Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer
PURPOSEWhether radiomics methods are useful in prediction of therapeutic response to neoadjuvant chemoradiotherapy (nCRT) is unclear. This study aimed to investigate multiple magnetic resonance imaging (MRI) sequence-based radiomics methods in evaluating therapeutic response to nCRT in patients with...
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| Format: | Article |
| Language: | English |
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Galenos Publishing House
2021-05-01
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| Series: | Diagnostic and Interventional Radiology |
| Online Access: |
http://www.dirjournal.org/archives/archive-detail/article-preview/radiomics-signature-as-a-new-biomarker-for-preoper/54315
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| _version_ | 1797691330447540224 |
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| author | Zhaohe Zhang Xiran Jiang Rui Zhang Tao Yu Shanshan Liu Yahong Luo |
| author_facet | Zhaohe Zhang Xiran Jiang Rui Zhang Tao Yu Shanshan Liu Yahong Luo |
| author_sort | Zhaohe Zhang |
| collection | DOAJ |
| description | PURPOSEWhether radiomics methods are useful in prediction of therapeutic response to neoadjuvant chemoradiotherapy (nCRT) is unclear. This study aimed to investigate multiple magnetic resonance imaging (MRI) sequence-based radiomics methods in evaluating therapeutic response to nCRT in patients with locally advanced rectal cancer (LARC).METHODSThis retrospective study enrolled patients with LARC (06/2014-08/2017) and divided them into nCRT-sensitive and nCRT-resistant groups according to postoperative tumor regression grading results. Radiomics features from preoperative MRI were extracted, followed by dimension reduction using the minimum redundancy maximum relevance filter. Three machine-learning classifiers and an ensemble classifier were used for therapeutic response prediction. Radiomics nomogram incorporating clinical parameters were constructed using logistic regression. The receiver operating characteristic (ROC), decision curves analysis (DCA) and calibration curves were also plotted to evaluate the prediction performance.RESULTSThe machine learning classifiers showed good prediction performance for therapeutic responses in LARC patients (n=189). The ROC curve showed satisfying performance (area under the curve [AUC], 0.830; specificity, 0.794; sensitivity, 0.815) in the validation group. The radiomics signature included 30 imaging features derived from axial T1-weighted imaging with contrast and sagittal T2-weighted imaging and exhibited good predictive power for nCRT. A radiomics nomogram integrating carcinoembryonic antigen levels and tumor diameter showed excellent performance with an AUC of 0.949 (95% confidence interval, 0.892–0.997; specificity, 0.909; sensitivity, 0.879) in the validation group. DCA confirmed the clinical usefulness of the nomogram model.CONCLUSIONThe radiomics method using multiple MRI sequences can be used to achieve individualized prediction of nCRT in patients with LARC before treatment. |
| first_indexed | 2024-03-12T02:11:50Z |
| format | Article |
| id | doaj.art-0a6204edf15b4e1a94ab123103672b0d |
| institution | Directory Open Access Journal |
| issn | 1305-3825 1305-3612 |
| language | English |
| last_indexed | 2024-03-12T02:11:50Z |
| publishDate | 2021-05-01 |
| publisher | Galenos Publishing House |
| record_format | Article |
| series | Diagnostic and Interventional Radiology |
| spelling | doaj.art-0a6204edf15b4e1a94ab123103672b0d2023-09-06T12:21:06ZengGalenos Publishing HouseDiagnostic and Interventional Radiology1305-38251305-36122021-05-0127330831410.5152/dir.2021.1967713049054Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancerZhaohe Zhang0Xiran Jiang1Rui Zhang2Tao Yu3Shanshan Liu4Yahong Luo5 Department of Medical Imaging, Liaoning Cancer Hospital and Institute, Shenyang, China Department of Biomedical Engineering, China Medical University, Shenyang, China Department of Colorectal surgery, Liaoning Cancer Hospital and Institute, Shenyang, China Department of Medical Imaging, Liaoning Cancer Hospital and Institute, Shenyang, China Department of Biomedical Engineering, China Medical University, Shenyang, China Department of Medical Imaging, Liaoning Cancer Hospital and Institute, Shenyang, China PURPOSEWhether radiomics methods are useful in prediction of therapeutic response to neoadjuvant chemoradiotherapy (nCRT) is unclear. This study aimed to investigate multiple magnetic resonance imaging (MRI) sequence-based radiomics methods in evaluating therapeutic response to nCRT in patients with locally advanced rectal cancer (LARC).METHODSThis retrospective study enrolled patients with LARC (06/2014-08/2017) and divided them into nCRT-sensitive and nCRT-resistant groups according to postoperative tumor regression grading results. Radiomics features from preoperative MRI were extracted, followed by dimension reduction using the minimum redundancy maximum relevance filter. Three machine-learning classifiers and an ensemble classifier were used for therapeutic response prediction. Radiomics nomogram incorporating clinical parameters were constructed using logistic regression. The receiver operating characteristic (ROC), decision curves analysis (DCA) and calibration curves were also plotted to evaluate the prediction performance.RESULTSThe machine learning classifiers showed good prediction performance for therapeutic responses in LARC patients (n=189). The ROC curve showed satisfying performance (area under the curve [AUC], 0.830; specificity, 0.794; sensitivity, 0.815) in the validation group. The radiomics signature included 30 imaging features derived from axial T1-weighted imaging with contrast and sagittal T2-weighted imaging and exhibited good predictive power for nCRT. A radiomics nomogram integrating carcinoembryonic antigen levels and tumor diameter showed excellent performance with an AUC of 0.949 (95% confidence interval, 0.892–0.997; specificity, 0.909; sensitivity, 0.879) in the validation group. DCA confirmed the clinical usefulness of the nomogram model.CONCLUSIONThe radiomics method using multiple MRI sequences can be used to achieve individualized prediction of nCRT in patients with LARC before treatment. http://www.dirjournal.org/archives/archive-detail/article-preview/radiomics-signature-as-a-new-biomarker-for-preoper/54315 |
| spellingShingle | Zhaohe Zhang Xiran Jiang Rui Zhang Tao Yu Shanshan Liu Yahong Luo Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer Diagnostic and Interventional Radiology |
| title | Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer |
| title_full | Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer |
| title_fullStr | Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer |
| title_full_unstemmed | Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer |
| title_short | Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer |
| title_sort | radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer |
| url |
http://www.dirjournal.org/archives/archive-detail/article-preview/radiomics-signature-as-a-new-biomarker-for-preoper/54315
|
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