Intralesional and perilesional radiomics strategy based on different machine learning for the prediction of international society of urological pathology grade group in prostate cancer
Abstract Objective To develop and evaluate a intralesional and perilesional radiomics strategy based on different machine learning model to differentiate International Society of Urological Pathology (ISUP) grade > 2 group and ISUP ≤ 2 prostate cancers (PCa). Methods 340 case of PCa patients conf...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
|
| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-025-01812-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849767254661529600 |
|---|---|
| author | Zhiping Li Liqin Yang Ximing Wang Huijing Xu Wen Chen Shuchao Kang Yasheng Huang Chang Shu Feng Cui Yongsheng Zhang |
| author_facet | Zhiping Li Liqin Yang Ximing Wang Huijing Xu Wen Chen Shuchao Kang Yasheng Huang Chang Shu Feng Cui Yongsheng Zhang |
| author_sort | Zhiping Li |
| collection | DOAJ |
| description | Abstract Objective To develop and evaluate a intralesional and perilesional radiomics strategy based on different machine learning model to differentiate International Society of Urological Pathology (ISUP) grade > 2 group and ISUP ≤ 2 prostate cancers (PCa). Methods 340 case of PCa patients confirmed by radical prostatectomy pathology were obtained from two hospitals. The patients were divided into training, internal validation, and external validation groups. Radiomic features were extracted from T2-weighted imaging, and four distinct radiomic feature models were constructed: intralesional, perilesional, combined tumoral and perilesional, and intralesional and perilesional image fusion. Four machine learning classifiers logistic regression (LR), random forest (RF), extra trees (ET), and multilayer perceptron (MLP) were employed for model training and evaluation to select the optimal model. The performance of each model was assessed by calculating the area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Results The AUCs for the RF classifier were higher than that of LR, ET, and MLP, and was selected as the final radiomic model. The nomogram model integrating perilesional, combined intralesional and perilesional, and intralesional and perilesional image fusion had an AUC of 0.929, 0.734, 0.743 for the training, internal, and external validation cohorts, respectively, which was higher than that of the individual intralesional, perilesional, combined intralesional and perilesional, and intralesional and perilesional image fusion models. Conclusions The proposed nomogram established from perilesional, combined intralesional and perilesional, and intralesional and perilesional image fusion radiomic has the potential to predict the differentiation degree of ISUP PCa patients. Clinical trial number Not applicable. |
| format | Article |
| id | doaj-art-e55e552afa594bf4864b918a48b005a7 |
| institution | DOAJ |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-e55e552afa594bf4864b918a48b005a72025-08-20T03:04:17ZengBMCBMC Medical Imaging1471-23422025-07-0125111410.1186/s12880-025-01812-zIntralesional and perilesional radiomics strategy based on different machine learning for the prediction of international society of urological pathology grade group in prostate cancerZhiping Li0Liqin Yang1Ximing Wang2Huijing Xu3Wen Chen4Shuchao Kang5Yasheng Huang6Chang Shu7Feng Cui8Yongsheng Zhang9Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityDepartment of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityDepartment of Radiology, The First Affiliated Hospital of Soochow UniversityDepartment of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityDepartment of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityDepartment of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityDepartment of Urology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityDepartment of Pathology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityDepartment of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityDepartment of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityAbstract Objective To develop and evaluate a intralesional and perilesional radiomics strategy based on different machine learning model to differentiate International Society of Urological Pathology (ISUP) grade > 2 group and ISUP ≤ 2 prostate cancers (PCa). Methods 340 case of PCa patients confirmed by radical prostatectomy pathology were obtained from two hospitals. The patients were divided into training, internal validation, and external validation groups. Radiomic features were extracted from T2-weighted imaging, and four distinct radiomic feature models were constructed: intralesional, perilesional, combined tumoral and perilesional, and intralesional and perilesional image fusion. Four machine learning classifiers logistic regression (LR), random forest (RF), extra trees (ET), and multilayer perceptron (MLP) were employed for model training and evaluation to select the optimal model. The performance of each model was assessed by calculating the area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Results The AUCs for the RF classifier were higher than that of LR, ET, and MLP, and was selected as the final radiomic model. The nomogram model integrating perilesional, combined intralesional and perilesional, and intralesional and perilesional image fusion had an AUC of 0.929, 0.734, 0.743 for the training, internal, and external validation cohorts, respectively, which was higher than that of the individual intralesional, perilesional, combined intralesional and perilesional, and intralesional and perilesional image fusion models. Conclusions The proposed nomogram established from perilesional, combined intralesional and perilesional, and intralesional and perilesional image fusion radiomic has the potential to predict the differentiation degree of ISUP PCa patients. Clinical trial number Not applicable.https://doi.org/10.1186/s12880-025-01812-zMagnetic resonance imagingPerilesional radiomicsMachine learningProstate cancerInternational society of urological pathology grading |
| spellingShingle | Zhiping Li Liqin Yang Ximing Wang Huijing Xu Wen Chen Shuchao Kang Yasheng Huang Chang Shu Feng Cui Yongsheng Zhang Intralesional and perilesional radiomics strategy based on different machine learning for the prediction of international society of urological pathology grade group in prostate cancer BMC Medical Imaging Magnetic resonance imaging Perilesional radiomics Machine learning Prostate cancer International society of urological pathology grading |
| title | Intralesional and perilesional radiomics strategy based on different machine learning for the prediction of international society of urological pathology grade group in prostate cancer |
| title_full | Intralesional and perilesional radiomics strategy based on different machine learning for the prediction of international society of urological pathology grade group in prostate cancer |
| title_fullStr | Intralesional and perilesional radiomics strategy based on different machine learning for the prediction of international society of urological pathology grade group in prostate cancer |
| title_full_unstemmed | Intralesional and perilesional radiomics strategy based on different machine learning for the prediction of international society of urological pathology grade group in prostate cancer |
| title_short | Intralesional and perilesional radiomics strategy based on different machine learning for the prediction of international society of urological pathology grade group in prostate cancer |
| title_sort | intralesional and perilesional radiomics strategy based on different machine learning for the prediction of international society of urological pathology grade group in prostate cancer |
| topic | Magnetic resonance imaging Perilesional radiomics Machine learning Prostate cancer International society of urological pathology grading |
| url | https://doi.org/10.1186/s12880-025-01812-z |
| work_keys_str_mv | AT zhipingli intralesionalandperilesionalradiomicsstrategybasedondifferentmachinelearningforthepredictionofinternationalsocietyofurologicalpathologygradegroupinprostatecancer AT liqinyang intralesionalandperilesionalradiomicsstrategybasedondifferentmachinelearningforthepredictionofinternationalsocietyofurologicalpathologygradegroupinprostatecancer AT ximingwang intralesionalandperilesionalradiomicsstrategybasedondifferentmachinelearningforthepredictionofinternationalsocietyofurologicalpathologygradegroupinprostatecancer AT huijingxu intralesionalandperilesionalradiomicsstrategybasedondifferentmachinelearningforthepredictionofinternationalsocietyofurologicalpathologygradegroupinprostatecancer AT wenchen intralesionalandperilesionalradiomicsstrategybasedondifferentmachinelearningforthepredictionofinternationalsocietyofurologicalpathologygradegroupinprostatecancer AT shuchaokang intralesionalandperilesionalradiomicsstrategybasedondifferentmachinelearningforthepredictionofinternationalsocietyofurologicalpathologygradegroupinprostatecancer AT yashenghuang intralesionalandperilesionalradiomicsstrategybasedondifferentmachinelearningforthepredictionofinternationalsocietyofurologicalpathologygradegroupinprostatecancer AT changshu intralesionalandperilesionalradiomicsstrategybasedondifferentmachinelearningforthepredictionofinternationalsocietyofurologicalpathologygradegroupinprostatecancer AT fengcui intralesionalandperilesionalradiomicsstrategybasedondifferentmachinelearningforthepredictionofinternationalsocietyofurologicalpathologygradegroupinprostatecancer AT yongshengzhang intralesionalandperilesionalradiomicsstrategybasedondifferentmachinelearningforthepredictionofinternationalsocietyofurologicalpathologygradegroupinprostatecancer |