Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysis
Abstract Background Prostate cancer (PCa) is definitively diagnosed by systematic prostate biopsy (SBx) with 13 cores. This method, however, can increase the risk of urinary retention, infection and bleeding due to the excessive number of biopsy cores. Methods We retrospectively analyzed 622 patient...
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2025-01-01
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| Online Access: | https://doi.org/10.1186/s12885-024-13391-3 |
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| author | Zhenlin Chen Zhihao Li Ruiling Dou Shaoqin Jiang Shaoshan Lin Zequn Lin Yue Xu Ciquan Liu Zijie Zheng Yewen Lin Mengqiang Li |
| author_facet | Zhenlin Chen Zhihao Li Ruiling Dou Shaoqin Jiang Shaoshan Lin Zequn Lin Yue Xu Ciquan Liu Zijie Zheng Yewen Lin Mengqiang Li |
| author_sort | Zhenlin Chen |
| collection | DOAJ |
| description | Abstract Background Prostate cancer (PCa) is definitively diagnosed by systematic prostate biopsy (SBx) with 13 cores. This method, however, can increase the risk of urinary retention, infection and bleeding due to the excessive number of biopsy cores. Methods We retrospectively analyzed 622 patients who underwent SBx with prostate multiparametric MRI (mpMRI) from two centers between January 2014 to June 2022. The MRI data were collected to manually segment Regions of Interest (ROI) of the tumor layer by layer. ROI reconstructions were fused to form outline of the volume of interest (VOI), which were exported and applied to subsequent extraction of radiomics features. The t-tests, Mann-Whitney U-tests and chi-squared tests were performed to evaluate the significance of features. The logistic regression was used for calculating the PCa risk score (PCS). The PCS model was trained to optimize the SBx core number, utilizing both mpMRI radiomics and clinical features. Results The predicted number of SBx cores was determined by PCS model. Optimal core numbers of SBx for PCS subgroups 1–5 were calculated as 13, 10, 8, 6, and 6, respectively. Accuracies of predicted core numbers were high: 100%, 95.8%, 91.7%, 90.6%, and 92.7% for PCS subgroups 1–5. Optimized SBx reduced core rate by 41.9%. Leakage rates for PCa and clinically significant PCa were 8.2% and 3.4%, respectively. The optimized SBx also demonstrated high accuracy on the validation set. Conclusion The optimization PCS model described in this study could therefore effectively reduce the number of systematic biopsy cores obtained from patients with high PCS, especially for biopsy cores far away from suspicious lesions. This method can enhance patient experience without reducing tumor detection rate. |
| format | Article |
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| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
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| series | BMC Cancer |
| spelling | doaj-art-11ad09a3c9c049c89b91568196d981bd2025-01-26T12:38:00ZengBMCBMC Cancer1471-24072025-01-0125111110.1186/s12885-024-13391-3Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysisZhenlin Chen0Zhihao Li1Ruiling Dou2Shaoqin Jiang3Shaoshan Lin4Zequn Lin5Yue Xu6Ciquan Liu7Zijie Zheng8Yewen Lin9Mengqiang Li10Department of Urology, Fujian Union Hospital, Fujian Medical UniversityCenter of Reproductive Medicine, Fujian Maternity and Child Health Hospital, Fujian Medical UniversityDepartment of Urology, Fujian Union Hospital, Fujian Medical UniversityDepartment of Urology, Fujian Union Hospital, Fujian Medical UniversityDepartment of Urology, Fujian Union Hospital, Fujian Medical UniversityDepartment of Urology, Fujian Union Hospital, Fujian Medical UniversityDepartment of Urology, Fujian Union Hospital, Fujian Medical UniversityDepartment of Urology, Fujian Union Hospital, Fujian Medical UniversityDepartment of Urology, Fujian Union Hospital, Fujian Medical UniversityDepartment of Urology, Fujian Union Hospital, Fujian Medical UniversityDepartment of Urology, Fujian Union Hospital, Fujian Medical UniversityAbstract Background Prostate cancer (PCa) is definitively diagnosed by systematic prostate biopsy (SBx) with 13 cores. This method, however, can increase the risk of urinary retention, infection and bleeding due to the excessive number of biopsy cores. Methods We retrospectively analyzed 622 patients who underwent SBx with prostate multiparametric MRI (mpMRI) from two centers between January 2014 to June 2022. The MRI data were collected to manually segment Regions of Interest (ROI) of the tumor layer by layer. ROI reconstructions were fused to form outline of the volume of interest (VOI), which were exported and applied to subsequent extraction of radiomics features. The t-tests, Mann-Whitney U-tests and chi-squared tests were performed to evaluate the significance of features. The logistic regression was used for calculating the PCa risk score (PCS). The PCS model was trained to optimize the SBx core number, utilizing both mpMRI radiomics and clinical features. Results The predicted number of SBx cores was determined by PCS model. Optimal core numbers of SBx for PCS subgroups 1–5 were calculated as 13, 10, 8, 6, and 6, respectively. Accuracies of predicted core numbers were high: 100%, 95.8%, 91.7%, 90.6%, and 92.7% for PCS subgroups 1–5. Optimized SBx reduced core rate by 41.9%. Leakage rates for PCa and clinically significant PCa were 8.2% and 3.4%, respectively. The optimized SBx also demonstrated high accuracy on the validation set. Conclusion The optimization PCS model described in this study could therefore effectively reduce the number of systematic biopsy cores obtained from patients with high PCS, especially for biopsy cores far away from suspicious lesions. This method can enhance patient experience without reducing tumor detection rate.https://doi.org/10.1186/s12885-024-13391-3Prostate cancerRadiomics featuresMpMRISystematic prostate biopsyOptimized model |
| spellingShingle | Zhenlin Chen Zhihao Li Ruiling Dou Shaoqin Jiang Shaoshan Lin Zequn Lin Yue Xu Ciquan Liu Zijie Zheng Yewen Lin Mengqiang Li Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysis BMC Cancer Prostate cancer Radiomics features MpMRI Systematic prostate biopsy Optimized model |
| title | Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysis |
| title_full | Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysis |
| title_fullStr | Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysis |
| title_full_unstemmed | Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysis |
| title_short | Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysis |
| title_sort | personalized optimization of systematic prostate biopsy core number based on mpmri radiomics features a large sample retrospective analysis |
| topic | Prostate cancer Radiomics features MpMRI Systematic prostate biopsy Optimized model |
| url | https://doi.org/10.1186/s12885-024-13391-3 |
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