Application value of biparametric magnetic resonance radiomics combined with prostate-specific antigen density in Gleason grade group of prostate cancer

Corresponding author: WANG Guoyu, hswangguoyu@163.com

DOI: 10.12201/bmr.202406.00029

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  Abstract: Objective: To investigate the diagnostic value of biparametric MRI (bpMRI) radiomics and prostate-specific antigen density (PSAD) in predicting low-grade and high-grade prostate cancerMethods: A total of 179 PCa patients were retrospectively analyzed and randomly assigned into training (n?=?125) and test (n = 54) cohorts. According to gleason grade group (GGG), GGG≤2 was defined as low-grade PCa, and GGG > 2 was defined as high-grade PCa. Radiomics features were extracted based on T2WI and ADC sequences, and maximum relevance and minimum redundancy and least absolute shrinkage and selection operator were used for feature selection and dimensionality reduction, and 5-fold cross validation was performed to retain the best radiomics features. Based on the best feature combination, univariate and multivariate logistic models were constructed, including three univariate models (ADC, T2WI and PSAD model) and two combined models (T2WI-ADC and T2WI-ADC-PSAD model). Receiver operating characteristic(ROC) curve and Delong’s test were used to evaluate the performance of each model. Finally, decision curve analysis (DCA) was used to evaluate the clinical utility of the model.Results: Among the single sequence models, the ADC model had the best diagnostic performance (AUC=0.713, 95%CI: 0.582-0.833), however, the T2WI-ADC combined model did not improve the diagnostic performance compared with ADC model (AUC=0.709, 95%CI: 0.572-0.830). Among all the models, T2WI-ADC-PSAD combined model had the best diagnostic performance (AUC=0.772, 95%CI: 0.661-0.874). Delong’s test showed that there was no significant difference in AUC between the combined model and the other models in the test cohort (P > 0.05). The DCA showed that the T2WI-ADC-PSAD model provided a higher net benefit for clinical decision-making when the threshold probability within the relevant range.Conclusion: BpMRI radiomics combined with PSAD can improve the detection of high-grade PCa, and guide patient treatment decisions.

  Key words: Biparametric MRI; Prostate-Specific Antigen Density; Radiomics; Prediction model; Gleason grade group

  Submit time: 19 June 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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