Objective: Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the patients' responses to NACT varies significantly among different subgroups. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy response prediction of the NACT at an early stage. Methods: For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. Using this cluster as the input, an SVM based classifier was developed and optimized to create a final marker, indicating the likelihood of the patient being responsive to the NACT treatment. To validate this scheme, a total of 42 ovarian cancer patients were retrospectively collected. A nested leave-one-out cross-validation was adopted for model performance assessment. Results: The results demonstrate that the new method yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.745. Meanwhile, the model achieved overall accuracy of 76.2%, positive predictive value of 70%, and negative predictive value of 78.1%. Conclusion: This study provides meaningful information for the development of radiomics based image markers in NACT response prediction.

翻译：目的：新辅助化疗（NACT）是晚期卵巢癌患者的治疗方案之一。然而，由于肿瘤异质性的特点，不同亚组患者对NACT的反应存在显著差异。为解决这一临床难题，本研究旨在开发一种新型影像标志物，以实现早期高精度预测NACT治疗反应。方法：为此，我们首先计算了共1373个影像组学特征以量化肿瘤特性，这些特征可分为三类：几何特征、强度特征和纹理特征。其次，采用主成分分析算法对所有特征进行优化，以生成紧凑且信息丰富的特征簇。以此特征簇为输入，开发并优化了基于支持向量机（SVM）的分类器，最终构建出可指示患者对NACT治疗反应可能性的标志物。为验证该方案，回顾性收集了共42例卵巢癌患者数据，并采用嵌套留一法交叉验证评估模型性能。结果：结果表明，新方法的AUC（受试者工作特征曲线下面积）为0.745。同时，模型总体准确率为76.2%，阳性预测值为70%，阴性预测值为78.1%。结论：本研究为开发基于影像组学的NACT反应预测影像标志物提供了有意义的信息。