Wenjuan Ma

Background: Assessment of lymph node involvement was incorporated into the 2023 International Federation of Gynecology and Obstetrics (FIGO) staging system as a critical determinant of patient management and prognostication. This study aims to validate the prognostic significance of metastatic lymph node characteristics and their correlation with molecular classification. Methods: We conducted a retrospective analysis of 131 patients with stage IIIC endometrial carcinoma who underwent surgical intervention at the Tianjin Medical University Cancer Institute and Hospital from 2010 to 2025. The patients were stratified based on various characteristics of metastatic lymph nodes. Progression-free survival (PFS) was estimated using Kaplan–Meier curves, and group comparisons were performed using the log-rank test. Univariate Cox proportional hazards regression was conducted to identify independent prognostic factors for PFS. Results: In accordance with the 2023 FIGO staging system, within the stage IIIC cohort, patients with concurrent pelvic and para-aortic lymph node metastases and those in the stage IIIC1 cohort with macrometastasis demonstrated decreased PFS (HR = 2.748; 95% CI = 1.227–6.151; p = 0.011, and HR = 4.039; 95% CI = 1.232–13.240; p = 0.021, respectively). Furthermore, patients with lymph nodes showing extracapsular extension had significantly reduced PFS in the stage IIIC, stage IIIC1, and stage IIIC1ii cohorts (HR = 10.12; 95% CI = 2.38–42.99; p < 0.001; HR = 3.58; 95% CI = 1.242–10.31; p = 0.011; and HR = 2.32; 95% CI = 0.83–6.71; p = 0.037, respectively). The number of involved lymph nodes, the largest diameter of metastatic foci in lymph nodes, and the presence or absence of extracapsular extension of metastatic lymph nodes were significantly different among the NSMP, MMRd, and p53abn molecular classifications (p = 0.033, p < 0.001, and p = 0.002, respectively). Patients with extracapsular extension of metastatic lymph nodes had worse PFS than those without in the MMRd group (p = 0.018). Conclusions: Extracapsular extension (ECE) of metastatic lymph nodes is the strongest predictor of worse progression-free survival in stage IIIC endometrial carcinoma, especially in the IIIC1 and MMRd subgroups, supporting its value for risk stratification and prognostic assessment.

Background: A deep learning (DL) model based on preoperative computed tomography (CT) has been proposed for estimating recurrence in patients with ovarian cancer. However, the inherent opacity of DL models complicates the interpretation of their output, limiting their clinical applicability. The aim of this study was thus to generate histopathologic evidence supporting such DL prediction models and to construct a clustering-based analytical framework for identifying patients with risk factors for ovarian cancer. Methods: A retrospective study was conducted in which preoperative CT data were collected from patients with high-grade serous ovarian cancer treated with radical tumor resection from January 2013 to December 2019 at three tertiary care centers. Unsupervised clustering was performed with 1,280 DL model-driven features, and the associations between clusters and histopathological features were analyzed. Multivariate regression was used to investigate the added value of DL outputs for histopathologic correlations. Results: A total of 418 patients [median age 55 years, interquartile range (IQR), 30–77 years] were evaluated. Unsupervised clusters 3 and 4 were associated with the positive status of P53, P16, and Ki-67, along with invasion of the omentum, rectum, and pelvic wall (PConclusions: The DL model could effectively extract histopathological features of high-grade serous ovarian cancer from CT images.

Background: Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer. This study aimed to build an artificial intelligence (AI) model to predict ALN metastasis based on pre-treatment dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) of breast cancer combined with radiomics algorithms. Methods: Pre-treatment DCE-MRI dataset of 166 patients with pathologically confirmed breast cancer diagnosis from January 2017 to August 2020 was collected, and all patients were randomly divided into a training group and test group with a ratio of 7:3. Each patient underwent pre-enhancement as well as post-enhancement 1–6 MRI, and a total of 7,224 two-dimensional (2D) and 9,863 three-dimensional (3D) features were extracted, respectively. Radiomics models based on 2D, 3D, pre-enhancement, and the first post-enhancement images were established using the least absolute shrinkage selection operator (LASSO) algorithm based on machine learning, and the area under the curve (AUC), accuracy, sensitivity, and specificity of the models were calculated. Results: The mean AUC, accuracy, sensitivity, and specificity of the 10-fold cross-validation of the 3D radiomics-based model were 82%, 82%, 83%, and 81%, respectively. The C-index of the combined model with combining radiomics features and clinical features was 90%, the AUC was 90%, the specificity was 91%, the sensitivity was 77% and the accuracy was 84%. Conclusions: The comprehensive prediction model using DCE-MRI image combined with clinical features can accurately predict ALN metastasis in breast cancer.

Artificial intelligence (AI) is significantly transforming surgery by enhancing precision, decision-making, and patient outcomes. This bibliometric analysis examines AI’s impact on surgery, highlighting research trends, key contributors, and evolving themes from 1998 to 2024. Utilizing data from the Web of Science Core Collection and analyzed through the Bibliometrix tool, the study reviews publication trends, author impact, institutional contributions, country-specific research activities, and keyword frequency. A total of 821 articles were examined, revealing a 14.53% annual growth rate in publications, increasing from one in 1998 to 328 in 2023. Influential contributors include 10 157 authors, notably HASHIMOTO DA and ITO M. Prominent institutions such as Harvard University and Stanford University, along with leading countries like the USA and China, play major roles in this field. High-frequency keywords identify core research areas: surgery, artificial intelligence, classification, diagnosis, and outcomes. Thematic evolution shows a shift from foundational concepts to advanced applications and interdisciplinary collaborations. AI integration into surgical practices is revolutionizing the field, driving advancements in precision, efficiency, and patient care. The study underscores significant research growth, influential contributors, and key trends, emphasizing the importance of continued interdisciplinary collaboration and innovation. Future research should focus on enhancing AI applications, addressing data quality and security challenges, and expanding into diverse surgical contexts to further improve surgical outcomes and patient care. AI in surgery is a rapidly evolving and promising field for innovation, with its full potential reliant on enhanced collaboration across disciplines.

Background: Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The value of magnetic resonance imaging (MRI)‐based tumor heterogeneity in assessing ALN metastasis in BC is unclear.Purpose: To assess the value of deep learning (DL)‐derived kinetic heterogeneity parameters based on BC dynamic contrast‐enhanced (DCE)‐MRI to infer the ALN status.Study Type: Retrospective.Subjects: 1256/539/153/115 patients in the training cohort, internal validation cohort, and external validation cohorts I and II, respectively.Field Strength/Sequence: 1.5 T/3.0 T, non‐contrast T1‐weighted spin‐echo sequence imaging (T1WI), DCE‐T1WI, and diffusion‐weighted imaging.Assessment: Clinical pathological and MRI semantic features were obtained by reviewing histopathology and MRI reports. The segmentation of the tumor lesion on the first phase of T1WI DCE‐MRI images was applied to other phases after registration. A DL architecture termed convolutional recurrent neural network (ConvRNN) was developed to generate the KH^image (kinetic heterogeneity of DCE‐MRI image) score that indicated the ALN status in patients with BC. The model was trained and optimized on training and internal validation cohorts, tested on two external validation cohorts. We compared ConvRNN model with other 10 models and the subgroup analyses of tumor size, magnetic field strength, and molecular subtype were also evaluated.Statistical Tests: Chi‐squared, Fisher's exact, Student's t, Mann–Whitney U tests, and receiver operating characteristics (ROC) analysis were performed. P < 0.05 was considered significant.Results: The ConvRNN model achieved area under the curve (AUC) of 0.802 in the internal validation cohort and 0.785–0.806 in the external validation cohorts. The ConvRNN model could well evaluate the ALN status of the four molecular subtypes (AUC = 0.685–0.868). The patients with larger tumor sizes (>5 cm) were more susceptible to ALN metastasis with KH^image scores of 0.527–0.827.Data Conclusion: A ConvRNN model outperformed traditional models for determining the ALN status in patients with BC.Level of Evidence: 3Technical Efficacy: Stage 2