AUTHOR=Jin Pengfei , Yang Liqin , Qiao Xiaomeng , Hu Chunhong , Hu Chenhan , Wang Ximing , Bao Jie 

TITLE=Utility of Clinical–Radiomic Model to Identify Clinically Significant Prostate Cancer in Biparametric MRI PI-RADS V2.1 Category 3 Lesions

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.840786

DOI=10.3389/fonc.2022.840786

ISSN=2234-943X

ABSTRACT=Purpose: To determine the predictive performance of the integrated model based on clinical factors and radiomic features for the accurate identification of clinically significant prostate cancer (csPCa) among PI-RADS 3 lesions.
Materials and Methods: A retrospective study of 103 patients with PI-RADS 3 lesions who underwent pre-operative 3.0 Tesla MRI was performed. Patients were randomly divided into the training set and the testing set at a ratio of 7:3. Radiomic features were extracted from axial T2WI, DWI and ADC images of each patient. The Minimum redundancy maximum relevance (mRMR)  and LASSO feature selection methods were used to identify the radiomic features and construct a radiomic model for csPCa identification. Moreover, multivariable logistic regression analysis was used to integrate the clinical factors with radiomic feature model to further improve the accuracy of csPCa identification, and the two are presented in the form of normogram. The performance of the integrated model was compared with radiomic model and clinical model on testing set. 
Results: N = 4 radiomic features were selected and used for radiomic model construction producing a radiomic score (Radscore). Radscore was significantly different between the csPCa and the non-csPCa patients (training set: P < 0.001; testing set: P =0.035). Multivariable Logistic regression analysis showed that age and PSA could be used as independent predictors for csPCa identification. The clinical-radiomic model produced the AUC in the testing set was 0.88 (95%CI: 0.75-1.00), which was similar to clinical model (AUC=0.85, 95%CI: 0.52-0.90) (P =0.048) and higher than the radiomic model (AUC=0.71, 95%CI: 0.68-1.00) (P < 0.001). The decision curve analysis implies that the clinical-radiomic model could be beneficial in identify csPCa among PI-RADS 3 lesions.
Conclusion: The clinical-radiomic model could effectively identify csPCa among biparametric PI-RADS 3 lesions, and thus could help avoid unnecessary biopsy and improve the life quality of patients.
Keywords: Radiomics, Clinically significant prostate cancer, PI-RADS score 3, Nomogram