Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer
ObjectivesThis study aims to develop and evaluate multiparametric MRI (MP-MRI)-based radiomic models as a noninvasive diagnostic method to predict several biological characteristics of prostate cancer.MethodsA total of 252 patients were retrospectively included who underwent radical prostatectomy an...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-02-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.839621/full |
| _version_ | 1798024209300979712 |
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| author | Xuhui Fan Ni Xie Jingwen Chen Tiewen Li Rong Cao Hongwei Yu Meijuan He Zilin Wang Yihui Wang Hao Liu Han Wang Han Wang Han Wang Xiaorui Yin |
| author_facet | Xuhui Fan Ni Xie Jingwen Chen Tiewen Li Rong Cao Hongwei Yu Meijuan He Zilin Wang Yihui Wang Hao Liu Han Wang Han Wang Han Wang Xiaorui Yin |
| author_sort | Xuhui Fan |
| collection | DOAJ |
| description | ObjectivesThis study aims to develop and evaluate multiparametric MRI (MP-MRI)-based radiomic models as a noninvasive diagnostic method to predict several biological characteristics of prostate cancer.MethodsA total of 252 patients were retrospectively included who underwent radical prostatectomy and MP-MRI examinations. The prediction characteristics of this study were as follows: Ki67, S100, extracapsular extension (ECE), perineural invasion (PNI), and surgical margin (SM). Patients were divided into training cohorts and validation cohorts in the ratio of 4:1 for each group. After lesion segmentation manually, radiomic features were extracted from MP-MRI images and some clinical factors were also included. Max relevance min redundancy (mRMR) and recursive feature elimination (RFE) based on random forest (RF) were adopted to select features. Six classifiers were included (SVM, KNN, RF, decision tree, logistic regression, XGBOOST) to find the best diagnostic performance among them. The diagnostic efficiency of the construction models was evaluated by ROC curves and quantified by AUC.ResultsRF performed best among the six classifiers for the four groups according to AUC values (Ki67 = 0.87, S100 = 0.80, ECE = 0.85, PNI = 0.82). The performance of SVM was relatively the best for SM (AUC = 0.77). The number and importance of DCE features ranked first in the models of each group. The combined models of MP-MRI and clinical characteristics showed no significant difference compared with MP-MRI models according to Delong’s tests.ConclusionsRadiomics models based on MP-MRI have the potential to predict biological characteristics and are expected to be a noninvasive method to evaluate the risk stratification of prostate cancer. |
| first_indexed | 2024-04-11T17:58:41Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-11T17:58:41Z |
| publishDate | 2022-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-775eebdfee0245b4bdfbb87711fe2b1f2022-12-22T04:10:34ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-02-011210.3389/fonc.2022.839621839621Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate CancerXuhui Fan0Ni Xie1Jingwen Chen2Tiewen Li3Rong Cao4Hongwei Yu5Meijuan He6Zilin Wang7Yihui Wang8Hao Liu9Han Wang10Han Wang11Han Wang12Xiaorui Yin13Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaInstitution for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Research and Development, Yizhun Medical AI Technology Co. Ltd., Beijing, ChinaDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaInstitution for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Radiology, Jiading Branch of Shanghai General Hospital, Shanghai, ChinaDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaObjectivesThis study aims to develop and evaluate multiparametric MRI (MP-MRI)-based radiomic models as a noninvasive diagnostic method to predict several biological characteristics of prostate cancer.MethodsA total of 252 patients were retrospectively included who underwent radical prostatectomy and MP-MRI examinations. The prediction characteristics of this study were as follows: Ki67, S100, extracapsular extension (ECE), perineural invasion (PNI), and surgical margin (SM). Patients were divided into training cohorts and validation cohorts in the ratio of 4:1 for each group. After lesion segmentation manually, radiomic features were extracted from MP-MRI images and some clinical factors were also included. Max relevance min redundancy (mRMR) and recursive feature elimination (RFE) based on random forest (RF) were adopted to select features. Six classifiers were included (SVM, KNN, RF, decision tree, logistic regression, XGBOOST) to find the best diagnostic performance among them. The diagnostic efficiency of the construction models was evaluated by ROC curves and quantified by AUC.ResultsRF performed best among the six classifiers for the four groups according to AUC values (Ki67 = 0.87, S100 = 0.80, ECE = 0.85, PNI = 0.82). The performance of SVM was relatively the best for SM (AUC = 0.77). The number and importance of DCE features ranked first in the models of each group. The combined models of MP-MRI and clinical characteristics showed no significant difference compared with MP-MRI models according to Delong’s tests.ConclusionsRadiomics models based on MP-MRI have the potential to predict biological characteristics and are expected to be a noninvasive method to evaluate the risk stratification of prostate cancer.https://www.frontiersin.org/articles/10.3389/fonc.2022.839621/fullradiomicsprostate cancermagnetic resonance imagingbiological characteristicsrisk stratification |
| spellingShingle | Xuhui Fan Ni Xie Jingwen Chen Tiewen Li Rong Cao Hongwei Yu Meijuan He Zilin Wang Yihui Wang Hao Liu Han Wang Han Wang Han Wang Xiaorui Yin Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer Frontiers in Oncology radiomics prostate cancer magnetic resonance imaging biological characteristics risk stratification |
| title | Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer |
| title_full | Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer |
| title_fullStr | Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer |
| title_full_unstemmed | Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer |
| title_short | Multiparametric MRI and Machine Learning Based Radiomic Models for Preoperative Prediction of Multiple Biological Characteristics in Prostate Cancer |
| title_sort | multiparametric mri and machine learning based radiomic models for preoperative prediction of multiple biological characteristics in prostate cancer |
| topic | radiomics prostate cancer magnetic resonance imaging biological characteristics risk stratification |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.839621/full |
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