Cancer, characterized by the uncontrolled proliferation of cells, is one of the leading causes of death globally, with approximately one in five people developing the disease in their lifetime. While many driver genes were identified decades ago, and most cancers can be classified based on morphology and progression, there is still a significant gap in knowledge about genetic aberrations and nuclear DNA damage. The study of two critical groups of genes-tumor suppressors, which inhibit proliferation and promote apoptosis, and oncogenes, which regulate proliferation and survival-can help to understand the genomic causes behind tumorigenesis, leading to more personalized approaches to diagnosis and treatment. Aberration of tumor suppressors, which undergo two-hit and loss-of-function mutations, and oncogenes, activated forms of proto-oncogenes that experience one-hit and gain-of-function mutations, are responsible for the dysregulation of key signaling pathways that regulate cell division, such as p53, Rb, Ras/Raf/ERK/MAPK, PI3K/AKT, and Wnt/β-catenin. Modern breakthroughs in genomics research, like next-generation sequencing, have provided efficient strategies for mapping unique genomic changes that contribute to tumor heterogeneity. Novel therapeutic approaches have enabled personalized medicine, helping address genetic variability in tumor suppressors and oncogenes. This comprehensive review examines the molecular mechanisms behind tumor-suppressor genes and oncogenes, the key signaling pathways they regulate, epigenetic modifications, tumor heterogeneity, and the drug resistance mechanisms that drive carcinogenesis. Moreover, the review explores the clinical application of sequencing techniques, multiomics, diagnostic procedures, pharmacogenomics, and personalized treatment and prevention options, discussing future directions for emerging technologies.

Randomized clinical trials represent the gold standard for the introduction of innovative therapies in medical oncology, and they provide the highest level of evidence to ascertain the clinical activity of new drugs or novel combinations. However, the current infrastructure of clinical trials supporting innovative drug approvals is challenged by an increased body of knowledge concerning tumor biology and therapy resistance, a fast-growing armamentarium of novel anti-cancer compounds, an impressively upscaled data analysis capacity, as well as increasing costs related to clinical trials' management. In this scenario, modern clinical trial designs need to evolve to expedite new drug approvals by tailoring patients' treatment strategies according to their medical needs. Balanced, patient-oriented, clinical trial designs are eagerly warranted to increase their efficiency, to include the fast-pace of technological innovations and scientific discoveries, and ultimately to face the challenges of the modern medical oncology field.

With the aid of ever-increasing computing resources, many deep learning algorithms have been proposed to aid in diagnostic workup for clinicians. However, existing studies usually selected informative patches from whole-slide images for the training of the deep learning model, requiring labor-intensive labeling efforts. This work aimed to improve diagnostic accuracy through the statistic features extracted from hematoxylin and eosin-stained slides.

We designed a computational pipeline for the diagnosis of inverted urothelial papilloma (IUP) of the bladder from its cancer mimics using statistical features automatically extracted from whole-slide images. Whole-slide images from 225 cases of common and uncommon urothelial lesions (64 IUPs; 69 inverted urothelial carcinomas [UCInvs], and 92 low-grade urothelial carcinoma [UCLG]) were analyzed.

We identified 68 image features in total that were significantly different between IUP and UCInv and 42 image features significantly different between IUP and UCLG. Our method integrated multiple types of image features and achieved high AUCs (the AUCs) of 0.913 and 0.920 for classifying IUP from UCInv and conventional UC, respectively. Moreover, we constructed an ensemble classifier to test the prediction accuracy of IUP from an external validation cohort, which provided a new workflow to diagnose rare cancer subtypes and test the models with limited validation samples.

Our data suggest that the proposed computational pipeline can robustly and accurately capture histopathologic differences between IUP and other UC subtypes. The proposed workflow and related findings have the potential to expand the clinician's armamentarium for accurate diagnosis of urothelial malignancies and other rare tumors.

The progression of malignant tumors leads to the development of secondary tumors in various organs, including bones, the brain, liver, and lungs. This metastatic process severely impacts the prognosis of patients, significantly affecting their quality of life and survival rates. Research efforts have consistently focused on the intricate mechanisms underlying this process and the corresponding clinical management strategies. Consequently, a comprehensive understanding of the biological foundations of tumor metastasis, identification of pivotal signaling pathways, and systematic evaluation of existing and emerging therapeutic strategies are paramount to enhancing the overall diagnostic and treatment capabilities for metastatic tumors. However, current research is primarily focused on metastasis within specific cancer types, leaving significant gaps in our understanding of the complex metastatic cascade, organ-specific tropism mechanisms, and the development of targeted treatments. In this study, we examine the sequential processes of tumor metastasis, elucidate the underlying mechanisms driving organ-tropic metastasis, and systematically analyze therapeutic strategies for metastatic tumors, including those tailored to specific organ involvement. Subsequently, we synthesize the most recent advances in emerging therapeutic technologies for tumor metastasis and analyze the challenges and opportunities encountered in clinical research pertaining to bone metastasis. Our objective is to offer insights that can inform future research and clinical practice in this crucial field.

Artificial intelligence (AI)-based systems are being rapidly integrated into the fields of health and social care. Although such systems can substantially improve the provision of care, diverse and marginalized populations are often incorrectly or insufficiently represented within these systems. This review aims to assess the influence of AI on health and social care among these populations, particularly with regard to issues related to inclusivity and regulatory concerns.

We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six leading databases were searched, and 129 articles were selected for this review in line with predefined eligibility criteria.

This research revealed disparities in AI outcomes, accessibility, and representation among diverse groups due to biased data sources and a lack of representation in training datasets, which can potentially exacerbate inequalities in care delivery for marginalized communities.

AI development practices, legal frameworks, and policies must be reformulated to ensure that AI is applied in an equitable manner. A holistic approach must be used to address disparities, enforce effective regulations, safeguard privacy, promote inclusion and equity, and emphasize rigorous validation.

Cancer stem cells (CSCs) play a central role in tumor progression, recurrence, and resistance to conventional therapies, making them a critical focus in oncology research. This review provides a comprehensive analysis of CSC biology, emphasizing their self-renewal, differentiation, and dynamic interactions with the tumor microenvironment (TME). Key signaling pathways, including Wnt, Notch, and Hedgehog, are discussed in detail to highlight their potential as therapeutic targets. Current methodologies for isolating CSCs are critically examined, addressing their advantages and limitations in advancing precision medicine. Emerging technologies, such as CRISPR/Cas9 and single-cell sequencing, are explored for their transformative potential in unraveling CSC heterogeneity and informing therapeutic strategies. The review also underscores the pivotal role of the TME in supporting CSC survival, promoting metastasis, and contributing to therapeutic resistance. Challenges arising from CSC-driven tumor heterogeneity and dormancy are analyzed, along with strategies to mitigate these barriers, including novel therapeutics and targeted approaches. Ethical considerations and the integration of artificial intelligence in designing CSC-specific therapies are discussed as essential elements of future research. The manuscript advocates for a multi-disciplinary approach that combines innovative technologies, advanced therapeutics, and collaborative research to address the complexities of CSCs. By bridging existing gaps in knowledge and fostering advancements in personalized medicine, this review aims to guide the development of more effective cancer treatment strategies, ultimately improving patient outcomes.

Artificial intelligence broadly refers to computer systems that simulate intelligent behaviour with minimal human intervention. Emphasizing patient-centered care, research has explored patients' perspectives on artificial intelligence in medical care, indicating general acceptance of the technology but also concerns about supervision. However, these views have not been systematically examined from the perspective of patients with cancer, whose opinions may differ given the distinct psychosocial toll of the disease.

This protocol describes a scoping review aimed at summarizing the existing literature on the attitudes of patients with cancer toward the use of artificial intelligence in their medical care. The primary goal is to identify knowledge gaps and highlight opportunities for future research.

This scoping review protocol will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA-ScR). The electronic databases MEDLINE (OVID), EMBASE, PsycINFO, and CINAHL will be searched for peer-reviewed primary research articles published in academic journals. We will have two independent reviewers screen the articles retrieved from the literature search and select relevant studies based on our inclusion criteria, with a third reviewer resolving any disagreements. We will then compile the data from the included articles into a narrative summary and discuss the implications for clinical practice and future research.

To our knowledge, this will be the first scoping review to map the existing literature on the attitudes of patients with cancer regarding artificial intelligence in their medical care.

The rapid rise of artificial intelligence (AI) is transforming society; yet, the education of health care providers in this field is lagging. In health care, where AI promises to facilitate diagnostic accuracy, and allow for personalized treatment, bridging the knowledge and skill gaps for providers becomes vital. This article explores the challenges of AI education, such as the emergence of self-proclaimed experts during the pandemic, and the need for comprehensive training in AI language, mechanics, and ethics. It advocates for a new breed of health care professionals who are both practitioners and informaticians, who are capable through initial training or through continuing professional development of harnessing AI's potential. Interdisciplinary collaboration, ongoing education, and incentives are proposed to ensure health care benefits from AI's trajectory. This perspective article explores the hurdles and the imperative of creating educational programming designed specifically to help health care professionals augment their practice with AI.

The incidence of cancer continues to increase worldwide, resulting in significant physical, emotional, and financial challenges for individuals, families, communities, and healthcare systems. Cancer is projected to be responsible for approximately 10 million deaths in 2020, accounting for one in six deaths globally. Prostate, colon, lung, and breast cancers are the most common types of cancer. In India, it is estimated that there will be around 2.7 million cancer patients by 2020. Personalized medicine has the potential to offer an alternative approach to cancer treatment. Precision medicine, often known as personalized medicine, is a new cancer treatment technique that focuses on tailoring medication to each patient's specific genetic, biochemical, and lifestyle factors. The goal is to optimize tumor response while minimizing therapy side effects, resulting in improved patient care and quality of life. Personalized medicine allows for the creation of focused medicines that address specific gene mutations by leveraging knowledge about a patient's cancer, including its genetic makeup. Ongoing research seeks to detect gene modifications in diverse cancer types, produce novel diagnostic tools, and develop treatments that particularly target these genetic changes. In recent years, personalized medicine has achieved major advances in the treatment of solid tumors, with the promise to improve treatment precision, reduce side effects, as well as enhance outcomes for patients in cancer therapy. This review aims to objectively evaluate the transformation of cancer treatment, emphasizing the shift towards a more precise methodology.

Modern antiretroviral therapy is associated with reduced rates of HIV-related morbidity and mortality. HIV viral suppression and retention in care are critically important outcomes requiring successful continuous patient engagement. However, barriers to such engagement are complex and require innovative solutions.

A multistakeholder group of experts comprising clinicians and service delivery researchers assembled to clarify what constitutes engagement in HIV care and identify overarching themes that inform strategies in this field. This article captures this expert opinion and adds relevant literature on selected current best practices.

The multistakeholder group felt strongly that a better understanding of the facilitators of continuous care engagement was critical. Unlike 'retention in care,' 'engagement in care' for an individual is nuanced, flexible, evolves and requires ongoing communication between patients, providers, and other key stakeholders. The following approaches highlight care engagement strategies at different stakeholder levels: 1) patient-level: personalized care and incentivization; 2) clinic-level: wraparound, co-localized, patient-centered low-barrier care, a diverse multidisciplinary team, patient support networks, and expanded use of telemedicine; 3) healthcare system-level: utilization of external partnerships. We propose a series of diverse and complementary approaches based on a more nuanced understanding of the qualitative aspects of engagement in care.

The treatment for liver cancer has transitioned from traditional surgical resection to interventional therapies, which have become increasingly popular among patients due to their minimally invasive nature and significant local efficacy. However, with advancements in treatment technologies, accurately assessing patient response and predicting long-term survival has become a crucial research topic. Over the past decade, machine algorithms have made remarkable progress in the medical field, particularly in hepatology and prognosis studies of hepatocellular carcinoma (HCC). Machine algorithms, including deep learning and machine learning, can identify prognostic patterns and trends by analyzing vast amounts of clinical data. Despite significant advancements, several issues remain unresolved in the prognosis prediction of liver cancer using machine algorithms. Key challenges and main controversies include effectively integrating multi-source clinical data to improve prediction accuracy, addressing data privacy and ethical concerns, and enhancing the transparency and interpretability of machine algorithm decision-making processes. This paper aims to systematically review and analyze the current applications and potential of machine algorithms in predicting the prognosis of patients undergoing interventional therapy for liver cancer, providing theoretical and empirical support for future research and clinical practice.

Breast cancer is the most common malignant tumor among women worldwide and remains one of the leading causes of death among women. Its incidence and mortality rates are continuously rising. In recent years, with the rapid advancement of deep learning (DL) technology, DL has demonstrated significant potential in breast cancer diagnosis, prognosis evaluation, and treatment response prediction. This paper reviews relevant research progress and applies DL models to image enhancement, segmentation, and classification based on large-scale datasets from TCGA and multiple centers. We employed foundational models such as ResNet50, Transformer, and Hover-net to investigate the performance of DL models in breast cancer diagnosis, treatment, and prognosis prediction. The results indicate that DL techniques have significantly improved diagnostic accuracy and efficiency, particularly in predicting breast cancer metastasis and clinical prognosis. Furthermore, the study emphasizes the crucial role of robust databases in developing highly generalizable models. Future research will focus on addressing challenges related to data management, model interpretability, and regulatory compliance, ultimately aiming to provide more precise clinical treatment and prognostic evaluation programs for breast cancer patients.

Understanding the similarities of patients with cancer is essential to advancing personalized medicine, improving patient outcomes, and developing more effective and individualized treatments. It enables researchers to discover important patterns, biomarkers, and treatment strategies that can have a significant impact on cancer research and oncology. In addition, the identification of previously successfully treated patients supports oncologists in making treatment decisions for a new patient who is clinically or molecularly similar to the previous patient.

The planned review aims to systematically summarize, map, and describe existing evidence to understand how patient similarity is defined and used in cancer research and clinical care.

To systematically identify relevant studies and to ensure reproducibility and transparency of the review process, a comprehensive literature search will be conducted in several bibliographic databases, including Web of Science, PubMed, LIVIVIVO, and MEDLINE, covering the period from 1998 to February 2024. After the initial duplicate deletion phase, a study selection phase will be applied using Rayyan, which consists of 3 distinct steps: title and abstract screening, disagreement resolution, and full-text screening. To ensure the integrity and quality of the selection process, each of these steps is preceded by a pilot testing phase. This methodological process will culminate in the presentation of the final research results in a structured form according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart. The protocol has been registered in the Journal of Medical Internet Research.

This protocol outlines the methodologies used in conducting the scoping review. A search of the specified electronic databases and after removing duplicates resulted in 1183 unique records. As of March 2024, the review process has moved to the full-text evaluation phase. At this stage, data extraction will be conducted using a pretested chart template.

The scoping review protocol, centered on these main concepts, aims to systematically map the available evidence on patient similarity among patients with cancer. By defining the types of data sources, approaches, and methods used in the field, and aligning these with the research questions, the review will provide a foundation for future research and clinical application in personalized cancer care. This protocol will guide the literature search, data extraction, and synthesis of findings to achieve the review's objectives.

DERR1-10.2196/58705.

The accurate diagnosis of temporomandibular disorders continues to be a challenge, despite the existence of internationally agreed-upon diagnostic criteria. The purpose of this study is to review applications of deep learning models in the diagnosis of temporomandibular joint arthropathies.

An electronic search was conducted on PubMed, Scopus, Embase, Google Scholar, IEEE, arXiv, and medRxiv up to June 2023. Studies that reported the efficacy (outcome) of prediction, object detection or classification of TMJ arthropathies by deep learning models (intervention) of human joint-based or arthrogenous TMDs (population) in comparison to reference standard (comparison) were included. To evaluate the risk of bias, included studies were critically analysed using the quality assessment of diagnostic accuracy studies (QUADAS-2). Diagnostic odds ratios (DOR) were calculated. Forrest plot and funnel plot were created using STATA 17 and MetaDiSc.

Full text review was performed on 46 out of the 1056 identified studies and 21 studies met the eligibility criteria and were included in the systematic review. Four studies were graded as having a low risk of bias for all domains of QUADAS-2. The accuracy of all included studies ranged from 74% to 100%. Sensitivity ranged from 54% to 100%, specificity: 85%-100%, Dice coefficient: 85%-98%, and AUC: 77%-99%. The datasets were then pooled based on the sensitivity, specificity, and dataset size of seven studies that qualified for meta-analysis. The pooled sensitivity was 95% (85%-99%), specificity: 92% (86%-96%), and AUC: 97% (96%-98%). DORs were 232 (74-729). According to Deek's funnel plot and statistical evaluation (p =.49), publication bias was not present.

Deep learning models can detect TMJ arthropathies high sensitivity and specificity. Clinicians, and especially those not specialized in orofacial pain, may benefit from this methodology for assessing TMD as it facilitates a rigorous and evidence-based framework, objective measurements, and advanced analysis techniques, ultimately enhancing diagnostic accuracy.

Artificial intelligence (AI) holds exciting potential to revolutionize healthcare delivery in the United States. However, there are concerns about its potential to perpetuate disparities among historically marginalized populations.

Following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses, we conducted a narrative review of current literature on AI and health disparities in the United States. We aimed to answer the question, Does AI have the potential to reduce or eliminate health disparities, or will its use further exacerbate these disparities?

We searched the Ovid MEDLINE electronic database to identify and retrieve publications discussing AI and its impact on racial/ethnic health disparities. Articles were included if they discussed AI as a tool to mitigate racial health disparities with or without bias in developing and using AI.

This review included 65 articles. We identified six themes of limitations in AI that impact health equity: (1) biases in AI can perpetuate and exacerbate racial and ethnic inequities; (2) equity in algorithms should be a priority; (3) lack of diversity in the field of AI is concerning; (4) the need for regulation and testing algorithms for accuracy; (5) ethical standards for AI in health care are needed; and (6) the importance of promoting transparency and accountability.

While AI promises to enhance healthcare outcomes and address equity concerns, risks and challenges are associated with its implementation. To maximize the use of AI, it must be approached with an equity lens during all phases of development.

The application of three- and four-dimensional (3D/4D) printing in cancer research represents a significant advancement in understanding and addressing the complexities of cancer biology. 3D/4D materials provide more physiologically relevant environments compared to traditional two-dimensional models, allowing for a more accurate representation of the tumor microenvironment that enables researchers to study tumor progression, drug responses, and interactions with surrounding tissues under conditions similar to in vivo conditions. The dynamic nature of 4D materials introduces the element of time, allowing for the observation of temporal changes in cancer behavior and response to therapeutic interventions. The use of 3D/4D printing in cancer research holds great promise for advancing our understanding of the disease and improving the translation of preclinical findings to clinical applications. Accordingly, this review aims to briefly discuss 3D and 4D printing and their advantages and limitations in the field of cancer. Moreover, new techniques such as 5D/6D printing and artificial intelligence (AI) are also introduced as methods that could be used to overcome the limitations of 3D/4D printing and opened promising ways for the fast and precise diagnosis and treatment of cancer.

Molecular biomarkers are central to personalised medicine for human cancer patients. It is gaining traction as part of standard veterinary clinical practice for dogs and cats with cancer. Molecular biomarkers can be somatic or germline genomic alterations and can be ascertained from tissues or body fluids using various techniques. This review discusses how these genomic alterations can be determined and the findings used in clinical settings as diagnostic, prognostic, predictive, and screening biomarkers. We showcase the somatic and germline genomic alterations currently available to date for testing dogs and cats in a clinical setting, discussing their utility in each biomarker class. We also look at some emerging molecular biomarkers that are promising for clinical use. Finally, we discuss the hurdles that need to be overcome in going 'bench to bedside', i.e., the translation from discovery of genomic alterations to adoption by veterinary clinicians. As we understand more of the genomics underlying canine and feline tumours, molecular biomarkers will undoubtedly become a mainstay in delivering precision veterinary care to dogs and cats with cancer.

Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.

Patient care workflows are highly multimodal and intertwined: the intersection of data outputs provided from different disciplines and in different formats remains one of the main challenges of modern oncology. Artificial Intelligence (AI) has the potential to revolutionize the current clinical practice of oncology owing to advancements in digitalization, database expansion, computational technologies, and algorithmic innovations that facilitate discernment of complex relationships in multimodal data. Within oncology, radiation therapy (RT) represents an increasingly complex working procedure, involving many labor-intensive and operator-dependent tasks. In this context, AI has gained momentum as a powerful tool to standardize treatment performance and reduce inter-observer variability in a time-efficient manner. This review explores the hurdles associated with the development, implementation, and maintenance of AI platforms and highlights current measures in place to address them. In examining AI's role in oncology workflows, we underscore that a thorough and critical consideration of these challenges is the only way to ensure equitable and unbiased care delivery, ultimately serving patients' survival and quality of life.

Traditional health care systems face long-standing challenges, including patient diversity, geographical disparities, and financial constraints. The emergence of artificial intelligence (AI) in health care offers solutions to these challenges. AI, a multidisciplinary field, enhances clinical decision-making. However, imbalanced AI models may enhance health disparities.

This systematic review aims to investigate the economic performance and equity impact of AI in diagnostic imaging for skin, neurological, and pulmonary diseases. The research question is "To what extent does the use of AI in imaging exams for diagnosing skin, neurological, and pulmonary diseases result in improved economic outcomes, and does it promote equity in health care systems?"

The study is a systematic review of economic and equity evaluations following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and CHEERS (Consolidated Health Economic Evaluation Reporting Standards) guidelines. Eligibility criteria include articles reporting on economic evaluations or equity considerations related to AI-based diagnostic imaging for specified diseases. Data will be collected from PubMed, Embase, Scopus, Web of Science, and reference lists. Data quality and transferability will be assessed according to CHEC (Consensus on Health Economic Criteria), EPHPP (Effective Public Health Practice Project), and Welte checklists.

This systematic review began in March 2023. The literature search identified 9,526 publications and, after full-text screening, 9 publications were included in the study. We plan to submit a manuscript to a peer-reviewed journal once it is finalized, with an expected completion date in January 2024.

AI in diagnostic imaging offers potential benefits but also raises concerns about equity and economic impact. Bias in algorithms and disparities in access may hinder equitable outcomes. Evaluating the economic viability of AI applications is essential for resource allocation and affordability. Policy makers and health care stakeholders can benefit from this review's insights to make informed decisions. Limitations, including study variability and publication bias, will be considered in the analysis. This systematic review will provide valuable insights into the economic and equity implications of AI in diagnostic imaging. It aims to inform evidence-based decision-making and contribute to more efficient and equitable health care systems.

DERR1-10.2196/48544.

Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management strategies. However, the development of robust models remains unsatisfactory. We aimed to utilize artificial intelligence and single-cell analysis to address this issue. Two independent datasets were screened from the Gene Expression Omnibus (GEO) database and processed to obtain overlapping differentially expressed genes (DEGs) in stage II-IV vs. stage I diseases. Three explainable machine learning algorithms were integrated to construct models that could determine the tumor stage and extract important characteristic genes as diagnostic biomarkers. Correlations between cancer-associated fibroblast (CAF) infiltration and characteristic gene expression were analyzed using TIMER2.0 and their relationship with survival rates was comprehensively explored via the Kaplan-Meier plotter (KM-plotter) online database. The specific expression of characteristic genes in fibroblast subsets was investigated through single-cell analysis. A novel fibroblast subset signature was explored to predict immune checkpoint inhibitor (ICI) response and oncogene mutation through Tumor Immune Dysfunction and Exclusion (TIDE) and artificial neural network algorithms, respectively. We found that Support Vector Machine-Shapley Additive Explanations (SVM-SHAP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) successfully diagnosed early-stage OC (stage I). The area under the receiver operating characteristic curves (AUCs) of these models exceeded 0.990. Their overlapping characteristic gene, secreted frizzled-related protein 2 (SFRP2), was a risk factor that affected the overall survival of OC patients with stage II-IV disease (log-rank test: p < 0.01) and was specifically expressed in a fibroblast subset. Finally, the SFRP2+ fibroblast signature served as a novel predictor in evaluating ICI response and exploring pan-cancer tumor protein P53 (TP53) mutation (AUC = 0.853, 95% confidence interval [CI]: 0.829-0.877). In conclusion, the models based on SVM-SHAP, XGBoost, and RF enabled the early detection of OC for clinical decision making, and SFRP2+ fibroblast signature used in diagnostic models can inform OC treatment selection and offer pan-cancer TP53 mutation detection.