Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study.

OBJECTIVE: To investigate whether pretreatment MRI-based radiomics of locally advanced rectal cancer (LARC) and/or the surrounding mesorectal compartment (MC) can predict pathologic complete response (pCR), neoadjuvant rectal (NAR) score, and tumor regression grade (TRG). METHODS: One hundred thirty-two consecutive patients with LARC who underwent neoadjuvant chemoradiation and total mesorectal excision (TME) were retrospectively collected from 2 centers in the USA and Italy. The primary tumor and surrounding MC were segmented on the best available T2-weighted sequence (axial, coronal, or sagittal). Three thousand one hundred ninety radiomic features were extracted using a python package. The most salient radiomic features as well as MRI parameter and clinical-based features were selected using recursive feature elimination. A logistic regression classifier was built to distinguish between any 2 binned categories in the considered endpoints: pCR, NAR, and TRG. Repeated k-fold validation was performed and AUCs calculated. RESULTS: There were 24, 87, and 21 T4, T3, and T2 LARCs, respectively (median age 63 years, 32 to 86). For NAR and TRG, the best classification performance was obtained using both the tumor and MC segmentations. The AUCs for classifying NAR 0 versus 2, pCR, and TRG 0/1 versus 2/3 were 0.66 (95% CI, 0.60-0.71), 0.80 (95% CI, 0.74-0.85), and 0.80 (95% CI, 0.77-0.82), respectively. CONCLUSION: Radiomics of pretreatment MRIs can predict pCR, TRG, and NAR score in patients with LARC undergoing neoadjuvant treatment and TME with moderate accuracy despite extremely heterogenous image data. Both the tumor and MC contain important prognostic information. KEY POINTS: • Machine learning of rectal cancer on images from the pretreatment MRI can predict important patient outcomes with moderate accuracy. • The tumor and the tissue around it both contain important prognostic information.

Diffusion-weighted MRI in inflammatory bowel disease.

Cross-sectional MRI is an attractive alternative to endoscopy for the objective assessment of patients with inflammatory bowel disease (IBD). Diffusion-weighted imaging is a specialised technique that maps the diffusion of water molecules in biological tissues and can be done without intravenous gadolinium contrast injection. Diffusion-weighted imaging further expands the capability of traditional MRI sequences in IBD. However, the use of quantitative parameters, such as the apparent diffusion coefficient, is limited by low reproducibility. The Nancy score is a luminal disease activity index applied in diffusion-weighted imaging, and comprises only qualitative parameters. The score is accurate in Crohn's disease and ulcerative colitis, and requires no fasting or bowel preparation for assessment of colonic disease. However, deficiency of anatomic detail limits the use of diffusion-weighted imaging for assessment of intra-abdominal Crohn's disease complications. The contribution of such imaging in the prediction of disease course and treatment response in patients with IBD remains to be determined.