Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics

• Published in: Radiotherapy and oncology 2020-01, Vol.142, p.246-252
• Main Authors: Bulens, Philippe, Couwenberg, Alice, Intven, Martijn, Debucquoy, Annelies, Vandecaveye, Vincent, Van Cutsem, Eric, D'Hoore, André, Wolthuis, Albert, Mukherjee, Pritam, Gevaert, Olivier, Haustermans, Karin
• Format: Article
• Language: English
• Subjects: Aged; Chemoradiotherapy; Clinical Trials as Topic; Cohort Studies; Diffusion Magnetic Resonance Imaging - methods; Female; Humans; Magnetic resonance imaging; Male; Middle Aged; Models, Statistical; Neoplasm Staging; Predictive Value of Tests; Radiomics; Rectal cancer; Rectal Neoplasms - diagnostic imaging; Rectal Neoplasms - drug therapy; Rectal Neoplasms - radiotherapy; Rectal Neoplasms - therapy; Response prediction; Treatment Outcome
• ISSN: 0167-8140; 1879-0887; 1879-0887
• DOI: 10.1016/j.radonc.2019.07.033

•MRI radiomics predict the response to chemoradiation in patients with rectal cancer.•RMRI-based radiomics models do not outperform a four-feature semantic MRI model.•MRI models provide the potential for non-invasive selection of responding patients.•These findings can be used to tailor the treatment for patients with rectal cancer. In well-responding patients to chemoradiotherapy for locally advanced rectal cancer (LARC), a watch-and-wait strategy can be considered. To implement organ-sparing strategies, accurate patient selection is needed. We investigate the use of MRI-based radiomics models to predict tumor response to improve patient selection. Models were developed in a cohort of 70 patients and validated in an external cohort of 55 patients. Patients received chemoradiation followed by surgery and underwent T2-weighted and diffusion-weighted MRI (DW-MRI) before and after chemoradiation. The outcome measure was (near-)complete pathological tumor response (ypT0-1N0). Tumor segmentation was done on T2-images and transferred to b800-images and ADC maps, after which quantitative and four semantic features were extracted. We combined features using principal component analysis and built models using LASSO regression analysis. The best models based on precision and performance were selected for validation. 21/70 patients (30%) achieved ypT0-1N0 in the development cohort versus 13/55 patients (24%) in the validation cohort. Three models (t2_dwi_pre_post, semantic_dwi_adc_pre, semantic_dwi_post) were identified with an area-under-the-curve (AUC) of 0.83 (95% CI 0.70–0.95), 0.86 (95% CI 0.75–0.98) and 0.84 (95% CI 0.75–0.94) respectively. Two models (t2_dwi_pre_post, semantic_dwi_post) validated well in the external cohort with AUCs of 0.83 (95% CI 0.70–0.95) and 0.86 (95% CI 0.76–0.97). These models however did not outperform a previously established four-feature semantic model. Prediction models based on MRI radiomics non-invasively predict tumor response after chemoradiation for rectal cancer and can be used as an additional tool to identify patients eligible for an organ-preserving treatment.