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Pathologic Complete Response Prediction after Neoadjuvant Chemoradiation Therapy for Rectal Cancer Using Radiomics and Deep Embedding Network of MRI

Authors
 Seunghyun Lee  ;  Joonseok Lim  ;  Jaeseung Shin  ;  Sungwon Kim  ;  Heasoo Hwang 
Citation
 APPLIED SCIENCES-BASEL, Vol.11(20) : 9494, 2021-10 
Journal Title
APPLIED SCIENCES-BASEL
Issue Date
2021-10
Keywords
convolutional neural network (CNN) ; magnetic resonance imaging (MRI) ; neoadjuvant chemoradiation therapy (nCRT) ; pathologic complete response (pCR) ; radiomics ; rectal cancer
Abstract
Assessment of magnetic resonance imaging (MRI) after neoadjuvant chemoradiation therapy (nCRT) is essential in rectal cancer staging and treatment planning. However, when predicting the pathologic complete response (pCR) after nCRT for rectal cancer, existing works either rely on simple quantitative evaluation based on radiomics features or partially analyze multi-parametric MRI. We propose an effective pCR prediction method based on novel multi-parametric MRI embedding. We first seek to extract volumetric features of tumors that can be found only by analyzing multiple MRI sequences jointly. Specifically, we encapsulate multiple MRI sequences into multi-sequence fusion images (MSFI) and generate MSFI embedding. We merge radiomics features, which capture important characteristics of tumors, with MSFI embedding to generate multi-parametric MRI embedding and then use it to predict pCR using a random forest classifier. Our extensive experiments demonstrate that using all given MRI sequences is the most effective regardless of the dimension reduction method. The proposed method outperformed any variants with different combinations of feature vectors and dimension reduction methods or different classification models. Comparative experiments demonstrate that it outperformed four competing baselines in terms of the AUC and F1-score. We use MRI sequences from 912 patients with rectal cancer, a much larger sample than in any existing work.
Files in This Item:
T202105166.pdf Download
DOI
10.3390/app11209494
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Sungwon(김성원) ORCID logo https://orcid.org/0000-0001-5455-6926
Shin, Jaeseung(신재승) ORCID logo https://orcid.org/0000-0002-6755-4732
Lim, Joon Seok(임준석) ORCID logo https://orcid.org/0000-0002-0334-5042
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/187152
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