He Mengge, Singh Rahul, Wang Mandi, Ho Grace, Wong Esther M F, Chiu Keith W H, Leung Anthony K T, Tse Ka Yu, Ip Philip P C, Hwang Andy, Han Lujun, Lee Elaine Y P
Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China.
Department of Radiology, Queen Mary Hospital, Hong Kong, China.
Cancer Imaging. 2025 Jul 3;25(1):85. doi: 10.1186/s40644-025-00906-9.
Platinum resistance carries poor prognosis in epithelial ovarian carcinoma (EOC). This study aimed to assess the value of radiomics model based on contrast-enhanced CT (ceCT) in predicting response to platinum-based chemotherapy in EOC.
Patients with histologically confirmed EOC and pre-treatment ceCT were retrospectively recruited from 5 centres. All patients underwent standard platinum-based chemotherapy and optimal cytoreduction. Platinum sensitivity was determined by whether it recurred within six months after platinum-based chemotherapy. The whole tumour volume was manually segmented on the baseline ceCT. Radiomics features were extracted using the open-source package PyRadiomics (version 3.0.1). Patients from centres A-C were randomly divided into training and internal validation sets in 4:1 ratio. Patients from the centres D and E were assigned as independent external validation sets. Spearman's rank correlation followed by 5-fold stratified cross validation (SCV) elastic net repeated for 100 times, and Mann-Whitney U test were deployed for feature reduction and selection. Adaptive synthetic sampling was applied to minimize class biases. Extra Trees classifier across 10-fold SCV was used for model building. The area under curve (AUC), calibration curve assessment, and decision curve analysis (DCA) were deployed to evaluate model performance and translational clinical utility.
Seven hundred and three EOC patients (51.6 ± 9.3 years) were recruited. The training data (n = 608) yielded the following classification metrics: AUC (0.917), sensitivity (83.9%), specificity (94.4%), and accuracy (91.7%) in the internal validation set. The external validation set using centre D (n = 44) had AUC (0.877), sensitivity (76.5%), specificity (92.6%), and accuracy (86.4%); while centre E (n = 51) had AUC (0.845), sensitivity (73.3%), specificity (86.1%), and accuracy (82.4%) in predicting platinum sensitivity. DCA illustrated net clinical benefit in internal validation set and both external validation sets.
The proposed CT-based radiomics model could be useful in predicting platinum sensitivity in EOC with potential in guiding personalized treatment in EOC.
铂耐药在上皮性卵巢癌（EOC）中预后较差。本研究旨在评估基于对比增强CT（ceCT）的放射组学模型在预测EOC对铂类化疗反应中的价值。
从5个中心回顾性招募组织学确诊为EOC且有治疗前ceCT的患者。所有患者均接受标准铂类化疗及最佳肿瘤细胞减灭术。铂敏感性通过铂类化疗后6个月内是否复发来确定。在基线ceCT上手动分割整个肿瘤体积。使用开源软件包PyRadiomics（版本3.0.1）提取放射组学特征。来自A - C中心的患者以4:1的比例随机分为训练集和内部验证集。来自D和E中心的患者被指定为独立的外部验证集。采用Spearman等级相关分析，随后进行5折分层交叉验证（SCV）弹性网络重复100次，并采用Mann - Whitney U检验进行特征降维和选择。应用自适应合成采样来最小化类别偏差。采用10折SCV的Extra Trees分类器进行模型构建。采用曲线下面积（AUC）、校准曲线评估和决策曲线分析（DCA）来评估模型性能和转化临床效用。
共招募703例EOC患者（51.6±9.3岁）。训练数据（n = 608）在内部验证集中得出以下分类指标：AUC（0.917）、敏感性（83.9%）、特异性（94.4%）和准确性（91.7%）。使用D中心（n = 44）的外部验证集在预测铂敏感性时，AUC为（0.877）、敏感性（76.5%）、特异性（92.6%）和准确性（86.4%）；而E中心（n = 51）的AUC为（0.845）、敏感性（73.3%）、特异性（86.1%）和准确性（82.4%）。DCA表明在内部验证集和两个外部验证集中均有净临床获益。
所提出的基于CT的放射组学模型可用于预测EOC中的铂敏感性，在指导EOC的个性化治疗方面具有潜力。
