Stefan Johannes Fransen

Objectives: To determine whether AI-reconstructed prostate MRI at reduced acquisition times maintains prostate cancer (PCa) detection performance comparable to conventional scans. Materials and methods: This multicenter, retrospective, consecutive-cohort study included 120 multi-coil T2-weighted prostate MRI scans from the University Medical Center Groningen (UMCG) and 312 publicly available scans from New York University (NYU). An AI model trained on the NYU data was tested on retrospectively undersampled UMCG scans at acceleration factors R = 3 and R = 6 (i.e., data reduction in k-space). Eight experienced radiologists participated in a multi-reader multi-case PCa detection study. Diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUROC). Histopathology and PI-RADS ≤ 2 findings served as reference standards. Multiple image quality metrics were subjectively evaluated using a 4-point Likert scale. Results: No statistically significant reduction in PCa detection was observed at an MRI acceleration up to R = 6 ( p = 0.08). AUROC values were 0.86 (95% CI: 0.74–0.90) for R = 1, 0.82 (0.72–0.88) for R = 3, and 0.80 (0.70–0.86) for R = 6. Compared to R = 1, R = 3 scans were rated by radiologists to have significantly improved sharpness (+0.2, p < 0.05) and lower noise (+0.1, p < 0.05). Overall visual quality at R = 6 remained comparable to R = 1 (2.81 at R6 vs. 2.74 at R1). Conclusion: AI-driven reconstruction enabled a sixfold acceleration of T2-weighted prostate MRI (0:33–1:27 min) without a statistically significant reduction in PCa detection, while preserving perceived image quality. However, the decreasing diagnostic performance at higher accelerations warrants further prospective evaluation. Key Points: Question This study investigated whether deep learning reconstruction enables three- to sixfold acceleration without reducing radiologists’ detection of clinically significant prostate cancer . Findings In a multi-reader multi-case study with eight radiologists, three- and sixfold acceleration showed no significant change in area under the receiver operating characteristic curve . Clinical relevance Deep learning reconstruction shortened T2-weighted acquisition times at sixfold acceleration while preserving perceived image quality and diagnostic performance across acceleration factors .

Objectives: This study compared two uncertainty quantification (UQ) metrics to rule out prostate MRI scans with a high-confidence artificial intelligence (AI) prediction and investigated the resulting potential radiologist’s workload reduction in a clinically significant prostate cancer (csPCa) detection pathway. Materials and methods: This retrospective study utilized 1612 MRI scans from three institutes for csPCa (Gleason Grade Group ≥ 2) assessment. We compared the standard diagnostic pathway (radiologist reading) to an AI-based rule-out pathway in terms of efficacy and accuracy in diagnosing csPCa. In the rule-out pathway, 15 AI submodels (trained on 7756 cases) diagnosed each MRI scan, and any prediction deemed uncertain was referred to a radiologist for reading. We compared the mean (meanUQ) and variability (varUQ) of predictions using the DeLong test on the area under the receiver operating characteristic curves (AUROC). The level of workload reduction of the best UQ method was determined based on a maintained sensitivity at non-inferior specificity using the margins 0.05 and 0.10. Results: The workload reduction of the proposed pathway was institute-specific: up to 20% at a 0.10 non-inferiority margin ( p < 0.05) and non-significant workload reduction at a 0.05 margin. VarUQ-based rule out gave higher but non-significant AUROC scores than meanUQ in certain selected cases (+0.05 AUROC, p > 0.05). Conclusion: MeanUQ and varUQ showed promise in AI-based rule-out csPCa detection. Using varUQ in an AI-based csPCa detection pathway could reduce the number of scans radiologists need to read. The varying performance of the UQ rule-out indicates the need for institute-specific UQ thresholds. Key Points: Question AI can autonomously assess prostate MRI scans with high certainty at a non-inferior performance compared to radiologists, potentially reducing the workload of radiologists . Findings The optimal ratio of AI-model and radiologist readings is institute-dependent and requires calibration . Clinical relevance Semi-autonomous AI-based prostate cancer detection with variational UQ scores shows promise in reducing the number of scans radiologists need to read . Graphical

Objectives: This study explores the methods employed by commercially available AI products to accelerate MRI protocols and investigates the strength of their diagnostic image quality assessment. Materials and methods: All commercial AI products for MRI acceleration were identified from the exhibitors presented at the RSNA 2023 and ECR 2024 annual meetings. Peer-reviewed scientific articles describing validation of clinical performance were searched for each product. Information was extracted regarding the MRI acceleration technique, achieved acceleration, diagnostic performance metrics, test cohort, and hallucinatory artifacts. The strength of the diagnostic image quality was assessed using scientific evidence levels ranging from “product’s technical feasibility for clinical purposes” to “product’s economic impact on society”. Results: Out of 1046 companies, 14 products of 14 companies were included. No scientific articles were found for four products (29%). For the remaining ten products (71%), 21 articles were retrieved. Four acceleration methods were identified: noise reduction, raw data reconstruction, personalized scanning protocols, and synthetic image generation. Only a limited number of articles prospectively demonstrated impact on patient outcomes (n = 4, 19%), and no articles discussed an evaluation in a prospective cohort of > 100 patients or performed an economic analysis. None of the articles performed an analysis of hallucinatory artifacts. Conclusion: Currently, commercially available AI products for MRI acceleration can be categorized into four main methods. The acceleration methods lack prospective scientific evidence on clinical performance in large cohorts and economic analysis, which would help to get a better insight into their diagnostic performance and enable safe and effective clinical implementation. Key Points: Question There is a growing interest in AI products that reduce MRI scan time, but an overview of these methods and their scientific evidence is missing. Findings Only a limited number of articles (n = 4, 19%) prospectively demonstrated the impact of the software for accelerating MRI on diagnostic performance metrics. Clinical relevance Although various commercially available products shorten MRI acquisition time, more studies in large cohorts are needed to get a better insight into the diagnostic performance of AI-constructed MRI.

The proposed deep learning model based on MRI and clinical characteristics showed good performance in predicting progression from low-risk prostate cancer to clinically significant prostate cancer.

Objectives: This study investigated patients’ acceptance of artificial intelligence (AI) for diagnosing prostate cancer (PCa) on MRI scans and the factors influencing their trust in AI diagnoses. Materials and methods: A prospective, multicenter study was conducted between January and November 2023. Patients undergoing prostate MRI were surveyed about their opinions on hypothetical AI assessment of their MRI scans. The questionnaire included nine items: four on hypothetical scenarios of combinations between AI and the radiologist, two on trust in the diagnosis, and three on accountability for misdiagnosis. Relationships between the items and independent variables were assessed using multivariate analysis. Results: A total of 212 PCa suspicious patients undergoing prostate MRI were included. The majority preferred AI involvement in their PCa diagnosis alongside a radiologist, with 91% agreeing with AI as the primary reader and 79% as the secondary reader. If AI has a high certainty diagnosis, 15% of the respondents would accept it as the sole decision-maker. Autonomous AI outperforming radiologists would be accepted by 52%. Higher educated persons tended to accept AI when it would outperform radiologists (p < 0.05). The respondents indicated that the hospital (76%), radiologist (70%), and program developer (55%) should be held accountable for misdiagnosis. Conclusions: Patients favor AI involvement alongside radiologists in PCa diagnosis. Trust in AI diagnosis depends on the patient’s education level and the AI performance, with autonomous AI acceptance by a small majority on the condition that AI outperforms a radiologist. Respondents held the hospital, radiologist, and program developers accountable for misdiagnosis in descending order of accountability. Clinical relevance statement: Patients show a high level of acceptance for AI-assisted prostate cancer diagnosis on MRI, either alongside radiologists or fully autonomous, particularly if it demonstrates superior performance to radiologists alone. Key Points: Prostate cancer suspicious patients may accept autonomous AI based on performance. Patients prefer AI involvement alongside a radiologist in diagnosing prostate cancer. Patients indicate accountability for AI should be shared among multiple stakeholders.

The increasing incidence of prostate cancer cases worldwide has led to a tremendous demand for multiparametric MRI (mpMRI). In order to relieve the pressure on healthcare, reducing mpMRI scan time is necessary. This review focuses on recent techniques proposed for faster mpMRI acquisition, specifically shortening T2W and DWI sequences while adhering to the PI‐RADS (Prostate Imaging Reporting and Data System) guidelines. Speeding up techniques in the reviewed studies rely on more efficient sampling of data, ranging from the acquisition of fewer averages or b‐values to adjustment of the pulse sequence. Novel acquisition methods based on undersampling techniques are often followed by suitable reconstruction methods typically incorporating synthetic priori information. These reconstruction methods often use artificial intelligence for various tasks such as denoising, artifact correction, improvement of image quality, and in the case of DWI, for the generation of synthetic high b‐value images or apparent diffusion coefficient maps. Reduction of mpMRI scan time is possible, but it is crucial to maintain diagnostic quality, confirmed through radiological evaluation, to integrate the proposed methods into the standard mpMRI protocol. Additionally, before clinical integration, prospective studies are recommended to validate undersampling techniques to avoid potentially inaccurate results demonstrated by retrospective analysis. This review provides an overview of recently proposed techniques, discussing their implementation, advantages, disadvantages, and diagnostic performance according to PI‐RADS guidelines compared to conventional methods.Level of Evidence: 3Technical Efficacy: Stage 3

Magnetic Resonance Imaging (MRI) offers strong soft tissue contrast but suffers from long acquisition times and requires tedious annotation from radiologists. Traditionally, these challenges have been addressed separately with reconstruction and image analysis algorithms. To see if performance could be improved by treating both as end-to-end, we hosted the K2S challenge, in which challenge participants segmented knee bones and cartilage from 8× undersampled k-space. We curated the 300-patient K2S dataset of multicoil raw k-space and radiologist quality-checked segmentations. 87 teams registered for the challenge and there were 12 submissions, varying in methodologies from serial reconstruction and segmentation to end-to-end networks to another that eschewed a reconstruction algorithm altogether. Four teams produced strong submissions, with the winner having a weighted Dice Similarity Coefficient of 0.910 ± 0.021 across knee bones and cartilage. Interestingly, there was no correlation between reconstruction and segmentation metrics. Further analysis showed the top four submissions were suitable for downstream biomarker analysis, largely preserving cartilage thicknesses and key bone shape features with respect to ground truth. K2S thus showed the value in considering reconstruction and image analysis as end-to-end tasks, as this leaves room for optimization while more realistically reflecting the long-term use case of tools being developed by the MR community.