An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer
Abstract Objective To investigate whether radiomics based on ultrasound images can predict lymphovascular invasion (LVI) of rectal cancer (RC) before surgery. Methods A total of 203 patients with RC were enrolled retrospectively, and they were divided into a training set (143 patients) and a validat...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-05-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-022-00813-6 |
| _version_ | 1817988225558380544 |
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| author | Yu-quan Wu Rui-zhi Gao Peng Lin Rong Wen Hai-yuan Li Mei-yan Mou Feng-huan Chen Fen Huang Wei-jie Zhou Hong Yang Yun He Ji Wu |
| author_facet | Yu-quan Wu Rui-zhi Gao Peng Lin Rong Wen Hai-yuan Li Mei-yan Mou Feng-huan Chen Fen Huang Wei-jie Zhou Hong Yang Yun He Ji Wu |
| author_sort | Yu-quan Wu |
| collection | DOAJ |
| description | Abstract Objective To investigate whether radiomics based on ultrasound images can predict lymphovascular invasion (LVI) of rectal cancer (RC) before surgery. Methods A total of 203 patients with RC were enrolled retrospectively, and they were divided into a training set (143 patients) and a validation set (60 patients). We extracted the radiomic features from the largest gray ultrasound image of the RC lesion. The intraclass correlation coefficient (ICC) was applied to test the repeatability of the radiomic features. The least absolute shrinkage and selection operator (LASSO) was used to reduce the data dimension and select significant features. Logistic regression (LR) analysis was applied to establish the radiomics model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the comprehensive performance of the model. Results Among the 203 patients, 33 (16.7%) were LVI positive and 170 (83.7%) were LVI negative. A total of 5350 (90.1%) radiomic features with ICC values of ≥ 0.75 were reported, which were subsequently subjected to hypothesis testing and LASSO regression dimension reduction analysis. Finally, 15 selected features were used to construct the radiomics model. The area under the curve (AUC) of the training set was 0.849, and the AUC of the validation set was 0.781. The calibration curve indicated that the radiomics model had good calibration, and DCA demonstrated that the model had clinical benefits. Conclusion The proposed endorectal ultrasound-based radiomics model has the potential to predict LVI preoperatively in RC. |
| first_indexed | 2024-04-14T00:31:35Z |
| format | Article |
| id | doaj.art-f62a48df01b642358ee28a09079601a4 |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-04-14T00:31:35Z |
| publishDate | 2022-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-f62a48df01b642358ee28a09079601a42022-12-22T02:22:32ZengBMCBMC Medical Imaging1471-23422022-05-0122111010.1186/s12880-022-00813-6An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancerYu-quan Wu0Rui-zhi Gao1Peng Lin2Rong Wen3Hai-yuan Li4Mei-yan Mou5Feng-huan Chen6Fen Huang7Wei-jie Zhou8Hong Yang9Yun He10Ji Wu11Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityThe Second Clinical Medical College, Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical UniversityAbstract Objective To investigate whether radiomics based on ultrasound images can predict lymphovascular invasion (LVI) of rectal cancer (RC) before surgery. Methods A total of 203 patients with RC were enrolled retrospectively, and they were divided into a training set (143 patients) and a validation set (60 patients). We extracted the radiomic features from the largest gray ultrasound image of the RC lesion. The intraclass correlation coefficient (ICC) was applied to test the repeatability of the radiomic features. The least absolute shrinkage and selection operator (LASSO) was used to reduce the data dimension and select significant features. Logistic regression (LR) analysis was applied to establish the radiomics model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the comprehensive performance of the model. Results Among the 203 patients, 33 (16.7%) were LVI positive and 170 (83.7%) were LVI negative. A total of 5350 (90.1%) radiomic features with ICC values of ≥ 0.75 were reported, which were subsequently subjected to hypothesis testing and LASSO regression dimension reduction analysis. Finally, 15 selected features were used to construct the radiomics model. The area under the curve (AUC) of the training set was 0.849, and the AUC of the validation set was 0.781. The calibration curve indicated that the radiomics model had good calibration, and DCA demonstrated that the model had clinical benefits. Conclusion The proposed endorectal ultrasound-based radiomics model has the potential to predict LVI preoperatively in RC.https://doi.org/10.1186/s12880-022-00813-6UltrasoundRadiomicsModelRectal cancerLymphovascular invasion |
| spellingShingle | Yu-quan Wu Rui-zhi Gao Peng Lin Rong Wen Hai-yuan Li Mei-yan Mou Feng-huan Chen Fen Huang Wei-jie Zhou Hong Yang Yun He Ji Wu An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer BMC Medical Imaging Ultrasound Radiomics Model Rectal cancer Lymphovascular invasion |
| title | An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer |
| title_full | An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer |
| title_fullStr | An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer |
| title_full_unstemmed | An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer |
| title_short | An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer |
| title_sort | endorectal ultrasound based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer |
| topic | Ultrasound Radiomics Model Rectal cancer Lymphovascular invasion |
| url | https://doi.org/10.1186/s12880-022-00813-6 |
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