Prediction of clinically significant prostate cancer using radiomics models in real-world clinical practice: a retrospective multicenter study
- Open Access
- 01-12-2024
- Prostate Cancer
- Original Article
Published in:
- Authors
- Jie Bao
- Xiaomeng Qiao
- Yang Song
- Yueting Su
- Libiao Ji
- Junkang Shen
- Guang Yang
- Hailin Shen
- Ximing Wang
- Chunhong Hu
- Published in
- Insights into Imaging | Issue 1/2024
Abstract
Purpose
To develop and evaluate machine learning models based on MRI to predict clinically significant prostate cancer (csPCa) and International Society of Urological Pathology (ISUP) grade group as well as explore the potential value of radiomics models for improving the performance of radiologists for Prostate Imaging Reporting and Data System (PI-RADS) assessment.
Material and methods
A total of 1616 patients from 4 tertiary care medical centers were retrospectively enrolled. PI-RADS assessments were performed by junior, senior, and expert-level radiologists. The radiomics models for predicting csPCa were built using 4 machine-learning algorithms. The PI-RADS were adjusted by the radiomics model. The relationship between the Rad-score and ISUP was evaluated by Spearman analysis.
Results
The radiomics models made using the random forest algorithm yielded areas under the receiver operating characteristic curves (AUCs) of 0.874, 0.876, and 0.893 in an internal testing cohort and external testing cohorts, respectively. The AUC of the adjusted_PI-RADS was improved, and the specificity was improved at a slight sacrifice of sensitivity. The participant-level correlation showed that the Rad-score was positively correlated with ISUP in all testing cohorts (r > 0.600 and p < 0.0001).
Conclusions
This radiomics model resulted as a powerful, non-invasive auxiliary tool for accurately predicting prostate cancer aggressiveness. The radiomics model could reduce unnecessary biopsies and help improve the diagnostic performance of radiologists’ PI-RADS. Yet, prospective studies are still needed to validate the radiomics models further.
Critical relevance statement
The radiomics model with MRI may help to accurately screen out clinically significant prostate cancer, thereby assisting physicians in making individualized treatment plans.
Key points
• The diagnostic performance of the radiomics model using the Random Forest algorithm is comparable to the Prostate Imaging Reporting and Data System (PI-RADS) obtained by radiologists.
• The performance of the adjusted Prostate Imaging Reporting and Data System (PI-RADS) was improved, which implied that the radiomics model could be a potential radiological assessment tool.
• The radiomics model lowered the percentage of equivocal cases. Moreover, the Rad-scores can be used to characterize prostate cancer aggressiveness.
Graphical Abstract
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- Title
- Prediction of clinically significant prostate cancer using radiomics models in real-world clinical practice: a retrospective multicenter study
- Authors
-
Jie Bao
Xiaomeng Qiao
Yang Song
Yueting Su
Libiao Ji
Junkang Shen
Guang Yang
Hailin Shen
Ximing Wang
Chunhong Hu
- Publication date
- 01-12-2024
- Publisher
- Springer Vienna
- Keywords
-
Prostate Cancer
Prostate Cancer - Published in
-
Insights into Imaging / Issue 1/2024
Electronic ISSN: 1869-4101 - DOI
- https://doi.org/10.1186/s13244-024-01631-w
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