Radiomic Features of Primary Rectal Cancers on Baseline T 2 -Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study

• Published in: Journal of magnetic resonance imaging Vol. 52; no. 5; pp. 1531 - 1541
• Main Authors: Antunes, Jacob T, Ofshteyn, Asya, Bera, Kaustav, Wang, Erik Y, Brady, Justin T, Willis, Joseph E, Friedman, Kenneth A, Marderstein, Eric L, Kalady, Matthew F, Stein, Sharon L, Purysko, Andrei S, Paspulati, Rajmohan, Gollamudi, Jayakrishna, Madabhushi, Anant, Viswanath, Satish E
• Format: Journal Article
• Language: English
• Published: United States 01.11.2020
• Subjects: Chemoradiotherapy; Humans; Magnetic Resonance Imaging; Neoadjuvant Therapy; Rectal Neoplasms - diagnostic imaging; Rectal Neoplasms - therapy; Retrospective Studies; radiomics; pathologic complete response; machine learning; rectal cancer
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.27140

Twenty-five percent of rectal adenocarcinoma patients achieve pathologic complete response (pCR) to neoadjuvant chemoradiation and could avoid proctectomy. However, pretreatment clinical or imaging markers are lacking in predicting response to chemoradiation. Radiomic texture features from MRI have recently been associated with therapeutic response in other cancers. To construct a radiomics texture model based on pretreatment MRI for identifying patients who will achieve pCR to neoadjuvant chemoradiation in rectal cancer, including validation across multiple scanners and sites. Retrospective. In all, 104 rectal cancer patients staged with MRI prior to long-course chemoradiation followed by proctectomy; curated from three institutions. 1.5T-3.0T, axial higher resolution T -weighted turbo spin echo sequence. Pathologic response was graded on postsurgical specimens. In total, 764 radiomic features were extracted from single-slice sections of rectal tumors on processed pretreatment T -weighted MRI. Three feature selection schemes were compared for identifying radiomic texture descriptors associated with pCR via a discovery cohort (one site, N = 60, cross-validation). The top-selected radiomic texture features were used to train and validate a random forest classifier model for pretreatment identification of pCR (two external sites, N = 44). Model performance was evaluated via area under the curve (AUC), accuracy, sensitivity, and specificity. Laws kernel responses and gradient organization features were most associated with pCR (P ≤ 0.01); as well as being commonly identified across all feature selection schemes. The radiomics model yielded a discovery AUC of 0.699 ± 0.076 and a hold-out validation AUC of 0.712 with 70.5% accuracy (70.0% sensitivity, 70.6% specificity) in identifying pCR. Radiomic texture features were resilient to variations in magnetic field strength as well as being consistent between two different expert annotations. Univariate analysis revealed no significant associations of baseline clinicopathologic or MRI findings with pCR (P = 0.07-0.96). Radiomic texture features from pretreatment MRIs may enable early identification of potential pCR to neoadjuvant chemoradiation, as well as generalize across sites. 3 TECHNICAL EFFICACY STAGE: 2.