Ming Du

Cervical cancer (CC) is the fourth-most common and lethal cancer in women worldwide, with ~ 23% of cases in Chinese patients. We sought to characterize real-world treatment patterns and decision drivers for locally advanced CC (LACC) in China. We conducted a cross-sectional, 3-phase survey (quantitative development, cognitive, and quantitative interviews) of 360 physicians across 21 cities in China. Primary outcomes included treatment patterns and decision drivers for Chinese patients with LACC. Cancer stage was reported per International Federation of Gynecology and Obstetrics (FIGO) 2018. Respondents were gynecologists (55.6%), radiation oncologists (36.1%), and medical oncologists (8.3%), who reported 67.7% patients had LACC (stage IB3-IVA). Treatment patterns varied by stage: chemoradiotherapy (CRT) alone was used by 18.1%, 42.2%, and 52.1% of respondents for stages IB3-IIA2, IIB, and III-IVA CC, respectively, with surgery-based treatment used by 67.1%, 42.0%, and 26.6% of respondents. Among patients with LACC receiving concurrent CRT, 57.7% received external-beam radiotherapy (EBRT) plus brachytherapy (BT). The most important LACC treatment decision drivers were treatment efficacy (91.7%), CC stage (89.7%), and safety (88.6%). Only 63.9% reported that both EBRT and BT could be performed at their hospitals. Radiotherapy-related issues were reported as the top unmet medical need by 66.4%. There is a significant LACC burden among female patients in China, and deviations from guideline-concordant concurrent CRT. Addressing implementation gaps in concurrent CRT adoption requires improved radiotherapy access, greater multidisciplinary team use, guideline-aligned quality control, enhanced physician and patient education, and improved affordability. Future research should incorporate patient perspectives to improve patient-centered care and outcomes.

PURPOSE: Current prognostic systems are inadequate for intermediate trophoblastic tumors (ITTs). The aim of this study was to develop a machine learning (ML)-based model to predict progression-free survival (PFS) in patients with ITT and implement a web-based tool for individualized risk stratification. MATERIALS AND METHODS: We analyzed a retrospective cohort of 236 patients with ITT treated at a national tertiary center between 2000 and 2024. A multimodal feature selection strategy—integrating Cox regression, LASSO, Gradient Boosting Machine (GBM), and Random Survival Forest (RSF)—was used to identify robust predictors from clinicopathologic and inflammatory variables. The final prognostic model was constructed using the RSF approach. Model performance was evaluated through a rigorous nested 5-fold cross-validation framework to prevent data leakage. RESULTS: Five key predictors were identified: International Federation of Gynecology and Obstetrics stage, interval from antecedent pregnancy, Ki-67 index, neutrophil-to-lymphocyte ratio, and systemic immune-inflammation index. The RSF model demonstrated robust discrimination with a cross-validated concordance index of 0.816 (95% CI, 0.721 to 0.895) and excellent calibration (Integrated Brier Score, 0.113). Decision-curve analysis confirmed clinical utility within the relevant 20%-60% threshold range. An interactive web tool (Shinyapps) was deployed to generate real-time individualized PFS predictions with 95% confidence intervals. CONCLUSION: To our knowledge, this study presents the first ML-based prognostic model specifically for ITT. By integrating immune-inflammatory markers with traditional clinicopathologic features, the RSF model offers superior risk stratification compared with anatomic staging or alternative models. The developed online tool serves as a proof-of-concept prototype to facilitate future external validation and research on personalized clinical decision making for this rare malignancy.

Objective. In this study, we utilized gonadotropin-releasing hormone analogue-modified indocyanine green (GnRHa-ICG) to improve the accuracy of intraoperative recognition and resection of endometriotic lesions. Methods. Gonadotropin-releasing hormone receptor (GnRHR) expression was detected in endometriosis tissues and cell lines via immunohistochemistry and western blotting. The in vitro binding capacities of GnRHa, GnRHa-ICG, and ICG were determined using fluorescence microscopy and flow cytometry. In vivo imaging was performed in mouse models of endometriosis using a near-infrared fluorescence (NIRF) imaging system and fluorescence navigation system. The ex vivo binding capacity was determined using confocal fluorescence microscopy. Results. GnRHa-ICG exhibited a significantly stronger binding capacity to endometriotic cells and tissues than ICG. In mice with endometriosis, GnRHa-ICG specifically imaged endometriotic tissues (EMTs) after intraperitoneal administration, whereas ICG exhibited signals in the intestine. GnRHa-ICG showed the highest fluorescence signals in the EMTs at 2h and a good signal-to-noise ratio at 48h postadministration. Compared with traditional surgery under white light, targeted NIRF imaging-guided surgery completely resected endometriotic lesions with a sensitivity of 97.3 and specificity of 77.8. No obvious toxicity was observed in routine blood tests, serum biochemicals, or histopathology in mice. Conclusions. GnRHa-ICG specifically recognized and localized endometriotic lesions and guided complete resection of lesions with high accuracy.

As a cancer type potentially dominated by copy number variations, ovarian cancer shows hyperploid karyotypes and large-scale chromosome alterations, which might be promising biomarkers correlated with tumor metastasis and chemoresistance. Experimental studies have provided more information about the roles of aneuploids and polyploids in ovarian cancer. However, ploidy evaluation of ovarian cancer cell lines is still limited, even in some ploidy-related research. Herein, the ploidy landscape of 51 ovarian cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE) were analyzed, and the ploidy statuses of 13 human ovarian cancer cell lines and 2 murine cell lines were evaluated using G-banding and flow cytometry. Most human ovarian cancer cell lines were aneuploid, with modal numbers of 52–86 and numerical complexity ranging from 5 to 12. A2780, COV434 and TOV21G were screened as diploid cell lines, with a modal number of 46, a low aneuploid score and a near-diploid ploidy value. Two murine cell lines, both OV2944-HM1 and ID-8, were near-tetraploid. Integrated information on karyotypes, aneuploid score and ploidy value supplied references for a nondiploid model construction and a parallel analysis of diploid versus aneuploid. Moreover, the gene expression profiles were compared between diploid and aneuploid cell lines. The functions of differentially expressed genes were mainly enriched in terms of protein function regulation, TGF-β signaling and cell adhesion molecules. Genes downregulated in the aneuploid group were mainly related to metabolism and protein function regulation, and genes upregulated in the aneuploid group were mainly involved in immune regulation. Differentially expressed genes were randomly distributed on all chromosomes, while chromosome 1 alteration might contribute to immune-related alterations in aneuploid cell lines. Chromosome 19 alteration might be potentially significant for aneuploid ovarian cancer cell lines and patients, which needs further verification in ploidy research.