Objectives: High-grade serous ovarian carcinoma (HGSOC) is typically diagnosed at an advanced stage with extensive peritoneal metastases, making treatment challenging. Neoadjuvant chemotherapy (NACT) is often used to reduce tumor burden before surgery, but about 40% of patients show limited response. Radiomics, combined with machine learning (ML), offers a promising non-invasive method for predicting NACT response by analyzing computed tomography (CT) imaging data. This study aimed to improve response prediction in HGSOC patients undergoing NACT by integration different feature selection methods. Materials and methods: A framework for selecting robust radiomics features was introduced by employing an automated randomisation algorithm to mimic inter-observer variability, ensuring a balance between feature robustness and prediction accuracy. Four response metrics were used: chemotherapy response score (CRS), RECIST, volume reduction (VolR), and diameter reduction (DiaR). Lesions in different anatomical sites were studied. Pre- and post-NACT CT scans were used for feature extraction and model training on one cohort, and an independent cohort was used for external testing. Results: The best prediction performance was achieved using all lesions combined for VolR prediction, with an AUC of 0.83. Omental lesions provided the best results for CRS prediction (AUC 0.77), while pelvic lesions performed best for DiaR (AUC 0.76). Conclusion: The integration of robustness into the feature selection processes ensures the development of reliable models and thus facilitates the implementation of the radiomics models in clinical applications for HGSOC patients. Future work should explore further applications of radiomics in ovarian cancer, particularly in real-time clinical settings.

翻译：目的：高级别浆液性卵巢癌（HGSOC）通常在晚期确诊，伴有广泛的腹膜转移，使得治疗极具挑战性。新辅助化疗（NACT）常用于术前减轻肿瘤负荷，但约40%的患者反应有限。影像组学结合机器学习（ML），通过分析计算机断层扫描（CT）影像数据，为预测NACT反应提供了一种有前景的非侵入性方法。本研究旨在通过整合不同的特征选择方法，改善接受NACT的HGSOC患者的反应预测。材料与方法：通过采用自动化随机化算法模拟观察者间的变异性，引入了一个用于选择稳健影像组学特征的框架，以确保特征稳健性与预测准确性之间的平衡。使用了四种反应评估指标：化疗反应评分（CRS）、RECIST标准、体积缩减率（VolR）和直径缩减率（DiaR）。研究了不同解剖部位的病灶。在一个队列中使用NACT前后的CT扫描进行特征提取和模型训练，并使用一个独立队列进行外部测试。结果：使用所有病灶组合进行VolR预测时获得了最佳预测性能，AUC为0.83。网膜病灶为CRS预测提供了最佳结果（AUC 0.77），而盆腔病灶在DiaR预测上表现最佳（AUC 0.76）。结论：将稳健性整合到特征选择过程中确保了可靠模型的开发，从而促进了影像组学模型在HGSOC患者临床应用中的实施。未来的工作应探索影像组学在卵巢癌中的进一步应用，特别是在实时临床环境中。