MRI Based Radiomics Compared With the PI-RADS V2.1 in the Prediction of Clinically Significant Prostate Cancer: Biparametric vs Multiparametric MRI

Abstract

PurposeTo compare the performance of radiomics to that of the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 scoring system in the detection of clinically significant prostate cancer (csPCa) based on biparametric magnetic resonance imaging (bpMRI) vs. multiparametric MRI (mpMRI).MethodsA total of 204 patients with pathological results were enrolled between January 2018 and December 2019, with 142 patients in the training cohort and 62 patients in the testing cohort. The radiomics model was compared with the PI-RADS v2.1 for the diagnosis of csPCa based on bpMRI and mpMRI by using receiver operating characteristic (ROC) curve analysis.ResultsThe radiomics model based on bpMRI and mpMRI signatures showed high predictive efficiency but with no significant differences (AUC = 0.975 vs 0.981, p=0.687 in the training cohort, and 0.953 vs 0.968, p=0.287 in the testing cohort, respectively). In addition, the radiomics model outperformed the PI-RADS v2.1 in the diagnosis of csPCa regardless of whether bpMRI (AUC = 0.975 vs. 0.871, p= 0.030 for the training cohort and AUC = 0.953 vs. 0.853, P = 0.024 for the testing cohort) or mpMRI (AUC = 0.981 vs. 0.880, p= 0.030 for the training cohort and AUC = 0.968 vs. 0.863, P = 0.016 for the testing cohort) was incorporated.ConclusionsOur study suggests the performance of bpMRI- and mpMRI-based radiomics models show no significant difference, which indicates that omitting DCE imaging in radiomics can simplify the process of analysis. Adding radiomics to PI-RADS v2.1 may improve the performance to predict csPCa.

MRI‐based radiomic features for identifying recurrent prostate cancer after proton radiation therapy

“…Radiomic features have been shown to enhance the accuracy of PI‐RADS system 7 . While most of these were done on treatment‐naïve prostate data, detecting PCa following proton therapy is sparse 13,27,28 . While PI‐RADS demonstrates high correlation between readers, there is still a substantial discordance rate based on imaging features and levels of expertise 29 .…”

“… 7 While most of these were done on treatment‐naïve prostate data, detecting PCa following proton therapy is sparse. 13 , 27 , 28 While PI‐RADS demonstrates high correlation between readers, there is still a substantial discordance rate based on imaging features and levels of expertise. 29 Therefore, we believe that radiomics could be a promising and exceptionally reliable tool that supports clinical decision‐making with less mobility for the patient.…”

Could Biparametric MRI Replace Multiparametric MRI in the Management of Prostate Cancer?

“…The DCE sequence is required because a PI-RADS 3 lesion indicates an equivocal lesion with a significant chance of developing clinically significant prostate cancer, whereas a PI-RADS 4 lesion indicates that clinically significant cancer is likely to be present [ 26 ]. Researchers state that using DCE for PI-RADS 3 can change the grading to PI-RADS 4 if the enhancement is focal [ 75 ]. Another role for bpMRI that has been mentioned is for AS, as long as the lesion does not upgrade in terms of PI-RADS.…”

Evaluating the Feasibility of AI-Predicted mpMRI Image Features for Predicting Prostate Cancer Aggressiveness: a Multicenter Study

“…Radiomics research involves the extraction of high-throughput and quantitative features from multimodal medical images and the use of machine learning algorithms to transform these features into high-dimensional mining information related to tumor pathophysiology, which may aid in clinical diagnosis and decision-making [9,10] . Radiomic models based on mpMRI had been used for risk strati cation of PCa patients [11][12][13][14] . Several studies have shown that the features extracted from T2WI and ADC are helpful for classifying the Gleason score [15][16][17] .…”