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Background: The Prostate Imaging-Reporting and Data System (PI-RADS) calls for reporting the prostate index lesion and the location within the transition (TZ) or peripheral zone (PZ) and location on a corresponding sector map. The aim of this study was to train a deep learning DL-based algorithm for automatic prostate sector mapping and to validate its' performance., Methods: An automatic 24-sector grid-map (ASG) of the prostate was developed, based on an automatic zone-specific deep learning segmentation of the prostate. To evaluate the efficacy of the method, fiducials for random locations within the prostate were placed, and the corresponding sectors were determined for 50 mpMRI datasets. The reference standard was defined in a consensus read by two expert uroradiologists. Annotated fiducial locations were evaluated automatically by the ASG and by four radiologists in two reads with and without the help of a superimposed sector grid-map and the success rate was compared., Results: The ASG algorithm identified the correct prostate sector of the annotated lesions in 80 % (40/50 reads) of the cases and outperformed readings of the four radiologists with 55 % (109/200), p < 0.0001. The added use of the 24 ASG map significantly improved the rate of correct sector annotation for the four radiologists to 71 % (141/200), p < 0.004., Conclusion: The 24 ASG map was effective for prostate sector segmentation and significantly improved location reporting of prostate lesions., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Prof. Bernd Hamm reports grant money from companies or nonprofit organizations to the Department of Radiology (outside of submitted work) from Abbott, Actelion Pharmaceuticals, Bayer Schering Pharma, Bayer Vital, BRACCO Group, Bristol-Myers Squibb, Charité Research Organization GmbH, Krebshilfe, Stiftung für Herzforschung, Essex Pharma, EU Programmes, Fibrex Medical Inc., Focused Ultrasound Surgery Foundation, Fraunhofer Gesellschaft, Guerbet, INC Research, InSightec Ltd., IPSEN Pharma, Kendle/MorphoSys AG, Lilly GmbH, Lundbeck GmbH, MeVis Medical Solutions AG, Nexus Oncology, Novartis, Parexel CRO Service, Perceptive, Pfizer GmbH, Philipps, Sanofi-Aventis S.A, Siemens, Spectranetics GmbH, Terumo Medical Corporation, TNS Healthcare GmbH, Toshiba, UCB Pharma, Wyeth Pharma and Zukunftsfond Berlin (TSB). Prof. Tobias Penzkofer was supported by the Berlin Institute of Health (Clinician Scientist Grant, Platform Grant), Ministry of Education and Research (BMBF) and reports research agreements (no personal payments, outside of submitted work) with AGO, Aprea AB, ARCAGY-GINECO, Astellas Pharma Global Inc. (APGD), Astra Zeneca, Clovis Oncology, Inc., Dohme Corp, Holaira, Incyte Corporation, Karyopharm, Lion Biotechnologies, Inc., MedImmune, Merck Sharp, Millennium Pharmaceuticals, Inc., Morphotec Inc., NovoCure Ltd., PharmaMar S.A. and PharmaMar USA, Inc., Roche, Siemens Healthineers, and TESARO Inc. and fees for a book translation (Elsevier). Dr. Nick Lasse Beetz and Dr. Charlie Hamm are participants of the BIH Charité Junior Clinician Scientist Program funded by Charité – Universitätsmedizin Berlin and the Berlin Institute of Health at Charité (BIH). Dr. Thula Walter-Rittel reports payments from Bayer Vital and Novartis Pharmaceuticals and Astellas Pharmaceuticals outside the current scope of this paper. Anne Frisch, Franziska Dräger, Matthias Haas, Lukas Baumgärtner, Alexander Hartenstein, Hannes Cash, and Sebastian Hofbauer declare that they have no conflicts of interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: Radiological reporting is transitioning to quantitative analysis, requiring large-scale multi-center validation of biomarkers. A major prerequisite and bottleneck for this task is the voxelwise annotation of image data, which is time-consuming for large cohorts. In this study, we propose an iterative training workflow to support and facilitate such segmentation tasks, specifically for high-resolution thoracic CT data., Methods: Our study included 132 thoracic CT scans from clinical practice, annotated by 13 radiologists. In three iterative training experiments, we aimed to improve and accelerate segmentation of the heart and mediastinum. Each experiment started with manual segmentation of 5-25 CT scans, which served as training data for a nnU-Net. Further iterations incorporated AI pre-segmentation and human correction to improve accuracy, accelerate the annotation process, and reduce human involvement over time., Results: Results showed consistent improvement in AI model quality with each iteration. Resampled datasets improved the Dice similarity coefficients for both the heart (DCS 0.91 [0.88; 0.92]) and the mediastinum (DCS 0.95 [0.94; 0.95]). Our AI models reduced human interaction time by 50 % for heart and 70 % for mediastinum segmentation in the most potent iteration. A model trained on only five datasets achieved satisfactory results (DCS > 0.90)., Conclusions: The iterative training workflow provides an efficient method for training AI-based segmentation models in multi-center studies, improving accuracy over time and simultaneously reducing human intervention. Future work will explore the use of fewer initial datasets and additional pre-processing methods to enhance model quality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Purpose: The ever-increasing volume of medical imaging data and interest in Big Data research brings challenges to data organization, categorization, and retrieval. Although the radiological value chain is almost entirely digital, data structuring has been widely performed pragmatically, but with insufficient naming and metadata standards for the stringent needs of image analysis. To enable automated data management independent of naming and metadata, this study focused on developing a convolutional neural network (CNN) that classifies medical images based solely on voxel data., Method: A 3D CNN (3D-ResNet18) was trained using a dataset of 31,602 prostate MRI volumes with 10 different sequence types of 1243 patients. A five-fold cross-validation approach with patient-based splits was chosen for training and testing. Training was repeated with a gradual reduction in training data assessing classification accuracies to determine the minimum training data required for sufficient performance. The trained model and developed method were tested on three external datasets., Results: The model achieved an overall accuracy of 99.88 % ± 0.13 % in classifying typical prostate MRI sequence types. When being trained with approximately 10 % of the original cohort (112 patients), the CNN still achieved an accuracy of 97.43 % ± 2.10 %. In external testing the model achieved sensitivities of > 90 % for 10/15 tested sequence types., Conclusions: The herein developed CNN enabled automatic and reliable sequence identification in prostate MRI. Ultimately, such CNN models for voxel-based sequence identification could substantially enhance the management of medical imaging data, improve workflow efficiency and data quality, and allow for robust clinical AI workflows., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: G.L.B. Stock in Siemens. C.A.H. Seed funding from University Medicine Greifswald; payment for lectures from Bracco Imaging Deutschland. F.B. Research project Sondierungsprojekt zu KI in der Pflege (SOKIP), funded by German Federal Ministry of Research and Education; AI Lighthouse Project Green Consumption Assistant with ecosia and TU Berlin, funded by German Federal Ministry for the Environment; Research grant on Prozessentwicklung und -begleitung zum KI-Einsatz in der Pflege (ProKIP), funded by the German Federal Ministry of Research; Research grant on KI in der Pflege-Sturz / Delir / Medikation (KIP-SDM), funded by the German Federal Ministry of Research; Research grant on Reduction of the Impact of untreated Waste Water on the Environment in case of torrential Rain (RIWWER), funded by the German Federal Ministry for Economic Affairs and Climate Action; Research grant on Citizen-based Monitoring for Peace & Security in the Era of Synthetic Media and Deepfakes, funded by the German Foundation for Peace Research. T.P. Berlin Institute of Health (Clinician Scientist Grant, Platform Grant), Ministry of Education and Research (BMBF, 01KX2021, 01KX2121, 68GX21001A), German Research Foundation (DFG, SFB 1340/2), Horizon 2020 (952172); AGO, Aprea AB, ARCAGY-GINECO, Astellas Pharma Global Inc. (APGD), Astra Zeneca, Clovis Oncology, Dohme Corp, Holaira, Incyte Corporation, Karyopharm, Lion Biotechnologies, MedImmune, Merck Sharp, Millennium Pharmaceuticals, Morphotec Inc., NovoCure Ltd., PharmaMar S.A. and PharmaMar USA, Roche, Siemens Healthineers, and TESARO; royalties from Elsevier; patent pending., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Purpose: To compare the performance of lesion detection and Prostate Imaging-Reporting and Data System (PI-RADS) classification between a deep learning-based algorithm (DLA), clinical reports and radiologists with different levels of experience in prostate MRI., Methods: This retrospective study included 121 patients who underwent prebiopsy MRI and prostate biopsy. More than five radiologists (Reader groups 1, 2: residents; Readers 3, 4: less-experienced radiologists; Reader 5: expert) independently reviewed biparametric MRI (bpMRI). The DLA results were obtained using bpMRI. The reference standard was based on pathologic reports. The diagnostic performance of the PI-RADS classification of DLA, clinical reports, and radiologists was analyzed using AUROC. Dichotomous analysis (PI-RADS cutoff value ≥ 3 or 4) was performed, and the sensitivities and specificities were compared using McNemar's test., Results: Clinically significant cancer [CSC, Gleason score ≥ 7] was confirmed in 43 patients (35.5%). The AUROC of the DLA (0.828) for diagnosing CSC was significantly higher than that of Reader 1 (AUROC, 0.706; p = 0.011), significantly lower than that of Reader 5 (AUROC, 0.914; p = 0.013), and similar to clinical reports and other readers (p = 0.060-0.661). The sensitivity of DLA (76.7%) was comparable to those of all readers and the clinical reports at a PI-RADS cutoff value ≥ 4. The specificity of the DLA (85.9%) was significantly higher than those of clinical reports and Readers 2-3 and comparable to all others at a PI-RADS cutoff value ≥ 4., Conclusions: The DLA showed moderate diagnostic performance at a level between those of residents and an expert in detecting and classifying according to PI-RADS. The performance of DLA was similar to that of clinical reports from various radiologists in clinical practice., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Objectives: Radiomics has shown to provide novel diagnostic and predictive disease information based on quantitative image features in study settings. However, limited data yielded contradictory results and important questions regarding the validity of the methods remain to be answered. The purpose of this study was to evaluate how clinical imaging techniques affect the stability of radiomics features by using 3D printed anthropomorphic CT phantom to test for repeatability and reproducibility of quantitative parameters., Methods: 48 PET/CT validated lymph nodes of prostate cancer patients (24 metastatic, 24 non-metastatic) were used as a template to create a customized 3D printed anthropomorphic phantom. We subsequently scanned the phantom five times with a routine abdominal CT protocol. Images were reconstructed using iterative reconstruction and two soft tissue kernels and one bone kernel. Radiomics features were extracted and assessed for repeatability and susceptibility towards image reconstruction settings using concordance correlation coefficients., Results: Our analysis revealed 19 of 86 features (22 %) as highly repeatable (CCC ≥ 0.85) with low susceptibility towards image reconstruction protocols. Most features analyzed depicted critical non-repeatability with CCC's < 0.75 even under entirely consistent imaging acquisition settings. Edge enhancing kernels result in higher variances between the scans and differences in repeatability and reproducibility were detected between PSMA-positive and negative lymph nodes with overall more stable features seen in tumor positive lymph nodes., Conclusions: Both, repeatability and reproducibility play a crucial role in the validation process of radiomics features in clinical routine. This phantom study shows that most radiomics features in contrast to previous studies, including phantom and clinical, do not depict sufficient intra-scanner repeatability to serve as reliable diagnostic tools., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Purpose: To evaluate if size-based cut-offs based on MR imaging can successfully assess clinically significant prostate cancer (csPCA). The goal was to improve the currently applied size-based differentiation criterion in PI-RADS., Methods and Materials: MRIs of 293 patients who had undergone 3 T MR imaging with subsequent confirmation of prostate cancer on systematic and targeted MRI/TRUS-fusion biopsy were re-read by three radiologists. All identifiable tumors were measured on T2WI for lesions originating in the transition zone (TZ) and on DWI for lesions from the peripheral zone (PZ) and tabulated against their Gleason grade., Results: 309 lesions were analyzed, 213 (68.9 %) in the PZ and 96 (31.1 %) in the TZ. ROC-Analysis showed a stronger correlation between lesion size and clinically significant (defined as Gleason Grade Group ≥ 2) prostate cancer (PCa) for the PZ (AUC = 0.73) compared to the TZ (AUC = 0.63). The calculated Youden index resulted in size cut-offs of 14 mm for PZ and 21 mm for TZ tumors., Conclusion: Size cut-offs can be used to stratify prostate cancer with different optimal size thresholds in the peripheral zone and transition zone. There was a clearer separation of clinically significant tumors in peripheral zone cancers compared to transition zone cancers. Future iterations of PI-RADS could therefore take different size-based cut-offs for peripheral zone and transition zone cancers into account., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Objective: To investigate 3T pulmonary magnetic resonance imaging (MRI) for characterization of solid pulmonary lesions in immunocompromised patients and to differentiate infectious from malignant lesions., Materials and Methods: Thirty-eight pulmonary lesions in 29 patients were evaluated. Seventeen patients were immunocompromised (11 infections and 6 lymphomas) and 12 served as controls (4 bacterial pneumonias, 8 solid tumors). Ten of the 15 infections were acute. Signal intensities (SI) were measured in the lesion, chest wall muscle, and subcutaneous fat. Scaled SIs as Non-enhanced Imaging Characterization Quotients ((SI Lesion -SI Muscle )/(SI Fat -SI Muscle )*100) were calculated from the T2-weighted images using the mean SI (T2-NICQ mean ) or the 90th percentile of SI (T2-NICQ 90th ) of the lesion. Simple quotients were calculated by dividing the SI of the lesion by the SI of chest wall muscle (e.g. T1-Q mean : SI Lesion /SI Muscle )., Results: Infectious pulmonary lesions showed a higher T2-NICQ mean (40.1 [14.6-56.0] vs. 20.9 [2.4-30.1], p<0.05) and T2-NICQ 90th (74.3 [43.8-91.6] vs. 38.5 [15.8-48.1], p<0.01) than malignant lesions. T1-Q mean was higher in malignant lesions (0.85 [0.68-0.94] vs. 0.93 [0.87-1.09], p<0.05). Considering infections only, T2-NICQ 90th was lower when anti-infectious treatment was administered >24h prior to MRI (81.8 [71.8-97.6] vs. 41.4 [26.6-51.1], p<0.01). Using Youden's index (YI), the optimal cutoff to differentiate infectious from malignant lesions was 43.1 for T2-NICQ mean (YI=0.42, 0.47 sensitivity, 0.95 specificity) and 55.5 for T2-NICQ 90th (YI=0.61, 0.71 sensitivity, 0.91 specificity). Combining T2-NICQ 90th and T1-Q mean increased diagnostic performance (YI=0.72, 0.77 sensitivity, 0.95 specificity)., Conclusion: Considering each quotient alone, T2-NICQ 90th showed the best diagnostic performance and could allow differentiation of acute infectious from malignant pulmonary lesions with high specificity. Combining T2-NICQ 90th with T1-Q mean increased overall performance, especially regarding sensitivity., (Copyright © 2017 Elsevier B.V. All rights reserved.)