  Objective Neoadjuvant chemotherapy (NACT) is one kind of treatment for
advanced stage ovarian cancer patients. However, due to the nature of tumor
heterogeneity, the clinical outcomes to NACT vary significantly among different
subgroups. Partial responses to NACT may lead to suboptimal debulking surgery,
which will result in adverse prognosis. To address this clinical challenge, the
purpose of this study is to develop a novel image marker to achieve high
accuracy prognosis prediction of 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. This cluster was used as input for developing and optimizing
support vector machine (SVM) based classifiers, which indicated the likelihood
of receiving suboptimal cytoreduction after the NACT treatment. Two different
kernels for SVM algorithm were explored and compared. A total of 42 ovarian
cancer cases were retrospectively collected to validate the scheme. A nested
leave-one-out cross-validation framework was adopted for model performance
assessment. Results The results demonstrated that the model with a Gaussian
radial basis function kernel SVM yielded an AUC (area under the ROC [receiver
characteristic operation] curve) of 0.806. Meanwhile, this model achieved
overall accuracy (ACC) of 83.3%, positive predictive value (PPV) of 81.8%, and
negative predictive value (NPV) of 83.9%. Conclusion This study provides
meaningful information for the development of radiomics based image markers in
NACT treatment outcome prediction.
