Prediction of Complete Pathological Response to Neo-Adjuvant Chemoradiotherapy Using Magnetic Resonance Imaging-Based Radiomics Analysis in Locally Advanced Rectal Cancer.

Abstract

PURPOSE/OBJECTIVE(S)\nIn locally advanced rectal cancer (LARC) patients treated with neoadjuvant chemoradiotherapy (CRT), surgery could be omitted in patients with complete pathological response (pCR) without compromising progression-free and overall survivals. Our objective is to develop a radiomic model based on Magnetic Resonance Imaging (MRI) and contrast-enhanced computed tomography (CT) scans to predict the pathologic complete response (pCR) to neoadjuvant CRT in LARC.\n\n\nMATERIALS/METHODS\nAll patients treated for a LARC with neoadjuvant CRT and subsequent surgery in 2 centers in the West of France (Insitut of Cancerologie Brittany Occidental (ICBO) and Nantes) were considered. Both pre-CRT pelvic MRIs and contrast-enhanced CT-scans were mandatory for inclusion. The tumor was manually segmented on the T2-weighted and diffusion axial sequences and on the contrast-enhanced CT-scan. Eighty-eight radiomic parameters (15 shape and geometry features, 11 first-order and 62 second-order) were extracted from each sequence using the in-house MirasÓ software, with a total of 1056 features by patient. The overall cohort was randomly split into two independent cohorts (training: 70% and testing: 30%). A strict feature set selection workflow based on the Spearman s correlation coefficient and the Area Under the Curve (AUC) was developed to reduce the number of features. Based on these selected features, three pCR prediction models (clinical, radiomics and combined: clinical\u202f+\u202fradiomics) were developed on the training set only with a random forest approach and a Bootstrap internal validation with n = 1000 replications. An optimal cut-off maximizing the model s performance was defined on the training set. Each model was then evaluated on the testing set, based on the AUC and the C-statistic calculated with the pre-defined cut-off. Finally, a posteriori harmonization using the ComBat approach was applied to account for imaging modalities heterogeneity.\n\n\nRESULTS\nOf the 124 included patients, 14 had a complete response (11,3%). In the training set, the clinical model based on 2 parameters (initial T-stage and degree of tumor differentiation) obtained an AUC of 0.67 and a C-statistic of 0.65. The radiomic model based on 1 parameter (entropy histogram derived from T2 sequence) obtained an AUC of 1 and a C-statistic of 1. The combined clinical and radiomic model was strictly identical to the radiomics model and thus had the same performance. On the testing set, models resulted in a C-statistic of 0.70/0.73/0.73 for the clinical, radiomics and combined models, respectively. Finally, after harmonization, the radiomic model achieved a C-statistic of 0.77 on the testing set while the combined resulted in a C-statistic of 0 .75.\n\n\nCONCLUSION\nRadiomic model based on T2-weighted pre-therapeutic MRIs sequences could help to predict pCR after neoadjuvant CRT in LARC.