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Purpose: Patients with locally advanced rectal cancer (LARC) achieving pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (CRT) have significantly improved long term survival. Preoperative detection of pCR may enable a conservative therapeutic approach in some patients. The purpose of the current prospective pilot study was to assess multiparametric qualitative and quantitative MR, PET, PET-MR and tumor texture features in predicting pCR to CRT in patients with LARC. Material and Methods: Eighteen LARC patients underwent staging with FDG-PET and MR-rectum and 15 had post-CRT restaging. Response was assessed qualitatively and quantitatively. SUV (tumor/background), SUV/ADC, and tumor texture parameters derived via machine learning algorithms (MLA) from PET and multiple MR sequences and were correlated with histopathology. Results: A third of patients had pCR. Sensitivity, specificity & accuracy of PET, MR and combined PET-MR were 90, 60, & 80; 90, 20 & 66.7; 90, 80 & 86.7, respectively. Differences did not reach statistical significance. Quantitatively, only tumor-muscle (SUV/ADC) ratio improved prediction of pCR. Of all texture features assessed using MLA, only the classifier trained on pre-treatment PET was significant (p = 0.034; accuracy, 92.8%). Combined PET and MR texture features did not improve performance. Conclusion: Combined PET-MR may improve specificity compared with PET or MR alone, although this needs to be validated in a larger cohort. Tumor to muscle SUV/ADC ratios post-therapy and texture features on baseline PET show promise in improving prediction of pCR post-CRT in LARC.

advances in molecular imaging

Multiparameteric PET-MR Assessment of Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: PET, MR, PET-MR and Tumor Texture Analysis: A Pilot Study