Improving risk stratification of PI-RADS 3 + 1 lesions of the peripheral zone: expert lexicon of terms, multi-reader performance and contribution of artificial intelligence.
Authors
Affiliations (13)
Affiliations (13)
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
- Heidelberg University Medical School, Heidelberg, Germany.
- Breast Diagnostics Munich, Sonnenstraße 29, Munich, Germany.
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Department of Urology, Heidelberg University Hospital, Heidelberg, Germany.
- Junior Clinical Cooperation Unit 'Multiparametric Methods for Early Detection of Prostate Cancer', German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany.
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany. [email protected].
- Heidelberg University Medical School, Heidelberg, Germany. [email protected].
- National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany. [email protected].
- German Cancer Consortium (DKTK), Heidelberg, Germany. [email protected].
Abstract
According to PI-RADS v2.1, peripheral PI-RADS 3 lesions are upgraded to PI-RADS 4 if dynamic contrast-enhanced MRI is positive (3+1 lesions), however those lesions are radiologically challenging. We aimed to define criteria by expert consensus and test applicability by other radiologists for sPC prediction of PI-RADS 3+1 lesions and determine their value in integrated regression models. From consecutive 3 Tesla MR examinations performed between 08/2016 to 12/2018 we identified 85 MRI examinations from 83 patients with a total of 94 PI-RADS 3+1 lesions in the official clinical report. Lesions were retrospectively assessed by expert consensus with construction of a newly devised feature catalogue which was utilized subsequently by two additional radiologists specialized in prostate MRI for independent lesion assessment. With reference to extended fused targeted and systematic TRUS/MRI-biopsy histopathological correlation, relevant catalogue features were identified by univariate analysis and put into context to typically available clinical features and automated AI image assessment utilizing lasso-penalized logistic regression models, also focusing on the contribution of DCE imaging (feature-based, bi- and multiparametric AI-enhanced and solely bi- and multiparametric AI-driven). The feature catalog enabled image-based lesional risk stratification for all readers. Expert consensus provided 3 significant features in univariate analysis (adj. p-value <0.05; most relevant feature T2w configuration: "irregular/microlobulated/spiculated", OR 9.0 (95%CI 2.3-44.3); adj. p-value: 0.016). These remained after lasso penalized regression based feature reduction, while the only selected clinical feature was prostate volume (OR<1), enabling nomogram construction. While DCE-derived consensus features did not enhance model performance (bootstrapped AUC), there was a trend for increased performance by including multiparametric AI, but not biparametric AI into models, both for combined and AI-only models. PI-RADS 3+1 lesions can be risk-stratified using lexicon terms and a key feature nomogram. AI potentially benefits more from DCE imaging than experienced prostate radiologists. Not applicable.