Ultrasound-based radiomics for predicting the five major histological subtypes of epithelial ovarian cancer

• Published in: BMC medical imaging Vol. 25; no. 1; pp. 122 - 13
• Main Authors: Yang, Yang, Ji, Xinyu, Li, Sen, Gao, Xuemeng, Wang, Yitong, Huang, Ying
• Format: Journal Article
• Language: English
• Published: London BioMed Central 15.04.2025; BioMed Central Ltd; BMC
• Subjects: Adult; Aged; Antigens; Biomarkers; Biopsy; Cancer; Cancer therapies; Carbohydrates; Carcinoma; Carcinoma, Ovarian Epithelial - classification; Carcinoma, Ovarian Epithelial - diagnostic imaging; Carcinoma, Ovarian Epithelial - pathology; Cell culture; Chemotherapy; Epithelial ovarian cancer; Female; Health aspects; Heterogeneity; Histological subtype; Hospitals; Humans; Image processing; Image segmentation; Imaging; Machine learning; Medical imaging; Medical imaging equipment; Medicine; Medicine & Public Health; Middle Aged; Mortality; Oncology, Experimental; Ovarian cancer; Ovarian Neoplasms - diagnostic imaging; Ovarian Neoplasms - pathology; Pathology; Patients; Radiology; Radiomics; Reproductive system; Strategic planning (Business); Surgery; Taiwan; Tumors; Ultrasonic imaging; Ultrasonography - methods; Ultrasound; Ultrasound-based; United States; United States; Taiwan; Netherlands; United States > US; China; Japan; Histological subtype; Epithelial ovarian cancer; Ultrasound-based; Radiomics
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-025-01624-1

Background Computational approaches have been proposed using radiomics in order to assess tumour heterogeneity, which is motivated by the concept that biomedical images may contain underlying pathophysiology information and has the potential to quantitatively measure the heterogeneity of intra- and intertumours. Ovarian cancer has the highest mortality among malignant tumours of female reproductive system and can be further divided into many subtypes with different management strategies and prognosis. The purpose of our study is to develop and validate ultrasound-based radiomics models to distinguish the five major histological subtypes of epithelial ovarian cancer. Methods From January 2018 to August 2022, 1209 eligible ovarian cancer patients were enrolled. There were two subjects in this study: all patients ( n  = 1209) and patients with the five major histological subtypes ( n  = 1039). After image segmentation manually, radiomics features were extracted and some clinical characteristics were added. Nine feature selection methods were used to select the optimal predictive features. Seven classifiers were carried out to construct models. Choose the combination with the best predictive performance as the final result. Results As for low-grade serous carcinoma, endometrioid carcinoma, and clear cell carcinoma, the models yields AUCs below 0.80 in the 10-fold cross-validation in the two groups. As for mucinous carcinoma, the AUCs were 0.83(95% CI , 0.74–0.93) and 0.89(95% CI , 0.83–0.95) in the validation cohorts and 0.80(95% CI , 0.73–0.87) and 0.86(95% CI , 0.78–0.94) in the 10-fold cross-validation in the two groups, respectively. As for high-grade serous carcinoma (HGSC), the models showed AUCs of 0.87(95% CI , 0.83–0.91) and 0.85(95% CI , 0.81–0.89) in the validation cohorts and 0.87(95% CI , 0.85–0.89) and 0.84(95% CI , 0.81–0.87) in the 10-fold cross-validation in the two groups, respectively, and exhibited high consistency between the predicted results and the actual outcomes, and brought great net benefits for patients. Conclusions The ultrasound-based radiomics models in discriminating HGSC and non-HGSC showed good predictive performance, as well as high consistency between the predicted results and the actual outcomes, and brought significant net benefits for patients.