BACKGROUND:The diagnostic performance of prostate magnetic resonance imaging (MRI) critically depends on image quality and reader expertise. The PROBASE trial is a prospective population- and prostate-specific antigen (PSA)-based prostate cancer (PCa) screening study enrolling men aged 45 yr. OBJECTIVE:In this predefined substudy, we evaluated the impact of MRI quality and expert reference reading on clinically significant PCa (csPCa; International Society of Urological Pathology grade ≥2) detection. DESIGN, SETTING, AND PARTICIPANTS:This analysis included 516 participants who underwent multiparametric MRI and combined MRI-targeted biopsy and systematic biopsy after screening with PSA ≥3 ng/ml. Local Prostate Imaging Reporting and Data System (PI-RADS) scores were compared with reference readings by experienced uroradiologists. MRI quality was assessed using Prostate Imaging Quality (version 1; PI-QUAL [v1]). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS:Detection rates, positive predictive values (PPV), and negative predictive values (NPV) were calculated at an underlying biopsy threshold of PI-RADS score of ≥4. Image quality was compared between the study sites. RESULTS AND LIMITATIONS:Reference reading yielded higher NPV (92% vs 86%), PPV (57% vs 50%), and sensitivity (83% vs 69%) than local reading. Local reading missed substantially more csPCa in PI-RADS 1-2 (17.1% vs 2.9%) and resulted in more false-positive MRI findings in PI-RADS 4-5 (n = 76 vs n = 59) than reference reading. Lower MRI quality (PI-QUAL 1-3/5) was associated with reduced csPCa detection (46% vs 62%) and true negative rate (84% vs 95%). PI-QUAL differed between the study sites. PI-RADS classification of reference reading missed small or diffusely infiltrative csPCa, often in lower quality scans. Limitations include using local interpretations to define biopsy targets, limiting retrospective comparison, and possibly underestimating reference-reader performance. CONCLUSIONS:Expert reference MRI reading substantially improves csPCa detection and can reduce unnecessary biopsies in a screening setting. High-quality imaging, standardized protocols, and experienced reading should be considered essential components of future MRI-based screening strategies. TRIAL REGISTRATION:The trial is registered with the ISRCTN (International Randomized Controlled Trial Number) registry, registration number ISRCTN37591328, and can be accessed at https://www.isrctn.com/ISRCTN37591328. The study protocol can be accessed at doi: 10.1016/j.eururo.2013.05.022.

Q-space trajectory imaging (QTI) provides promising markers of tissue microstructure, but clinical translation requires shorter acquisitions, faster analysis, and more robust parameter estimation at high spatial resolution. To address these barriers, we trained a voxel-wise multilayer perceptron (MLP) to infer QTI-derived scalar parameters directly from the diffusion signal. We established reference QTI parameters of the brain in 18 healthy subjects using constrained fitting on 50-min QTI scans. The MLP was trained to estimate those reference parameters from a five-minute subset of the diffusion data. We compared the MLP with the constrained fit applied to the same short-protocol input, computing normalized root mean squared error, peak signal-to-noise ratio, and structural similarity with respect to the reference. Here, the MLP consistently achieved better performance metrics, with normalized root mean squared errors up to two-fold lower. For one whole-brain dataset, MLP inference reduced computation time from more than an hour with constrained fitting to a few seconds. Robustness to lower SNR was tested in a separate 1.7 mm isotropic voxel size acquisition of the full protocol, in which the MLP retained lower errors and less visually apparent noise. Finally, we demonstrate qualitative feasibility in two glioma patients scanned with the short protocol. We conclude that a simple MLP can provide high-quality QTI parameter estimates from short tensor-valued diffusion acquisitions. This enables five-minute, high-resolution QTI and may encourage further clinical studies of markers such as microscopic fractional anisotropy.

PURPOSE:To increase performance and generalization ability of artificial intelligence prostate cancer detection systems by simulating physiological size changes of the bladder and rectum and, thereby, associated deformations of the prostate and its lesions. MATERIALS AND METHODS:This retrospective study included 1028 bi-parametric MRI examinations of men (age range: 40-90 years) performed between 2014 and 2019, divided into training/test sets (771/257). We integrated an 'anatomy-informed' transformation into the training of nnU-Net, by simulating soft-tissue deformations of the prostate resulting from size changes of the rectum and bladder. The effects of these strategies were evaluated using free-response receiver operating characteristic (FROC) to assess lesion-level performance, along with a variant: weighted alternative FROC (wAFROC), which prioritizes patient-level effects with localization criteria. Change in sensitivity was tested using a clustered McNemar test. Patient-level performance was assessed with standard and localized receiver operating characteristics (ROC/LROC) analysis. RESULTS:On the independent test set, the anatomy-informed model simulating changes of both rectum and bladder significantly increased lesion-level detection of true positive lesions by 18.8% (from 48 to 57, p = 0.01) and demonstrated significantly higher performance in the wAFROC analysis (from 0.597 to 0.639, p < 0.01). Patient-level ROC increased slightly (from 0.779 to 0.782, p = 0.89), while LROC analysis demonstrated increased performance (from 0.471 to 0.546). CONCLUSION:Simulation of rectum and bladder size variations during model training led to significant improvement in lesion detection performance, which may be crucial for diagnostics and therapeutic measures depending on correct lesion localization, e.g. MRI-guided biopsies or focal therapy regimes.

Purpose: To complement 1.5-minute measurements of common tensor-valued diffusion MRI (dMRI) markers with rapid constrained fitting. Methods: Fast dMRI protocols for obtaining rotational invariants of the cumulant expansion (RICE) were paired with constrained weighted linear least squares (CWLLS) to stabilize the more fragile WLLS fit. A compact constraint set was formulated, including a novel mean-dependent upper bound on total diffusional variance. Evaluation used diffusion tensor distribution (DTD) simulations, healthy-volunteer data with a resolution-dependent SNR experiment, and a glioma patient dataset. A 5-minute q-space trajectory imaging (QTI) protocol served as a reference. Results: Across experiments, CWLLS reduced unphysical estimates and fit outliers in parameters such as microscopic FA and isotropic diffusivity variance. In simulations, it narrowed error distributions most clearly in the CSF-dominant case, while some metrics showed a bias-variance trade-off. In vivo, CWLLS removed negative variance estimates, truncated out-of-bounds tails, and reduced artifacts in fluid-contaminated voxels while preserving anatomical contrast. It also retained more stable maps than WLLS at higher resolution, although both estimators degraded in the lowest-SNR setting. Notably, the new mean-dependent variance bound was violated in 15.4 Conclusion: CWLLS for fast RICE yielded high-quality parameter maps at an online-ready computational cost. This may enhance the reliability of dMRI tissue characterization and strengthen the path toward clinical translation.

The optimal approach for prostate cancer (PC) screening, including the ideal starting age and most effective diagnostic method, remains under investigation. We evaluated the diagnostic performance of magnetic resonance imaging (MRI)-targeted biopsy (TBx) and systematic biopsy (SBx) in detecting clinically significant PC (csPC) in men aged 45-50 yr in PROBASE, a prospective, randomized trial of a risk-adapted screening strategy. A total of 525 participants with elevated prostate-specific antigen (≥3 ng/ml) underwent MRI followed by biopsy. Of the 209 PC cases detected, 148 (71%) were csPC. SBx identified 94% of csPC cases, while TBx detected 74% (p ≤ 0.05). SBx also diagnosed significantly more low-grade PCs than TBx (p < 0.001). These findings suggest that relying solely on MRI-TBx may lead to underdiagnosis of csPC. Combining SBx with TBx remains the most effective strategy for early detection of PC in young men undergoing screening. Future research should explore optimization strategies to reduce unnecessary biopsies while maintaining high detection rates for csPC. This trial is registered on the ISRCTN registry as ISRCTN37591328 (https://www.isrctn.com/ISRCTN37591328). The study protocol can be accessed at https://doi.org/10.1016/j.eururo.2013.05.022.

BACKGROUND: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. METHODS: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). RESULTS: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. CONCLUSIONS: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. CLINICAL TRIAL NUMBER:Not applicable.

OBJECTIVES:To assess variability of maximum diameter measurements of prostate lesions in MRI assessing patient repositioning, rater and sequence effects. METHODS:Forty-two patients were included retrospectively, who received a clinical bi-/multiparametric prostate MRI examination and agreed to have the T2-weighted (T2WI) and diffusion weighted-imaging (DWI) sequences scanned twice. Maximum diameter measurements of prostate lesions mentioned in the clinical radiologist reports were performed by four readers in multiple reading sessions for determination of inter-sequence (between two DWI sequences), inter-scan (between clinical and additional scan), intra-rater and inter-rater variability. The primary calculated metrics were the repeatability and reproducibility coefficient (RC/RDC), including pooled RC/RDC. RESULTS:Variability measured by RCs/RDCs was lowest for measurements obtained within the same reading session, with inter-scan RCs up to 5.6 mm/6.5 mm for T2WI/DWI, pooled RCs of 4.8 mm/5.8 mm, respectively, and inter-sequence RDCs of 5.4 mm-5.9 mm, pooled RDC 5.8 mm. Measurements performed in separate reading sessions demonstrated significantly higher variability for both settings in the majority of cases (RCs: up to 10.9 mm/11.7 mm/10.2 mm for T2WI/DWI/inter-sequence, p ≤ 0.002), pooled RCs/RDCs 9.2 mm-9.9 mm. Measurements necessarily generated in different reading sessions, i.e., intra-rater or inter-rater, demonstrated high variability (RCs/RDCs up to 11.4 mm/11.5 mm for T2WI/DWI). CONCLUSIONS:Prostate lesion measurements demonstrate considerable variability. When measured in one reading session by one rater, lesion diameter differences below the pooled RCs of 4.8 mm, 95 %-CI [3.9, 5.6] for T2WI and 5.8 mm, 95 %-CI [4.7, 7.1] for DWI should not necessarily assumed to be true biological change, as these differences may result from measurement- or repositioning-based variability alone. Caution needs to be taken assessing size changes.

Background and objective: Biparametric magnetic resonance imaging (bpMRI), excluding dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), is a potential replacement for multiparametric MRI (mpMRI) in diagnosing clinically significant prostate cancer (csPCa). An extensive international multireader multicase observer study was conducted to assess the noninferiority of bpMRI to mpMRI in csPCa diagnosis. Methods: An observer study was conducted with 400 mpMRI examinations from four European centers, excluding examinations with prior prostate treatment or csPCa (Gleason grade [GG] >= 2) findings. Readers assessed bpMRI and mpMRI sequentially, assigning lesion-specific Prostate Imaging Reporting and Data System (PI-RADS) scores (3-5) and a patient-level suspicion score (0-100). The noninferiority of patient-level bpMRI versus mpMRI csPCa diagnosis was evaluated using the area under the receiver operating curve (AUROC) alongside the sensitivity and specificity at PI-RADS >= 3 with a 5% margin. The secondary outcomes included insignificant prostate cancer (GG1) diagnosis, diagnostic evaluations at alternative risk thresholds, decision curve analyses (DCAs), and subgroup analyses considering reader expertise. Histopathology and >= 3 yr of follow-up were used for the reference standard. Key findings and limitations: Sixty-two readers (45 centers and 20 countries) participated. The prevalence of csPCa was 33% (133/400); bpMRI and mpMRI showed similar AUROC values of 0.853 (95% confidence interval [CI], 0.819-0.887) and 0.859 (95% CI, 0.826-0.893), respectively, with a noninferior difference of -0.6% (95% CI, -1.2% to 0.1%, p < 0.001). At PI-RADS >= 3, bpMRI and mpMRI had sensitivities of 88.6% (95% CI, 84.8-92.3%) and 89.4% (95% CI, 85.8-93.1%), respectively, with a noninferior difference of -0.9% (95% CI, -1.7% to 0.0%, p < 0.001), and specificities of 58.6% (95% CI, 52.3- 63.1%) and 57.7% (95% CI, 52.3-63.1%), respectively, with a noninferior difference of 0.9% (95% CI, 0.0-1.8%, p < 0.001). At alternative risk thresholds, mpMRI increased sensitivity at the expense of reduced specificity. DCA demonstrated the highest net benefit for an mpMRI pathway in cancer-averse scenarios, whereas a bpMRI pathway showed greater benefit for biopsy-averse scenarios. A subgroup analysis indicated limited additional benefit of DCE MRI for nonexperts. Limitations included that biopsies were conducted based on mpMRI imaging, and reading was performed in a sequential order. Conclusions and clinical implications: It has been found that bpMRI is noninferior to mpMRI in csPCa diagnosis at AUROC, along with the sensitivity and specificity at PIRADS >= 3, showing its value in individuals without prior csPCa findings and prostate treatment. Additional randomized prospective studies are required to investigate the generalizability of outcomes. (c) 2024 The Authors. Published by Elsevier B.V. on behalf of European Association of Urology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).

Importance Artificial intelligence (AI) assistance in magnetic resonance imaging (MRI) assessment for prostate cancer shows promise for improving diagnostic accuracy but lacks large-scale observational evidence. Objective To evaluate whether use of AI-assisted assessment for diagnosing clinically significant prostate cancer (csPCa) on MRI is superior to unassisted readings. Design, Setting, and Participants This diagnostic study was conducted between March and July 2024 to compare unassisted and AI-assisted diagnostic performance using the AI system developed within the international Prostate Imaging-Cancer AI (PI-CAI) Consortium. The study involved 61 readers (34 experts and 27 nonexperts) from 53 centers across 17 countries. Readers assessed prostate magnetic resonance images both with and without AI assistance, providing Prostate Imaging Reporting and Data System (PI-RADS) annotations from 3 to 5 (higher PI-RADS indicated a higher likelihood of csPCa) and patient-level suspicion scores ranging from 0 to 100 (higher scores indicated a greater likelihood of harboring csPCa). Biparametric prostate MRI examinations were included for 780 men from the PI-CAI study who were included in the newly-conducted observer study. All men within the PI-CAI study had suspicion of harboring prostate cancer, sufficient diagnostic image quality, and no prior clinically significant cancer findings. Disease presence was defined by histopathology, and absence was determined by 3 or more years of follow-up. The AI system was recalibrated using 420 Dutch examinations to generate lesion-detection maps, with AI scores ranging from 1 to 10, in which 10 indicates the highest likelihood of csPCa. The remaining 360 examinations, originating from 3 Dutch centers and 1 Norwegian center, were included in the observer study. Main Outcomes and Measures The primary outcome was diagnosis of csPCa, evaluated using the area under the receiver operating characteristic curve and sensitivity and specificity at a PI-RADS threshold of 3 or more. The secondary outcomes included analysis at alternate operating points and reader expertise. Results Among the 360 examinations of 360 men (median age, 65 years [IQR, 62-70 years]) who were included for testing, 122 (34%) harbored csPCa. AI assistance was associated with significantly improved performance, achieving a 3.3% increase in the area under the receiver operating characteristic curve (95% CI, 1.8%-4.9%; P < .001), from 0.882 (95% CI, 0.854-0.910) in unassisted assessments to 0.916 (95% CI, 0.893-0.938) with AI assistance. Sensitivity improved by 2.5% (95% CI, 1.1%-3.9%; P < .001), from 94.3% (95% CI, 91.9%-96.7%) to 96.8% (95% CI, 95.2%-98.5%), and specificity increased by 3.4% (95% CI, 0.8%-6.0%; P = .01), from 46.7% (95% CI, 39.4%-54.0%) to 50.1% (95% CI, 42.5%-57.7%), at a PI-RADS score of 3 or more. Secondary analyses demonstrated similar performance improvements across alternate operating points and a greater benefit of AI assistance for nonexpert readers. Conclusions and Relevance The findings of this diagnostic study of patients suspected of harboring prostate cancer suggest that AI assistance was associated with improved radiologic diagnosis of clinically significant disease. Further research is required to investigate the generalization of outcomes and effects on workflow improvement within prospective settings.

Despite academic success, radiomics-based machine learning algorithms have not reached clinical practice, partially due to limited repeatability/reproducibility. To address this issue, this work aims to identify a stable subset of radiomics features in prostate MRI for radiomics modelling. A prospective study was conducted in 43 patients who received a clinical MRI examination and a research exam with repetition of T2-weighted and two different diffusion-weighted imaging (DWI) sequences with repositioning in between. Radiomics feature (RF) extraction was performed from MRI segmentations accounting for intra-rater and inter-rater effects, and three different image normalization methods were compared. Stability of RFs was assessed using the concordance correlation coefficient (CCC) for different comparisons: rater effects, inter-scan (before and after repositioning) and inter-sequence (between the two diffusion-weighted sequences) variability. In total, only 64 out of 321 (~ 20%) extracted features demonstrated stability, defined as CCC ≥ 0.75 in all settings (5 high-b value, 7 ADC- and 52 T2-derived features). For DWI, primarily intensity-based features proved stable with no shape feature passing the CCC threshold. T2-weighted images possessed the largest number of stable features with multiple shape (7), intensity-based (7) and texture features (28). Z-score normalization for high-b value images and muscle-normalization for T2-weighted images were identified as suitable.

Accurate risk stratification is essential to prevent over- and undertreatment in newly diagnosed prostate cancer (PCa). In a pilot study with 49 men (36 PCa patients, 13 controls), we integrated non-invasive data modalities collected in a real-world clinical setting: radiomics from multiparametric magnetic resonance imaging (T2/ADC index-lesion features), routine and extended serological parameters, multimodal liquid biopsy features (copy number variations, chromosomal instability, methylation, fragmentation in plasma/urinary cfDNA), plus clinical and lifestyle factors. Pairwise Spearman analyses revealed significant inter-modality correlations. Our holistic PCa characterization showed that imaging phenotypes reflect tumor stage and molecular aggressiveness: lower ADC metrics correlated inversely with genomic cfDNA features, while ADC/T2 lesion volumes correlated positively with molecular signals. These results indicate that routine imaging and serological data may capture systemic tumor biology. Our proof-of-concept demonstrates feasibility of combining multiple data modalities in real-world clinical workflows and supports developing integrative models for non-invasive PCa risk stratification. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was realized through support by the German Federal Ministry for Economic Affairs and Climate Action (funding # 01MT21004A) and the Dieter Morszeck Foundation. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the ethical committee of the University of Heidelberg (Approval No. S-130/2021) and was performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to study inclusion. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data sets generated and analyzed during the current study are available from the corresponding author on reasonable request.

BACKGROUND AND OBJECTIVE:While magnetic resonance imaging (MRI)-guided targeted biopsy (TBx) is becoming an integral part of early detection of prostate cancer (PC), its role in screening of younger men remains unclear. We analyzed the additional value of systematic biopsy (SBx) in improving detection of clinically significant PC (csPC). METHODS:A total of 525 men aged 45-54 yr with confirmed prostate-specific antigen ≥3.0 ng/ml underwent multiparametric MRI followed by combined TBx and SBx between February 2014 and August 2023 within a multicenter prospective screening trial in Germany. Software-based MRI/ultrasound fusion TBx (2 cores per lesion) combined with SBx was performed via a transrectal or transperineal approach. The primary objective was to analyze differences in csPC detection rates between SBx and TBx in relation to MRI. Secondary objectives were detection rates by International Society of Urological Pathology grade group (GG) and the distribution of SBx and/or TBx findings. KEY FINDINGS AND LIMITATIONS:PC was detected in 209 men (39%), of which 148/209 cases were csPC (71%; GG ≥2). SBx missed 24/148 csPC cases (16%) and TBx missed 49/148 (33%). SBx detected 25 more low-risk PC cases than TBx (51 vs 26). For 64% of the cases in which SBx detected higher GG than TBx (n = 89, including GG 1), the positive cores were located within MRI-detected lesions. Five GG ≥3 PC cases were not identified on MRI. Limitations include the lack of centralized MRI review before biopsy, variability in biopsy technique, retrospective subgroup analysis, and short follow-up. CONCLUSIONS AND CLINICAL IMPLICATIONS:A relevant proportion of csPC cases were missed by two-core TBx, although they were correctly identified on MRI, suggesting limitations in targeting accuracy and/or the fusion technique. SBx cores or targeted perilesional sampling, particularly in young men with smaller prostate volume, might be a valuable complement to TBx to ensure reliable and early detection of (cs)PC in this age group. PATIENT SUMMARY:We looked at the effectiveness of systematic biopsy (usually 12 cores taken from the prostate gland) and targeted biopsy (cores from a suspicious area seen on a scan) of the prostate among men aged 45-54 years as part of a prostate cancer screening trial. The results show that using only two targeted cores per lesion seen on an MRI (magnetic resonance imaging) scan may miss a significant number of clinically relevant prostate cancers. One reason could be that MRI-targeted biopsies are not always perfectly accurate. To improve diagnostic accuracy in younger men, it may be necessary to take additional systematic tissue samples, or at least more samples from around any suspicious area. This trial is registered on ISRCTN as ISRCTN37591328.

Risk calculators (RCs) improve patient selection for prostate biopsy with clinical/demographic information, recently with prostate MRI using the prostate imaging reporting and data system (PI-RADS). Fully-automated deep learning (DL) analyzes MRI data independently, and has been shown to be on par with clinical radiologists, but has yet to be incorporated into RCs. The goal of this study is to re-assess the diagnostic quality of RCs, the impact of replacing PI-RADS with DL predictions, and potential performance gains by adding DL besides PI-RADS. One thousand six hundred twenty-seven consecutive examinations from 2014 to 2021 were included in this retrospective single-center study, including 517 exams withheld for RC testing. Board-certified radiologists assessed PI-RADS during clinical routine, then systematic and MRI/Ultrasound-fusion biopsies provided histopathological ground truth for significant prostate cancer (sPC). nnUNet-based DL ensembles were trained on biparametric MRI predicting the presence of sPC lesions (UNet-probability) and a PI-RADS-analogous five-point scale (UNet-Likert). Previously published RCs were validated as is; with PI-RADS substituted by UNet-Likert (UNet-Likert-substituted RC); and with both UNet-probability and PI-RADS (UNet-probability-extended RC). Together with a newly fitted RC using clinical data, PI-RADS and UNet-probability, existing RCs were compared by receiver-operating characteristics, calibration, and decision-curve analysis. Diagnostic performance remained stable for UNet-Likert-substituted RCs. DL contained complementary diagnostic information to PI-RADS. The newly-fitted RC spared 49

Background: Due to superior image quality and daily adaptive planning, MR-guided stereotactic body radiation therapy (MRgSBRT) has the potential to further widen the therapeutic window in radiotherapy of localized prostate cancer. This study reports on acute toxicity rates and patient-reported outcomes after MR-guided adaptive ultrahypofractionated radiotherapy for localized prostate cancer within the prospective, multicenter phase II SMILE trial. Materials and methods: A total of 69 patients with localized prostate cancer underwent MRgSBRT with daily online plan adaptation. Inclusion criteria comprised a tumor stage ≤ T3a, serum PSA value ≤ 20 ng/ml, ISUP Grade group ≤ 4. A dose of 37.5 Gy was prescribed to the PTV in five fractions on alternating days with an optional simultaneous boost of 40 Gy to the dominant intraprostatic lesion defined by multiparametric MRI. Acute genitourinary (GU-) and gastrointestinal (GI-) toxicity, as defined by CTCAE v. 5.0 and RTOG as well as patient-reported outcomes according to EORTC QLQ-C30 and -PR25 scores were analyzed at completion of radiotherapy, 6 and 12 weeks after radiotherapy and compared to baseline symptoms. Results: There were no toxicity-related treatment discontinuations. At the 12-week follow-up visit, no grade 3 + toxicities were reported according to CTCAE. Up until the 12-week visit, in total 16 patients (23 %) experienced a grade 2 GU or GI toxicity. Toxicity rates peaked at the end of radiation therapy and subsided within the 12-week follow-up period. At the 12-week follow-up visit, no residual grade 2 GU toxicities were reported and 1 patient (1 %) had residual grade 2 enteritic symptoms. With exception to a significant improvement in the emotional functioning score following MRgSBRT, no clinically meaningful changes in the global health status nor in relevant subscores were reported. Conclusion: Daily online-adaptive MRgSBRT for localized prostate cancer resulted in an excellent overall toxicity profile without any major negative impact on quality of life.

Abstract Background To investigate the ability of artificial intelligence (AI)-based and semi-quantitative dynamic contrast enhanced (DCE) multiparametric MRI (mpMRI), performed within [18F]-PSMA-1007 PET/MRI, in differentiating benign from malignant prostate tissues in patients with primary prostate cancer (PC). Results A total of seven patients underwent whole-body [18F]-PSMA-1007 PET/MRI examinations including a pelvic mpMRI protocol with T2w, diffusion weighted imaging (DWI) and DCE image series. Conventional analysis included visual reading of PET/MRI images and Prostate Imaging Reporting & Data System (PI-RADS) scoring of the prostate. On the prostate level, we performed manual segmentations for time-intensity curve parameter formation and semi-quantitative analysis based on DCE segmentation data of PC-suspicious lesions. Moreover, we applied a recently introduced deep learning (DL) pipeline previously trained on 1010 independent MRI examinations with systematic biopsy-enhanced histopathological targeted biopsy lesion ground truth in order to perform AI-based lesion detection, prostate segmentation and derivation of a deep learning PI-RADS score. DICE coefficients between manual and automatic DL-acquired segmentations were compared. On patient-based analysis, PET/MRI revealed PC-suspicious lesions in the prostate gland in 6/7 patients (Gleason Score-GS ≥ 7b) that were histologically confirmed. Four of these patients also showed lymph node metastases, while two of them had bone metastases. One patient with GS 6 showed no PC-suspicious lesions. Based on DCE segmentations, a distinction between PC-suspicious and normal appearing tissue was feasible with the parameters fitted maximum contrast ratio (FMCR) and wash-in-slope. DICE coefficients (manual vs. deep learning) were comparable with literature values at a mean of 0.44. Further, the DL pipeline could identify the intraprostatic PC-suspicious lesions in all six patients with clinically significant PC. Conclusion Firstly, semi-quantitative DCE analysis based on manual segmentations of time-intensity curves was able to distinguish benign from malignant tissues. Moreover, DL analysis of the MRI data could detect clinically significant PC in all cases, demonstrating the feasibility of AI-supported approaches in increasing diagnostic certainty of PSMA-radioligand PET/MRI.

Level of Evidence5Technical Efficacy Stage1

Data augmentation (DA) is a key factor in medical image analysis, such as in prostate cancer (PCa) detection on magnetic resonance images. State-of-the-art computer-aided diagnosis systems still rely on simplistic spatial transformations to preserve the pathological label post transformation. However, such augmentations do not substantially increase the organ as well as tumor shape variability in the training set, limiting the model's ability to generalize to unseen cases with more diverse localized soft-tissue deformations. We propose a new anatomy-informed transformation that leverages information from adjacent organs to simulate typical physiological deformations of the prostate and generates unique lesion shapes without altering their label. Due to its lightweight computational requirements, it can be easily integrated into common DA frameworks. We demonstrate the effectiveness of our augmentation on a dataset of 774 biopsy-confirmed examinations, by evaluating a state-of-the-art method for PCa detection with different augmentation settings.

Background and objective:Artificial intelligence (AI)-powered conversational agents are increasingly finding application in health care, as these can provide patient education at any time. However, their effectiveness in medical settings remains largely unexplored. This study aimed to assess the impact of the chatbot "PROState cancer Conversational Agent" (PROSCA), which was trained to provide validated support from diagnostic tests to treatment options for men facing prostate cancer (PC) diagnosis. Methods:The chatbot PROSCA, developed by urologists at Heidelberg University Hospital and SAP SE, was evaluated through a randomized controlled trial (RCT). Patients were assigned to either the chatbot group, receiving additional access to PROSCA alongside standard information by urologists, or the control group (1:1), receiving standard information. A total of 112 men were included, of whom 103 gave feedback at study completion. Key findings and limitations:Over time, patients' information needs decreased significantly more in the chatbot group than in the control group (p = 0.035). In the chatbot group, 43/54 men (79.6%) used PROSCA, and all of them found it easy to use. Of the men, 71.4% agreed that the chatbot improved their informedness about PC and 90.7% would like to use PROSCA again. Limitations are study sample size, single-center design, and specific clinical application. Conclusions and clinical implications:With the introduction of the PROSCA chatbot, we created and evaluated an innovative, evidence-based AI health information tool as an additional source of information for PC. Our RCT results showed significant benefits of the chatbot in reducing patients' information needs and enhancing their understanding of PC. This easy-to-use AI tool provides accurate, timely, and accessible support, demonstrating its value in the PC diagnosis process. Future steps include further customization of the chatbot's responses and integration with the existing health care systems to maximize its impact on patient outcomes. Patient summary:This study evaluated an artificial intelligence-powered chatbot-PROSCA, a digital tool designed to support men facing prostate cancer diagnosis by providing validated information from diagnosis to treatment. Results showed that patients who used the chatbot as an additional tool felt better informed than those who received standard information from urologists. The majority of users appreciated the ease of use of the chatbot and expressed a desire to use it again; this suggests that PROSCA could be a valuable resource to improve patient understanding in prostate cancer diagnosis.