Ultrasound in medicine & biology · 2026
Doppler-based Radiomics Integrated With Frozen Section Analysis Enhances Borderline Ovarian Tumor Prediction: A Multicenter Study.
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Zhang Shanshan, Wang Wenqian, Liu Xiaomin, Lou Xuezhen, Xu Lei, Yang Jing, Hu Xi, Lv Bingjian, Sun Litao, Chen Jian, Qin Jiale
Аннотация
OBJECTIVE: To develop a Doppler-based radiomics model that utilizes tumor characteristics related to neovascularization, with or without integrating intraoperative frozen section analysis (FSA), to enhance borderline ovarian tumors (BOT) prediction.
METHODS: In this retrospective multicenter study, patients diagnosed with BOT or epithelial ovarian cancer (EOC) between January 2010 and October 2022 across five centers were included. Data from Center I were randomly divided into training (80%) and validation (20%) sets, while Centers II-V provided the test set. Three ultrasound-radiomics models were developed: color Doppler features (CD), grayscale features (GS), and combining both (GSCD). An Integration Model was developed by incorporating clinical variables, FSA and the best-performing radiomics model. Color Doppler signals were acquired through three channels with RGB information. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC).
RESULTS: A total of 1244 patients with a median age of 49 years (IQR, 37-58 years) were included, consisting of 520 patients with BOT and 724 EOC. The GSCD exhibited the best predictive performance for BOT among the three ultrasound-radiomics models, showing an AUC of 0.975 (95%CI, 0.962-0.985) in the training set, with a sensitivity of 92.9% and a specificity of 88.9% in the validation set. The Integration Model further improved the performance, achieving an AUC of 0.997 (95%CI, 0.993-0.998) in the training set, with a sensitivity of 87.2% and a specificity of 98.7% in the validation set. Additionally, both the GSCD and Integration Model effectively distinguished BOT from Stage I EOC, with AUCs of 0.919 (95% CI: 0.849-0.963) and 0.936 (95% CI: 0.871-0.974) in the validation set.
CONCLUSION: We developed a Doppler-based radiomics model utilizing red-green-blue information. This model, irrespective of integration with FSA, demonstrated improved accuracy in predicting BOT and stratified the FIGO Stage EOC.
METHODS: In this retrospective multicenter study, patients diagnosed with BOT or epithelial ovarian cancer (EOC) between January 2010 and October 2022 across five centers were included. Data from Center I were randomly divided into training (80%) and validation (20%) sets, while Centers II-V provided the test set. Three ultrasound-radiomics models were developed: color Doppler features (CD), grayscale features (GS), and combining both (GSCD). An Integration Model was developed by incorporating clinical variables, FSA and the best-performing radiomics model. Color Doppler signals were acquired through three channels with RGB information. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC).
RESULTS: A total of 1244 patients with a median age of 49 years (IQR, 37-58 years) were included, consisting of 520 patients with BOT and 724 EOC. The GSCD exhibited the best predictive performance for BOT among the three ultrasound-radiomics models, showing an AUC of 0.975 (95%CI, 0.962-0.985) in the training set, with a sensitivity of 92.9% and a specificity of 88.9% in the validation set. The Integration Model further improved the performance, achieving an AUC of 0.997 (95%CI, 0.993-0.998) in the training set, with a sensitivity of 87.2% and a specificity of 98.7% in the validation set. Additionally, both the GSCD and Integration Model effectively distinguished BOT from Stage I EOC, with AUCs of 0.919 (95% CI: 0.849-0.963) and 0.936 (95% CI: 0.871-0.974) in the validation set.
CONCLUSION: We developed a Doppler-based radiomics model utilizing red-green-blue information. This model, irrespective of integration with FSA, demonstrated improved accuracy in predicting BOT and stratified the FIGO Stage EOC.