Dataset Information

Development and validation of an MRI-based model to predict response to chemoradiotherapy for rectal cancer.

ABSTRACT: BACKGROUND AND PURPOSE:To safely implement organ preserving treatment strategies for patients with rectal cancer, well-considered selection of patients with favourable response is needed. In this study, we develop and validate an MRI-based response predicting model. METHODS:A multivariate model using T2-volumetric and DWI parameters before and 6?weeks after chemoradiation (CRT) was developed using a cohort of 85 rectal cancer patients and validated in an external cohort of 55 patients that underwent preoperative CRT. RESULTS:Twenty-two patients (26%) achieved ypT0-1N0 response in the development cohort versus 13 patients (24%) in the validation cohort. Two T2-volumetric parameters (?Volume% and Sphere_post) and two DWI parameters (ADC_avg_post and ADCratio_avg) were retained in a model predicting (near-)complete response (ypT0-1N0). In the development cohort, this model had a good predictive performance (AUC?=?0.89; 95% CI 0.80-0.98). Validation of the model in an external cohort resulted in a similar performance (AUC?=?0.88 95% CI 0.79-0.98). CONCLUSION:An MRI-based prediction model of (near-)complete pathological response following CRT in rectal cancer patients, shows a high predictive performance in an external validation cohort. The clinically relevant features in the model make it an interesting tool for implementation of organ-preserving strategies in rectal cancer.

SUBMITTER: Bulens P

PROVIDER: S-EPMC5990635 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and validation of an MRI-based model to predict response to chemoradiotherapy for rectal cancer.

Bulens Philippe P Couwenberg Alice A Haustermans Karin K Debucquoy Annelies A Vandecaveye Vincent V Philippens Marielle M Zhou Mu M Gevaert Olivier O Intven Martijn M

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 20180131 3

<h4>Background and purpose</h4>To safely implement organ preserving treatment strategies for patients with rectal cancer, well-considered selection of patients with favourable response is needed. In this study, we develop and validate an MRI-based response predicting model.<h4>Methods</h4>A multivariate model using T2-volumetric and DWI parameters before and 6 weeks after chemoradiation (CRT) was developed using a cohort of 85 rectal cancer patients and validated in an external cohort of 55 pati ...[more]

PMID: 29395287

Similar Datasets

Project description:ObjectivesTo develop a prognostic prediction MRI-based nomogram model for locally advanced rectal cancer (LARC) treated with neoadjuvant therapy.MethodsThis was a retrospective analysis of 233 LARC (MRI-T stage 3-4 (mrT) and/or MRI-N stage 1-2 (mrN), M0) patients who had undergone neoadjuvant radiotherapy and total mesorectal excision (TME) surgery with baseline MRI and operative pathology assessments at our institution from March 2015 to March 2018. The patients were sequentially allocated to training and validation cohorts at a ratio of 4:3 based on the image examination date. A nomogram model was developed based on the univariate logistic regression analysis and multivariable Cox regression analysis results of the training cohort for disease-free survival (DFS). To evaluate the clinical usefulness of the nomogram, Harrell's concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) were conducted in both cohorts.ResultsThe median follow-up times were 43.2 months (13.3-61.3 months) and 32.0 months (12.3-39.5 months) in the training and validation cohorts. Multivariate Cox regression analysis identified MRI-detected extramural vascular invasion (mrEMVI), pathological T stage (ypT) and perineural invasion (PNI) as independent predictors. Lymphovascular invasion (LVI) (which almost reached statistical significance in multivariate regression analysis) and three other independent predictors were included in the nomogram model. The nomogram showed the best predictive ability for DFS (C-index: 0.769 (training cohort) and 0.776 (validation cohort)). It had a good 3-year DFS predictive capacity [area under the curve, AUC=0.843 (training cohort) and 0.771 (validation cohort)]. DCA revealed that the use of the nomogram model was associated with benefits for the prediction of 3-year DFS in both cohorts.ConclusionWe developed and validated a novel nomogram model based on MRI factors and pathological factors for predicting DFS in LARC treated with neoadjuvant therapy. This model has good predictive value for prognosis, which could improve the risk stratification and individual treatment of LARC patients.

Dataset Information

Development and validation of an MRI-based model to predict response to chemoradiotherapy for rectal cancer.

Publications

Development and validation of an MRI-based model to predict response to chemoradiotherapy for rectal cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure