Prediction of clinically significant prostate cancer using radiomics models in real-world clinical practice: a retrospective multicenter study

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.

[The value of machine learning models based on biparametric MRI for diagnosis of prostate cancer and clinically significant prostate cancer]

Objective: To evaluate the value of machine learning (ML) models based on biparametric magnetic resonance imaging (bpMRI) for diagnosis of prostate cancer (PCa) and clinically significant prostate cancer (csPCa). Methods: A total of 1 368 patients, aged from 30 to 92 (69.4±8.2) years, from 3 tertiary medical centers in Jiangsu Province were retrospectively collected from May 2015 to December 2020, including 412 cases of csPCa, 242 cases of clinically insignificant prostate cancer (ciPCa) and 714 cases of benign prostate lesions. The data of center 1 and center 2 were randomly divided into training cohort and internal testing cohort at a ratio of 7∶3 by random number sampling without replacement using Python Random package, and the data of center 3 were used as the independent external testing cohort. The training cohort includs 243 cases of csPCa, 135 cases of ciPCa and 384 cases of benign lesions, the internal testing cohort includs 104 cases of csPCa, 58 cases of ciPCa and 165 cases of benign lesions, and the external testing cohort includs 65 cases of csPCa, 49 cases of ciPCa and 165 cases of benign lesions. The radiomics features were extracted on T₂-weighted imaging, diffusion-weighted imaging and apparent diffusion coefficient map, and optimal radiomics features were selected by using Pearson correlation coefficient method and analysis of variance. The ML models were built using two ML algorithms, including support vector machine and random forest (RF) and were further tested in the internal testing cohort and external testing cohort. Finally, the PI-RADS scores evaluated by the radiologists were adjusted by the ML models which had superior diagnostic performance, namely adjusted PI-RADS. The receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the ML models and PI-RADS. DeLong test was used to compare the areas under curve (AUC) of models with those of PI-RADS. Results: For PCa diagnosis, in internal testing cohort, the AUC of ML model using RF algorithm and PI-RADS were 0.869 (95%CI: 0.830-0.908) and 0.874 (95%CI: 0.836-0.913), respectively, and the difference between the model and PI-RADS did not reach to the statistical significance (P=0.793). In the external testing cohort, the AUC of model and PI-RADS were 0.845 (95%CI: 0.794-0.897) and 0.915 (95%CI: 0.880-0.951), respectively, and the difference was statistically significant (P=0.01). For csPCa diagnosis, the AUC of ML model using RF algorithm and PI-RADS were 0.874 (95%CI: 0.834-0.914) and 0.892 (95%CI: 0.857-0.927), respectively, in internal testing cohort, and the difference between the model and PI-RADS was not statistically significant (P=0.341). In the external testing cohort, the AUC of model and PI-RADS were 0.876 (95%CI: 0.831-0.920) and 0.884 (95%CI: 0.841-0.926), respectively, and the difference between the model and PI-RADS was not statistically significant (P=0.704). When PI-RADS assessment was adjusted with the assistance of ML models, the specificities increased from 63.0% to 80.0% in the internal testing cohort and from 92.7% to 93.3% in the external test group in diagnosing PCa. In diagnosing csPCa, the specificities increased from 52.5% to 72.6% in the internal testing cohort and from 75.2% to 79.9% in the external testing cohort. Conclusions: The ML models based on bpMRI showed comparable diagnostic performance to PI-RADS assessed by senior radiologists and achieved good generalization ability in both diagnosing PCa and csPCa. The specificities of the PI-RADS were improved by ML models.