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CT radiomics identifying non‐responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer

BACKGROUND AND PURPOSE: Early detection of non‐response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no r...

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Autores principales: Zhang, Zinan, Yi, Xiaoping, Pei, Qian, Fu, Yan, Li, Bin, Liu, Haipeng, Han, Zaide, Chen, Changyong, Pang, Peipei, Lin, Huashan, Gong, Guanghui, Yin, Hongling, Zai, Hongyan, Chen, Bihong T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9939108/
https://www.ncbi.nlm.nih.gov/pubmed/35912919
http://dx.doi.org/10.1002/cam4.5086
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author Zhang, Zinan
Yi, Xiaoping
Pei, Qian
Fu, Yan
Li, Bin
Liu, Haipeng
Han, Zaide
Chen, Changyong
Pang, Peipei
Lin, Huashan
Gong, Guanghui
Yin, Hongling
Zai, Hongyan
Chen, Bihong T.
author_facet Zhang, Zinan
Yi, Xiaoping
Pei, Qian
Fu, Yan
Li, Bin
Liu, Haipeng
Han, Zaide
Chen, Changyong
Pang, Peipei
Lin, Huashan
Gong, Guanghui
Yin, Hongling
Zai, Hongyan
Chen, Bihong T.
author_sort Zhang, Zinan
collection PubMed
description BACKGROUND AND PURPOSE: Early detection of non‐response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT. MATERIALS AND METHODS: Patients with LARC who were treated with nCRT were retrospectively enrolled between March 2009 and March 2019. Traditional radiological characteristics were analyzed by visual inspection and radiomic features were analyzed through computational methods from the pretreatment radiotherapy planning CT images. Differentiation models were constructed using radiomic methods and clinicopathological characteristics for predicting non‐response to nCRT. Model performance was assessed for classification efficiency, calibration, discrimination, and clinical application. RESULTS: This study enrolled a total of 215 patients, including 151 patients in the training cohort (50 non‐responders and 101 responders) and 64 patients in the validation cohort (21 non‐responders and 43 responders). For predicting non‐response, the model constructed with an ensemble machine learning method had higher performance with area under the curve (AUC) values of 0.92 and 0.89 as compared to the model constructed with the logistic regression method (AUC: 0.72 and 0.71 for the training and validation cohorts, respectively). Both decision curve and calibration curve analyses confirmed that the ensemble machine learning model had higher prediction performance. CONCLUSION: Pretreatment CT radiomics achieved satisfying performance in predicting non‐response to nCRT and could be helpful to assist in treatment planning for patients with LARC.
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spelling pubmed-99391082023-02-20 CT radiomics identifying non‐responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer Zhang, Zinan Yi, Xiaoping Pei, Qian Fu, Yan Li, Bin Liu, Haipeng Han, Zaide Chen, Changyong Pang, Peipei Lin, Huashan Gong, Guanghui Yin, Hongling Zai, Hongyan Chen, Bihong T. Cancer Med RESEARCH ARTICLES BACKGROUND AND PURPOSE: Early detection of non‐response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT. MATERIALS AND METHODS: Patients with LARC who were treated with nCRT were retrospectively enrolled between March 2009 and March 2019. Traditional radiological characteristics were analyzed by visual inspection and radiomic features were analyzed through computational methods from the pretreatment radiotherapy planning CT images. Differentiation models were constructed using radiomic methods and clinicopathological characteristics for predicting non‐response to nCRT. Model performance was assessed for classification efficiency, calibration, discrimination, and clinical application. RESULTS: This study enrolled a total of 215 patients, including 151 patients in the training cohort (50 non‐responders and 101 responders) and 64 patients in the validation cohort (21 non‐responders and 43 responders). For predicting non‐response, the model constructed with an ensemble machine learning method had higher performance with area under the curve (AUC) values of 0.92 and 0.89 as compared to the model constructed with the logistic regression method (AUC: 0.72 and 0.71 for the training and validation cohorts, respectively). Both decision curve and calibration curve analyses confirmed that the ensemble machine learning model had higher prediction performance. CONCLUSION: Pretreatment CT radiomics achieved satisfying performance in predicting non‐response to nCRT and could be helpful to assist in treatment planning for patients with LARC. John Wiley and Sons Inc. 2022-08-01 /pmc/articles/PMC9939108/ /pubmed/35912919 http://dx.doi.org/10.1002/cam4.5086 Text en © 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle RESEARCH ARTICLES
Zhang, Zinan
Yi, Xiaoping
Pei, Qian
Fu, Yan
Li, Bin
Liu, Haipeng
Han, Zaide
Chen, Changyong
Pang, Peipei
Lin, Huashan
Gong, Guanghui
Yin, Hongling
Zai, Hongyan
Chen, Bihong T.
CT radiomics identifying non‐responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer
title CT radiomics identifying non‐responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer
title_full CT radiomics identifying non‐responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer
title_fullStr CT radiomics identifying non‐responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer
title_full_unstemmed CT radiomics identifying non‐responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer
title_short CT radiomics identifying non‐responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer
title_sort ct radiomics identifying non‐responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer
topic RESEARCH ARTICLES
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9939108/
https://www.ncbi.nlm.nih.gov/pubmed/35912919
http://dx.doi.org/10.1002/cam4.5086
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