DataSheet_1_Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma.pdf

BackgroundPathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy response score (CRS) for omental tumor deposits. The main limitation of CRS is that it requires surgical sampling after initial neoadjuvant chemotherapy (NACT) treatment. Earlier and non-invasive response predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures to predict neoadjuvant response before NACT using CRS as a gold standard.MethodsOmental CT-based radiomics models, yielding a simplified fully interpretable radiomic signature, were developed using Elastic Net logistic regression and compared to predictions based on omental tumor volume alone. Models were developed on a single institution cohort of neoadjuvant-treated HGSOC (n = 61; 41% complete response to NCT) and tested on an external test cohort (n = 48; 21% complete response).ResultsThe performance of the comprehensive radiomics models and the fully interpretable radiomics model was significantly higher than volume-based predictions of response in both the discovery and external test sets when assessed using G-mean (geometric mean of sensitivity and specificity) and NPV, indicating high generalizability and reliability in identifying non-responders when using radiomics. The performance of a fully interpretable model was similar to that of comprehensive radiomics models.ConclusionsCT-based radiomics allows for predicting response to NACT in a timely manner and without the need for abdominal surgery. Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing. A radiomic signature based on five robust predictive features provides improved clinical interpretability and may thus facilitate clinical acceptance and application.

背景：对于高级浆液性卵巢癌（HGSOC）患者在接受新辅助治疗（NACT）后的病理反应，采用网膜肿瘤沉积物的化疗反应评分（CRS）进行评估。CRS的主要局限性在于它需要在初次新辅助化疗（NACT）治疗后进行手术取样。更早且非侵入性的反应预测因子可以改善患者分层。本研究开发了一种基于计算机断层扫描（CT）的影像组学测量方法，以预测NACT前的治疗反应，并以CRS作为金标准。方法：利用弹性网络逻辑回归开发了基于网膜CT的影像组学模型，产生了简化和可完全解释的影像组学特征，并将其与仅基于网膜肿瘤体积的预测进行了比较。模型是在单一机构的新辅助治疗HGSOC队列（n = 61；NCT完全缓解率为41%）上开发的，并在外部测试队列（n = 48；NCT完全缓解率为21%）上进行了测试。结果：在发现集和外部测试集上，使用G-mean（敏感性和特异性的几何平均值）和NPV（阴性预测值）评估时，综合影像组学模型和可完全解释的影像组学模型在预测反应方面的性能显著高于基于体积的预测，这表明在使用影像组学时识别非反应者的泛化性和可靠性较高。可完全解释模型的性能与综合影像组学模型相似。结论：基于CT的影像组学允许及时预测NACT的反应，且无需进行腹部手术。将NACT前的影像组学添加到体积测量中，可以改善预测HGSOC NACT反应的模型性能，并在外部测试中表现出稳健性。基于五个稳健预测特征的影像组学特征提供了更好的临床可解释性，从而可能促进临床接受和应用。