Metabolic and Texture Features of PET/CT Scan to Predict Response in Patients with Locally Advanced Rectal Cancer Treated by Concurrent Neoadjuvant Chemoradiotherapy

Abstract Background: Neoadjuvant chemoradiotherapy (nCRT) before surgical resection is the standard treatment for patients with locally advanced rectal cancer (LARC). Radiomics can be used as noninvasive biomarker for response prediction. The purpose of our study was to evaluate the potential of PET/CT texture features to predict the responses of LARC subjects treated with nCRT.Methods: One week prior to nCRT, patients underwent Positron Emission Tomography/Computed Tomography (PET/CT) scan and then received concurrent nCRT. For each patient, intensity, shape and texture-based features were derived from PET/CT images using the IBEX software. The logistic regression classifier was used to identify the responders from non-responders. Results: In this study, 23 patients with LARC were included. The patients’ responses included 5 patients with Grade 0, 7 with Grade 1, 6 with Grade 2, and 5 with Grade 3 according to American Joint Committee on Cancer/College of American Pathologists (AJCC/CAP) pathologic grading. In quantitative texture features analysis, the dissimilarity feature had the highest performance [Area under Curve (AUC) = 0.65] and in metabolic parameters analysis the best performance was for total lesion glycolysis (TLG; AUC= 0.61)Conclusions: In conclusion, performance of quantitative texture features is better than metabolic parameters but their performance should be improved.