MRI-based delta-radiomics are predictive of pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

Abstract:

RATIONALE AND OBJECTIVES:To investigate the capability of delta-radiomics to predict pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS:This retrospective study enrolled 165 consecutive patients with LARC (training set, n = 116; test set, n = 49) who received nCRT before surgery. All patients underwent pre- and post-nCRT MRI examination from which radiomics features were extracted. A delta-radiomics feature was defined as the percentage change in a radiomics feature from pre- to post-nCRT MRI. A data reduction and feature selection process including the least absolute shrinkage and selection operator algorithm was performed for building T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) delta-radiomics signature. Logistic regression was used to build a T2WI and DWI combined radiomics model. Receiver operating characteristic analysis was performed to assess diagnostic performance. Delong method was used to compare the performance of delta-radiomics model with that of magnetic resonance tumor regression grade (mrTRG). RESULTS:Twenty-seven of 165 patients (16.4%) achieved pCR. T2WI and DWI delta-radiomics signature, and the combined model showed good predictive performance for pCR. The combined model achieved the highest areas under the receiver operating characteristic curves of 0.91 (95% confidence interval: 0.85-0.98) and 0.91 (95% confidence interval: 0.83-0.99) in the training and test sets, respectively (significantly greater than those for mrTRG; training set, p < 0.001; test set, p = 0.04). CONCLUSION:MRI-based delta-radiomics can help predict pCR after nCRT in patients with LARC with better performance than mrTRG.

journal_name

Acad Radiol

journal_title

Academic radiology

authors

Wan L,Peng W,Zou S,Ye F,Geng Y,Ouyang H,Zhao X,Zhang H

doi

10.1016/j.acra.2020.10.026

subject

Has Abstract

pub_date

2020-11-11 00:00:00

eissn

1076-6332

issn

1878-4046

pii

S1076-6332(20)30614-0

pub_type

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