Yang Yang, Ji Xinyu, Li Sen, Gao Xuemeng, Wang Yitong, Huang Ying
Department of Ultrasound, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Department of Ultrasound, Shengjing Hospital, China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning Province, 110004, China.
BMC Med Imaging. 2025 Apr 15;25(1):122. doi: 10.1186/s12880-025-01624-1.
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.
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.
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.
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.
为了评估肿瘤异质性，人们提出了利用放射组学的计算方法，这一方法的依据是生物医学图像可能包含潜在病理生理学信息的概念，并且有潜力定量测量肿瘤内和肿瘤间的异质性。卵巢癌在女性生殖系统恶性肿瘤中死亡率最高，并且可进一步分为许多具有不同管理策略和预后的亚型。我们研究的目的是开发并验证基于超声的放射组学模型，以区分上皮性卵巢癌的五种主要组织学亚型。
2018年1月至2022年8月，纳入了1209例符合条件的卵巢癌患者。本研究有两个研究对象：所有患者（n = 1209）和具有五种主要组织学亚型的患者（n = 1039）。手动进行图像分割后，提取放射组学特征并添加一些临床特征。使用九种特征选择方法来选择最佳预测特征。采用七种分类器构建模型。选择预测性能最佳的组合作为最终结果。
对于低级别浆液性癌、子宫内膜样癌和透明细胞癌，两组在10倍交叉验证中的模型曲线下面积（AUC）均低于0.80。对于黏液性癌，验证队列中的AUC分别为0.83（95%CI，0.74 - 0.93）和0.89（95%CI，0.83 - 0.95），两组10倍交叉验证中的AUC分别为0.80（95%CI，0.73 - 0.87）和0.86（95%CI，0.78 - 0.94）。对于高级别浆液性癌（HGSC），验证队列中的模型AUC分别为0.87（95%CI，0.83 - 0.91）和0.85（95%CI，0.81 - 0.89），两组10倍交叉验证中的AUC分别为0.87（95%CI，0.85 - 0.89）和0.84（95%CI，0.81 - 0.87），预测结果与实际结果之间表现出高度一致性，并为患者带来了巨大的净效益。
基于超声的放射组学模型在鉴别HGSC和非HGSC方面表现出良好的预测性能，预测结果与实际结果之间具有高度一致性，并为患者带来了显著的净效益。
