CT radiomics identifying non‐responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer
Abstract 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 w...
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Wiley
2023-02-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.5086 |
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| author | Zinan Zhang Xiaoping Yi Qian Pei Yan Fu Bin Li Haipeng Liu Zaide Han Changyong Chen Peipei Pang Huashan Lin Guanghui Gong Hongling Yin Hongyan Zai Bihong T. Chen |
| author_facet | Zinan Zhang Xiaoping Yi Qian Pei Yan Fu Bin Li Haipeng Liu Zaide Han Changyong Chen Peipei Pang Huashan Lin Guanghui Gong Hongling Yin Hongyan Zai Bihong T. Chen |
| author_sort | Zinan Zhang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-9cae42001dbc408ba34e508dbdba1c3e |
| institution | OA Journals |
| issn | 2045-7634 |
| language | English |
| publishDate | 2023-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-9cae42001dbc408ba34e508dbdba1c3e2025-08-20T02:23:32ZengWileyCancer Medicine2045-76342023-02-011232463247310.1002/cam4.5086CT radiomics identifying non‐responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancerZinan Zhang0Xiaoping Yi1Qian Pei2Yan Fu3Bin Li4Haipeng Liu5Zaide Han6Changyong Chen7Peipei Pang8Huashan Lin9Guanghui Gong10Hongling Yin11Hongyan Zai12Bihong T. Chen13Department of Radiology (Xiangya Hospital) Central South University Changsha Hunan P.R. ChinaDepartment of Radiology (Xiangya Hospital) Central South University Changsha Hunan P.R. ChinaDepartment of General Surgery (Xiangya Hospital) Central South University Changsha Hunan P.R. ChinaDepartment of Radiology (Xiangya Hospital) Central South University Changsha Hunan P.R. ChinaDepartment of Oncology (Xiangya Hospital) Central South University Changsha Hunan P.R. ChinaDepartment of Radiology (Xiangya Hospital) Central South University Changsha Hunan P.R. ChinaDepartment of Radiology (Xiangya Hospital) Central South University Changsha Hunan P.R. ChinaDepartment of Radiology (Xiangya Hospital) Central South University Changsha Hunan P.R. ChinaDepartment of Pharmaceuticals and Diagnosis GE Healthcare Changsha P.R. ChinaDepartment of Pharmaceuticals and Diagnosis GE Healthcare Changsha P.R. ChinaDepartment of Pathology, Xiangya Hospital Central South University Changsha Hunan P.R. ChinaDepartment of Pathology, Xiangya Hospital Central South University Changsha Hunan P.R. ChinaDepartment of General Surgery (Xiangya Hospital) Central South University Changsha Hunan P.R. ChinaDepartment of Diagnostic Radiology City of Hope National Medical Center Duarte California USAAbstract 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.https://doi.org/10.1002/cam4.5086CT radiomicslocally advanced colorectal cancerneoadjuvant chemoradiotherapypredictionradiotherapy planningtreatment response |
| spellingShingle | Zinan Zhang Xiaoping Yi Qian Pei Yan Fu Bin Li Haipeng Liu Zaide Han Changyong Chen Peipei Pang Huashan Lin Guanghui Gong Hongling Yin Hongyan Zai Bihong T. Chen CT radiomics identifying non‐responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer Cancer Medicine CT radiomics locally advanced colorectal cancer neoadjuvant chemoradiotherapy prediction radiotherapy planning treatment response |
| 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 | CT radiomics locally advanced colorectal cancer neoadjuvant chemoradiotherapy prediction radiotherapy planning treatment response |
| url | https://doi.org/10.1002/cam4.5086 |
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