Update on PI-RADS Version 2.1 Diagnostic Performance Benchmarks for Prostate MRI: Systematic Review and Meta-Analysis.

Background Prostate MRI for the detection of clinically significant prostate cancer (csPCa) is standardized by the Prostate Imaging Reporting and Data System (PI-RADS), currently in version 2.1. A systematic review and meta-analysis infrastructure with a 12-month update cycle was established to evaluate the diagnostic performance of PI-RADS over time. Purpose To provide estimates of diagnostic accuracy and cancer detection rates (CDRs) of PI-RADS version 2.1 categories for prostate MRI, which is required for further evidence-based patient management. Materials and Methods A systematic search of PubMed, Embase, Cochrane Library, and multiple trial registers (English-language studies published from March 1, 2019, to August 30, 2022) was performed. Studies that reported data on diagnostic accuracy or CDRs of PI-RADS version 2.1 with csPCa as the primary outcome were included. For the meta-analysis, pooled estimates for sensitivity, specificity, and CDRs were derived from extracted data at the lesion level and patient level. Sensitivity and specificity for PI-RADS greater than or equal to 3 and PI-RADS greater than or equal to 4 considered as test positive were investigated. In addition to individual PI-RADS categories 1-5, subgroup analyses of subcategories (ie, 2+1, 3+0) were performed. Results A total of 70 studies (11 686 lesions, 13 330 patients) were included. At the patient level, with PI-RADS greater than or equal to 3 considered positive, meta-analysis found a 96% summary sensitivity (95% CI: 95, 98) and 43% specificity (95% CI: 33, 54), with an area under the summary receiver operating characteristic (SROC) curve of 0.86 (95% CI: 0.75, 0.93). For PI-RADS greater than or equal to 4, meta-analysis found an 89% sensitivity (95% CI: 85, 92) and 66% specificity (95% CI: 58, 74), with an area under the SROC curve of 0.89 (95% CI: 0.85, 0.92). CDRs were as follows: PI-RADS 1, 6%; PI-RADS 2, 5%; PI-RADS 3, 19%; PI-RADS 4, 54%; and PI-RADS 5, 84%. The CDR was 12% (95% CI: 7, 19) for transition zone 2+1 lesions and 19% (95% CI: 12, 29) for 3+0 lesions (P = .12). Conclusion Estimates of diagnostic accuracy and CDRs for PI-RADS version 2.1 categories are provided for quality benchmarking and to guide further evidence-based patient management. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Tammisetti and Jacobs in this issue.

A Machine Learning Framework Reduces the Manual Workload for Systematic Reviews of the Diagnostic Performance of Prostate Magnetic Resonance Imaging.

Prostate magnetic resonance imaging has become the imaging standard for prostate cancer in various clinical settings, with interpretation standardized according to the Prostate Imaging Reporting and Data System (PI-RADS). Each year, hundreds of scientific studies that report on the diagnostic performance of PI-RADS are published. To keep up with this ever-increasing evidence base, systematic reviews and meta-analyses are essential. As systematic reviews are highly resource-intensive, we investigated whether a machine learning framework can reduce the manual workload and speed up the screening process (title and abstract). We used search results from a living systematic review of the diagnostic performance of PI-RADS (1585 studies, of which 482 were potentially eligible after screening). A naïve Bayesian classifier was implemented in an active learning environment for classification of the titles and abstracts. Our outcome variable was the percentage of studies that can be excluded after 95% of relevant studies have been identified by the classifier (work saved over sampling: WSS@95%). In simulation runs of the entire screening process (controlling for classifier initiation and the frequency of classifier updating), we obtained a WSS@95% value of 28% (standard error of the mean ±0.1%). Applied prospectively, our classification framework would translate into a significant reduction in manual screening effort. Patient summary: Systematic reviews of scientific evidence are labor-intensive and take a lot of time. For example, many studies on prostate cancer diagnosis via MRI (magnetic resonance imaging) are published every year. We describe the use of machine learning to reduce the manual workload in screening search results. For a review of MRI for prostate cancer diagnosis, this approach reduced the screening workload by about 28%.

Prediction of upgrade to clinically significant prostate cancer in patients under active surveillance: Performance of a fully automated AI-algorithm for lesion detection and classification.

BACKGROUND: Multiparametric MRI (mpMRI) improves the detection of aggressive prostate cancer (PCa) subtypes. As cases of active surveillance (AS) increase and tumor progression triggers definitive treatment, we evaluated whether an AI-driven algorithm can detect clinically significant PCa (csPCa) in patients under AS. METHODS: Consecutive patients under AS who received mpMRI (PI-RADSv2.1 protocol) and subsequent MR-guided ultrasound fusion (targeted and extensive systematic) biopsy between 2017 and 2020 were retrospectively analyzed. Diagnostic performance of an automated clinically certified AI-driven algorithm was evaluated on both lesion and patient level regarding the detection of csPCa. RESULTS: Analysis of 56 patients resulted in 93 target lesions. Patient level sensitivity and specificity of the AI algorithm was 92.5%/31% for the detection of ISUP ≥ 1 and 96.4%/25% for the detection of ISUP ≥ 2, respectively. The only case of csPCa missed by the AI harbored only 1/47 Gleason 7a core (systematic biopsy; previous and subsequent biopsies rendered non-csPCa). CONCLUSIONS: AI-augmented lesion detection and PI-RADS scoring is a robust tool to detect progression to csPCa in patients under AS. Integration in the clinical workflow can serve as reassurance for the reader and streamline reporting, hence improve efficiency and diagnostic confidence.

Quantitative Analysis of Diffusion Weighted Imaging May Improve Risk Stratification of Prostatic Transition Zone Lesions.

BACKGROUND/AIM: To investigate whether quantitative analysis of diffusion weighted images allows for improved risk stratification of transition zone lesions in prostate magnetic resonance imaging (MRI) evaluated according to PI-RADSv2.1 [Prostate Imaging Reporting and Data System, target variable: clinically significant prostate cancer (csPCa)]. PATIENTS AND METHODS: Consecutive patients with transition zone lesions in 3T prostate MRI were enrolled in the study. All lesions on MRI were histopathologically verified by transperineal MRI-TRUS fusion biopsy. Two blinded radiologists re-evaluated all lesions according to PI-RADSv2.1. A consensus reading was performed after reading of all cases. Additionally, mean apparent diffusion coefficient values (mADC) were derived from blinded lesion segmentation. ROC analysis was performed for PI-RADS categories and PI-RADS categories with separate subcategories and diffusion coefficient values (ADC). Data were examined for optimal mADC cut-off values that improve stratification of csPCa and benign lesions. RESULTS: Among 85 patients (mean age=66.2 years), 98 transition zone lesions were detected. Biopsy confirmed csPCa in 24/98 cases. Area under the curve (AUC) was 0.89/0.90 for reader 1, 0.92/0.91 for reader 2 and 0.92/0.91 for the consensus reading (5 category analysis/analysis with subcategories separately). Inter-reader agreement was substantial, with lower PI-RADS categories assigned by the more experienced reader (p<0.05). AUC for mADC alone was 0.81. When a cut-off threshold of 950 µm2/s mADC is used to downgrade PI-RADS 3 lesions to PI-RADS 2, biopsy could be avoided in all benign PI-RADS 3 cases. CONCLUSION: Quantitative analysis of diffusion weighted images may help avoid unnecessary biopsies of transition zone PI-RADS 3 lesions.

Cancer detection rates of the PI-RADSv2.1 assessment categories: systematic review and meta-analysis on lesion level and patient level.

BACKGROUND: The Prostate Imaging Reporting and Data System, version 2.1 (PI-RADSv2.1) standardizes reporting of multiparametric MRI of the prostate. Assigned assessment categories are a risk stratification algorithm, higher categories indicate a higher probability of clinically significant cancer compared to lower categories. PI-RADSv2.1 does not define these probabilities numerically. We conduct a systematic review and meta-analysis to determine the cancer detection rates (CDR) of the PI-RADSv2.1 assessment categories on lesion level and patient level. METHODS: Two independent reviewers screen a systematic PubMed and Cochrane CENTRAL search for relevant articles (primary outcome: clinically significant cancer, index test: prostate MRI reading according to PI-RADSv2.1, reference standard: histopathology). We perform meta-analyses of proportions with random-effects models for the CDR of the PI-RADSv2.1 assessment categories for clinically significant cancer. We perform subgroup analysis according to lesion localization to test for differences of CDR between peripheral zone lesions and transition zone lesions. RESULTS: A total of 17 articles meet the inclusion criteria and data is independently extracted by two reviewers. Lesion level analysis includes 1946 lesions, patient level analysis includes 1268 patients. On lesion level analysis, CDR are 2% (95% confidence interval: 0-8%) for PI-RADS 1, 4% (1-9%) for PI-RADS 2, 20% (13-27%) for PI-RADS 3, 52% (43-61%) for PI-RADS 4, 89% (76-97%) for PI-RADS 5. On patient level analysis, CDR are 6% (0-20%) for PI-RADS 1, 9% (5-13%) for PI-RADS 2, 16% (7-27%) for PI-RADS 3, 59% (39-78%) for PI-RADS 4, 85% (73-94%) for PI-RADS 5. Higher categories are significantly associated with higher CDR (P < 0.001, univariate meta-regression), no systematic difference of CDR between peripheral zone lesions and transition zone lesions is identified in subgroup analysis. CONCLUSIONS: Our estimates of CDR demonstrate that PI-RADSv2.1 stratifies lesions and patients as intended. Our results might serve as an initial evidence base to discuss management strategies linked to assessment categories.

Validation of color Doppler ultrasound and computed tomography in the radiologic assessment of non-malignant acute splanchnic vein thrombosis.

INTRODUCTION: International guidelines propose color Doppler ultrasound (CDUS) and contrast-enhanced computed tomography (CT) as primary imaging techniques in the diagnosis of acute splanchnic vein thrombosis. However, their reliability in this context is poorly investigated. Therefore, the aim of our study was to validate CDUS and CT in the radiologic assessment of acute splanchnic vein thrombosis, using direct transjugular spleno-portography as gold standard. MATERIALS AND METHODS: 49 patients with non-malignant acute splanchnic vein thrombosis were included in a retrospective, multicenter analysis. The thrombosis' extent in five regions of the splanchnic venous system (right and left intrahepatic portal vein, main trunk of the portal vein, splenic vein, superior mesenteric vein) and the degree of thrombosis (patent, partial thrombosis, complete thrombosis) were assessed by portography, CDUS and CT in a blinded manner. Reliability of CDUS and CT with regard to portography as gold standard was analyzed by calculating Cohen's kappa. RESULTS: Results of CDUS and CT were consistent with portography in 76.6% and 78.4% of examinations, respectively. Cohen's kappa demonstrated that CDUS and CT delivered almost equally reliable results with regard to the portographic gold standard (k = 0.634 [p < 0.001] vs. k = 0.644 [p < 0.001]). In case of findings non-consistent with portography there was no clear trend to over- or underestimation of the degree of thrombosis in both CDUS (60.0% vs. 40.0%) and CT (59.5% vs. 40.5%). CONCLUSIONS: CDUS and CT are equally reliable tools in the radiologic assessment of non-malignant acute splanchnic vein thrombosis.

Chondrogenic Bone Tumors: The Importance of Imaging Characteristics. / Chondrogene Knochentumoren: Bildgebung als Wegweiser?

BACKGROUND: Chondrogenic tumors are the most frequent primary bone tumors. Malignant chondrogenic tumors represent about one quarter of malignant bone tumors. Benign chondrogenic bone tumors are frequent incidental findings at imaging. Radiological parameters may be helpful for identification, characterization, and differential diagnosis. METHODS: Systematic PubMed literature research. Identification and review of studies analyzing and describing imaging characteristics of chondrogenic bone tumors. RESULTS AND CONCLUSIONS: The 2020 World Health Organization (WHO) classification system differentiates between benign, intermediate (locally aggressive or rarely metastasizing), and malignant chondrogenic tumors. On imaging, typical findings of differentiated chondrogenic tumors are lobulated patterns with a high signal on T2-weighted magnetic resonance imaging (MRI) and ring- and arc-like calcifications on conventional radiography and computed tomography (CT). Depending on the entity, the prevalence of this chondrogenic pattern differs. While high grade tumors may be identified due to aggressive imaging patterns, the differentiation between benign and intermediate grade chondrogenic tumors is challenging, even in an interdisciplinary approach. KEY POINTS: · The WHO defines benign, intermediate, and malignant chondrogenic bone tumors. · Frequent benign tumors: osteochondroma and enchondroma; Frequent malignant tumor: conventional chondrosarcoma. · Differentiation between enchondroma versus low-grade chondrosarcoma is challenging for radiologists and pathologists. · Pain, deep scalloping, cortical destruction, bone expansion, soft tissue component: favor chondrosarcoma. · Potential malignant transformation of osteochondroma: progression after skeletal maturity, cartilage cap thickness (> 2 cm adult; > 3 cm child). · Potentially helpful advanced imaging methods: Dynamic MRI, texture analysis, FDG-PET/CT. CITATION FORMAT: · Engel H, Herget GW, Füllgraf H et al. Chondrogenic Bone Tumors: The Importance of Imaging Characteristics. Fortschr Röntgenstr 2021; 193: 262 - 274.

MRI phenotype of the prostate: Transition zone radiomics analysis improves explanation of prostate-specific antigen (PSA) serum level compared to volume measurement alone.

PURPOSE: To determine the value of a radiomics MRI phenotype of the transition zone to explain PSA level in patients with low suspicion for clinically significant cancer to confirm hyperplastic changes. MATERIALS AND METHODS: T2 weighted images from 36 consecutive PI-RADS 2 and 3 cases with volume adapted systematic transperineal biopsy as reference standard (all biopsies negative, 34.8 biopsy cores per patient in average, mean PSA level 10.77 ng/mL) are manually segmented to define transition zone (TZ) volume. 54 radiomic features (RF) are derived for each TZ. RF are tested for significant correlation with PSA level, Bonferroni correction is applied. We build regression models to explain PSA level with a) TZ volume b) RF c) TZ volume+RF. We apply all models to a control group with clinically significant transition zone cancer. RESULTS: TZ volume is moderately correlated with PSA level (r = 0.44). 5/54 RF are significantly correlated with PSA level (r: 0.53-0.69, p < 0.05). Inclusion of each of these five features into the regression model significantly improves the explanatory value for PSA level (p < 0.05). Furthermore, RF alone better explain PSA level compared to TZ volume alone (p < 0.01). A systematic and significant trend for positive residuals is observed when regression models are applied to the malignant control group. CONCLUSION: A radiomics analysis of the transition zone has the potential to improve explanation of corresponding PSA level in patients with low suspicion. This knowledge may reassure radiologists to read prostate MRI cases as unremarkable, despite present hyperplastic changes.

Direct reprogramming of fibroblasts into renal tubular epithelial cells by defined transcription factors.

Direct reprogramming by forced expression of transcription factors can convert one cell type into another. Thus, desired cell types can be generated bypassing pluripotency. However, direct reprogramming towards renal cells remains an unmet challenge. Here, we identify renal cell fate-inducing factors on the basis of their tissue specificity and evolutionarily conserved expression, and demonstrate that combined expression of Emx2, Hnf1b, Hnf4a and Pax8 converts mouse and human fibroblasts into induced renal tubular epithelial cells (iRECs). iRECs exhibit epithelial features, a global gene expression profile resembling their native counterparts, functional properties of differentiated renal tubule cells and sensitivity to nephrotoxic substances. Furthermore, iRECs integrate into kidney organoids and form tubules in decellularized kidneys. Our approach demonstrates that reprogramming factors can be identified by targeted in silico analysis. Renal tubular epithelial cells generated ex vivo by forced expression of transcription factors may facilitate disease modelling, drug and nephrotoxicity testing, and regenerative approaches.