Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant re
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IMAGING INFORMATICS AND ARTIFICIAL INTELLIGENCE
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 Hiram Shaish 1 & Andrew Aukerman 2 & Rami Vanguri 2 & Antonino Spinelli 3,4 & Paul Armenta 5 & Sachin Jambawalikar 1 & Jasnit Makkar 1 & Stuart Bentley-Hibbert 1 & Armando Del Portillo 2 & Ravi Kiran 6 & Lara Monti 4 & Christiana Bonifacio 7 & Margarita Kirienko 3 & Kevin L Gardner 2 & Lawrence Schwartz 1 & Deborah Keller 6 Received: 17 March 2020 / Revised: 17 April 2020 / Accepted: 15 May 2020 # European Society of Radiology 2020
Abstract 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. Keywords Rectal cancer . Machine learning . Magnetic resonance imaging . Neoadjuvant therapy Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-06968-6) contains supplementary material, which is available to authorized users. * Hiram Shaish [email protected]
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Division Colon and Rectal Surgery Unit, Humanitas Clinical and Research Center, V
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