Radiomic Features of Primary Rectal Cancers on Baseline T2‐Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study. Issue 5 (26th March 2020)

• Record Type: Journal Article
• 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.
• Is Part Of: Journal of magnetic resonance imaging. Volume 52:Issue 5(2020)
• Page Start: 1531
• Page End: 1541
• Publication Date: 2020-03-26
• Subjects: radiomics -- rectal cancer -- pathologic complete response -- machine learning; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.27140
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: 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. Purpose: 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. Study Type: Retrospective. Subjects: In all, 104 rectal cancer patients staged with MRI prior to long‐course chemoradiation followed by proctectomy; curated from three institutions. Field Strength/Sequence: 1.5T–3.0T, axial higher resolution T2 ‐weighted turbo spin echo sequence. Assessment: Pathologic response was graded on postsurgical specimens. In total, 764 radiomic features were extracted from single‐slice sections of rectal tumors on processed pretreatment T2 ‐weighted MRI. Statistical Tests: 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. Results: 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). Data Conclusion: Radiomic texture features from pretreatment MRIs may enable early identification of potential pCR to neoadjuvant chemoradiation, as well as generalize across sites. Level of Evidence: 3 Technical Efficacy Stage: 2