Towards the Definition of Radiomic Features and Clinical Indices to Enhance the Diagnosis of Clinically Significant Cancers in PI-RADS 4 and 5 Lesions

Simple Summary Lesions scored as PI-RADS 4 and 5 may include false positives and tools aimed to reduce them are highly needed. MRI texture analysis of standard sequences seems to improve the detection of clinically significant cancer (csPC) in PI-RADS 4/5 lesions. Multivariate models considering both clinical and radiomic features achieved promising diagnostic values.Abstract: Objectives Lesions classified as PI-RADS 4/5, according to the Prostate Imaging-Reporting and Data System (PI-RADS) guidelines, may include false positives. This study aims to identify promising radiomic features that may support the detection of clinically significant tumours among PI-RADS 4/5 lesions on MRI. Methods: Patients undergoing a 3T magnet multiparametric MRI (mpMRI) for clinical suspicion of prostate cancer (PC) or active surveillance were retrospectively enrolled. Pathological results utilizing MRI-targeted biopsy specimens were considered the ground truth. Clinical (age, PSA, PSA density) and MRI parameters (prostate volume, mean apparent diffusion coefficient/ADC) were collected. Lesions were manually contoured on axial T2-weighted images and ADC maps. Radiomic features were extracted with Pyradiomics. Clinical and radiomic features best correlating with histopathological results were selected. Diagnostic values were assessed on validation samples. Results: The final cohort included 99 patients (mean age, 69.2 +/- 6.8 years) and 111 PI-RADS 4/5 lesions. At pathology, 79 lesions (71%) were identified as clinically significant cancers (Gleason score >= 7). Radiomic, clinical, and MRI features best correlating with histopathology were selected. The best predictive clinical and radiomic multivariate model showed the following diagnostic values: sensitivity, 79%; specificity, 80%; positive predictive value (PPV), 91%; negative predictive value (NPV), 63%; accuracy, 79%. A radiomic multivariate model based exclusively on peripheral zone lesions showed more promising values: sensitivity, 86%; specificity, 80%; PPV, 93%; NPV, 70%; accuracy, 84%. Conclusions: Radiomic MRI feature analysis can potentially improve the accuracy of mpMRI in discriminating between clinically significant cancers in PI-RADS 4 and 5 lesions.

Automated summary

Radiomic MRI feature analysis has the potential to improve the accuracy of mpMRI in discriminating between clinically significant cancers in PI-RADS 4 and 5 lesions.