Obesity is a global pandemic that has been associated with the development of breast, endometrial, large intestine, renal, esophageal, and pancreatic cancer. Obesity is also involved in the development of cardiovascular disease and type 2 diabetes mellitus. Recently, an increase in the incidence of obesity-related cancers has been reported. Multiple myeloma (MM) is the second most common hematological malignancy, after lymphoma. The aim of this review is to examine the epidemiological data on obesity and MM, assess the effect of obesity on MM outcomes, evaluate the possible mechanisms through which obesity might increase the incidence of MM and provide the effects of obesity management on MM. Current evidence indicates that obesity may have an impact on the progression of monoclonal gammopathy of undetermined significance (MGUS) to MM and increase the prevalence of MM. However, data regarding the effect of obesity on MGUS incidence are controversial; further studies are needed to examine whether obesity affects the development of MGUS or the progression of MGUS to MM. In addition, obesity affects MM outcomes. Increased BMI is associated with decreased survival in patients with MM, while data regarding the effect of obesity on newly diagnosed MM subjects and autologous stem cell transplantation are limited. Interestingly, the obesity paradox may also apply to patients with relapsed/refractory MM who are overweight or obese, because they may have a survival advantage. The pathophysiological pathways linking obesity to MM are very complicated and include bone marrow adipose tissue; adipokines, such as adiponectin, leptin, resistin, and visfatin; inflammatory cytokines and growth factors, such as TNF-α and IL-6; hormones including insulin and the insulin-like growth factor system as well as sex hormones. In terms of the effect of pharmacological management of obesity, orlistat has been shown to alter the proliferation of MM cells, whereas no data exist on glucagon-like peptide-1 receptor agonists, naltrexone/bupropion, or phentermine/topiramate. Bariatric surgery may be associated with a reduction in the incidence of MM, however, further studies are needed.

Esophageal cancer is a unique and complex heterogeneous malignancy, with substantial tumor heterogeneity: at the cellular levels, tumors are composed of tumor and stromal cellular components; at the genetic levels, they comprise genetically distinct tumor clones; at the phenotypic levels, cells in distinct microenvironmental niches acquire diverse phenotypic features. This heterogeneity affects almost every process of esophageal cancer progression from onset to metastases and recurrence, etc. Intertumoral and intratumoral heterogeneity are major obstacles in the treatment of esophageal cancer, but also offer the potential to manipulate the heterogeneity themselves as a new therapeutic strategy. The high-dimensional, multi-faceted characterization of genomics, epigenomics, transcriptomics, proteomics, metabonomics, etc. of esophageal cancer has opened novel horizons for dissecting tumor heterogeneity. Artificial intelligence especially machine learning and deep learning algorithms, are able to make decisive interpretations of data from multi-omics layers. To date, artificial intelligence has emerged as a promising computational tool for analyzing and dissecting esophageal patient-specific multi-omics data. This review provides a comprehensive review of tumor heterogeneity from a multi-omics perspective. Especially, we discuss the novel techniques single-cell sequencing and spatial transcriptomics, which have revolutionized our understanding of the cell compositions of esophageal cancer and allowed us to determine novel cell types. We focus on the latest advances in artificial intelligence in integrating multi-omics data of esophageal cancer. Artificial intelligence-based multi-omics data integration computational tools exert a key role in tumor heterogeneity assessment, which will potentially boost the development of precision oncology in esophageal cancer.

Urologic cancers (UC) account for 13.1% of all new cancer cases and 7.9% of all cancer-related deaths. A growing body of evidence has indicated a potential causal link between obesity and UC. The aim of the present review is to appraise in a critical and integrative manner evidence from meta-analyses and mechanistic studies on the role of obesity in four prevalent UC (kidney-KC, prostate-PC, urinary bladder-UBC, and testicular cancer-TC). Special emphasis is given on Mendelian Randomization Studies (MRS) corroborating a genetic causal association between obesity and UC, as well as on the role of classical and novel adipocytokines. Furthermore, the molecular pathways that link obesity to the development and progression of these cancers are reviewed. Available evidence indicates that obesity confers increased risk for KC, UBC, and advanced PC (20-82%, 10-19%, and 6-14%, respectively), whereas for TC adult height (5-cm increase) may increase the risk by 13%. Obese females tend to be more susceptible to UBC and KC than obese males. MRS have shown that a higher genetic-predicted BMI may be causally linked to KC and UBC but not PC and TC. Biological mechanisms that are involved in the association between excess body weight and UC include the Insulin-like Growth Factor axis, altered availability of sex hormones, chronic inflammation and oxidative stress, abnormal secretion of adipocytokines, ectopic fat deposition, dysbiosis of the gastrointestinal and urinary tract microbiomes and circadian rhythm dysregulation. Anti-hyperglycemic and non-steroidal anti-inflammatory drugs, statins, and adipokine receptor agonists/antagonists show potential as adjuvant cancer therapies. Identifying obesity as a modifiable risk factor for UC may have significant public health implications, allowing clinicians to tailor individualized prevention strategies for patients with excess body weight.

Excess body weight is a global health problem due to sedentary lifestyle and unhealthy diet, affecting 2 billion population worldwide. Obesity is a major risk factor for metabolic diseases. Notably, the metabolic risk of obesity largely depends on body weight distribution, of which visceral adipose tissues but not subcutaneous fats are closely associated with obesity comorbidities, including type 2 diabetes, non-alcoholic fatty liver disease, cardiovascular disease and certain types of cancer. Latest multi-omics and mechanistical studies reported the crucial involvement of genetic and epigenetic alterations, adipokines dysregulation, immunity changes, imbalance of white and brown adipose tissues, and gut microbial dysbiosis in mediating the pathogenic association between visceral adipose tissues and comorbidities. In this review, we explore the epidemiology of excess body weight and the up-to-date mechanism of how excess body weight and obesity lead to chronic complications. We also examine the utilization of visceral fat measurement as an accurate clinical parameter for risk assessment in healthy individuals and clinical outcome prediction in obese subjects. In addition, current approaches for the prevention and treatment of excess body weight and its related metabolic comorbidities are further discussed.

The circadian rhythm is regulated by an intrinsic time-tracking system, composed both of a central and a peripheral clock, which influences the cycles of activities and sleep of an individual over 24 h. At the molecular level, the circadian rhythm begins when two basic helix-loop-helix/Per-ARNT-SIM (bHLH-PAS) proteins, BMAL-1 and CLOCK, interact with each other to produce BMAL-1/CLOCK heterodimers in the cytoplasm. The BMAL-1/CLOCK target genes encode for the repressor components of the clock, cryptochrome (Cry1 and Cry2) and the Period proteins (Per1, Per2 and Per3). It has been recently demonstrated that the disruption of circadian rhythm is associated with an increased risk of developing obesity and obesity-related diseases. In addition, it has been demonstrated that the disruption of the circadian rhythm plays a key role in tumorigenesis. Further, an association between the circadian rhythm disruptions and an increased incidence and progression of several types of cancer (e.g., breast, prostate, colorectal and thyroid cancer) has been found. As the perturbation of circadian rhythm has adverse metabolic consequences (e.g., obesity) and at the same time tumor promoter functions, this manuscript has the aim to report how the aberrant circadian rhythms affect the development and prognosis of different types of obesity-related cancers (breast, prostate, colon rectal and thyroid cancer) focusing on both human studies and on molecular aspects.

Glioma represents a dominant primary intracranial malignancy in the central nervous system. Artificial intelligence that mainly includes machine learning, and deep learning computational approaches, presents a unique opportunity to enhance clinical management of glioma through improving tumor segmentation, diagnosis, differentiation, grading, treatment, prediction of clinical outcomes (prognosis, and recurrence), molecular features, clinical classification, characterization of the tumor microenvironment, and drug discovery. A growing body of recent studies apply artificial intelligence-based models to disparate data sources of glioma, covering imaging modalities, digital pathology, high-throughput multi-omics data (especially emerging single-cell RNA sequencing and spatial transcriptome), etc. While these early findings are promising, future studies are required to normalize artificial intelligence-based models to improve the generalizability and interpretability of the results. Despite prominent issues, targeted clinical application of artificial intelligence approaches in glioma will facilitate the development of precision medicine of this field. If these challenges can be overcome, artificial intelligence has the potential to profoundly change the way patients with or at risk of glioma are provided with more rational care.

Hepatocellular carcinoma (HCC) is estimated to be the third leading cause of cancer-related mortality and is characterized by low survival rates. Nonalcoholic fatty liver disease (NAFLD) is emerging as a leading cause of HCC, whose rates are increasing, owing to the increasing prevalence of NAFLD. The pathogenesis of NAFLD-associated HCC is multifactorial: insulin resistance, obesity, diabetes and the low-grade hepatic inflammation, which characterizes NAFLD, seem to play key roles in the development and progression of HCC. The diagnosis of NAFLD-associated HCC is based on imaging in the presence of liver cirrhosis, preferably computerized tomography or magnetic resonance imaging, but liver biopsy for histological confirmation is usually required in the absence of liver cirrhosis. Some preventive measures have been recommended for NAFLD-associated HCC, including weight loss, cessation of even moderate alcohol drinking and smoking, as well as the use of metformin, statins and aspirin. However, these preventive measures are mainly based on observational studies, thus they need validation in trials of different design before introducing in clinical practice. The treatment of NAFLD should be tailored on an individual basis and should be ideally determined by a multidisciplinary team. In the last two decades, new medications, including tyrosine kinase inhibitors and immune checkpoints inhibitors, have improved the survival of patients with advanced HCC, but trials specifically designed for patients with NAFLD-associated HCC are scarce. The aim of this review was to overview evidence on the epidemiology and pathophysiology of NAFLD-associated HCC, then to comment on imaging tools for its appropriate screening and diagnosis, and finally to critically summarize the currently available options for its prevention and treatment.

Small cell lung cancer (SCLC) is a recalcitrant cancer with an urgent need for novel therapeutics, preclinical models, and elucidation of the molecular pathways responsible for its rapid resistance. Recently, there have been many significant advancements in our knowledge of SCLC that led to the development of novel treatments. This review will go over the recent attempts to provide new molecular subcategorization of SCLC, recent breakthroughs in various systemic treatments including immunotherapy, targeted therapy, cellular therapy, as well as advancements in radiation therapy.

Declining thymic functions associated either with old age (i.e., age-related thymic involution), or with acute involution as a result of stress, infectious disease, or cytoreductive therapies (e.g., chemotherapy/radiotherapy), have been associated with cancer development. A key mechanism underlying such increased cancer risk is the thymus-dependent debilitation of adaptive immunity, which is responsible for orchestrating immunoediting mechanisms and tumor immune surveillance. In the past few years, a blooming set of evidence has intriguingly linked obesity with cancer development and progression. The majority of such studies has focused on obesity-driven chronic inflammation, steroid/sex hormone and adipokine production, and hyperinsulinemia, as principal factors affecting the tumor microenvironment and driving the development of primary malignancy. However, experimental observations about the negative impact of obesity on T cell development and maturation have existed for more than half a century. Here, we critically discuss the molecular and cellular mechanisms of obesity-driven thymic involution as a previously underrepresented intermediary pathology leading to cancer development and progression. This knowledge could be especially relevant in the context of childhood obesity, because impaired thymic function in young individuals leads to immune system abnormalities, and predisposes to various pediatric cancers. A thorough understanding behind the molecular and cellular circuitries governing obesity-induced thymic involution could therefore help towards the rationalized development of targeted thymic regeneration strategies for obese individuals at high risk of cancer development.

Cancer 'stemness' is fundamental to cancer existence. It defines the ability of cancer cells to indefinitely perpetuate as well as differentiate. Cancer stem cell populations within a growing tumor also help evade the inhibitory effects of chemo- as well as radiation-therapies, in addition to playing an important role in cancer metastases. NF-κB and STAT-3 are representative transcription factors (TFs) that have long been associated with cancer stemness, thus presenting as attractive targets for cancer therapy. The growing interest in non-coding RNAs (ncRNAs) in the recent years has provided further insight into the mechanisms by which TFs influence cancer stem cell characteristics. There is evidence for a direct regulation of TFs by ncRNAs, such as, microRNAs (miRNAs), long non-coding RNAs (lncRNAs) as well as circular RNAs (circRNAs), and vice versa. Additionally, the TF-ncRNAs regulations are often indirect, involving ncRNA-target genes or the sponging of other ncRNA species by individual ncRNAs. The information is rapidly evolving and this review provides a comprehensive review of TF-ncRNAs interactions with implications on cancer stemness and in response to therapies. Such knowledge will help uncover the many levels of tight regulations that control cancer stemness, providing novel opportunities and targets for therapy in the process.

The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.

Cancer cells adapt to varying stress conditions to survive through plasticity. Stem cells exhibit a high degree of plasticity, allowing them to generate more stem cells or differentiate them into specialized cell types to contribute to tissue development, growth, and repair. Cancer cells can also exhibit plasticity and acquire properties that enhance their survival. TGF-β is an unrivaled growth factor exploited by cancer cells to gain plasticity. TGF-β-mediated signaling enables carcinoma cells to alter their epithelial and mesenchymal properties through epithelial-mesenchymal plasticity (EMP). However, TGF-β is a multifunctional cytokine; thus, the signaling by TGF-β can be detrimental or beneficial to cancer cells depending on the cellular context. Those cells that overcome the anti-tumor effect of TGF-β can induce epithelial-mesenchymal transition (EMT) to gain EMP benefits. EMP allows cancer cells to alter their cell properties and the tumor immune microenvironment (TIME), facilitating their survival. Due to the significant roles of TGF-β and EMP in carcinoma progression, it is essential to understand how TGF-β enables EMP and how cancer cells exploit this plasticity. This understanding will guide the development of effective TGF-β-targeting therapies that eliminate cancer cell plasticity.

Despite the development of cancer therapies, the success of most treatments has been impeded by drug resistance. The crucial role of tumor cell plasticity has emerged recently in cancer progression, cancer stemness and eventually drug resistance. Cell plasticity drives tumor cells to reversibly convert their cell identity, analogous to differentiation and dedifferentiation, to adapt to drug treatment. This phenotypical switch is driven by alteration of the transcriptome. Several pluripotent factors from the KLF and SOX families are closely associated with cancer pathogenesis and have been revealed to regulate tumor cell plasticity. In this review, we particularly summarize recent studies about KLF4, KLF5 and SOX factors in cancer development and evolution, focusing on their roles in cancer initiation, invasion, tumor hierarchy and heterogeneity, and lineage plasticity. In addition, we discuss the various regulation of these transcription factors and related cutting-edge drug development approaches that could be used to drug "undruggable" transcription factors, such as PROTAC and PPI targeting, for targeted cancer therapy. Advanced knowledge could pave the way for the development of novel drugs that target transcriptional regulation and could improve the outcome of cancer therapy.

Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (PIN1) is a member of a family of peptidyl-prolyl isomerases that specifically recognizes and binds phosphoproteins, catalyzing the rapid cis-trans isomerization of phosphorylated serine/threonine-proline motifs, which leads to changes in the structures and activities of the targeted proteins. Through this complex mechanism, PIN1 regulates many hallmarks of cancer including cell autonomous metabolism and the crosstalk with the cellular microenvironment. Many studies showed that PIN1 is largely overexpressed in cancer turning on a set of oncogenes and abrogating the function of tumor suppressor genes. Among these targets, recent evidence demonstrated that PIN1 is involved in lipid and glucose metabolism and accordingly, in the Warburg effect, a characteristic of tumor cells. As an orchestra master, PIN1 finely tunes the signaling pathways allowing cancer cells to adapt and take advantage from a poorly organized tumor microenvironment. In this review, we highlight the trilogy among PIN1, the tumor microenvironment and the metabolic program rewiring.

Transcription factors (TFs) are indispensable for the modulation of various signaling pathways associated with normal cell homeostasis and disease conditions. Among cancer-related TFs, FOXM1 is a critical molecule that regulates multiple aspects of cancer cells, including growth, metastasis, recurrence, and stem cell features. FOXM1 also impact the outcomes of targeted therapies, chemotherapies, and immune checkpoint inhibitors (ICIs) in various cancer types. Recent advances in cancer research strengthen the cancer-specific role of FOXM1, providing a rationale to target FOXM1 for developing targeted therapies. This review compiles the recent studies describing the pivotal role of FOXM1 in promoting metastasis of various cancer types. It also implicates the contribution of FOXM1 in the modulation of chemotherapeutic resistance, antitumor immune response/immunotherapies, and the potential of small molecule inhibitors of FOXM1.

Quiescence is a state of cell cycle arrest, allowing cancer cells to evade anti-proliferative cancer therapies. Quiescent cancer stem cells are thought to be responsible for treatment resistance in glioblastoma, an aggressive brain cancer with poor patient outcomes. However, the regulation of quiescence in glioblastoma cells involves a myriad of intrinsic and extrinsic mechanisms that are not fully understood. In this review, we synthesise the literature on quiescence regulatory mechanisms in the context of glioblastoma and propose an ecological perspective to stemness-like phenotypes anchored to the contemporary concepts of niche theory. From this perspective, the cell cycle regulation is multiscale and multidimensional, where the niche dimensions extend to extrinsic variables in the tumour microenvironment that shape cell fate. Within this conceptual framework and powered by ecological niche modelling, the discovery of microenvironmental variables related to hypoxia and mechanosignalling that modulate proliferative plasticity and intratumor immune activity may open new avenues for therapeutic targeting of emerging biological vulnerabilities in glioblastoma.

Obesity results from a chronic excessive accumulation of adipose tissue due to a long-term imbalance between energy intake and expenditure. Available epidemiological and clinical data strongly support the links between obesity and certain cancers. Emerging clinical and experimental findings have improved our understanding of the roles of key players in obesity-associated carcinogenesis such as age, sex (menopause), genetic and epigenetic factors, gut microbiota and metabolic factors, body shape trajectory over life, dietary habits, and general lifestyle. It is now widely accepted that the cancer-obesity relationship depends on the site of cancer, the systemic inflammatory status, and micro environmental parameters such as levels of inflammation and oxidative stress in transforming tissues. We hereby review recent advances in our understanding of cancer risk and prognosis in obesity with respect to these players. We highlight how the lack of their consideration contributed to the controversy over the link between obesity and cancer in early epidemiological studies. Finally, the lessons and challenges of interventions for weight loss and better cancer prognosis, and the mechanisms of weight gain in survivors are also discussed.

Primary tumors secrete a variety of factors to turn distant microenvironments into favorable and fertile 'soil' for subsequent metastases. Among these 'seeding' factors that initiate pre-metastatic niche (PMN) formation, tumor-derived extracellular vesicles (EVs) are of particular interest as tumor EVs can direct organotropism depending on their surface integrin profiles. In addition, EVs also contain versatile, bioactive cargo, which include proteins, metabolites, lipids, RNA, and DNA fragments. The cargo incorporated into EVs is collectively shed from cancer cells and cancer-associated stromal cells. Increased understanding of how tumor EVs promote PMN establishment and detection of EVs in bodily fluids highlight how tumor EVs could serve as potential diagnostic and prognostic biomarkers, as well as provide a therapeutic target for metastasis prevention. This review focuses on tumor-derived EVs and how they direct organotropism and subsequently modulate stromal and immune microenvironments at distal sites to facilitate PMN formation. We also outline the progress made thus far towards clinical applications of tumor EVs.

Cancer ranks among the five leading causes of death in almost all countries and has important repercussions for individual and public health, the healthcare system, and society in general. Obesity increases the incidence of many types of cancer, but growing evidence suggests that physical activity may decrease risk for developing a variety of obesity-related cancer types, and, in some cases, may improve cancer prognosis and mortality rates. This review summarizes recent evidence on the effect of physical activity on obesity-related cancer prevention and survival. For some cancers, including breast, colorectal, and endometrial cancer, there is strong evidence for a preventative effect of exercise, but for many others, including gallbladder and kidney cancer, and multiple myeloma, evidence is inconsistent or largely lacking. Though many potential mechanisms have been proposed to explain the onco-protective effect of exercise, including improved insulin sensitivity, alterations in sex hormone availability, improved immune function and inflammation, myokine secretion, and modulation of intracellular signaling at the level of AMP kinase, the exact mechanism(s) of action within each cancer subtype remains poorly defined. Overall, a deeper understanding of how exercise can help against cancer and of the exercise parameters that can be altered to optimize exercise prescription is necessary and should be the subject of future investigation.

The prevalence of obesity has reached pandemic levels worldwide, leading to a lower quality of life and higher health costs. Obesity is a major risk factor for noncommunicable diseases, including cancer, although obesity is one of the major preventable causes of cancer. Lifestyle factors, such as dietary quality and patterns, are also closely related to the onset and development of obesity and cancer. However, the mechanisms underlying the complex association between diet, obesity, and cancer remain unclear. In the past few decades, microRNAs (miRNAs), a class of small non-coding RNAs, have been demonstrated to play critical roles in biological processes such as cell differentiation, proliferation, and metabolism, highlighting their importance in disease development and suppression and as therapeutic targets. miRNA expression levels can be modulated by diet and are involved in cancer and obesity-related diseases. Circulating miRNAs can also mediate cell-to-cell communications. These multiple aspects of miRNAs present challenges in understanding and integrating their mechanism of action. Here, we introduce a general consideration of the associations between diet, obesity, and cancer and review the current knowledge of the molecular functions of miRNA in each context. A comprehensive understanding of the interplay between diet, obesity, and cancer could be valuable for the development of effective preventive and therapeutic strategies in future.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer that is characterised by a prominent collagenous stromal reaction/desmoplasia surrounding tumour cells. Pancreatic stellate cells (PSCs) are responsible for the production of this stroma and have been shown to facilitate PDAC progression. Recently, extracellular vesicles (EVs), in particular, small extracellular vesicles (exosomes) have been a topic of interest in the field of cancer research for their emerging roles in cancer progression and diagnosis. EVs act as a form of intercellular communication by carrying their molecular cargo from one cell to another, regulating functions of the recipient cells. Although the knowledge of the bi-directional interactions between the PSCs and cancer cells that promote disease progression has advanced significantly over the past decade, studies on PSC-derived EVs in PDAC are currently rather limited. This review provides an overview of PDAC, pancreatic stellate cells and their interactions with cancer cells, as well as the currently known role of extracellular vesicles derived from PSCs in PDAC progression.

Lung cancer is the leading cause of cancer related death, and is divided into two major histological subtypes, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Histological transformation from NSCLC to SCLC has been reported as a mechanism of treatment resistance in patients who received tyrosine kinase inhibitors (TKIs) targeting EGFR, ALK and ROS1 or immunotherapies. The transformed histology could be due to therapy-induced lineage plasticity or clonal selection of pre-existing SCLC cells. Evidence supporting either mechanism exist in the literature. Here, we discuss potential mechanisms of transformation and review the current knowledge about cell of origin of NSCLC and SCLC. In addition, we summarize genomic alterations that are frequently observed in both "de novo" and transformed SCLC, such as TP53, RB1 and PIK3CA. We also discuss treatment options for transformed SCLC, including chemotherapy, radiotherapy, TKIs, immunotherapy and anti-angiogenic agents.

Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.

Cell-cell interactions instruct cell fate and function. These interactions are hijacked to promote cancer development. Single-cell transcriptomics and spatial transcriptomics have become powerful new tools for researchers to profile the transcriptional landscape of cancer at unparalleled genetic depth. In this review, we discuss the rapidly growing array of computational tools to infer cell-cell interactions from non-spatial single-cell RNA-sequencing and the limited but growing number of methods for spatial transcriptomics data. Downstream analyses of these computational tools and applications to cancer studies are highlighted. We finish by suggesting several directions for further extensions that anticipate the increasing availability of multi-omics cancer data.

Tumors consist of different genotypically distinct subpopulations-or subclones-of cells. These subclones can influence neighboring clones in a process called "clonal interaction." Conventionally, research on driver mutations in cancer has focused on their cell-autonomous effects that lead to an increase in fitness of the cells containing the driver. Recently, with the advent of improved experimental and computational technologies for investigating tumor heterogeneity and clonal dynamics, new studies have shown the importance of clonal interactions in cancer initiation, progression, and metastasis. In this review we provide an overview of clonal interactions in cancer, discussing key discoveries from a diverse range of approaches to cancer biology research. We discuss common types of clonal interactions, such as cooperation and competition, its mechanisms, and the overall effect on tumorigenesis, with important implications for tumor heterogeneity, resistance to treatment, and tumor suppression. Quantitative models-in coordination with cell culture and animal model experiments-have played a vital role in investigating the nature of clonal interactions and the complex clonal dynamics they generate. We present mathematical and computational models that can be used to represent clonal interactions and provide examples of the roles they have played in identifying and quantifying the strength of clonal interactions in experimental systems. Clonal interactions have proved difficult to observe in clinical data; however, several very recent quantitative approaches enable their detection. We conclude by discussing ways in which researchers can further integrate quantitative methods with experimental and clinical data to elucidate the critical-and often surprising-roles of clonal interactions in human cancers.

Obesity, defined by body mass index (BMI), is an established risk factor for specific renal cell carcinoma (RCC) subtypes such as clear cell RCC, the most common RCC histology. Many studies have identified an association between obesity and improved survival after diagnosis of RCC, a potential "obesity paradox." Clinically, there is uncertainty whether improved outcomes observed after diagnosis are driven by stage, type of treatment received, or artifacts of longitudinal changes in weight and body composition. The biological mechanisms underlying obesity's influence on RCC are not fully established, but multiomic and mechanistic studies suggest an impact on tumor metabolism, particularly fatty acid metabolism, angiogenesis, and peritumoral inflammation, which are known to be key biological hallmarks of clear cell RCC. Conversely, high-intensity exercise associated with increased muscle mass may be a risk factor for renal medullary carcinoma, a rare RCC subtype that predominantly occurs in individuals with sickle hemoglobinopathies. Herein, we highlight methodologic challenges associated with studying the influence of obesity on RCC and review the clinical evidence and potential underlying mechanisms associating RCC with BMI and body composition.

Based on the advantages of revealing the functional status and molecular expression of tumor cells, positron emission tomography (PET) imaging has been performed in numerous types of malignant diseases for diagnosis and monitoring. However, insufficient image quality, the lack of a convincing evaluation tool and intra- and interobserver variation in human work are well-known limitations of nuclear medicine imaging and restrict its clinical application. Artificial intelligence (AI) has gained increasing interest in the field of medical imaging due to its powerful information collection and interpretation ability. The combination of AI and PET imaging potentially provides great assistance to physicians managing patients. Radiomics, an important branch of AI applied in medical imaging, can extract hundreds of abstract mathematical features of images for further analysis. In this review, an overview of the applications of AI in PET imaging is provided, focusing on image enhancement, tumor detection, response and prognosis prediction and correlation analyses with pathology or specific gene mutations in several types of tumors. Our aim is to describe recent clinical applications of AI-based PET imaging in malignant diseases and to focus on the description of possible future developments.

Obesity, defined by excessive body fat, is a highly complex condition affecting numerous physiological processes, such as metabolism, proliferation, and cellular homeostasis. These multifaceted effects impact cells and tissues throughout the host, including immune cells as well as cancer biology. Because of the multifaceted nature of obesity, common parameters used to define it (such as body mass index in humans) can be problematic, and more nuanced methods are needed to characterize the pleiotropic metabolic effects of obesity. Obesity is well-accepted as an overall negative prognostic factor for cancer incidence, progression, and outcome. This is in part due to the meta-inflammatory and immunosuppressive effects of obesity. Immunotherapy is increasingly used in cancer therapy, and there are many different types of immunotherapy approaches. The effects of obesity on immunotherapy have only recently been studied with the demonstration of an "obesity paradox", in which some immune therapies have been demonstrated to result in greater efficacy in obese subjects despite the direct adverse effects of obesity and excess body fat acting on the cancer itself. The multifactorial characteristics that influence the effects of obesity (age, sex, lean muscle mass, underlying metabolic conditions and drugs) further confound interpretation of clinical data and necessitate the use of more relevant preclinical models mirroring these variables in the human scenario. Such models will allow for more nuanced mechanistic assessment of how obesity can impact, both positively and negatively, cancer biology, host metabolism, immune regulation, and how these intersecting processes impact the delivery and outcome of cancer immunotherapy.

The family of mammalian E2F transcription factors (E2Fs) comprise of 8 members (E2F1-E2F8) classified as activators (E2F1-E2F3) and repressors (E2F4-E2F8) primarily regulating the expression of several genes related to cell proliferation, apoptosis and differentiation, mainly in a cell cycle-dependent manner. E2F activity is frequently controlled via the retinoblastoma protein (pRb), cyclins, p53 and the ubiquitin-proteasome pathway. Additionally, genetic or epigenetic changes result in the deregulation of E2F family genes expression altering S phase entry and apoptosis, an important hallmark for the onset and development of cancer. Although studies reveal E2Fs to be involved in several human malignancies, the mechanisms underlying the role of E2Fs in oral cancer lies nascent and needs further investigations. This review focuses on the role of E2Fs in oral cancer and the etiological factors regulating E2Fs activity, which in turn transcriptionally control the expression of their target genes, thus contributing to cell proliferation, metastasis, and drug/therapy resistance. Further, we will discuss therapeutic strategies for E2Fs, which may prevent oral tumor growth, metastasis, and drug resistance.

In the recent decades, chimeric antigen receptor (CAR) therapy signaled a new revolutionary approach to cancer treatment. This method seeks to engineer immune cells expressing an artificially designed receptor, which would endue those cells with the ability to recognize and eliminate tumor cells. While some CAR therapies received FDA approval and others are subject to clinical trials, many aspects of their workings remain elusive. Techniques of systems and computational biology have been frequently employed to explain the operating principles of CAR therapy and suggest further design improvements. In this review, we sought to provide a comprehensive account of those efforts. Specifically, we discuss various computational models of CAR therapy ranging in scale from organismal to molecular. Then, we describe the molecular and functional properties of costimulatory domains frequently incorporated in CAR structure. Finally, we describe the signaling cascades by which those costimulatory domains elicit cellular response against the target. We hope that this comprehensive summary of computational and experimental studies will further motivate the use of systems approaches in advancing CAR therapy.

Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.

Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.

Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.

The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.

Olive oil (OO) is the main source of added fat in the Mediterranean diet (MD). It is a mix of bioactive compounds, including monounsaturated fatty acids, phytosterols, simple phenols, secoiridoids, flavonoids, and terpenoids. There is a growing body of evidence that MD and OO improve obesity-related factors. In addition, obesity has been associated with an increased risk for several cancers: endometrial, oesophageal adenocarcinoma, renal, pancreatic, hepatocellular, gastric cardia, meningioma, multiple myeloma, colorectal, postmenopausal breast, ovarian, gallbladder, and thyroid cancer. However, the epidemiological evidence linking MD and OO with these obesity-related cancers, and their potential mechanisms of action, especially those involving the gut microbiota, are not clearly described or understood. The goals of this review are 1) to update the current epidemiological knowledge on the associations between MD and OO consumption and obesity-related cancers, 2) to identify the gut microbiota mechanisms involved in obesity-related cancers, and 3) to report the effects of MD and OO on these mechanisms.

Tumor cells evolve in tumor microenvironment composed of multiple cell types. Among these, endothelial cells (ECs) are the major players in tumor angiogenesis, which is a driver of tumor progression and metastasis. Increasing evidence suggests that ECs also contribute to tumor progression and metastasis as they modify their phenotypes to differentiate into mesenchymal cells through a process known as endothelial-mesenchymal transition (EndoMT). This plasticity of ECs is mediated by various cytokines, including transforming growth factor-β (TGF-β), and modulated by other stimuli depending on the cellular contexts. Recent lines of evidence have shown that EndoMT is involved in various steps of tumor progression, including tumor angiogenesis, intravasation and extravasation of cancer cells, formation of cancer-associated fibroblasts, and cancer therapy resistance. In this review, we summarize current updates on EndoMT, highlight the roles of EndoMT in tumor progression and metastasis, and underline targeting EndoMT as a potential therapeutic strategy.

Obesity has been closely related to cancer progression, recurrence, metastasis, and treatment resistance. We aim to review recent progress in the knowledge on the obese macroenvironment and the generated adipose tumor microenvironment (TME) inducing lipid metabolic dysregulation and their influence on carcinogenic processes. Visceral white adipose tissue expansion during obesity exerts systemic or macroenvironmental effects on tumor initiation, growth, and invasion by promoting inflammation, hyperinsulinemia, growth-factor release, and dyslipidemia. The dynamic relationship between cancer and stromal cells of the obese adipose TME is critical for cancer cell survival and proliferation as well. Experimental evidence shows that secreted paracrine signals from cancer cells can induce lipolysis in cancer-associated adipocytes, causing them to release free fatty acids and acquire a fibroblast-like phenotype. Such adipocyte delipidation and phenotypic change is accompanied by an increased secretion of cytokines by cancer-associated adipocytes and tumor-associated macrophages in the TME. Mechanistically, the availability of adipose TME free fatty acids and tumorigenic cytokines concomitant with the activation of angiogenic processes creates an environment that favors a shift in the cancer cells toward an aggressive phenotype associated with increased invasiveness. We conclude that restoring the aberrant metabolic alterations in the host macroenvironment and in adipose TME of obese subjects would be a therapeutic option to prevent cancer development. Several dietary, lipid-based, and oral antidiabetic pharmacological therapies could potentially prevent tumorigenic processes associated with the dysregulated lipid metabolism closely linked to obesity.

Reactive oxygen species (ROS) are common products of normal cellular metabolism, but their elevated levels can result in nucleotide modifications. These modified or noncanonical nucleotides often integrate into nascent DNA during replication, causing lesions that trigger DNA repair mechanisms such as the mismatch repair machinery and base excision repair. Four superfamilies of sanitization enzymes can effectively hydrolyze noncanonical nucleotides from the precursor pool and eliminate their unintended incorporation into DNA. Notably, we focus on the representative MTH1 NUDIX hydrolase, whose enzymatic activity is ostensibly nonessential under normal physiological conditions. Yet, the sanitization attributes of MTH1 are more prevalent when ROS levels are abnormally high in cancer cells, rendering MTH1 an interesting target for developing anticancer treatments. We discuss multiple MTH1 inhibitory strategies that have emerged in recent years, and the potential of NUDIX hydrolases as plausible targets for the development of anticancer therapeutics.

Altered energy metabolism is one of the hallmarks of tumorigenesis and essential for fulfilling the high demand for metabolic energy in a tumor through accelerating glycolysis and reprogramming the glycolysis metabolism through the Warburg effect. The dysregulated glucose metabolic pathways are coordinated not only by proteins coding genes but also by non-coding RNAs (ncRNAs) during the initiation and cancer progression. The ncRNAs are responsible for regulating numerous cellular processes under developmental and pathological conditions. Recent studies have shown that various ncRNAs such as microRNAs, circular RNAs, and long noncoding RNAs are extensively involved in rewriting glucose metabolism in human cancers. In this review, we demonstrated the role of ncRNAs in the progression of breast cancer with a focus on outlining the aberrant expression of glucose metabolic pathways. Moreover, we have discussed the existing and probable future applications of ncRNAs to regulate energy pathways along with their importance in the prognosis, diagnosis, and future therapeutics for human breast carcinoma.

Acute myeloid leukemia (AML) is a heterogeneous disease with a genetic, epigenetic, and transcriptional etiology mainly presenting somatic and germline abnormalities. AML incidence rises with age but can also occur during childhood. Pediatric AML (pAML) accounts for 15-20% of all pediatric leukemias and differs considerably from adult AML. Next-generation sequencing technologies have enabled the research community to "paint" the genomic and epigenomic landscape in order to identify pathology-associated mutations and other prognostic biomarkers in pAML. Although current treatments have improved the prognosis for pAML, chemoresistance, recurrence, and refractory disease remain major challenges. In particular, pAML relapse is commonly caused by leukemia stem cells that resist therapy. Marked patient-to-patient heterogeneity is likely the primary reason why the same treatment is successful for some patients but, at best, only partially effective for others. Accumulating evidence indicates that patient-specific clonal composition impinges significantly on cellular processes, such as gene regulation and metabolism. Although our understanding of metabolism in pAML is still in its infancy, greater insights into these processes and their (epigenetic) modulation may pave the way toward novel treatment options. In this review, we summarize current knowledge on the function of genetic and epigenetic (mis)regulation in pAML, including metabolic features observed in the disease. Specifically, we describe how (epi)genetic machinery can affect chromatin status during hematopoiesis, leading to an altered metabolic profile, and focus on the potential value of targeting epigenetic abnormalities in precision and combination therapy for pAML. We also discuss the possibility of using alternative epidrug-based therapeutic approaches that are already in clinical practice, either alone as adjuvant treatments and/or in combination with other drugs.

Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.

Cerebral ischemic stroke and glioma are the two leading causes of patient mortality globally. Despite physiological variations, 1 in 10 people who have an ischemic stroke go on to develop brain cancer, most notably gliomas. In addition, glioma treatments have also been shown to increase the risk of ischemic strokes. Stroke occurs more frequently in cancer patients than in the general population, according to traditional literature. Unbelievably, these events share multiple pathways, but the precise mechanism underlying their co-occurrence remains unknown. Transcription factors (TFs), the main components of gene expression programmes, finally determine the fate of cells and homeostasis. Both ischemic stroke and glioma exhibit aberrant expression of a large number of TFs, which are strongly linked to the pathophysiology and progression of both diseases. The precise genomic binding locations of TFs and how TF binding ultimately relates to transcriptional regulation remain elusive despite a strong interest in understanding how TFs regulate gene expression in both stroke and glioma. As a result, the importance of continuing efforts to understand TF-mediated gene regulation is highlighted in this review, along with some of the primary shared events in stroke and glioma.

Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently, artificial intelligence approaches, particularly machine learning and deep learning, are rapidly reshaping the full spectrum of clinical management for gastric cancer. Machine learning is formed from computers running repeated iterative models for progressively improving performance on a particular task. Deep learning is a subtype of machine learning on the basis of multilayered neural networks inspired by the human brain. This review summarizes the application of artificial intelligence algorithms to multi-dimensional data including clinical and follow-up information, conventional images (endoscope, histopathology, and computed tomography (CT)), molecular biomarkers, etc. to improve the risk surveillance of gastric cancer with established risk factors; the accuracy of diagnosis, and survival prediction among established gastric cancer patients; and the prediction of treatment outcomes for assisting clinical decision making. Therefore, artificial intelligence makes a profound impact on almost all aspects of gastric cancer from improving diagnosis to precision medicine. Despite this, most established artificial intelligence-based models are in a research-based format and often have limited value in real-world clinical practice. With the increasing adoption of artificial intelligence in clinical use, we anticipate the arrival of artificial intelligence-powered gastric cancer care.

Obesity is a chronic inflammatory condition that has been associated with different types of cancer. However, its role in melanoma incidence, progression, and response to immune-checkpoint-inhibitors (ICI) is still controversial. On the one hand, increased levels of lipids and adipokines can promote tumor proliferation and several genes associated with fatty acid metabolism have been found to be upregulated in melanomas. On the other hand, immunotherapy seems to be more effective in obese animal models, presumably due to an increase in CD8 + and subsequent decrease in PD-1 + T-cells in the tumor microenvironment. In humans, several studies have investigated the role of BMI (body mass index) and other adiposity-related parameters as potential prognostic markers of survival in advanced melanoma patients treated with ICI. The aim of this research has been to systematically review the scientific literature on studies evaluating the relationship between overweight/obesity and survival outcomes in patients with advanced melanoma treated with ICI and to perform a meta-analysis on those sharing common characteristics. After screening 1070 records identified through a literature search, 18 articles assessing the role of BMI-related exposure in relation to survival outcomes in ICI-treated patients with advanced melanoma were included in our review. In the meta-analysis of the association between overweight (defined as BMI>25 or BMI 25-30), overall survival (OS), and progression free survival (PFS), 7 studies were included, yielding a summary HR of 0.87 (95% CI: 0.74-1.03) and 0.96 (95% CI: 0.86-1.08), respectively. Our results show that, despite few suggestive findings, the use of BMI as a valuable predictor of melanoma patients' survival in terms of PFS and OS should not be currently recommended, due to the limited evidence available.

Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.

Primary cutaneous lymphomas (CLs) represent a heterogeneous group of T-cell lymphomas and B-cell lymphomas that present in the skin without evidence of extracutaneous involvement at time of diagnosis. CLs are largely distinct from their systemic counterparts in clinical presentation, histopathology, and biological behavior and, therefore, require different therapeutic management. Additional diagnostic burden is added by the fact that several benign inflammatory dermatoses mimic CL subtypes, requiring clinicopathological correlation for definitive diagnosis. Due to the heterogeneity and rarity of CL, adjunct diagnostic tools are welcomed, especially by pathologists without expertise in this field or with limited access to a centralized specialist panel. The transition into digital pathology workflows enables artificial intelligence (AI)-based analysis of patients' whole-slide pathology images (WSIs). AI can be used to automate manual processes in histopathology but, more importantly, can be applied to complex diagnostic tasks, especially suitable for rare disease like CL. To date, AI-based applications for CL have been minimally explored in literature. However, in other skin cancers and systemic lymphomas, disciplines that are recognized here as the building blocks for CLs, several studies demonstrated promising results using AI for disease diagnosis and subclassification, cancer detection, specimen triaging, and outcome prediction. Additionally, AI allows discovery of novel biomarkers or may help to quantify established biomarkers. This review summarizes and blends applications of AI in pathology of skin cancer and lymphoma and proposes how these findings can be applied to diagnostics of CL.