Combined Clinical and Radiomic Features Model for Prediction of Pain Relief in Palliative Radiotherapy for Spine Metastases

Abstract

Pain relief is a major concern in the radiation therapy for symptomatic bone metastases. There are previous studies about investigating the association between pain relief and clinical features. This retrospective study enrolled a total of 40 patients with painful spine metastasis who received palliative radiation therapy from 2018 to 2019. Exclusion criteria were: (1) NRS 0-1; (2) reirradiation; (3) treated with SBRT; (4) metal artifacts close to target spine and (5) other extraspinal metastases apart from target spine in radiation field. Pain response defined by international consensus criteria. The 5 clinical predictive factors referred from previous study included NRS, primary tumor sites, performance status (PS), and added biologically effective dose (BED10) in this analysis. As for radiomic features, 321 features were extracted from three specified models by CT scans, which was categorizes as spinal canal model (SC model), spine model (S model), spine and surrounding tissues model (SS model), respectively. Recursive Feature Elimination and the importance score which provided by random forest method were applied to features for feature selection. A random forest model was designed to use the six selected features. The predictive performance of Random Forest models built on six radiomic features was compared with two other models, such as clinical features model, and a combined model with three clinical and three radiomic features. Internal validation was performed using ‘leave-one-out’ cross-validation method. Area under the curve (AUC) was used as predictive performance metrics. Overall 29 patients (72.5%) showed a response. The three radiomic features were extracted from SS (MajorAxisLength, RunLengthNonUniformity). The random forest models built on clinical features, radiomic features, and combination features achieved AUC of 0.47 (95%-CI 0.27-0.68), 0.70 (95%-CI 0.51-0.88), 0.77(95%-CI 0.60-0.93). We evaluate our CT-based radiomic model for prediction of response in palliative radiation therapy to spinal metastasis, and found that the combination features model seemed to be useful compared to other models.