Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics.

Details

Serval ID
serval:BIB_007714323738
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics.
Journal
Abdominal radiology
Author(s)
El Homsi M., Bane O., Fauveau V., Hectors S., Vietti Violi N., Sylla P., Ko H.B., Cuevas J., Carbonell G., Nehlsen A., Vanguri R., Viswanath S., Jambawalikar S., Shaish H., Taouli B.
ISSN
2366-0058 (Electronic)
Publication state
In Press
Peer-reviewed
Oui
Language
english
Notes
Publication types: Journal Article
Publication Status: aheadofprint
Abstract
To assess the role of pretreatment multiparametric (mp)MRI-based radiomic features in predicting pathologic complete response (pCR) of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiation therapy (nCRT).
This was a retrospective dual-center study including 98 patients (M/F 77/21, mean age 60 years) with LARC who underwent pretreatment mpMRI followed by nCRT and total mesorectal excision or watch and wait. Fifty-eight patients from institution 1 constituted the training set and 40 from institution 2 the validation set. Manual segmentation using volumes of interest was performed on T1WI pre-/post-contrast, T2WI and diffusion-weighted imaging (DWI) sequences. Demographic information and serum carcinoembryonic antigen (CEA) levels were collected. Shape, 1st and 2nd order radiomic features were extracted and entered in models based on principal component analysis used to predict pCR. The best model was obtained using a k-fold cross-validation method on the training set, and AUC, sensitivity and specificity for prediction of pCR were calculated on the validation set.
Stage distribution was T3 (n = 79) or T4 (n = 19). Overall, 16 (16.3%) patients achieved pCR. Demographics, MRI TNM stage, and CEA were not predictive of pCR (p range 0.59-0.96), while several radiomic models achieved high diagnostic performance for prediction of pCR (in the validation set), with AUCs ranging from 0.7 to 0.9, with the best model based on high b-value DWI demonstrating AUC of 0.9 [95% confidence intervals: 0.67, 1], sensitivity of 100% [100%, 100%], and specificity of 81% [66%, 96%].
Radiomic models obtained from pre-treatment MRI show good to excellent performance for the prediction of pCR in patients with LARC, superior to clinical parameters and CEA. A larger study is needed for confirmation of these results.
Keywords
Diffusion, Magnetic resonance imaging, Radiomics, Rectal cancer
Pubmed
Web of science
Create date
10/01/2024 11:07
Last modification date
20/01/2024 7:11
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