Enhanced ISUP grade prediction in prostate cancer using multi-center radiomics data.
Background: To explore the predictive value of radiomics features extracted from anatomical ROIs in differentiating the International Society of Urological Pathology (ISUP) grading in prostate cancer patients. Methods: This study included 1,500 prostate cancer patients from a multi-center study. The...
| Publicado en: | Abdominal Radiology Vol. 50; no. 9; pp. 4301 - 4311 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187189757&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187189757 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Sep2025 vid: 50 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187189757 183474735 10.1007/s00261-025-04858-3 187189757 ppf: 4301 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Enhanced ISUP grade prediction in prostate cancer using multi-center radiomics data. aug: au: Liu, Yuying Han, Xueqing Chen, Haohui Zhang, Qirui affil: https://ror.org/01rxvg760 Department of Computer Science and Technology, Nanjing University, Nanjing, China sug: ab: Background: To explore the predictive value of radiomics features extracted from anatomical ROIs in differentiating the International Society of Urological Pathology (ISUP) grading in prostate cancer patients. Methods: This study included 1,500 prostate cancer patients from a multi-center study. The peripheral zone (PZ) and central gland (CG, transition zone + central zone) of the prostate were segmented using deep learning algorithms and were defined as the regions of interest (ROI) in this study. A total of 12,918 image-based features were extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), and diffusion-weighted imaging (DWI) images of these two ROIs. Synthetic minority over-sampling technique (SMOTE) algorithm was used to address the class imbalance problem. Feature selection was performed using Pearson correlation analysis and random forest regression. A prediction model was built using the random forest classification algorithm. Kruskal-Wallis H test, ANOVA, and Chi-Square Test were used for statistical analysis. Results: A total of 20 ISUP grading-related features were selected, including 10 from the PZ ROI and 10 from the CG ROI. On the test set, the combined PZ + CG radiomics model exhibited better predictive performance, with an AUC of 0.928 (95% CI: 0.872, 0.966), compared to the PZ model alone (AUC: 0.838; 95% CI: 0.722, 0.920) and the CG model alone (AUC: 0.904; 95% CI: 0.851, 0.945). Conclusion: This study demonstrates that radiomic features extracted based on anatomical sub-region of the prostate can contribute to enhanced ISUP grade prediction. The combination of PZ + GG can provide more comprehensive information with improved accuracy. Further validation of this strategy in the future will enhance its prospects for improving decision-making in clinical settings. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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