BMC women's health · 2026
Combining transvaginal ultrasound radiomics with clinical-ultrasound semantic features for the identification of high-risk endometrial lesions.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Yao Xiaohong, Ye Xuandan, Chen Lixia, He Yubing, Wu Jiahui, Kang Siliang, Liu Fan, Zhu Lirong, Zheng Jian
Аннотация
OBJECTIVE: Preoperative risk stratification of high-risk endometrial lesions remains a clinical challenge. This study aimed to preliminarily explore an integrated machine learning approach combining transvaginal ultrasound (TVUS) radiomics, clinical indicators, and ultrasound semantic attributes to assist in clinical triage.
METHODS: TVUS images (n = 956) from 239 patients were retrospectively analyzed across two centers. Clinical and ultrasound semantic features were evaluated blindly. Within a patient-level 5-fold cross-validation scheme repeated across five independent random seeds, 1,125 radiomics features were extracted and selected via nested LASSO regression to compute a Rad-score. Frameworks using nine machine learning algorithms were evaluated via Area Under the Curve (AUC), DeLong tests, and Decision Curve Analysis (DCA).
RESULTS: The optimal validation AUCs for the Radiomics, Clinical-Semantic, and Combined models were 0.7589, 0.8965, and 0.9077 (CatBoost), respectively. The Combined framework yielded a statistically significant increase in AUC values compared with the Radiomics model (P < 0.001) and showed significant differences over the Clinical-Semantic model across seven algorithms (all P < 0.05). Endometrial-myometrial junction appearance was identified as the primary predictor. DCA indicated that the combined model's soft-voting configuration optimized initial triage at a 0.20 threshold, while its CatBoost configuration sustained higher net benefit across medium-to-high risk thresholds (0.30-0.60).
CONCLUSION: Integrating clinical-ultrasound semantic features with radiomics offers an objective, non-invasive approach to potentially assist in individualized risk stratification. This combined framework may provide a potential stratified decision-support pathway for further clinical evaluation.
METHODS: TVUS images (n = 956) from 239 patients were retrospectively analyzed across two centers. Clinical and ultrasound semantic features were evaluated blindly. Within a patient-level 5-fold cross-validation scheme repeated across five independent random seeds, 1,125 radiomics features were extracted and selected via nested LASSO regression to compute a Rad-score. Frameworks using nine machine learning algorithms were evaluated via Area Under the Curve (AUC), DeLong tests, and Decision Curve Analysis (DCA).
RESULTS: The optimal validation AUCs for the Radiomics, Clinical-Semantic, and Combined models were 0.7589, 0.8965, and 0.9077 (CatBoost), respectively. The Combined framework yielded a statistically significant increase in AUC values compared with the Radiomics model (P < 0.001) and showed significant differences over the Clinical-Semantic model across seven algorithms (all P < 0.05). Endometrial-myometrial junction appearance was identified as the primary predictor. DCA indicated that the combined model's soft-voting configuration optimized initial triage at a 0.20 threshold, while its CatBoost configuration sustained higher net benefit across medium-to-high risk thresholds (0.30-0.60).
CONCLUSION: Integrating clinical-ultrasound semantic features with radiomics offers an objective, non-invasive approach to potentially assist in individualized risk stratification. This combined framework may provide a potential stratified decision-support pathway for further clinical evaluation.