Interest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities.While AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people's daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model's behavior. Prompts such as "Write this note for a specialist consultant" and "Write this note for the patient's mother" will produce markedly different content.Foundation models and generative AI represent a major revolution in AI's capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.

Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of "mountains" (genes altered in a high percentage of tumors) and a much larger number of "hills" (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or "drive" tumorigenesis. A typical tumor contains two to eight of these "driver gene" mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.

Background: Current prognostic models lack the use of pre-operative CT images to predict recurrence in endometrial cancer (EC) patients. Our study aimed to investigate the potential of radiomic features extracted from pre-surgical CT scans to accurately predict disease-free survival (DFS) among EC patients. Methods: Contrast-Enhanced CT (CE-CT) scans from 81 EC cases were used to extract the radiomic features from semi-automatically contoured volumes of interest. We employed a 10-fold cross-validation approach with a 6:4 training to test set and utilized data augmentation and balancing techniques. Univariate analysis was applied for feature reduction leading to the development of three distinct machine learning (ML) models for the prediction of DFS: LASSO-Cox, CoxBoost and Random Forest (RFsrc). Results: In the training set, the ML models demonstrated AUCs ranging from 0.92 to 0.93, sensitivities from 0.96 to 1.00 and specificities from 0.77 to 0.89. In the test set, AUCs ranged from 0.86 to 0.90, sensitivities from 0.89 to 1.00 and specificities from 0.73 to 0.90. Patients classified as having a high recurrence risk prediction by ML models exhibited significantly worse DSF (p-value < 0.001) across all models. Conclusions: Our findings demonstrate the potential of radiomics in predicting EC recurrence. While further validation studies are needed, our results underscore the promising role of radiomics in forecasting EC outcomes.

The primary aim of this study was to develop and validate radiomics models, applied to ultrasound images, capable of differentiating from other cancers high‐risk endometrial cancer, as defined jointly by the European Society for Medical Oncology, European Society of Gynaecological Oncology and European Society for Radiotherapy & Oncology (ESMO‐ESGO‐ESTRO) in 2016. The secondary aim was to develop and validate radiomics models for differentiating low‐risk endometrial cancer from other endometrial cancers.

Hysteroscopy is a commonly used technique for diagnosing endometrial lesions. It is essential to develop an objective model to aid clinicians in lesion diagnosis, as each type of lesion has a distinct treatment, and judgments of hysteroscopists are relatively subjective. This study constructs a convolutional neural network model that can automatically classify endometrial lesions using hysteroscopic images as input.All histopathologically confirmed endometrial lesion images were obtained from the Shengjing Hospital of China Medical University, including endometrial hyperplasia without atypia, atypical hyperplasia, endometrial cancer, endometrial polyps, and submucous myomas. The study included 1851 images from 454 patients. After the images were preprocessed (histogram equalization, addition of noise, rotations, and flips), a training set of 6478 images was input into a tuned VGGNet-16 model; 250 images were used as the test set to evaluate the model's performance. Thereafter, we compared the model's results with the diagnosis of gynecologists.The overall accuracy of the VGGNet-16 model in classifying endometrial lesions is 80.8%. Its sensitivity to endometrial hyperplasia without atypia, atypical hyperplasia, endometrial cancer, endometrial polyp, and submucous myoma is 84.0%, 68.0%, 78.0%, 94.0%, and 80.0%, respectively; for these diagnoses, the model's specificity is 92.5%, 95.5%, 96.5%, 95.0%, and 96.5%, respectively. When classifying lesions as benign or as premalignant/malignant, the VGGNet-16 model's accuracy, sensitivity, and specificity are 90.8%, 83.0%, and 96.0%, respectively. The diagnostic performance of the VGGNet-16 model is slightly better than that of the three gynecologists in both classification tasks. With the aid of the model, the overall accuracy of the diagnosis of endometrial lesions by gynecologists can be improved.The VGGNet-16 model performs well in classifying endometrial lesions from hysteroscopic images and can provide objective diagnostic evidence for hysteroscopists.

Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) is assigned after exclusion of the defining features of the other three molecular subtypes and includes patients with heterogeneous clinical outcomes. In this study, we employ artificial intelligence (AI)-powered histopathology image analysis to differentiate between p53abn and NSMP EC subtypes and consequently identify a sub-group of NSMP EC patients that has markedly inferior progression-free and disease-specific survival (termed 'p53abn-like NSMP'), in a discovery cohort of 368 patients and two independent validation cohorts of 290 and 614 from other centers. Shallow whole genome sequencing reveals a higher burden of copy number abnormalities in the 'p53abn-like NSMP' group compared to NSMP, suggesting that this group is biologically distinct compared to other NSMP ECs. Our work demonstrates the power of AI to detect prognostically different and otherwise unrecognizable subsets of EC where conventional and standard molecular or pathologic criteria fall short, refining image-based tumor classification. This study's findings are applicable exclusively to females.© 2024. The Author(s).

A European consensus conference on endometrial carcinoma was held in 2014 to produce multidisciplinary evidence-based guidelines on selected questions. Given the large body of literature on the management of endometrial carcinoma published since 2014, the European Society of Gynaecological Oncology (ESGO), the European SocieTy for Radiotherapy & Oncology (ESTRO) and the European Society of Pathology (ESP) jointly decided to update these evidence-based guidelines and to cover new topics in order to improve the quality of care for women with endometrial carcinoma across Europe and worldwide. ESGO/ESTRO/ESP nominated an international multidisciplinary development group consisting of practicing clinicians and researchers who have demonstrated leadership and expertise in the care and research of endometrial carcinoma (27 experts across Europe). To ensure that the guidelines are evidence-based, the literature published since 2014, identified from a systematic search was reviewed and critically appraised. In the absence of any clear scientific evidence, judgment was based on the professional experience and consensus of the development group. The guidelines are thus based on the best available evidence and expert agreement. Prior to publication, the guidelines were reviewed by 191 independent international practitioners in cancer care delivery and patient representatives. The guidelines comprehensively cover endometrial carcinoma staging, definition of prognostic risk groups integrating molecular markers, pre- and intra-operative work-up, fertility preservation, management for early, advanced, metastatic, and recurrent disease and palliative treatment. Principles of radiotherapy and pathological evaluation are also defined.Copyright © 2021 International Gynecologic Cancer Society and European Society of Gynecological Oncology [Published by BMJ]; Springer Verlag GmbH Berlin Heidelberg, part of Springer Nature. Published by Elsevier B.V. All rights reserved.

Endometrial cancer (EC) is the seventh most common tumor in women, and prognosis of recurrent and metastatic disease is poor. Cervical cancer (CC) represents the fifth most common gynecological cancer. While ECs are more common in developed countries, the incidence of CC has decreased due to the recent implementation of large screening and vaccination programs. Until very recently, patients with advanced or unresectable EC or CC had very limited treatment options and were receiving in first line setting platinum/taxane-based chemotherapy (CT). Significant progress in the treatment of gynecological cancers has occurred in the last few years, with the use of innovative targeted therapies and immunotherapy. However, targeting the immune system in patients with gynecological tumors remains challenging and is not always successful. In ovarian cancer, several immunotherapy treatment regimens have been investigated (as monotherapy and combination therapy in first and subsequent lines of treatment) and showed poor responses. Therefore, we specifically focused our review on EC and CC for their specific immune-related features and therapeutic results demonstrated with immunotherapy. We report recent and current immunotherapy-based clinical trials and provide a review of emerging data that are likely to impact immunotherapy development based on increased biomarkers’ identification to monitor response and overcome resistance.

Cancer-associated fibroblasts (CAFs), a stromal cell population with cell-of-origin, phenotypic and functional heterogeneity, are the most essential components of the tumor microenvironment (TME). Through multiple pathways, activated CAFs can promote tumor growth, angiogenesis, invasion and metastasis, along with extracellular matrix (ECM) remodeling and even chemoresistance. Numerous previous studies have confirmed the critical role of the interaction between CAFs and tumor cells in tumorigenesis and development. However, recently, the mutual effects of CAFs and the tumor immune microenvironment (TIME) have been identified as another key factor in promoting tumor progression. The TIME mainly consists of distinct immune cell populations in tumor islets and is highly associated with the antitumor immunological state in the TME. CAFs interact with tumor-infiltrating immune cells as well as other immune components within the TIME via the secretion of various cytokines, growth factors, chemokines, exosomes and other effector molecules, consequently shaping an immunosuppressive TME that enables cancer cells to evade surveillance of the immune system. In-depth studies of CAFs and immune microenvironment interactions, particularly the complicated mechanisms connecting CAFs with immune cells, might provide novel strategies for subsequent targeted immunotherapies. Herein, we shed light on recent advances regarding the direct and indirect crosstalk between CAFs and infiltrating immune cells and further summarize the possible immunoinhibitory mechanisms induced by CAFs in the TME. In addition, we present current related CAF-targeting immunotherapies and briefly describe some future perspectives on CAF research in the end.

Regulatory T (Treg) cells reportedly play crucial roles in tumor angiogenesis as well as antitumor immunity. In order to explore their therapeutic potential, we investigated the precise prognostic impact of Treg markers in endometrial carcinoma.

Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with high incidence and poor prognosis. Although immunotherapy has brought significant survival benefits to advanced UCEC patients, traditional evaluation indicators cannot accurately identify all potential beneficiaries of immunotherapy. Consequently, it is necessary to construct a new scoring system to predict patient prognosis and responsiveness of immunotherapy.

HER2(+) gastric cancer (GC) can benefit from trastuzumab. However, the impact of additional trastuzumab in preoperative treatment on immune cells remains largely unknown.

Platinum-based chemotherapy is the cornerstone treatment for female high-grade serous ovarian carcinoma (HGSOC), but choosing an appropriate treatment for patients hinges on their responsiveness to it. Currently, no available biomarkers can promptly predict responses to platinum-based treatment. Therefore, we developed the Pathologic Risk Classifier for HGSOC (PathoRiCH), a histopathologic image-based classifier. PathoRiCH was trained on an in-house cohort (n = 394) and validated on two independent external cohorts (n = 284 and n = 136). The PathoRiCH-predicted favorable and poor response groups show significantly different platinum-free intervals in all three cohorts. Combining PathoRiCH with molecular biomarkers provides an even more powerful tool for the risk stratification of patients. The decisions of PathoRiCH are explained through visualization and a transcriptomic analysis, which bolster the reliability of our model's decisions. PathoRiCH exhibits better predictive performance than current molecular biomarkers. PathoRiCH will provide a solid foundation for developing an innovative tool to transform the current diagnostic pipeline for HGSOC.© 2024. The Author(s).

Immune checkpoint inhibitors, including antibodies that block programmed cell death protein-1 (PD-1) and PD-L1, have transformed the management of many cancers. However, the majority of patients have primary or acquired resistance to these immunotherapies. There is a significant unmet need for predictive biomarkers that can reliably identify patients who derive a clinically meaningful response from PD-1/PD-L1 blockade. High tumor mutational burden (TMB-H) has shown promise as a biomarker in lung cancer, but the broad applicability of TMB-H as a biomarker of response across all solid tumors is unclear. The FDA has approved the PD-1 inhibitor, pembrolizumab, as a therapy for all solid tumors with TMB equal to or greater than 10 mutations/megabase as measured by the FoundationOne CDx assay. This approval was based on an exploratory analysis of the KEYNOTE-158 study, which was a single-arm, phase II multi-cohort study of pembrolizumab for select, previously treated advanced solid tumors. Here, we elucidate the caveats of using TMB as a biomarker with a universal threshold across all solid tumors. While we recognize the importance of this and other FDA pan-cancer approvals, several questions about TMB as a predictive biomarker remain unanswered. In this perspective, we discuss clinical trial evidence in this area. We review the relationship between TMB and the tumor immune microenvironment. We highlight the risks of extrapolating evidence from a limited number of tumor histologies to all solid tumors, and we propose avenues for future research.©2020 American Association for Cancer Research.

Endometrial cancer (EC) is the most common gynecologic cancer. The overall survival remains unsatisfying due to the lack of effective treatment screening approaches. Immunotherapy as a promising therapy has been applied for EC treatment, but still fails in many cases. Therefore, there is a strong need to optimize the screening approach for clinical treatment. In this study, we employed co-expression network (GCN) analysis to mine immune-related GCN modules and key genes and further constructed an immune-related risk score model (IRSM). The IRSM was proved effective as an independent predictor of poor prognosis. The roles of IRSM-related genes in EC were confirmed by IHC. The molecular basis, tumor immune microenvironment and clinical characteristics of the IRSM were revealed. Moreover, the IRSM effectiveness was associated with immunotherapy and chemotherapy. Patients in the low-risk group were more sensitive to immunotherapy and chemotherapy than those in the high-risk group. Interestingly, the patients responding to immunotherapy were also more sensitive to chemotherapy. Overall, we developed an IRSM which could be used to predict the prognosis, immunotherapy response and chemotherapy sensitivity of EC patients. Our analysis not only improves the treatment of EC but also offers targets for personalized therapeutic interventions.