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BMC Cancer

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01-12-2020 | Breast Cancer | Research article

Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI

Authors: Jiali Zhou, Jinghui Lu, Chen Gao, Jingjing Zeng, Changyu Zhou, Xiaobo Lai, Wenli Cai, Maosheng Xu

Published in: BMC Cancer | Issue 1/2020

Abstract

Background

The purpose of this study was to investigate the value of wavelet-transformed radiomic MRI in predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) for patients with locally advanced breast cancer (LABC).

Methods

Fifty-five female patients with LABC who underwent contrast-enhanced MRI (CE-MRI) examination prior to NAC were collected for the retrospective study. According to the pathological assessment after NAC, patient responses to NAC were categorized into pCR and non-pCR. Three groups of radiomic textures were calculated in the segmented lesions, including (1) volumetric textures, (2) peripheral textures, and (3) wavelet-transformed textures. Six models for the prediction of pCR were Model I: group (1), Model II: group (1) + (2), Model III: group (3), Model IV: group (1) + (3), Model V: group (2) + (3), and Model VI: group (1) + (2) + (3). The performance of predicting models was compared using the area under the receiver operating characteristic (ROC) curves (AUC).

Results

The AUCs of the six models for the prediction of pCR were 0.816 ± 0.033 (Model I), 0.823 ± 0.020 (Model II), 0.888 ± 0.025 (Model III), 0.876 ± 0.015 (Model IV), 0.885 ± 0.030 (Model V), and 0.874 ± 0.019 (Model VI). The performance of four models with wavelet-transformed textures (Models III, IV, V, and VI) was significantly better than those without wavelet-transformed textures (Model I and II). In addition, the inclusion of volumetric textures or peripheral textures or both did not result in any improvements in performance.

Conclusions

Wavelet-transformed textures outperformed volumetric and/or peripheral textures in the radiomic MRI prediction of pCR to NAC for patients with LABC, which can potentially serve as a surrogate biomarker for the prediction of the response of LABC to NAC.

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Title: Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI
Authors: Jiali Zhou
Jinghui Lu
Chen Gao
Jingjing Zeng
Changyu Zhou
Xiaobo Lai
Wenli Cai
Maosheng Xu
Publication date: 01-12-2020
Publisher: BioMed Central
Keywords: Breast Cancer
Breast Cancer
Cytostatic Therapy
Published in: BMC Cancer / Issue 1/2020
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-020-6523-2

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Abstract

Background

Methods

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Conclusions

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