Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies.

OBJECTIVES: To develop and test zone-specific prostate-specific antigen density (sPSAD) combined with PI-RADS to guide prostate biopsy decision strategies (BDS). METHODS: This retrospective study included consecutive patients, who underwent prostate MRI and biopsy (01/2012-10/2018). The whole gland and transition zone (TZ) were segmented at MRI using a retrained deep learning system (DLS; nnU-Net) to calculate PSAD and sPSAD, respectively. Additionally, sPSAD and PI-RADS were combined in a BDS, and diagnostic performances to detect Grade Group ≥ 2 (GG ≥ 2) prostate cancer were compared. Patient-based cancer detection using sPSAD was assessed by bootstrapping with 1000 repetitions and reported as area under the curve (AUC). Clinical utility of the BDS was tested in the hold-out test set using decision curve analysis. Statistics included nonparametric DeLong test for AUCs and Fisher-Yates test for remaining performance metrics. RESULTS: A total of 1604 patients aged 67 (interquartile range, 61-73) with 48% GG ≥ 2 prevalence (774/1604) were evaluated. By employing DLS-based prostate and TZ volumes (DICE coefficients of 0.89 (95% confidence interval, 0.80-0.97) and 0.84 (0.70-0.99)), GG ≥ 2 detection using PSAD was inferior to sPSAD (AUC, 0.71 (0.68-0.74)/0.73 (0.70-0.76); p < 0.001). Combining PI-RADS with sPSAD, GG ≥ 2 detection specificity doubled from 18% (10-20%) to 43% (30-44%; p < 0.001) with similar sensitivity (93% (89-96%)/97% (94-99%); p = 0.052), when biopsies were taken in PI-RADS 4-5 and 3 only if sPSAD was ≥ 0.42 ng/mL/cc as compared to all PI-RADS 3-5 cases. Additionally, using the sPSAD-based BDS, false positives were reduced by 25% (123 (104-142)/165 (146-185); p < 0.001). CONCLUSION: Using sPSAD to guide biopsy decisions in PI-RADS 3 lesions can reduce false positives at MRI while maintaining high sensitivity for GG ≥ 2 cancers. CLINICAL RELEVANCE STATEMENT: Transition zone-specific prostate-specific antigen density can improve the accuracy of prostate cancer detection compared to MRI assessments alone, by lowering false-positive cases without significantly missing men with ISUP GG ≥ 2 cancers. KEY POINTS: • Prostate biopsy decision strategies using PI-RADS at MRI are limited by a substantial proportion of false positives, not yielding grade group ≥ 2 prostate cancer. • PI-RADS combined with transition zone (TZ)-specific prostate-specific antigen density (PSAD) decreased the number of unproductive biopsies by 25% compared to PI-RADS only. • TZ-specific PSAD also improved the specificity of MRI-directed biopsies by 9% compared to the whole gland PSAD, while showing identical sensitivity.

First effectiveness data of lenvatinib and pembrolizumab as first-line therapy in advanced anaplastic thyroid cancer: a retrospective cohort study.

BACKGROUND: Anaplastic thyroid cancer (ATC) is a rare and aggressive neoplasm. We still lack effective treatment options, so survival rates remain very low. Here, we aimed to evaluate the activity of the combination of lenvatinib and pembrolizumab as systemic first-line therapy in ATC. METHODS: In a retrospective analysis, we investigated the activity and tolerability of combined lenvatinib (starting dose 14 to 24 mg daily) and pembrolizumab (200 mg every three weeks) as first-line therapy in an institutional cohort of ATC patients. RESULTS: Five patients with metastatic ATC received lenvatinib and pembrolizumab as systemic first-line therapy. The median progression-free survival was 4.7 (range 0.8-5.9) months, and the median overall survival was 6.3 (range 0.8-not reached) months. At the first follow-up, one patient had partial response, three patients had stable disease, and one patient was formally not evaluable due to interference of assessment by concomitant acute infectious thyroiditis. This patient was then stable for more than one year and was still on therapy at the data cutoff without disease progression. Further analyses revealed deficient DNA mismatch repair, high CD8+ lymphocyte infiltration, and low macrophage infiltration in this patient. Of the other patients, two had progressive disease after adverse drug reactions and therapy de-escalation, and two died after the first staging. For all patients, the PD-L1 combined positive score ranged from 12 to 100%. CONCLUSIONS: The combination of lenvatinib and pembrolizumab was effective and moderately tolerated in treatment-naïve ATC patients with occasional long-lasting response. However, we could not confirm the exceptional responses for this combination therapy reported before in pretreated patients.

Interactive Explainable Deep Learning Model Informs Prostate Cancer Diagnosis at MRI.

Background Clinically significant prostate cancer (PCa) diagnosis at MRI requires accurate and efficient radiologic interpretation. Although artificial intelligence may assist in this task, lack of transparency has limited clinical translation. Purpose To develop an explainable artificial intelligence (XAI) model for clinically significant PCa diagnosis at biparametric MRI using Prostate Imaging Reporting and Data System (PI-RADS) features for classification justification. Materials and Methods This retrospective study included consecutive patients with histopathologic analysis-proven prostatic lesions who underwent biparametric MRI and biopsy between January 2012 and December 2017. After image annotation by two radiologists, a deep learning model was trained to detect the index lesion; classify PCa, clinically significant PCa (Gleason score ≥ 7), and benign lesions (eg, prostatitis); and justify classifications using PI-RADS features. Lesion- and patient-based performance were assessed using fivefold cross validation and areas under the receiver operating characteristic curve. Clinical feasibility was tested in a multireader study and by using the external PROSTATEx data set. Statistical evaluation of the multireader study included Mann-Whitney U and exact Fisher-Yates test. Results Overall, 1224 men (median age, 67 years; IQR, 62-73 years) had 3260 prostatic lesions (372 lesions with Gleason score of 6; 743 lesions with Gleason score of ≥ 7; 2145 benign lesions). XAI reliably detected clinically significant PCa in internal (area under the receiver operating characteristic curve, 0.89) and external test sets (area under the receiver operating characteristic curve, 0.87) with a sensitivity of 93% (95% CI: 87, 98) and an average of one false-positive finding per patient. Accuracy of the visual and textual explanations of XAI classifications was 80% (1080 of 1352), confirmed by experts. XAI-assisted readings improved the confidence (4.1 vs 3.4 on a five-point Likert scale; P = .007) of nonexperts in assessing PI-RADS 3 lesions, reducing reading time by 58 seconds (P = .009). Conclusion The explainable AI model reliably detected and classified clinically significant prostate cancer and improved the confidence and reading time of nonexperts while providing visual and textual explanations using well-established imaging features. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chapiro in this issue.

A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [18F]FDG-PET/CT parameters.

BACKGROUND: In patients with non-small cell lung cancer (NSCLC), accuracy of [18F]FDG-PET/CT for pretherapeutic lymph node (LN) staging is limited by false positive findings. Our aim was to evaluate machine learning with routinely obtainable variables to improve accuracy over standard visual image assessment. METHODS: Monocentric retrospective analysis of pretherapeutic [18F]FDG-PET/CT in 491 consecutive patients with NSCLC using an analog PET/CT scanner (training + test cohort, n = 385) or digital scanner (validation, n = 106). Forty clinical variables, tumor characteristics, and image variables (e.g., primary tumor and LN SUVmax and size) were collected. Different combinations of machine learning methods for feature selection and classification of N0/1 vs. N2/3 disease were compared. Ten-fold nested cross-validation was used to derive the mean area under the ROC curve of the ten test folds ("test AUC") and AUC in the validation cohort. Reference standard was the final N stage from interdisciplinary consensus (histological results for N2/3 LNs in 96%). RESULTS: N2/3 disease was present in 190 patients (39%; training + test, 37%; validation, 46%; p = 0.09). A gradient boosting classifier (GBM) with 10 features was selected as the final model based on test AUC of 0.91 (95% confidence interval, 0.87-0.94). Validation AUC was 0.94 (0.89-0.98). At a target sensitivity of approx. 90%, test/validation accuracy of the GBM was 0.78/0.87. This was significantly higher than the accuracy based on "mediastinal LN uptake > mediastinum" (0.7/0.75; each p < 0.05) or combined PET/CT criteria (PET positive and/or LN short axis diameter > 10 mm; 0.68/0.75; each p < 0.001). Harmonization of PET images between the two scanners affected SUVmax and visual assessment of the LNs but did not diminish the AUC of the GBM. CONCLUSIONS: A machine learning model based on routinely available variables from [18F]FDG-PET/CT improved accuracy in mediastinal LN staging compared to established visual assessment criteria. A web application implementing this model was made available.

A concurrent, deep learning-based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists.

OBJECTIVES: To evaluate the effect of a deep learning-based computer-aided diagnosis (DL-CAD) system on experienced and less-experienced radiologists in reading prostate mpMRI. METHODS: In this retrospective, multi-reader multi-case study, a consecutive set of 184 patients examined between 01/2018 and 08/2019 were enrolled. Ground truth was combined targeted and 12-core systematic transrectal ultrasound-guided biopsy. Four radiologists, two experienced and two less-experienced, evaluated each case twice, once without (DL-CAD-) and once assisted by DL-CAD (DL-CAD+). ROC analysis, sensitivities, specificities, PPV and NPV were calculated to compare the diagnostic accuracy for the diagnosis of prostate cancer (PCa) between the two groups (DL-CAD- vs. DL-CAD+). Spearman's correlation coefficients were evaluated to assess the relationship between PI-RADS category and Gleason score (GS). Also, the median reading times were compared for the two reading groups. RESULTS: In total, 172 patients were included in the final analysis. With DL-CAD assistance, the overall AUC of the less-experienced radiologists increased significantly from 0.66 to 0.80 (p = 0.001; cutoff ISUP GG ≥ 1) and from 0.68 to 0.80 (p = 0.002; cutoff ISUP GG ≥ 2). Experienced radiologists showed an AUC increase from 0.81 to 0.86 (p = 0.146; cutoff ISUP GG ≥ 1) and from 0.81 to 0.84 (p = 0.433; cutoff ISUP GG ≥ 2). Furthermore, the correlation between PI-RADS category and GS improved significantly in the DL-CAD + group (0.45 vs. 0.57; p = 0.03), while the median reading time was reduced from 157 to 150 s (p = 0.023). CONCLUSIONS: DL-CAD assistance increased the mean detection performance, with the most significant benefit for the less-experienced radiologist; with the help of DL-CAD less-experienced radiologists reached performances comparable to that of experienced radiologists. KEY POINTS: • DL-CAD used as a concurrent reading aid helps radiologists to distinguish between benign and cancerous lesions in prostate MRI. • With the help of DL-CAD, less-experienced radiologists may achieve detection performances comparable to that of experienced radiologists. • DL-CAD assistance increases the correlation between PI-RADS category and cancer grade.

Non-contrast computed tomography features predict intraventricular hemorrhage growth.

OBJECTIVES: Non-contrast computed tomography (NCCT) markers are robust predictors of parenchymal hematoma expansion in intracerebral hemorrhage (ICH). We investigated whether NCCT features can also identify ICH patients at risk of intraventricular hemorrhage (IVH) growth. METHODS: Patients with acute spontaneous ICH admitted at four tertiary centers in Germany and Italy were retrospectively included from January 2017 to June 2020. NCCT markers were rated by two investigators for heterogeneous density, hypodensity, black hole sign, swirl sign, blend sign, fluid level, island sign, satellite sign, and irregular shape. ICH and IVH volumes were semi-manually segmented. IVH growth was defined as IVH expansion > 1 mL (eIVH) or any delayed IVH (dIVH) on follow-up imaging. Predictors of eIVH and dIVH were explored with multivariable logistic regression. Hypothesized moderators and mediators were independently assessed in PROCESS macro models. RESULTS: A total of 731 patients were included, of whom 185 (25.31%) suffered from IVH growth, 130 (17.78%) had eIVH, and 55 (7.52%) had dIVH. Irregular shape was significantly associated with IVH growth (OR 1.68; 95%CI [1.16-2.44]; p = 0.006). In the subgroup analysis stratified by the IVH growth type, hypodensities were significantly associated with eIVH (OR 2.06; 95%CI [1.48-2.64]; p = 0.015), whereas irregular shape (OR 2.72; 95%CI [1.91-3.53]; p = 0.016) in dIVH. The association between NCCT markers and IVH growth was not mediated by parenchymal hematoma expansion. CONCLUSIONS: NCCT features identified ICH patients at a high risk of IVH growth. Our findings suggest the possibility to stratify the risk of IVH growth with baseline NCCT and might inform ongoing and future studies. CLINICAL RELEVANCE STATEMENT: Non-contrast CT features identified ICH patients at a high risk of intraventricular hemorrhage growth with subtype-specific differences. Our findings may assist in the risk stratification of intraventricular hemorrhage growth with baseline CT and might inform ongoing and future clinical studies. KEY POINTS: • NCCT features identified ICH patients at a high risk of IVH growth with subtype-specific differences. • The effect of NCCT features was not moderated by time and location or indirectly mediated by hematoma expansion. • Our findings may assist in the risk stratification of IVH growth with baseline NCCT and might inform ongoing and future studies.

External validation of the diagnostic value of perihematomal edema characteristics in neoplastic and non-neoplastic intracerebral hemorrhage.

BACKGROUND AND PURPOSE: Neoplastic intracerebral hemorrhage (ICH) may be incorrectly identified as non-neoplastic ICH on imaging. Relative perihematomal edema (relPHE) on computed tomography (CT) has been proposed as a marker to discriminate neoplastic from non-neoplastic ICH but has not been externally validated. The purpose of this study was to evaluate the discriminatory power of relPHE in an independent cohort. METHODS: A total of 291 patients with acute ICH on CT and follow-up magnetic resonance imaging (MRI) were included in this single-center retrospective study. ICH subjects were dichotomized into non-neoplastic or neoplastic ICH based on the diagnosis on the follow-up MRI. ICH and PHE volumes and density values were derived from semi-manually segmented CT scans. Calculated PHE characteristics for discriminating neoplastic ICH were evaluated using receiver-operating characteristic (ROC) curves. ROC curve-associated cut-offs were calculated and compared between the initial and the validation cohort. RESULTS: A total of 116 patients (39.86%) with neoplastic ICH and 175 (60.14%) with non-neoplastic ICH were included. Median PHE volumes, relPHE, and relPHE adjusted for hematoma density were significantly higher in subjects with neoplastic ICH (all p values <0.001). ROC curves for relPHE had an area under the curve (AUC) of 0.72 (95% confidence interval [CI] 0.66-0.78) and an AUC of 0.81 (95% CI 0.76-0.87) for adjusted relPHE. The cut-offs were identical in the two cohorts, with >0.70 for relPHE and >0.01 for adjusted relPHE. CONCLUSIONS: Relative perihematomal edema and adjusted relPHE accurately discriminated neoplastic from non-neoplastic ICH on CT imaging in an external patient cohort. These results confirmed the findings of the initial study and may improve clinical decision making.

German S3 Evidence-Based Guidelines on Focal Therapy in Localized Prostate Cancer: The First Evidence-Based Guidelines on Focal Therapy.

BACKGROUND: Focal therapy (FT) is an option to treat localized prostate cancer (PCa) and preserve healthy prostate tissue in order to reduce known side effects from primary whole-gland treatment. The available FT modalities are manifold. Until now, national and international PCa guidelines have been cautious to propose recommendations regarding FT treatment since data from prospective controlled trials are lacking for most FT modalities. Moreover, none of the international guidelines provides a separate section on FT. In this purpose, we provide a synopsis of the consensus-based German S3 guidelines for a possible international use. SUMMARY: The recently published update of the German S3 guidelines, an evidence- and consensus-based guideline, provides a section on FT with recommendations for diagnostic work-up, indications, modalities, and follow-up. This section consists of 12 statements and recommendations for FT in the treatment of localized PCa. KEY MESSAGE: The German S3 guidelines on PCa are the first to incorporate recommendations for FT based on evidence and expert consensus including indication criteria for FT, pretreatment, and follow-up diagnostic pathways as well as an extended overview of FT techniques and the current supportive evidence.

An accelerometer-based guidance device for CT-guided procedures: an improved wireless prototype.

INTRODUCTION: The aim of the study was to demonstrate the feasibility of a prototype for accelerometer-based guidance for percutaneous CT-guided punctures and compare it with free-hand punctures. MATERIAL AND METHODS: The prototype enabled alignment with the CT coordinate system and a wireless connectivity. Its feasibility was tested in a swine cadaver model: 20 out-of-plane device-assisted punctures performed without intermittent control scans (one-step punctures) were evaluated regarding deviation to target and difference between planned and obtained angle. Thereafter, 22 device-assisted punctures were compared with 20 free-hand punctures regarding distance to target, deviation from the planned angle, number of control scans and procedure time. Differences were compared with the Mann-Whitney U-test (p < .05). RESULTS: The one-step punctures revealed a deviation to target of 0.26 ± 0.37 cm (axial plane) and 0.21 ± 0.19 cm (sagittal plane) and differences between planned and performed puncture angles of 0.9 ± 1.09° (axial plane) and 1.15 ± 0.91° (sagittal planes). In the comparative study, device-assisted punctures showed a significantly higher accuracy, 0.20 ± 0.17 cm vs. 0.30 ± 0.21 cm (p < .05) and lower number of required control scans, 1.3 ± 1.1 vs. 3.7 ± 0.9 (p < .05) compared with free-hand punctures. CONCLUSION: The accelerometer-based device proved to be feasible and demonstrated significantly higher accuracy and required significantly less control scans compared to free-hand puncture.

Enhancing the differentiation of pulmonary lymphoma and fungal pneumonia in hematological patients using texture analysis in 3-T MRI.

OBJECTIVES: To evaluate texture analysis in nonenhanced 3-T MRI for differentiating pulmonary fungal infiltrates and lymphoma manifestations in hematological patients and to compare the diagnostic performance with that of signal intensity quotients ("nonenhanced imaging characterization quotients," NICQs). METHODS: MR scans were performed using a speed-optimized imaging protocol without an intravenous contrast medium including axial T2-weighted (T2w) single-shot fast spin-echo and T1-weighted (T1w) gradient-echo sequences. ROIs were drawn within the lesions to extract first-order statistics from original images using HeterogeneityCAD and PyRadiomics. NICQs were calculated using signal intensities of the lesions, muscle, and fat. The standard of reference was histology or clinical diagnosis in follow-up. Statistical testing included ROC analysis, clustered ROC analysis, and DeLong test. Intra- and interrater reliability was tested using intraclass correlation coefficients (ICC). RESULTS: Thirty-three fungal infiltrates in 16 patients and 38 pulmonary lymphoma manifestations in 19 patients were included. Considering the leading lesion in each patient, diagnostic performance was excellent for T1w entropy (AUC 80.2%; p < 0.005) and slightly inferior for T2w energy (79.9%; p < 0.005), T1w uniformity (79.6%; p < 0.005), and T1w energy (77.0%; p < 0.01); the best AUC for NICQs was 72.0% for T2NICQmean (p < 0.05). Intra- and interrater reliability was good to excellent (ICC > 0.81) for these parameters except for moderate intrarater reliability of T1w energy (ICC = 0.64). CONCLUSIONS: T1w entropy, uniformity, and energy and T2w energy showed the best performances for differentiating pulmonary lymphoma and fungal pneumonia and outperformed NICQs. Results of the texture analysis should be checked for their intrinsic consistency to identify possible incongruities of single parameters. KEY POINTS: • Texture analysis in nonenhanced pulmonary MRI improves the differentiation of pulmonary lymphoma and fungal pneumonia compared with signal intensity quotients. • T1w entropy, uniformity, and energy along with T2w energy show the best performances for differentiating pulmonary lymphoma from fungal pneumonia. • The results of the texture analysis should be checked for their intrinsic consistency to identify possible incongruities of single parameters.

ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging.

Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists' workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. KEY POINTS: • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists' workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined.

Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network.

OBJECTIVE: To present a deep learning-based approach for semi-automatic prostate cancer classification based on multi-parametric magnetic resonance (MR) imaging using a 3D convolutional neural network (CNN). METHODS: Two hundred patients with a total of 318 lesions for which histological correlation was available were analyzed. A novel CNN was designed, trained, and validated using different combinations of distinct MRI sequences as input (e.g., T2-weighted, apparent diffusion coefficient (ADC), diffusion-weighted images, and K-trans) and the effect of different sequences on the network's performance was tested and discussed. The particular choice of modeling approach was justified by testing all relevant data combinations. The model was trained and validated using eightfold cross-validation. RESULTS: In terms of detection of significant prostate cancer defined by biopsy results as the reference standard, the 3D CNN achieved an area under the curve (AUC) of the receiver operating characteristics ranging from 0.89 (88.6% and 90.0% for sensitivity and specificity respectively) to 0.91 (81.2% and 90.5% for sensitivity and specificity respectively) with an average AUC of 0.897 for the ADC, DWI, and K-trans input combination. The other combinations scored less in terms of overall performance and average AUC, where the difference in performance was significant with a p value of 0.02 when using T2w and K-trans; and 0.00025 when using T2w, ADC, and DWI. Prostate cancer classification performance is thus comparable to that reported for experienced radiologists using the prostate imaging reporting and data system (PI-RADS). Lesion size and largest diameter had no effect on the network's performance. CONCLUSION: The diagnostic performance of the 3D CNN in detecting clinically significant prostate cancer is characterized by a good AUC and sensitivity and high specificity. KEY POINTS: • Prostate cancer classification using a deep learning model is feasible and it allows direct processing of MR sequences without prior lesion segmentation. • Prostate cancer classification performance as measured by AUC is comparable to that of an experienced radiologist. • Perfusion MR images (K-trans), followed by DWI and ADC, have the highest effect on the overall performance; whereas T2w images show hardly any improvement.

Hepatocellular adenomas: is there additional value in using Gd-EOB-enhanced MRI for subtype differentiation?

PURPOSE: To differentiate subtypes of hepatocellular adenoma (HCA) based on enhancement characteristics in gadoxetic acid (Gd-EOB) magnetic resonance imaging (MRI). MATERIALS AND METHODS: Forty-eight patients with 79 histopathologically proven HCAs who underwent Gd-EOB-enhanced MRI were enrolled (standard of reference: surgical resection). Two blinded radiologists performed quantitative measurements (lesion-to-liver enhancement) and evaluated qualitative imaging features. Inter-reader variability was tested. Advanced texture analysis was used to evaluate lesion heterogeneity three-dimensionally. RESULTS: Overall, there were 19 (24%) hepatocyte nuclear factor (HNF)-1a-mutated (HHCAs), 37 (47%) inflammatory (IHCAs), 5 (6.5%) b-catenin-activated (bHCA), and 18 (22.5%) unclassified (UHCAs) adenomas. In the hepatobiliary phase (HBP), 49.5% (39/79) of all adenomas were rated as hypointense and 50.5% (40/79) as significantly enhancing (defined as > 25% intralesional GD-EOB uptake). 82.5% (33/40) of significantly enhancing adenomas were IHCAs, while only 4% (1/40) were in the HHCA subgroup (p < 0.001). When Gd-EOB uptake behavior was considered in conjunction with established MRI features (binary regression model), the area under the curve (AUC) increased from 0.785 to 0.953 for differentiation of IHCA (atoll sign + hyperintensity), from 0.859 to 0.903 for bHCA (scar + hyperintensity), and from 0.899 to 0.957 for HHCA (steatosis + hypointensity). Three-dimensional region of interest (3D ROI) analysis showed significantly increased voxel heterogeneity for IHCAs (p = 0.038). CONCLUSION: Gd-EOB MRI is of added value for subtype differentiation of HCAs and reliably identifies the typical heterogeneous HBP uptake of IHCAs. Diagnostic accuracy can be improved significantly by the combined analysis of established morphologic MR appearances and intralesional Gd-EOB uptake. KEY POINTS: •Gd-EOB-enhanced MRI is of added value for subtype differentiation of HCA. •IHCA and HHCA can be identified reliably based on their typical Gd-EOB uptake patterns, and accuracy increases significantly when additionally taking established MR appearances into account. •The small numbers of bHCAs and UHCAs remain the source of diagnostic uncertainty.

Validation of the PI-RADS language: predictive values of PI-RADS lexicon descriptors for detection of prostate cancer.

OBJECTIVES: To assess the discriminatory power of lexicon terms used in PI-RADS version 2 to describe MRI features of prostate lesions. METHODS: Four hundred fifty-four patients were included in this retrospective, institutional review board-approved study. Patients received multiparametric (mp) MRI and subsequent prostate biopsy including MRI/transrectal ultrasound fusion biopsy and 10-core systematic biopsy. PI-RADS lexicon terms describing lesion characteristics on mpMRI were assigned to lesions by experienced readers. Positive and negative predictive values (PPV, NPV) of each lexicon term were assessed using biopsy results as a reference standard. RESULTS: From a total of 501 lesions, clinically significant prostate cancer (csPCa) was present in 175 lesions (34.9%). Terms related to findings of restricted diffusion showed PPVs of up to 52.0%/43.9% and NPV of up to 91.8%/89.7% (peripheral zone or PZ/transition zone or TZ). T2-weighted imaging (T2W)-related terms showed a wide range of predictive values. For PZ lesions, high PPVs were found for "markedly hypointense," "lenticular," "lobulated," and "spiculated" (PPVs between 67.2 and 56.7%). For TZ lesions, high PPVs were found for "water-drop-shaped" and "erased charcoal sign" (78.6% and 61.0%). The terms "encapsulated," "organized chaos," and "linear" showed to be good predictors for benignity with distinctively low PPVs between 5.4 and 6.9%. Most T2WI-related terms showed improved predictive values for TZ lesions when combined with DWI-related findings. CONCLUSIONS: Lexicon terms with high discriminatory power were identified (e.g., "markedly hypointense," "water-drop-shaped," "organized chaos"). DWI-related terms can be useful for excluding TZ cancer. Combining T2WI- with DWI findings in TZ lesions markedly improved predictive values. KEY POINTS: • Lexicon terms describing morphological and functional features of prostate lesions on MRI show a wide range of predictive values for prostate cancer. • Some T2-related terms have favorable PPVs, e.g., "water-drop-shaped" and "organized chaos" while others show less distinctive predictive values. DWI-related terms have noticeable negative predictive values in TZ lesions making DWI feature a useful tool for exclusion of TZ cancer. • Combining DWI- and T2-related lexicon terms for assessment of TZ lesions markedly improves PPVs. Most T2-related lexicon terms showed a significant decrease in PPV when combined with negative findings for "DW hyperintensity."

[Artificial intelligence and radiomics in MRI-based prostate diagnostics]. / Künstliche Intelligenz und Radiomics in der MRT-basierten Prostatadiagnostik.

CLINICAL/METHODICAL ISSUE: In view of the diagnostic complexity and the large number of examinations, modern radiology is challenged to identify clinically significant prostate cancer (PCa) with high sensitivity and specificity. Meanwhile overdiagnosis and overtreatment of clinically nonsignificant carcinomas need to be avoided. STANDARD RADIOLOGICAL METHODS: Increasingly, international guidelines recommend multiparametric magnetic resonance imaging (mpMRI) as first-line investigation in patients with suspected PCa. METHODICAL INNOVATIONS: Image interpretation according to the PI-RADS criteria is limited by interobserver variability. Thus, rapid developments in the field of automated image analysis tools, including radiomics and artificial intelligence (AI; machine learning, deep learning), give hope for further improvement in patient care. PERFORMANCE: AI focuses on the automated detection and classification of PCa, but it also attempts to stratify tumor aggressiveness according to the Gleason score. Recent studies present good to very good results in radiomics or AI-supported mpMRI diagnosis. Nevertheless, these systems are not widely used in clinical practice. ACHIEVEMENTS AND PRACTICAL RECOMMENDATIONS: In order to apply these innovative technologies, a growing awareness for the need of structured data acquisition, development of robust systems and an increased acceptance of AI as diagnostic support are needed. If AI overcomes these obstacles, it may play a key role in the quantitative and reproducible image-based diagnosis of ever-increasing prostate MRI examination volumes.

Image-guided Irreversible Electroporation of Localized Prostate Cancer: Functional and Oncologic Outcomes.

Background Irreversible electroporation (IRE) is a nonthermal ablative method based on the formation of nanoscale defects in cell membranes leading to cell death. Clinical experience with the technique for treatment of prostate cancer remains limited. Purpose To evaluate urogenital toxicity and oncologic outcome of MRI-transrectal US fusion-guided IRE of localized prostate cancer. Materials and Methods In this prospective study, men with biopsy-proven, treatment-naive, low- to intermediate-risk prostate cancer (prostate-specific antigen [PSA], ≤15 ng/mL; Gleason score, ≤3 + 4; clinical stage, ≤T2c; lesion size at multiparametric MRI, ≤20 mm) underwent focal MRI/transrectal US fusion-guided IRE between July 2014 and July 2017. Primary end point was the urogenital toxicity profile of focal IRE by using participant-reported questionnaires. Secondary end points were biochemical, histologic, and imaging measures of oncologic control. Analyses were performed by using nonparametric and χ2 test statistics. Results Thirty men were included (median age, 65.5 years); mean PSA level was 8.65 ng/mL and mean tumor size was 13.5 mm. One grade III adverse event (urethral stricture) was recorded. The proportion of men with erection sufficient for penetration was 83.3% (25 of 30) at baseline and 79.3% (23 of 29; P > .99) at 12 months. Leak-free and pad-free continence rate was 90% (27 of 30) at baseline and 86.2% (25 of 29; P > .99) at 12 months. Urogenital function remained stable at 12 months according to changes in the modified International Consultation on Incontinence Questionnaire Male Lower Urinary Tract Symptoms, or ICIQ-MLUTS, and the International Index of Erectile Function, or IIEF-5, questionnaires (P = .58 and P = .07, respectively). PSA level decreased from a baseline median value of 8.65 ng/mL (interquartile range, 5-11.4 ng/mL) to 2.35 ng/mL (interquartile range, 1-3.4 ng/mL) at 12 months (P < .001). At 6 months, 28 of 30 participants underwent posttreatment biopsy. The rate of in-field treatment failure was 17.9% (five of 28) as determined with multiparametric prostate MRI and targeted biopsies at 6 months. Conclusion After a median follow-up of 20 months, focal irreversible electroporation of localized prostate cancer was associated with low urogenital toxicity and promising oncologic outcomes. © RSNA, 2019 Online supplemental material is available for this article.

L1Base 2: more retrotransposition-active LINE-1s, more mammalian genomes.

LINE-1 (L1) insertions comprise as much as 17% of the human genome sequence, and similar proportions have been recorded for other mammalian species. Given the established role of L1 retrotransposons in shaping mammalian genomes, it becomes an important task to track and annotate the sources of this activity: full length elements, able to encode the cis and trans acting components of the retrotransposition machinery. The L1Base database (http://l1base.charite.de) contains annotated full-length sequences of LINE-1 transposons including putatively active L1s. For the new version of L1Base, a LINE-1 annotation tool, L1Xplorer, has been used to mine potentially active L1 retrotransposons from the reference genome sequences of 17 mammals. The current release of the human genome, GRCh38, contains 146 putatively active L1 elements or full length intact L1 elements (FLIs). The newest versions of the mouse, GRCm38 and the rat, Rnor_6.0, genomes contain 2811 and 492 FLIs, respectively. Most likely reflecting the current level of completeness of the genome project, the latest reference sequence of the common chimpanzee genome, PT 2.19, only contains 19 FLIs. Of note, the current assemblies of the dog, CF 3.1 and the sheep, OA 3.1, genomes contain 264 and 598 FLIs, respectively. Further developments in the new version of L1Base include an updated website with implementation of modern web server technologies. including a more responsive design for an improved user experience, as well as the addition of data sharing capabilities for L1Xplorer annotation.