CT‐based radiomic features to predict pathological response in rectal cancer: A retrospective cohort study. Issue 3 (9th May 2020)

• Record Type: Journal Article
• Authors: Yuan, Zhigang; Frazer, Marissa; Zhang, Geoffrey G; Latifi, Kujtim; Moros, Eduardo G; Feygelman, Vladimir; Felder, Seth; Sanchez, Julian; Dessureault, Sophie; Imanirad, Iman; Kim, Richard D; Harrison, Louis B; Hoffe, Sarah E; Frakes, Jessica M
• Is Part Of: Journal of medical imaging and radiation oncology. Volume 64:Issue 3(2020:Jun.)
• Page Start: 444
• Page End: 449
• Publication Date: 2020-05-09
• Subjects: neoadjuvant chemoradiation therapy -- pathologic response -- radiomics -- rectal cancer; Radiology, Medical -- Periodicals; Radiology, Medical -- Australasia -- Periodicals; 616.0757
• DOI: 10.1111/1754-9485.13044
• Languages: English
• ISSNs: 1754-9477

Abstract

Abstract: Introduction: Innovative biomarkers to predict treatment response in rectal cancer would be helpful in optimizing personalized treatment approaches. In this study, we aimed to develop and validate a CT‐based radiomic imaging biomarker to predict pathological response. Methods: We used two independent cohorts of rectal cancer patients to develop and validate a CT‐based radiomic imaging biomarker predictive of treatment response. A total of 91 rectal cancer cases treated from 2009 to 2018 were assessed for the tumour regression grade (TRG) (0 = pathological complete response, pCR; 1 = moderate response; 2 = partial response; 3 = poor response). Exploratory analysis was performed by combining pre‐treatment non‐contrast CT images and patterns of TRG. The models built from the training cohort were further assessed using the independent validation cohort. Results: The patterns of pathological response in training and validation groups were TRG 0 ( n = 14, 23.3%; n = 6, 19.4%), 1 ( n = 31, 51.7%; n = 15, 48.4%), 2 ( n = 12, 20.0%; n = 7, 22.6%) and 3 ( n = 3, 5.0%; n = 3, 9.7%), respectively. Separate predictive models were built and analysed from CT features for pathological response. For pathological response prediction, the model including 8 radiomic features by random forest method resulted in 83.9% accuracy in predicting TRG 0 vs TRG 1–3 in validation. Conclusion: The pre‐treatment CT‐based radiomic signatures were developed and validated in two independent cohorts. This imaging biomarker provided a promising way to predict pCR and select patients for non‐operative management.