MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins
Abstract Purpose To investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction. Methods and materials In total, 459 patients who underw...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2021-07-01
|
| Series: | Cancer Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40644-021-00414-6 |
| _version_ | 1830512374749069312 |
|---|---|
| author | Dong He Ximing Wang Chenchao Fu Xuedong Wei Jie Bao Xuefu Ji Honglin Bai Wei Xia Xin Gao Yuhua Huang Jianquan Hou |
| author_facet | Dong He Ximing Wang Chenchao Fu Xuedong Wei Jie Bao Xuefu Ji Honglin Bai Wei Xia Xin Gao Yuhua Huang Jianquan Hou |
| author_sort | Dong He |
| collection | DOAJ |
| description | Abstract Purpose To investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction. Methods and materials In total, 459 patients who underwent multiparametric MRI (mpMRI) before prostate biopsy were included. Radiomic features were extracted from both T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. Radiomics signatures were built using radiomic features on the training set, and integrated models were built by adding clinical characteristics. The areas under the receiver operating characteristic curves (AUCs) were calculated to assess the classification performance on the testing sets. Results The radiomics signatures for benign and malignant lesion discrimination achieved AUCs of 0.775 (T2WI), 0.863 (ADC) and 0.855 (ADC + T2WI). The corresponding integrated models improved the AUC to 0.851/0.912/0.905, respectively. The radiomics signatures for ECE achieved the highest AUC of 0.625 (ADC), and the corresponding integrated model achieved the highest AUC (0.728). The radiomics signatures for PSM prediction achieved AUCs of 0.614 (T2WI) and 0.733 (ADC). The corresponding integrated models reached AUCs of 0.680 and 0.766, respectively. Conclusions The MRI-based radiomics models, which took advantage of radiomic features on ADC and T2WI scans, showed good performance in discriminating benign and malignant prostate lesions and predicting ECE and PSM. Combining radiomics signatures and clinical factors enhanced the performance of the models, which may contribute to clinical diagnosis and treatment. |
| first_indexed | 2024-12-22T02:27:17Z |
| format | Article |
| id | doaj.art-39b8248dc9294e14967fb1c46d42c001 |
| institution | Directory Open Access Journal |
| issn | 1470-7330 |
| language | English |
| last_indexed | 2024-12-22T02:27:17Z |
| publishDate | 2021-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Imaging |
| spelling | doaj.art-39b8248dc9294e14967fb1c46d42c0012022-12-21T18:41:58ZengBMCCancer Imaging1470-73302021-07-012111910.1186/s40644-021-00414-6MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical marginsDong He0Ximing Wang1Chenchao Fu2Xuedong Wei3Jie Bao4Xuefu Ji5Honglin Bai6Wei Xia7Xin Gao8Yuhua Huang9Jianquan Hou10Department of Urology, The First Affiliated Hospital of SooChow UniversityDepartment of Radiology, The First Affiliated Hospital of SooChow UniversityDepartment of Urology, The First Affiliated Hospital of SooChow UniversityDepartment of Urology, The First Affiliated Hospital of SooChow UniversityDepartment of Radiology, The First Affiliated Hospital of SooChow UniversitySuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesDepartment of Urology, The First Affiliated Hospital of SooChow UniversityDepartment of Urology, The First Affiliated Hospital of SooChow UniversityAbstract Purpose To investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction. Methods and materials In total, 459 patients who underwent multiparametric MRI (mpMRI) before prostate biopsy were included. Radiomic features were extracted from both T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. Radiomics signatures were built using radiomic features on the training set, and integrated models were built by adding clinical characteristics. The areas under the receiver operating characteristic curves (AUCs) were calculated to assess the classification performance on the testing sets. Results The radiomics signatures for benign and malignant lesion discrimination achieved AUCs of 0.775 (T2WI), 0.863 (ADC) and 0.855 (ADC + T2WI). The corresponding integrated models improved the AUC to 0.851/0.912/0.905, respectively. The radiomics signatures for ECE achieved the highest AUC of 0.625 (ADC), and the corresponding integrated model achieved the highest AUC (0.728). The radiomics signatures for PSM prediction achieved AUCs of 0.614 (T2WI) and 0.733 (ADC). The corresponding integrated models reached AUCs of 0.680 and 0.766, respectively. Conclusions The MRI-based radiomics models, which took advantage of radiomic features on ADC and T2WI scans, showed good performance in discriminating benign and malignant prostate lesions and predicting ECE and PSM. Combining radiomics signatures and clinical factors enhanced the performance of the models, which may contribute to clinical diagnosis and treatment.https://doi.org/10.1186/s40644-021-00414-6Prostate cancerRadiomicsExtracapsular extensionPositive surgical margins |
| spellingShingle | Dong He Ximing Wang Chenchao Fu Xuedong Wei Jie Bao Xuefu Ji Honglin Bai Wei Xia Xin Gao Yuhua Huang Jianquan Hou MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins Cancer Imaging Prostate cancer Radiomics Extracapsular extension Positive surgical margins |
| title | MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins |
| title_full | MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins |
| title_fullStr | MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins |
| title_full_unstemmed | MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins |
| title_short | MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins |
| title_sort | mri based radiomics models to assess prostate cancer extracapsular extension and positive surgical margins |
| topic | Prostate cancer Radiomics Extracapsular extension Positive surgical margins |
| url | https://doi.org/10.1186/s40644-021-00414-6 |
| work_keys_str_mv | AT donghe mribasedradiomicsmodelstoassessprostatecancerextracapsularextensionandpositivesurgicalmargins AT ximingwang mribasedradiomicsmodelstoassessprostatecancerextracapsularextensionandpositivesurgicalmargins AT chenchaofu mribasedradiomicsmodelstoassessprostatecancerextracapsularextensionandpositivesurgicalmargins AT xuedongwei mribasedradiomicsmodelstoassessprostatecancerextracapsularextensionandpositivesurgicalmargins AT jiebao mribasedradiomicsmodelstoassessprostatecancerextracapsularextensionandpositivesurgicalmargins AT xuefuji mribasedradiomicsmodelstoassessprostatecancerextracapsularextensionandpositivesurgicalmargins AT honglinbai mribasedradiomicsmodelstoassessprostatecancerextracapsularextensionandpositivesurgicalmargins AT weixia mribasedradiomicsmodelstoassessprostatecancerextracapsularextensionandpositivesurgicalmargins AT xingao mribasedradiomicsmodelstoassessprostatecancerextracapsularextensionandpositivesurgicalmargins AT yuhuahuang mribasedradiomicsmodelstoassessprostatecancerextracapsularextensionandpositivesurgicalmargins AT jianquanhou mribasedradiomicsmodelstoassessprostatecancerextracapsularextensionandpositivesurgicalmargins |