Field Of Oncology Research Articles

FTO in Lung Cancer: Its Progression and Therapeutic Potential.

One of the most fatal and frequent malignancies on the planet is lung cancer. Its occurrence and development are the results of multifactorial and multigenic interactions. In recent years, RNA N6-methyladenosine transferase (FTO) has gained significant attention in the field of oncology. FTO is the first RNA demethylase to be found to control target mRNA demethylation. The growth, proliferation, and metastasis of tumor cells are greatly influenced by FTO. Recent studies have found that imbalanced m6A methylation regulatory proteins can induce disruption of downstream RNA metabolism, strongly affecting tumor development. This paper provides an overview of the relationship between FTO and lung cancer, discussing the mechanisms by which FTO is involved in lung cancer and its potential clinical applications.

Contemporary Molecular Markers for Predicting Systemic Treatment Response in Urothelial Bladder Cancer: A Narrative Review.

The search for dependable molecular biomarkers to enhance routine clinical practice is a compelling challenge across all oncology fields. Urothelial bladder carcinoma, known for its significant heterogeneity, presents difficulties in predicting responses to systemic therapies and outcomes post-radical cystectomy. Recent advancements in molecular cancer biology offer promising avenues to understand the disease's biology and identify emerging predictive biomarkers. Stratifying patients based on their recurrence risk post-curative treatment or predicting the efficacy of conventional and targeted therapies could catalyze personalized treatment selection and disease surveillance. Despite progress, reliable molecular biomarkers to forecast responses to systemic agents, in neoadjuvant, adjuvant, or palliative treatment settings, are still lacking, underscoring an urgent unmet need. This review aims to delve into the utilization of current and emerging molecular signatures across various stages of urothelial bladder carcinoma to predict responses to systemic therapy.

The Fletcher-Cox Pathway: A Unique View on Clinical Trial Education

PurposeThere currently are no established formal mentorship and training program for radiation oncology (RO) trainees to learn trial design, creation, writing, or implementation. There only exists informal training on analyzing clinical trials in RO residency programs. The integration of a longitudinal formal training and mentorship program for clinical trialists - consisting of clinical trial education, design, mentorship, and implementation during the RO residency education will give residents not only formal teaching in the subject, but strong tools and requisite mentorship in hopes to help them succeed as future academic physicians and leaders in the field of radiation oncology. Methods and MaterialsWe developed a clinical trial training pathway in 2018 at XXX, proposing it as a pilot program. The “Fletcher-Cox Pathway” was accepted with a highly positive response by trainees. and is is now offered as a standard option to RO trainees at our institution. ResultsWith the guidance of their principal investigator (PI), residents participating in this pathway design and submit a clinical trial for institutional review board (IRB) review. In 2019, after implementation of the pilot program, 4 of the incoming 7 residents joined the pathway. The program continues, and the current cohort of trainees have received training in clinical trials design andworked with dedicated mentor(s) regarding clinical trial ideas; their studies are currently accruing patients. ConclusionsThis pilot program has been viewed by trainees and mentors as successful; it highlights how a structured approach meets a clear need within RO training. We envision the creation of a national platform to increase access, whereby programs adopt this clinical trialist educational pathway, which ultimately leads to the development of a robust clinical trial community with communal resources. We hope that this not only improves and provides educational initiatives to all trainees but also initiates further collaboration.

Increasing Power in Phase III Oncology Trials With Multivariable Regression: An Empirical Assessment of 535 Primary End Point Analyses.

A previous study demonstrated that power against the (unobserved) true effect for the primary end point (PEP) of most phase III oncology trials is low, suggesting an increased risk of false-negative findings in the field of late-phase oncology. Fitting models with prognostic covariates is a potential solution to improve power; however, the extent to which trials leverage this approach, and its impact on trial interpretation at scale, is unknown. To that end, we hypothesized that phase III trials using multivariable PEP analyses are more likely to demonstrate superiority versus trials with univariable analyses. PEP analyses were reviewed from trials registered on ClinicalTrials.gov. Adjusted odds ratios (aORs) were calculated by logistic regressions. Of the 535 trials enrolling 454,824 patients, 69% (n = 368) used a multivariable PEP analysis. Trials with multivariable PEP analyses were more likely to demonstrate PEP superiority (57% [209 of 368] v 42% [70 of 167]; aOR, 1.78 [95% CI, 1.18 to 2.72]; P = .007). Among trials with a multivariable PEP model, 16 conditioned on covariates and 352 stratified by covariates. However, 108 (35%) of 312 trials with stratified analyses lost power by categorizing a continuous variable, which was especially common among immunotherapy trials (aOR, 2.45 [95% CI, 1.23 to 4.92]; P = .01). Trials increasing power by fitting multivariable models were more likely to demonstrate PEP superiority than trials with unadjusted analysis. Underutilization of conditioning models and empirical power loss associated with covariate categorization required by stratification were identified as barriers to power gains. These findings underscore the opportunity to increase power in phase III trials with conventional methodology and improve patient access to effective novel therapies.

Immunoprevention Strategies for Colorectal Cancer in Lynch Syndrome Carriers.

The immune revolution that swept the field of oncology in the mid-2010s with the advent of checkpoint inhibitors has led to a paradigm shift in approaches toward adapting new cancer prevention modalities. Cancer vaccines have emerged from this era with astounding potential as a durable intervention to prevent cancers especially for patients with hereditary susceptibilities such as Lynch syndrome carriers. This review covers new insights in the immunoprevention landscape for patients living with Lynch syndrome including highlights ranging from clinical trials exploring the use of chemoprevention agents to boost immune cellularity to investigative studies using novel vaccine approaches to induce long-term antitumor immunity.

Innovative Progress: Artificial Intelligence in the Realm of Oral Cancer

In recent years, there has been a notable increase in the global incidence of oral cancer, leading to significant morbidity and mortality, especially when diagnosed at advanced stages. The integration: In recent years, there has been a notable increase in the global incidence of oral cancer, leading to significant morbidity and mortality, especially when diagnosed at advanced stages. The integration of technology holds great promise for early detection and diagnosis, facilitating improved patient management for clinicians. The emergence of artificial intelligence (AI) presents a potential breakthrough in oral cancer screening, as it can meticulously analyze vast datasets from various imaging modalities, offering valuable support in the field of oncology. This review focuses on a spectrum of AI techniques utilized for early detection and diagnosis of oral cancer. Additionally, AI techniques may be employed for the effective treatment of oral cancer. Using the abundance of information acquired, this article provides an in-depth overview and discussion of AI's value and benefits in oral cancer screening, early detection, disease prediction, and therapy, among other areas. Furthermore, it identifies present limits and forecasts the hopeful future of AI in oral cancer research.

Oncology researchers’ and clinicians’ perceptions of complementary, alternative, and integrative medicine: an international, cross-sectional survey

BackgroundComplementary, alternative, and integrative medicine (CAIM) has become an increasingly popular supportive therapy option for patients with cancer. The objective of this study was to investigate how researchers and clinicians in the oncology field perceive CAIM.MethodsWe conducted an online, anonymous, cross-sectional survey for researchers and clinicians who have published their work in oncology journals that are indexed in MEDLINE. The link to the survey was sent to 47, 991 researchers and clinicians whose contact information was extracted from their publications. The survey included various multiple-choice questions, and one open-ended question at the end to allow for any additional comments.Results751 respondents completed the survey, and they mostly identified themselves as researchers (n = 329, 45.13%), or as both researchers and clinicians (n = 332, 45.45%) in the field of oncology. Over half of the respondents perceive mind–body therapies (n = 354, 54.97%) to be the most promising CAIM category with regards to the prevention, treatment, and/or management of diseases related to oncology, and many respondents agreed that most CAIM therapies are safe (n = 218, 37.39%), and that clinicians should receive training on CAIM therapies via formal (n = 225, 38.59%) and supplemental education (n = 290, 49.83%). However, many respondents were unsure when asked if most CAIM therapies are effective (n = 202, 34.77%).ConclusionsThe findings from this study demonstrated great current interest in the use of CAIM in oncology. This information can serve as a foundation for conducting additional research and creating customized educational materials for researchers and clinicians in oncology.

Tubal Cancer Clinical Management: Two Exceptional Scenarios and a Review of the Literature.

This article provides a literature review on tubal carcinoma to offer an updated insight into its preventative strategies, diagnosis, treatment and oncological surveillance. In addition to the search string utilized, the authors' focus extended to key scientific studies, consensus statements, guidelines and relevant case reports essential for the proper clinical management of the disease, providing a methodologically well-structured literature review combined with practical expertise in the oncological field. This article also includes two special clinical cases that emphasize the importance of understanding the physiopathology and the current state of the art in the anatomopathological advancements in tubal/ovarian/peritoneal carcinoma, often assimilated into a single clinical entity and to which many of the concepts extracted from the literature can apply.

Tumour mutational burden: clinical utility, challenges and emerging improvements.

Tumour mutational burden (TMB), defined as the total number of somatic non-synonymous mutations present within the cancer genome, varies across and within cancer types. A first wave of retrospective and prospective research identified TMB as a predictive biomarker of response to immune-checkpoint inhibitors and culminated in the disease-agnostic approval of pembrolizumab for patients with TMB-high tumours based on data from the Keynote-158 trial. Although the applicability of outcomes from this trial to all cancer types and the optimal thresholds for TMB are yet to be ascertained, research into TMB is advancing along three principal avenues: enhancement of TMB assessments through rigorous quality control measures within the laboratory process, including the mitigation of confounding factors such as limited panel scope and low tumour purity; refinement of the traditional TMB framework through the incorporation of innovative concepts such as clonal, persistentor HLA-corrected TMB, tumour neoantigen load and mutational signatures; and integration of TMB with established and emerging biomarkers such as PD-L1 expression, microsatellite instability, immune gene expression profiles and the tumour immune contexture. Given its pivotal functions in both the pathogenesis of cancer and the ability of the immune system to recognize tumours, a profound comprehension of the foundational principles and the continued evolution of TMB are of paramount relevance for the field of oncology.

Bringing the Genomic Revolution to Comparative Oncology: Human and Dog Cancers.

Dogs are humanity's oldest friend, the first species we domesticated 20,000-40,000years ago. In this unequaled collaboration, dogs have inadvertently but serendipitously been molded into a potent human cancer model. Unlike many common model species, dogs are raised in the same environment as humans and present with spontaneous tumors with human-like comorbidities, immunocompetency, and heterogeneity. In breast, bladder, blood, and several pediatric cancers, in-depth profiling of dog and human tumors has established the benefits of the dog model. In addition to this clinical and molecular similarity, veterinary studies indicate that domestic dogs have relatively high tumor incidence rates. As a result, there are a plethora of data for analysis, the statistical power of which is bolstered by substantial breed-specific variability. As such, dog tumors provide a unique opportunity to interrogate the molecular factors underpinning cancer and facilitate the modeling of new therapeutic targets. This review discusses the emerging field of comparative oncology, how it complements human and rodent cancer studies, and where challenges remain, given the rapid proliferation of genomic resources. Increasingly, it appears that human's best friend is becoming an irreplaceable component of oncology research.

Comprehensive Evaluation of Machine Learning and ANN Models for Breast Cancer Detection

Breast cancer is one of the most prevalent forms of cancer among women, and early diagnosis is of vital importance. In recent years, machine learning algorithms have demonstrated high accuracy in breast cancer detection, contributing to earlier diagnoses. Various machine learning models can analyze tumor characteristics and assist in cancer identification and treatment decisions. This project aims to comprehensively examine the performance of 14 different machine learning algorithms and a custom-developed Artificial Neural Network (ANN) model in breast cancer detection, using public Wisconsin dataset. Following data preprocessing, the training and testing stages are carried out, and the results are thoroughly analyzed to determine the model with the highest accuracy. The findings of this project will showcase the potential impact of machine learning algorithms in the clinical applications of breast cancer detection, as researchers and healthcare providers can leverage these advanced computational techniques to enhance the accuracy and efficiency of breast cancer diagnosis, ultimately leading to improved patient outcomes and the advancement of this critical field of oncology.

Inhibition of MSH6 augments the antineoplastic efficacy of cisplatin in non-small cell lung cancer as autophagy modulator

The altered response to chemotherapeutic agents predominantly stems from heightened single-point mutations within coding regions and dysregulated expression levels of genes implicated in drug resistance mechanisms. The identification of biomarkers based on mutation profiles and expression levels is pivotal for elucidating the underlying mechanisms of altered drug responses and for refining combinatorial therapeutic strategies in the field of oncology. Utilizing comprehensive bioinformatic analyses, we investigated the impact of eight mismatch repair (MMR) genes on overall survival across 23 cancer types, encompassing more than 7500 tumors, by integrating their mutation profiles. Among these genes, MSH6 emerged as the most predictive biomarker, characterized by a pronounced mutation frequency and elevated expression levels, which correlated with poorer patient survival outcomes.The wet lab experiments disclosed the impact of MSH6 in mediating altered drug responses. Cytotoxic assays conducted revealed that the depletion of MSH6 in H460 non-small lung cancer cells augmented the efficacy of cisplatin, carboplatin, and gemcitabine. Pathway analyses further delineated the involvement of MSH6 as a modulator, influencing the delicate equilibrium between the pro-survival and pro-death functions of autophagy.Our study elucidates the intricate interplay between MSH6, autophagy, and cisplatin efficacy, highlighting MSH6 as a potential therapeutic target to overcome cisplatin resistance. By revealing the modulation of autophagy pathways by MSH6 inhibition, our findings offer insights into novel approaches for enhancing the efficacy of cisplatin-based cancer therapy through targeted interventions.

Integrating frontiers: a holistic, quantum and evolutionary approach to conquering cancer through systems biology and multidisciplinary synergy.

Cancer therapy is facing increasingly significant challenges, marked by a wide range of techniques and research efforts centered around somatic mutations, precision oncology, and the vast amount of big data. Despite this abundance of information, the quest to cure cancer often seems more elusive, with the "war on cancer" yet to deliver a definitive victory. A particularly pressing issue is the development of tumor treatment resistance, highlighting the urgent need for innovative approaches. Evolutionary, Quantum Biology and System Biology offer a promising framework for advancing experimental cancer research. By integrating theoretical studies, translational methods, and flexible multidisciplinary clinical research, there's potential to enhance current treatment strategies and improve outcomes for cancer patients. Establishing stronger links between evolutionary, quantum, entropy and chaos principles and oncology could lead to more effective treatments that leverage an understanding of the tumor's evolutionary dynamics, paving the way for novel methods to control and mitigate cancer. Achieving these objectives necessitates a commitment to multidisciplinary and interprofessional collaboration at the heart of both research and clinical endeavors in oncology. This entails dismantling silos between disciplines, encouraging open communication and data sharing, and integrating diverse viewpoints and expertise from the outset of research projects. Being receptive to new scientific discoveries and responsive to how patients react to treatments is also crucial. Such strategies are key to keeping the field of oncology at the forefront of effective cancer management, ensuring patients receive the most personalized and effective care. Ultimately, this approach aims to push the boundaries of cancer understanding, treating it as a manageable chronic condition, aiming to extend life expectancy and enhance patient quality of life.

Myofascial pain syndrome in patients with cancer: a narrative review.

Myofascial pain syndrome (MPS) is a chronic musculoskeletal pain syndrome. The purpose of this review is to describe the epidemiological and treatment evidence and to address the future research agenda in patients with cancer. A narrative review of previous reports investigating the prevalence and treatment of MPS in the oncology field is presented. The target population is patients with cancer and cancer survivors. There have been three prospective and two retrospective studies investigating the prevalence of MPS. MPS is as high as 38%-45% in patients with advanced or incurable cancer and 11.9%-44.8% in cancer survivors. A total of nine reports investigated the efficacy of the following interventions: trigger point injection (TPI), myofascial techniques and ischaemic compression. TPI has been reported to be effective in four observational studies. One randomised study reported the efficacy of myofascial techniques, but two randomised studies reported no added beneficial effects of it in breast cancer survivors. Two randomised studies investigated the efficacy of ischaemic compression, but the obtained results were contradictory. MPS is highly prevalent. We should know that non-cancer pain is also common in both patients with cancer and survivors. In treating such pain, careful physical examination is essential. Then, non-pharmacological treatment should be considered as well as pharmacotherapy. As evidence regarding MPS in the oncology field is scarce, further research is warranted.

End-to-end reproducible AI pipelines in radiology using the cloud

Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address these issues remains challenging. Here, we show the potential of cloud-based infrastructure for implementing and sharing transparent and reproducible AI-based radiology pipelines. We demonstrate end-to-end reproducibility from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results. We successfully implement two distinct use cases, starting from recent literature on AI-based biomarkers for cancer imaging. Using cloud-hosted data and computing, we confirm the findings of these studies and extend the validation to previously unseen data for one of the use cases. Furthermore, we provide the community with transparent and easy-to-extend examples of pipelines impactful for the broader oncology field. Our approach demonstrates the potential of cloud resources for implementing, sharing, and using reproducible and transparent AI pipelines, which can accelerate the translation into clinical solutions.

NKG2D-bispecific enhances NK and CD8+ T cell antitumor immunity

BackgroundCancer immunotherapy approaches that elicit immune cell responses, including T and NK cells, have revolutionized the field of oncology. However, immunosuppressive mechanisms restrain immune cell activation within solid tumors so additional strategies to augment activity are required.MethodsWe identified the co-stimulatory receptor NKG2D as a target based on its expression on a large proportion of CD8+ tumor infiltrating lymphocytes (TILs) from breast cancer patient samples. Human and murine surrogate NKG2D co-stimulatory receptor-bispecifics (CRB) that bind NKG2D on NK and CD8+ T cells as well as HER2 on breast cancer cells (HER2-CRB) were developed as a proof of concept for targeting this signaling axis in vitro and in vivo.ResultsHER2-CRB enhanced NK cell activation and cytokine production when co-cultured with HER2 expressing breast cancer cell lines. HER2-CRB when combined with a T cell-dependent-bispecific (TDB) antibody that synthetically activates T cells by crosslinking CD3 to HER2 (HER2-TDB), enhanced T cell cytotoxicity, cytokine production and in vivo antitumor activity. A mouse surrogate HER2-CRB (mHER2-CRB) improved in vivo efficacy of HER2-TDB and augmented NK as well as T cell activation, cytokine production and effector CD8+ T cell differentiation.ConclusionWe demonstrate that targeting NKG2D with bispecific antibodies (BsAbs) is an effective approach to augment NK and CD8+ T cell antitumor immune responses. Given the large number of ongoing clinical trials leveraging NK and T cells for cancer immunotherapy, NKG2D-bispecifics have broad combinatorial potential.

The impact of multicentric datasets for the automated tumor delineation in primary prostate cancer using convolutional neural networks on 18F-PSMA-1007 PET

PurposeConvolutional Neural Networks (CNNs) have emerged as transformative tools in the field of radiation oncology, significantly advancing the precision of contouring practices. However, the adaptability of these algorithms across diverse scanners, institutions, and imaging protocols remains a considerable obstacle. This study aims to investigate the effects of incorporating institution-specific datasets into the training regimen of CNNs to assess their generalization ability in real-world clinical environments. Focusing on a data-centric analysis, the influence of varying multi- and single center training approaches on algorithm performance is conducted.MethodsnnU-Net is trained using a dataset comprising 161 18F-PSMA-1007 PET images collected from four distinct institutions (Freiburg: n = 96, Munich: n = 19, Cyprus: n = 32, Dresden: n = 14). The dataset is partitioned such that data from each center are systematically excluded from training and used solely for testing to assess the model's generalizability and adaptability to data from unfamiliar sources. Performance is compared through a 5-Fold Cross-Validation, providing a detailed comparison between models trained on datasets from single centers to those trained on aggregated multi-center datasets. Dice Similarity Score, Hausdorff distance and volumetric analysis are used as primary evaluation metrics.ResultsThe mixed training approach yielded a median DSC of 0.76 (IQR: 0.64–0.84) in a five-fold cross-validation, showing no significant differences (p = 0.18) compared to models trained with data exclusion from each center, which performed with a median DSC of 0.74 (IQR: 0.56–0.86). Significant performance improvements regarding multi-center training were observed for the Dresden cohort (multi-center median DSC 0.71, IQR: 0.58–0.80 vs. single-center 0.68, IQR: 0.50–0.80, p < 0.001) and Cyprus cohort (multi-center 0.74, IQR: 0.62–0.83 vs. single-center 0.72, IQR: 0.54–0.82, p < 0.01). While Munich and Freiburg also showed performance improvements with multi-center training, results showed no statistical significance (Munich: multi-center DSC 0.74, IQR: 0.60–0.80 vs. single-center 0.72, IQR: 0.59–0.82, p > 0.05; Freiburg: multi-center 0.78, IQR: 0.53–0.87 vs. single-center 0.71, IQR: 0.53–0.83, p = 0.23).ConclusionCNNs trained for auto contouring intraprostatic GTV in 18F-PSMA-1007 PET on a diverse dataset from multiple centers mostly generalize well to unseen data from other centers. Training on a multicentric dataset can improve performance compared to training exclusively with a single-center dataset regarding intraprostatic 18F-PSMA-1007 PET GTV segmentation. The segmentation performance of the same CNN can vary depending on the dataset employed for training and testing.

The effect of virtual reality versus standard-of-care treatment on pain perception during paediatric vaccination: Study protocol for a randomised controlled trial.

To study the effects of virtual reality (VR) on pain perception among a paediatric population while being vaccinated. We will also investigate the effects of VR on the fear experienced by children during the vaccination procedure, as well as parental satisfaction levels. Many studies, for example, in the fields of oncology, dermatology or plastic surgery, have described the benefits of using VR to reduce the perception of pain among paediatric populations. These results are encouraging, but their main limitations were the small sample sizes they included or their methodological design. This will be a randomised clinical trial. SPIRIT guidelines were followed to report this protocol, and we will use the CONSORT and CONSORT-EHEALTH guidelines to report the randomised clinical trial. The sample will comprise 148 children aged 3-14 years who will come for vaccination at the Tres Forques Health Center. The participants will be randomly allocated into intervention group (VR; n = 74) or (the control group standard-of-care; n = 74) at a 1:1 ratio. The intervention group will view experiences with a VR headset as a distraction measure. The study variables will be the level of pain and fear perceived by the child during vaccination, parental satisfaction with the vaccination procedure, and sociodemographic and vaccination variables. The start of the study is planned for September 2020, and the results will be expected in September 2021. This study aims to identify what measures reduce pain and fear in children during vaccination, which in turn, can help to improve the degree of parental satisfaction with these procedures. Vaccination is an independent function of the nursing profession. Identifying which distraction measures reduce the perception of pain and fear in the paediatric population will not only improve children's experiences but will also improve the satisfaction of both parents and children, thus increasing the degree of compliance with the vaccination schedule.

Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.

The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.

Uveal Melanoma: Comprehensive Review of Its Pathophysiology, Diagnosis, Treatment, and Future Perspectives.

Uveal melanoma (UM) is the most common intraocular malignancy in adults. Recent advances highlight the role of tumor-derived extracellular vesicles (TEV) and circulating hybrid cells (CHC) in UM tumorigenesis. Bridged with liquid biopsies, a novel technology that has shown incredible performance in detecting cancer cells or products derived from tumors in bodily fluids, it can significantly impact disease management and outcome. The aim of this comprehensive literature review is to provide a summary of current knowledge and ongoing advances in posterior UM pathophysiology, diagnosis, and treatment. The first section of the manuscript discusses the complex and intricate role of TEVs and CHCs. The second part of this review delves into the epidemiology, etiology and risk factors, clinical presentation, and prognosis of UM. Third, current diagnostic methods, ensued by novel diagnostic tools for the early detection of UM, such as liquid biopsies and artificial intelligence-based technologies, are of paramount importance in this review. The fundamental principles, limits, and challenges associated with these diagnostic tools, as well as their potential as a tracker for disease progression, are discussed. Finally, a summary of current treatment modalities is provided, followed by an overview of ongoing preclinical and clinical research studies to provide further insights on potential biomolecular pathway alterations and therapeutic targets for the management of UM. This review is thus an important resource for all healthcare professionals, clinicians, and researchers working in the field of ocular oncology.