Investigating Long-term Prognostication of CT-based Radiomics for Subgroup of High-risk Localized Prostate Cancer Patients Treated by Whole-pelvic Radiotherapy

Abstract

Abstract Background To investigate capability of planning computed tomography (CT)-based radiomics for prediction of long-term prognostication, for the first time, in subgroup of high-risk localized prostate cancer (PCa) patients treated by whole-pelvic radiotherapy (WPRT). Methods A total of 64 high-risk localized PCa patients [training cohort (n=45) and validation cohort (n=19)] were enrolled. The planning CT and clinical data were collected. The least absolute shrinkage selection operator (LASSO) was used for model training in conjunction with 3-fold cross validation. The predictive performance of the model was assessed using the Area-under-the-curve (AUC) values generated from receiver operating characteristic analysis. The resultant radiomics signature was used for calculation of radiomics score (Rad-score) for every patients. A cut-off of the Rad-score was suggested for classification of the risk of having progression within 6 years, based on the evaluation of model accuracy, sensitivity, and specificity. Results The model incorporated 2 features: the run entropy of gray level run length matrix after Laplacian of Gaussian (LoG) filtering with a sigma value of 2 mm (RE-GLRLMσ2mm); and the small area emphasis of gray level size zone matrix after LoG filtering with a sigma value of 4.5 mm (SAE-GLSZMσ4.5mm). AUC values of the training and testing cohorts were 0.76 and 0.71, respectively. With the cut-off as the third-quartile value for stratification into high-risk and low-risk group, the respective accuracy, sensitivity, and specificity of the radiomics signature were 77.8%, 83.3% and 55.6% in the training cohort and 84.2%, 86.7% and 75% in the testing cohort. Conclusions Radiomics signature based on pre-treatment planning CT images can be used as a potential biomarker for differentiating the risk of 6-year disease progression in high-risk localized PCa patients treated with WPRT. Further development is warranted that may help to support clinical decisions about follow-up and treatment options in this subgroup of patients.