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Purpose. To investigate the safety and feasibility of the use of a vascular closure device (VCD) after endovascular procedures in swine. Material and Methods. In a study on endovascular therapy, VCD (StarClose, Abbott Vascular, Il, USA) was used in 20 female swines to achieve immediate hemostasis after percutaneous right femoral artery (FA) access. 10 animals were sacrificed immediately after the study and 10 animals were sacrificed 28 days after the initial study. To ensure complete hemostasis and patency of the femoral artery, a CT-angiography of the puncture site was performed on day 1 (acute and chronic group) and day 28 (chronic group). After the sacrifice, the femoral artery was explanted and examined macroscopically for signs of VCD dysfunction. Results. Technical success rate was 100% with immediate hemostasis being achieved in all animals. No animals showed evidence of hematoma. During explantation, only small traces of coagulated blood were found in the acute group, while there were no signs of hematoma in the chronic group. CT-angiography immediately after VCD application as well as before sacrifice (chronic group) showed patency of the FA in all cases. Conclusion. The use of VCD to achieve hemostasis after endovascular studies in swine is feasible and safe.

Objective: Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments due to numerous hurdles impeding its translation into practice. The few existing real-world FL initiatives rarely communicate specific measures taken to overcome these hurdles. To bridge this significant knowledge gap, we propose a comprehensive guide for real-world FL in radiology. Minding efforts to implement real-world FL, there is a lack of comprehensive assessments comparing FL to less complex alternatives in challenging real-world settings, which we address through extensive benchmarking., Materials and Methods: We developed our own FL infrastructure within the German Radiological Cooperative Network (RACOON) and demonstrated its functionality by training FL models on lung pathology segmentation tasks across six university hospitals. Insights gained while establishing our FL initiative and running the extensive benchmark experiments were compiled and categorized into the guide., Results: The proposed guide outlines essential steps, identified hurdles, and implemented solutions for establishing successful FL initiatives conducting real-world experiments. Our experimental results prove the practical relevance of our guide and show that FL outperforms less complex alternatives in all evaluation scenarios., Discussion and Conclusion: Our findings justify the efforts required to translate FL into real-world applications by demonstrating advantageous performance over alternative approaches. Additionally, they emphasize the importance of strategic organization, robust management of distributed data and infrastructure in real-world settings. With the proposed guide, we are aiming to aid future FL researchers in circumventing pitfalls and accelerating translation of FL into radiological applications., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: Facial recognition technology in medical imaging, particularly with head scans, poses privacy risks due to identifiable facial features. This study evaluates the use of facial recognition software in identifying facial features from head CT scans and explores a defacing pipeline using TotalSegmentator to reduce re-identification risks while preserving data integrity for research., Methods: 1404 high-quality renderings from the UCLH EIT Stroke dataset, both with and without defacing were analysed. The performance of defacing with the face mask created by TotalSegmentator was compared to a state-of-the-art CT defacing algorithm. Face detection was performed using deep learning models. The cosine similarity between facial embeddings for intra- and inter-patient images was compared. A Support Vector Machine was trained on cosine similarity values to assess defacing performance, determining if two renderings came from the same patient. This analysis was conducted on defaced and non-defaced images using 5-fold cross-validation., Results: Faces were detected in 76.5 % of non-defaced images. Intra-patient images exhibited a median cosine similarity of 0.65 (IQR: 0.47-0.80), compared to 0.50 (IQR: 0.39-0.62) for inter-patient images. A binary classifier performed moderately on non-defaced images, achieving a ROC-AUC of 0.69 (SD = 0.01) and an accuracy of 0.65 (SD = 0.01) in distinguishing whether a scan belonged to the same or a different individual. Following defacing, performance declined markedly. Defacing with the TotalSegmentator decreased the ROC-AUC to 0.55 (SD = 0.02) and the accuracy to 0.56 (SD = 0.01), whereas the CTA-DEFACE algorithm brought the performance down to a ROC-AUC of 0.60 (SD = 0.02) and an accuracy of 0.59 (SD = 0.01). These results demonstrate the effectiveness of defacing algorithms in mitigating re-identification risks, with the TotalSegmentator providing slightly superior privacy protection., Conclusion: Facial recognition software can identify facial features from partial and complete head CT scan renderings. However, using the TotalSegmentator to deface images reduces re-identification risks to a near-chance level. We offer code to implement this privacy-preserving pipeline., Implications for Practice: Utilizing the TotalSegmentator framework, the proposed pipeline efficiently removes facial features from CT images, making it ideal for multi-site research and data sharing. It is a useful tool for radiographers and radiologists who must comply with medico-legal requirements necessitating the removal of facial features., Competing Interests: Conflict of interest statement C.A.H and T.A. receive funding from Berlin Institute of Health (Clinician Scientist Grant). T.P. receives funding from Berlin Institute of Health (Advanced Clinician Scientist Grant, Platform Grant), Ministry of Education and Research (BMBF, 01KX2021 (RACOON), 01KX2121 („NUM 2.0“, RACOON), 68GX21001A, 01ZZ2315D), German Research Foundation (DFG, SFB 1340/2), European Union (H2020, CHAIMELEON: 952172, DIGITAL, EUCAIM:101100633) 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., Holaira, Incyte Corporation, Karyopharm, Lion Biotechnologies, Inc., MedImmune, Merck Sharp & Dohme Corp, 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 B.V.). J.N. receives funding from Berlin Institute of Health (Digital Health Accelerator), European Union's Horizon Europe programme (COMFORT, 101079894) and reports personal fees from Eppdata GmbH outside the submitted work., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Purpose: This study evaluates the prognostic significance of pleural effusion (PE) in COVID-19 patients across thirteen centers in Germany, aiming to clarify its role in predicting clinical outcomes., Methods: In this retrospective analysis within the RACOON project (Radiological Cooperative Network of the COVID-19 pandemic), 1183 patients (29.3 % women, 70.7 % men) underwent chest CT to assess PE. We investigated PE's association with 30-day mortality, ICU admission, and the need for mechanical ventilation., Results: PE was detected in 31.5 % of patients, showing a significant correlation with 30-day mortality (47.5 % in non-survivors vs. 27.3 % in survivors, p < 0.001), with a hazard ratio of 2.22 (95 % CI 1.65-2.99, p < 0.001). No significant association was found between PE volume or density and mortality. ICU admissions were noted in 46.8 % of patients, while mechanical ventilation was required for 26.7 %., Conclusion: Pleural effusion is present in a significant portion of COVID-19 patients and independently predicts increased 30-day mortality, underscoring its value as a prognostic marker. Its identification, irrespective of volume or density, should be a priority in radiological reports to guide clinical decision-making., Competing Interests: Declaration of competing interest The authors have no competing interests to declare that are relevant to the content of this article., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

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.)

Rationale and Objectives: The prognostic role of computed tomography (CT)-defined skeletal muscle features in COVID-19 is still under investigation. The aim of the present study was to evaluate the prognostic role of CT-defined skeletal muscle area and density in patients with COVID-19 in a multicenter setting., Materials and Methods: This retrospective study is a part of the German multicenter project RACOON (Radiological Cooperative Network of the COVID-19 pandemic). The acquired sample included 1379 patients, 389 (28.2%) women and 990 (71.8%) men. In each case, chest CT was analyzed and pectoralis muscle area and density were calculated. Data were analyzed by means of descriptive statistics. Group differences were calculated using the Mann-Whitney-U test and Fisher's exact test. Univariable and multivariable logistic regression analyses were performed., Results: The 30-day mortality was 17.9%. Using median values as thresholds, low pectoralis muscle density (LPMD) was a strong and independent predictor of 30-day mortality, HR=2.97, 95%-CI: 1.52-5.80, p=0.001. Also in male patients, LPMD predicted independently 30-day mortality, HR=2.96, 95%-CI: 1.42-6.18, p=0.004. In female patients, the analyzed pectoralis muscle parameters did not predict 30-day mortality. For patients under 60 years of age, LPMD was strongly associated with 30-day mortality, HR=2.72, 95%-CI: 1.17;6.30, p=0.019. For patients over 60 years of age, pectoralis muscle parameters could not predict 30-day mortality., Conclusion: In male patients with COVID-19, low pectoralis muscle density is strongly associated with 30-day mortality and can be used for risk stratification. In female patients with COVID-19, pectoralis muscle parameters cannot predict 30-day mortality., 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 Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background: Deep learning (DL) accelerated controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA)-volumetric interpolated breath-hold examination (VIBE), provides high spatial resolution T1-weighted imaging of the upper abdomen. We aimed to investigate whether DL-CAIPIRINHA-VIBE can improve image quality, vessel conspicuity, and lesion detectability compared to a standard CAIPIRINHA-VIBE in renal imaging at 3 Tesla., Methods: In this prospective study, 50 patients with 23 solid and 45 cystic renal lesions underwent MRI with clinical MR sequences, including standard CAIPIRINHA-VIBE and DL-CAIPIRINHA-VIBE sequences in the nephrographic phase at 3 Tesla. Two experienced radiologists independently evaluated both sequences and multiplanar reconstructions (MPR) of the sagittal and coronal planes for image quality with a Likert scale ranging from 1 to 5 (5 =best). Quantitative measurements including the size of the largest lesion and renal lesion contrast ratios were evaluated., Results: DL-CAIPIRINHA-VIBE compared to standard CAIPIRINHA-VIBE showed significantly improved overall image quality, higher scores for renal border delineation, renal sinuses, vessels, adrenal glands, reduced motion artifacts and reduced perceived noise in nephrographic phase images (all p < 0.001). DL-CAIPIRINHA-VIBE with MPR showed superior lesion conspicuity and diagnostic confidence compared to standard CAIPIRINHA-VIBE. However, DL-CAIPIRINHA-VIBE presented a more synthetic appearance and more aliasing artifacts (p < 0.023). The mean size and signal intensity of renal lesions for DL-CAIPIRINHA-VIBE showed no significant differences compared to standard CAIPIRINHA-VIBE (p > 0.9)., Conclusions: DL-CAIPIRINHA-VIBE is well suited for kidney imaging in the nephrographic phase, provides good image quality, improved delineation of anatomic structures and renal lesions., Competing Interests: There’s a research agreement in place between Siemens Healthineers and the Institution. D.N. is an employee at Siemens Healthineers., (© 2024 The Authors.)

Importance: The magnetic resonance imaging (MRI) pathway for diagnosing clinically significant prostate cancer (csPCa; defined as International Society of Urological Pathology grade group ≥2) uses multiparametric MRI (mpMRI) for prostate biopsy (PB) decision-making. However, the intermediate impact on patient outcomes in men with negative MRI results avoiding PB and men with positive MRI results without PCa remains unknown., Objective: To assess the feasibility and safety of a community-based MRI diagnostic strategy in men with suspected PCa using 3-year active monitoring., Design, Setting, and Participants: This multisite, longitudinal cohort trial took place across 54 community-based urology practices and 2 radiology imaging centers at a referral academic institution in Berlin, Germany. Eligible participants aged 18 to 75 years with clinically suspected PCa were enrolled between September 2016 and December 2017 and monitored for 3 years. Final analysis was reported on December 23, 2023., Exposures: Participants underwent 3-T mpMRI. Men with findings suspected to be PCa were recommended for targeted PB (diagnostic phase). Men with negative mpMRI results or positive mpMRI results with benign findings at PB were systematically monitored for 3 years (monitoring phase). Clinical visits were recommended every 6 months., Main Outcomes and Measures: The total proportion of men avoiding PB and those with csPCa., Results: A total of 593 men (median [IQR] age, 64 [58-70] years) underwent mpMRI, with 286 (48%) having negative MRI results, 261 (44%) avoiding PB initially, and 242 (41%) avoiding PB over 3 years. csPCa was detected in 161 (27%) men after immediate PB, increasing to 172 (29%) men after 3 years. Seven men with negative MRI results were diagnosed with PCa by immediate PB (including 4 cases of csPCa), while 279 entered monitoring. Three-year monitoring was completed by 233 (84%) men, with 7 diagnoses of csPCa. Of 307 men with positive MRI results, 58 (19%) showed no PCa after immediate PB, of which 41 (71%) completed monitoring and 4 (7%) were diagnosed with csPCa., Conclusions and Relevance: In this cohort study, men with negative mpMRI results avoiding biopsy were not at elevated risk of csPCa. The study confirms the oncological safety of the prebiopsy MRI strategy of avoiding an immediate PB after negative MRI results when a programmatic safety net is in place.

Artificial intelligence (AI) has become commonplace in solving routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. AI has been shown to improve efficiency in medical image generation, processing, and interpretation, and various such AI models have been developed across research laboratories worldwide. However, very few of these, if any, find their way into routine clinical use, a discrepancy that reflects the divide between AI research and successful AI translation. The goal of this paper is to give an overview of the intersection of AI and medical imaging landscapes. We also want to inform the readers about the importance of using standards in their radiology workflow and the challenges associated with deploying AI models in the clinical workflow. The main focus of this paper is to examine the existing condition of radiology workflow and identify the challenges hindering the implementation of AI in hospital settings. This report reflects extensive weekly discussions and practical problem-solving expertise accumulated over multiple years by industry experts, imaging informatics professionals, research scientists, and clinicians. To gain a deeper understanding of the requirements for deploying AI models, we introduce a taxonomy of AI use cases, supplemented by real-world instances of AI model integration within hospitals. We will also explain how the need for AI integration in radiology can be addressed using the Medical Open Network for AI (MONAI). MONAI is an open-source consortium for providing reproducible deep learning solutions and integration tools for radiology practice in hospitals., (©Vikash Gupta, Barbaros Erdal, Carolina Ramirez, Ralf Floca, Bradley Genereaux, Sidney Bryson, Christopher Bridge, Jens Kleesiek, Felix Nensa, Rickmer Braren, Khaled Younis, Tobias Penzkofer, Andreas Michael Bucher, Ming Melvin Qin, Gigon Bae, Hyeonhoon Lee, M Jorge Cardoso, Sebastien Ourselin, Eric Kerfoot, Rahul Choudhury, Richard D White, Tessa Cook, David Bericat, Matthew Lungren, Risto Haukioja, Haris Shuaib. Originally published in JMIR AI (https://ai.jmir.org), 09.12.2024.)

Purpose: This observational study aims to provide a detailed clinical and imaging characterization/workup of acute intracerebral hemorrhage (ICH) due to either an underlying metastasis (mICH) or brain tumor (tICH) lesion., Methods: We conducted a retrospective, single-center study, evaluating patients presenting with occult ICH on initial CT imaging, classified as tICH or mICH on follow-up MRI imaging according to the H-Atomic classification. Demographic, clinical and radiological data were reviewed., Results: We included 116 patients (tICH: 20/116, 17.24%; mICH: 96/116, 82.76%). The most common malignancies causing ICH were lung cancer (27.59%), malignant melanoma (18.10%) and glioblastoma (10.34%). The three most common stroke-like symptoms observed were focal deficit (62/116, 53.45%), dizziness (42/116, 36.21%) and cognitive impairment (27/116, 23.28%). Highest mICH prevalence was seen in the occipital lobe (mICH: 28.13%, tICH: 0.00%; p = 0.004) with tICH more in the corpus callosum (tICH: 10.00%, mICH: 0.00%; p = 0.029). Anticoagulation therapy was only frequent in mICH patients (tICH: 0.00%, mICH: 5.21%; p = 0.586). Hemorrhage (tICH: 12682 mm 3 , mICH: 5708 mm 3 , p = 0.020) and edema volumes (tICH: 49389 mm 3 , mICH: 20972 mm 3 , p = 0.035) were significantly larger within tICH patients., Conclusion: More than half of the patients with neoplastic ICH exhibited stroke-like symptoms. Lung cancer was most common in mICH, glioblastoma in tICH. While clinical presentations were similar, significant differences in tumor location and treatments were discernible., Competing Interests: Declarations. Ethical approval: This single center retrospective study was approved by the ethics committee (Charité Berlin, Germany [protocol number EA1/035/20]) and written informed consent was waived by the institutional review boards. All study protocols and procedures were conducted in accordance with the Declaration of Helsinki. Consent to participate: Patient consent was not needed due to the retrospective nature of the study. Consent for publication: This manuscript doesn’t contain any individual person’s data in any form. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Standardized reporting of multiparametric prostate MRI (mpMRI) is widespread and follows international standards (Pi-RADS). However, quantitative measurements from mpMRI are not widely comparable. Although T2 mapping sequences can provide repeatable quantitative image measurements and extract reliable imaging biomarkers from mpMRI, they are often time-consuming. We therefore investigated the value of quantitative measurements on a highly accelerated T2 mapping sequence, in order to establish a threshold to differentiate benign from malignant lesions. For this purpose, we evaluated a novel, highly accelerated T2 mapping research sequence that enables high-resolution image acquisition with short acquisition times in everyday clinical practice. In this retrospective single-center study, we included 54 patients with clinically indicated MRI of the prostate and biopsy-confirmed carcinoma (n = 37) or exclusion of carcinoma (n = 17). All patients had received a standard of care biopsy of the prostate, results of which were used to confirm or exclude presence of malignant lesions. We used the linear mixed-effects model-fit by REML to determine the difference between mean values of cancerous tissue and healthy tissue. We found good differentiation between malignant lesions and normal appearing tissue in the peripheral zone based on the mean T2 value. Specifically, the mean T2 value for tissue without malignant lesions was (151.7 ms [95% CI: 146.9-156.5 ms] compared to 80.9 ms for malignant lesions [95% CI: 67.9-79.1 ms]; p < 0.001). Based on this assessment, a limit of 109.2 ms is suggested. Aditionally, a significant correlation was observed between T2 values of the peripheral zone and PI-RADS scores (p = 0.0194). However, no correlation was found between the Gleason Score and the T2 relaxation time. Using REML, we found a difference of -82.7 ms in mean values between cancerous tissue and healthy tissue. We established a cut-off-value of 109.2 ms to accurately differentiate between malignant and non-malignant prostate regions. The addition of T2 mapping sequences to routine imaging could benefit automated lesion detection and facilitate contrast-free multiparametric MRI of the prostate., Competing Interests: Declarations. Ethics Approval: For this IRB approved (No. 19-299), retrospective case-control study, the hospital's PACS was searched by an independent investigator for prostate MRIs between 08/2018 and 07/2019. Consent to Participate: For the retrospective case-control study, no consent to participate was needed. Consent to Publish: For the anonymized data, no consent to publish is needed. Competing Interests: R.S. and T.H. are employed by Siemens Healthineers. T.P. receives funding from Berlin Institute of Health (Advanced Clinician Scientist Grant, Platform Grant), Ministry of Education and Research (BMBF, 01KX2021 (RACOON), 01KX2121 („NUM 2.0“, RACOON), 68GX21001A, 01ZZ2315D), German Research Foundation (DFG, SFB 1340/2), European Union (H2020, CHAIMELEON: 952172, DIGITAL, EUCAIM:101100633) 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., Holaira, Incyte Corporation, Karyopharm, Lion Biotechnologies, Inc., MedImmune, Merck Sharp & Dohme Corp, 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 B.V.). A.M.B.: Bayer, Guebert, Siemens Healthineers (Consulting fees and travel Support)., (© 2024. The Author(s).)

Objectives: Compressed sensing allows for image reconstruction from sparsely sampled k-space data, which is particularly useful in dynamic contrast enhanced MRI (DCE-MRI). The aim of the study was to assess the diagnostic value of a volume-interpolated 3D T1-weighted spoiled gradient-echo sequence with variable density Cartesian undersampling and compressed sensing (CS) for head and neck MRI., Methods: Seventy-one patients with clinical indications for head and neck MRI were included in this study. DCE-MRI was performed at 3 Tesla magnet using CS-VIBE (variable density undersampling, temporal resolution 3.4 s, slice thickness 1 mm). Image quality was compared to standard Cartesian VIBE. Three experienced readers independently evaluated image quality and lesion conspicuity on a 5-point Likert scale and determined the DCE-derived time intensity curve (TIC) types., Results: CS-VIBE demonstrated higher image quality scores compared to standard VIBE with respect to overall image quality (4.3 ± 0.6 vs. 4.2 ± 0.7, p = 0.682), vessel contour (4.6 ± 0.4 vs. 4.4 ± 0.6, p < 0.001), muscle contour (4.4 ± 0.5 vs. 4.5 ± 0.6, p = 0.302), lesion conspicuity (4.5 ± 0.7 vs. 4.3 ± 0.9, p = 0.024) and showed improved fat saturation (4.8 ± 0.3 vs. 3.8 ± 0.4, p < 0.001) and movement artifacts were significantly reduced (4.6 ± 0.6 vs. 3.7 ± 0.7, p < 0.001). Standard VIBE outperformed CS-VIBE in the delineation of pharyngeal mucosa (4.2 ± 0.5 vs. 4.6 ± 0.6, p < 0.001). Lesion size in cases where a focal lesion was identified was similar for all readers for CS-VIBE and standard VIBE (p = 0.101). TIC curve assessment showed good interobserver agreement (k=0.717)., Conclusion: CS-VIBE with variable density Cartesian undersampling allows for DCE-MRI of the head and neck region with diagnostic, high image quality and high temporal resolution., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (© 2024. The Author(s).)

Background and Objective: Accurate magnetic resonance imaging (MRI) reporting is essential for transperineal prostate biopsy (TPB) planning. Although approved computer-aided diagnosis (CAD) tools may assist urologists in this task, evidence of improved clinically significant prostate cancer (csPCa) detection is lacking. Therefore, we aimed to document the diagnostic utility of using Prostate Imaging Reporting and Data System (PI-RADS) and CAD for biopsy planning compared with PI-RADS alone., Methods: A total of 262 consecutive men scheduled for TPB at our referral centre were analysed. Reported PI-RADS lesions and an US Food and Drug Administration-cleared CAD tool were used for TPB planning. PI-RADS and CAD lesions were targeted on TPB, while four (interquartile range: 2-5) systematic biopsies were taken. The outcomes were the (1) proportion of csPCa (grade group ≥2) and (2) number of targeted lesions and false-positive rate. Performance was tested using free-response receiver operating characteristic curves and the exact Fisher-Yates test., Key Findings and Limitations: Overall, csPCa was detected in 56% (146/262) of men, with sensitivity of 92% and 97% (p = 0.007) for PI-RADS- and CAD-directed TPB, respectively. In 4% (10/262), csPCa was detected solely by CAD-directed biopsies; in 8% (22/262), additional csPCa lesions were detected. However, the number of targeted lesions increased by 54% (518 vs 336) and the false-positive rate doubled (0.66 vs 1.39; p = 0.009). Limitations include biopsies only for men at clinical/radiological suspicion and no multidisciplinary review of MRI before biopsy., Conclusions and Clinical Implications: The tested CAD tool for TPB planning improves csPCa detection at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences., Patient Summary: The computer-aided diagnosis tool tested for transperineal prostate biopsy planning improves the detection of clinically significant prostate cancer at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences., (Copyright © 2024 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Purpose To determine whether the unsupervised domain adaptation (UDA) method with generated images improves the performance of a supervised learning (SL) model for prostate cancer (PCa) detection using multisite biparametric (bp) MRI datasets. Materials and Methods This retrospective study included data from 5150 patients (14 191 samples) collected across nine different imaging centers. A novel UDA method using a unified generative model was developed for PCa detection using multisite bpMRI datasets. This method translates diffusion-weighted imaging (DWI) acquisitions, including apparent diffusion coefficient (ADC) and individual diffusion-weighted (DW) images acquired using various b values, to align with the style of images acquired using b values recommended by Prostate Imaging Reporting and Data System (PI-RADS) guidelines. The generated ADC and DW images replace the original images for PCa detection. An independent set of 1692 test cases (2393 samples) was used for evaluation. The area under the receiver operating characteristic curve (AUC) was used as the primary metric, and statistical analysis was performed via bootstrapping. Results For all test cases, the AUC values for baseline SL and UDA methods were 0.73 and 0.79 ( P < .001), respectively, for PCa lesions with PI-RADS score of 3 or greater and 0.77 and 0.80 ( P < .001) for lesions with PI-RADS scores of 4 or greater. In the 361 test cases under the most unfavorable image acquisition setting, the AUC values for baseline SL and UDA were 0.49 and 0.76 ( P < .001) for lesions with PI-RADS scores of 3 or greater and 0.50 and 0.77 ( P < .001) for lesions with PI-RADS scores of 4 or greater. Conclusion UDA with generated images improved the performance of SL methods in PCa lesion detection across multisite datasets with various b values, especially for images acquired with significant deviations from the PI-RADS-recommended DWI protocol (eg, with an extremely high b value). Keywords: Prostate Cancer Detection, Multisite, Unsupervised Domain Adaptation, Diffusion-weighted Imaging, b Value Supplemental material is available for this article. © RSNA, 2024.

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.)

Rationale and Objectives: The prognostic role of pericardial effusion (PE) in Covid 19 is unclear. The aim of the present study was to estimate the prognostic role of PE in patients with Covid 19 in a large multicentre setting., Materials and Methods: This retrospective study is a part of the German multicenter project RACOON (Radiological Cooperative Network of the Covid 19 pandemic). The acquired sample comprises 1197 patients, 363 (30.3%) women and 834 (69.7%) men. In every case, chest computed tomography was analyzed for PE. Data about 30-day mortality, need for mechanical ventilation and need for intensive care unit (ICU) admission were collected. Data were evaluated by means of descriptive statistics. Group differences were calculated with Mann-Whitney test and Fisher exact test. Uni-and multivariable regression analyses were performed., Results: Overall, 46.4% of the patients were admitted to ICU, mechanical lung ventilation was performed in 26.6% and 30-day mortality was 24%. PE was identified in 159 patients (13.3%). The presence of PE was associated with 30-day mortality: HR= 1.54, CI 95% (1.05; 2.23), p = 0.02 (univariable analysis), and HR= 1.60, CI 95% (1.03; 2.48), p = 0.03 (multivariable analysis). Furthermore, density of PE was associated with the need for intubation (OR=1.02, CI 95% (1.003; 1.05), p = 0.03) and the need for ICU admission (OR=1.03, CI 95% (1.005; 1.05), p = 0.01) in univariable regression analysis. The presence of PE was associated with 30-day mortality in male patients, HR= 1.56, CI 95%(1.01-2.43), p = 0.04 (multivariable analysis). In female patients, none of PE values predicted clinical outcomes., Conclusion: The prevalence of PE in Covid 19 is 13.3%. PE is an independent predictor of 30-day mortality in male patients with Covid 19. In female patients, PE plays no predictive role., 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 Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

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., (© 2024. The Author(s).)

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., (© 2023. The Author(s).)

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.)

Background: Magnetic resonance imaging (MRI) is used to detect the prostate index lesion before targeted biopsy. However, the number of biopsy cores that should be obtained from the index lesion is unclear. The aim of this study is to analyze how many MRI-targeted biopsy cores are needed to establish the most relevant histopathologic diagnosis of the index lesion and to build a prediction model., Methods: We retrospectively included 451 patients who underwent 10-core systematic prostate biopsy and MRI-targeted biopsy with sampling of at least three cores from the index lesion. A total of 1587 biopsy cores were analyzed. The core sampling sequence was recorded, and the first biopsy core detecting the most relevant histopathologic diagnosis was identified. In a subgroup of 261 patients in whom exactly three MRI-targeted biopsy cores were obtained from the index lesion, we generated a prediction model. A nonparametric Bayes classifier was trained using the PI-RADS score, prostate-specific antigen (PSA) density, lesion size, zone, and location as covariates., Results: The most relevant histopathologic diagnosis of the index lesion was detected by the first biopsy core in 331 cases (73%), by the second in 66 cases (15%), and by the third in 39 cases (9%), by the fourth in 13 cases (3%), and by the fifth in two cases (<1%). The Bayes classifier correctly predicted which biopsy core yielded the most relevant histopathologic diagnosis in 79% of the subjects. PI-RADS score, PSA density, lesion size, zone, and location did not independently influence the prediction model., Conclusion: The most relevant histopathologic diagnosis of the index lesion was made on the basis of three MRI-targeted biopsy cores in 97% of patients. Our classifier can help in predicting the first MRI-targeted biopsy core revealing the most relevant histopathologic diagnosis; however, at least three MRI-targeted biopsy cores should be obtained regardless of the preinterventionally assessed covariates., (© 2022. The Author(s).)

Data-driven machine learning in medical research and diagnostics needs large-scale datasets curated by clinical experts. The generation of large datasets can be challenging in terms of resource consumption and time effort, while generalizability and validation of the developed models significantly benefit from variety in data sources. Training algorithms on smaller decentralized datasets through federated learning can reduce effort, but require the implementation of a specific and ambitious infrastructure to share data, algorithms and computing time. Additionally, it offers the opportunity of maintaining and keeping the data locally. Thus, data safety issues can be avoided because patient data must not be shared. Machine learning models are trained on local data by sharing the model and through an established network. In addition to commercial applications, there are also numerous academic and customized implementations of network infrastructures available. The configuration of these networks primarily differs, yet adheres to a standard framework composed of fundamental components. In this technical note, we propose basic infrastructure requirements for data governance, data science workflows, and local node set-up, and report on the advantages and experienced pitfalls in implementing the local infrastructure with the German Radiological Cooperative Network initiative as the use case example. We show how the infrastructure can be built upon some base components to reflect the needs of a federated learning network and how they can be implemented considering both local and global network requirements. After analyzing the deployment process in different settings and scenarios, we recommend integrating the local node into an existing clinical IT infrastructure. This approach offers benefits in terms of maintenance and deployment effort compared to external integration in a separate environment (e.g., the radiology department). This proposed groundwork can be taken as an exemplary development guideline for future applications of federated learning networks in clinical and scientific environments.

Objectives: SARS-CoV-2 virus infection can lead to acute respiratory distress syndrome (ARDS), which can be complicated by severe muscle wasting. Until now, data on muscle loss of critically ill COVID-19 patients are limited, while computed tomography (CT) scans for clinical follow-up are available. We sought to investigate the parameters of muscle wasting in these patients by being the first to test the clinical application of body composition analysis (BCA) as an intermittent monitoring tool., Materials: BCA was conducted on 54 patients, with a minimum of three measurements taken during hospitalization, totaling 239 assessments. Changes in psoas- (PMA) and total abdominal muscle area (TAMA) were assessed by linear mixed model analysis. PMA was calculated as relative muscle loss per day for the entire monitoring period, as well as for the interval between each consecutive scan. Cox regression was applied to analyze associations with survival. Receiver operating characteristic (ROC) analysis and Youden index were used to define a decay cut-off., Results: Intermittent BCA revealed significantly higher long-term PMA loss rates of 2.62% (vs. 1.16%, p < 0.001) and maximum muscle decay of 5.48% (vs. 3.66%, p = 0.039) per day in non-survivors. The first available decay rate did not significantly differ between survival groups but showed significant associations with survival in Cox regression (p = 0.011). In ROC analysis, PMA loss averaged over the stay had the greatest discriminatory power (AUC = 0.777) for survival. A long-term PMA decline per day of 1.84% was defined as a threshold; muscle loss beyond this cut-off proved to be a significant BCA-derived predictor of mortality., Conclusion: Muscle wasting in critically ill COVID-19 patients is severe and correlates with survival. Intermittent BCA derived from clinically indicated CT scans proved to be a valuable monitoring tool, which allows identification of individuals at risk for adverse outcomes and has great potential to support critical care decision-making., (© 2023. The Author(s).)

Background: The objective of this study was to assess the performance of the first publicly available automated 3D segmentation for spontaneous intracerebral hemorrhage (ICH) based on a 3D neural network before and after retraining., Methods: We performed an independent validation of this model using a multicenter retrospective cohort. Performance metrics were evaluated using the dice score (DSC), sensitivity, and positive predictive values (PPV). We retrained the original model (OM) and assessed the performance via an external validation design. A multivariate linear regression model was used to identify independent variables associated with the model's performance. Agreements in volumetric measurements and segmentation were evaluated using Pearson's correlation coefficients (r) and intraclass correlation coefficients (ICC), respectively. With 1040 patients, the OM had a median DSC, sensitivity, and PPV of 0.84, 0.79, and 0.93, compared to thoseo f 0.83, 0.80, and 0.91 in the retrained model (RM). However, the median DSC for infratentorial ICH was relatively low and improved significantly after retraining, at p < 0.001. ICH volume and location were significantly associated with the DSC, at p < 0.05. The agreement between volumetric measurements (r > 0.90, p > 0.05) and segmentations (ICC ≥ 0.9, p < 0.001) was excellent., Conclusion: The model demonstrated good generalization in an external validation cohort. Location-specific variances improved significantly after retraining. External validation and retraining are important steps to consider before applying deep learning models in new clinical settings.

Background: In patients with non-small cell lung cancer (NSCLC), accuracy of [ 18 F]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 [ 18 F]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 [ 18 F]FDG-PET/CT improved accuracy in mediastinal LN staging compared to established visual assessment criteria. A web application implementing this model was made available., (© 2023. The Author(s).)

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., (© 2023 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.)

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.

Background: Deep-learning-based computer-aided diagnosis (DL-CAD) systems using MRI for prostate cancer (PCa) detection have demonstrated good performance. Nevertheless, DL-CAD systems are vulnerable to high heterogeneities in DWI, which can interfere with DL-CAD assessments and impair performance. This study aims to compare PCa detection of DL-CAD between zoomed-field-of-view echo-planar DWI (z-DWI) and full-field-of-view DWI (f-DWI) and find the risk factors affecting DL-CAD diagnostic efficiency., Methods: This retrospective study enrolled 354 consecutive participants who underwent MRI including T2WI, f-DWI, and z-DWI because of clinically suspected PCa. A DL-CAD was used to compare the performance of f-DWI and z-DWI both on a patient level and lesion level. We used the area under the curve (AUC) of receiver operating characteristics analysis and alternative free-response receiver operating characteristics analysis to compare the performances of DL-CAD using f- DWI and z-DWI. The risk factors affecting the DL-CAD were analyzed using logistic regression analyses. P values less than 0.05 were considered statistically significant., Results: DL-CAD with z-DWI had a significantly better overall accuracy than that with f-DWI both on patient level and lesion level (AUC patient : 0.89 vs. 0.86; AUC lesion : 0.86 vs. 0.76; P < .001). The contrast-to-noise ratio (CNR) of lesions in DWI was an independent risk factor of false positives (odds ratio [OR] = 1.12; P < .001). Rectal susceptibility artifacts, lesion diameter, and apparent diffusion coefficients (ADC) were independent risk factors of both false positives (OR rectal susceptibility artifact  = 5.46; OR diameter, = 1.12; OR ADC  = 0.998; all P < .001) and false negatives (OR rectal susceptibility artifact  = 3.31; OR diameter  = 0.82; OR ADC  = 1.007; all P ≤ .03) of DL-CAD., Conclusions: Z-DWI has potential to improve the detection performance of a prostate MRI based DL-CAD., Trial Registration: ChiCTR, NO. ChiCTR2100041834 . Registered 7 January 2021., (© 2023. The Author(s).)

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., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Background: Noncontrast Computed Tomography (NCCT) features are promising markers for acute hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH). It remains unclear whether accurate identification of these markers is also reliable in raters with different levels of experience., Methods: Patients with acute spontaneous ICH admitted at four tertiary centers in Germany and Italy were retrospectively included from January 2017 to June 2020. In total, nine NCCT markers were rated by one radiology resident, one radiology fellow, and one neuroradiology fellow with different levels experience in ICH imaging. Interrater reliabilities of the resident and radiology fellow were evaluated by calculated Cohen's kappa (κ) statistics in reference to the neuroradiology fellow who was referred as the gold standard. Gold-standard ratings were evaluated by calculated interrater κ statistics. Global interrater reliabilities were evaluated by calculated Fleiss kappa statistics across all three readers. A comparison of receiver operating characteristics (ROCs) was used to evaluate differences in the diagnostic accuracy for predicting acute hematoma expansion (HE) among the raters., Results: Substantial-to-almost-perfect interrater concordance was found for the resident with interrater Cohen's kappa from 0.70 (95% CI 0.65-0.81) to 0.96 (95% CI 0.94-0.98). The interrater Cohen's kappa for the radiology fellow was moderate to almost perfect and ranged from 0.58 (95% CI 0.52-0.65) to 94 (95% CI 92-0.97). The intrarater gold-standard Cohen's kappa was almost perfect and ranged from 0.79 (95% CI 0.78-0.90) to 0.98 (95% CI 0.78-0.90). The global interrater Fleiss kappa ranged from 0.62 (95%CI 0.57-0.66) to 0.93 (95%CI 0.89-0.97). The diagnostic accuracy for the prediction of acute hematoma expansion (HE) was different for the island sign and fluid sign, with p -values < 0.05., Conclusion: The NCCT markers had a substantial-to-almost-perfect interrater agreement among raters with different levels of experience. Differences in the diagnostic accuracy for the prediction of acute HE were found in two out of nine NCCT markers. The study highlights the promising utility of NCCT markers for acute HE prediction.

The routine use of dynamic-contrast-enhanced MRI (DCE-MRI) of the liver using hepatocyte-specific contrast agent (HSCA) as the standard of care for the study of focal liver lesions is not widely accepted and opponents invoke the risk of a loss in near 100% specificity of extracellular contrast agents (ECA) and the need for prospective head-to-head comparative studies evaluating the diagnostic performance of both contrast agents. The Purpose of this prospective intraindividual study was to conduct a quantitative and qualitative head-to-head comparison of DCE-MRI using HSCA and ECA in patients with liver cirrhosis and HCC. Twenty-three patients with liver cirrhosis and proven HCC underwent two 3 T-MR examinations, one with ECA (gadoteric acid) and the other with HSCA (gadoxetic acid). Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), wash-in, wash-out, image quality, artifacts, lesion conspicuity, and major imaging features of LI-RADS v2018 were evaluated. Wash-in and wash-out were significantly stronger with ECA compared to HSCA (P < 0.001 and 0.006, respectively). During the late arterial phase (LAP), CNR was significantly lower with ECA (P = 0.005), while SNR did not differ significantly (P = 0.39). In qualitative analysis, ECA produced a better overall image quality during the portal venous phase (PVP) and delayed phase (DP) compared to HSCA (P = 0.041 and 0.008), showed less artifacts in the LAP and PVP (P = 0.003 and 0.034) and a higher lesion conspicuity in the LAP and PVP (P = 0.004 and 0.037). There was no significant difference in overall image quality during the LAP (P = 1), in artifacts and lesion conspicuity during the DP (P = 0.078 and 0.073) or in the frequency of the three major LI-RADS v2018 imaging features. In conclusion, ECA provides superior contrast of HCC-especially hypervascular HCC lesions-in DCE-MR in terms of better perceptibility of early enhancement and a stronger washout., (© 2022. The Author(s).)

Background: We evaluated the role of radiomics applied to contrast-enhanced computed tomography (CT) in the detection of lymph node (LN) metastases in patients with known lung cancer compared to 18 F-fluorodeoxyglucose positron emission tomography (PET)/CT as a reference., Methods: This retrospective analysis included 381 patients with 1,799 lymph nodes (450 malignant, 1,349 negative). The data set was divided into a training and validation set. A radiomics analysis with 4 filters and 6 algorithms resulting in 24 different radiomics signatures and a bootstrap algorithm (Bagging) with 30 bootstrap iterations was performed. A decision curve analysis was applied to generate a net benefit to compare the radiomics signature to two expert radiologists as one-by-one and as a prescreening tool in combination with the respective radiologist and only the radiologists., Results: All 24 modeling methods showed good and reliable discrimination for malignant/benign LNs (area under the curve 0.75-0.87). The decision curve analysis showed a net benefit for the least absolute shrinkage and selection operator (LASSO) classifier for the entire probability range and outperformed the expert radiologists except for the high probability range. Using the radiomics signature as a prescreening tool for the radiologists did not improve net benefit., Conclusions: Radiomics showed good discrimination power irrespective of the modeling technique in detecting LN metastases in patients with known lung cancer. The LASSO classifier was a suitable diagnostic tool and even outperformed the expert radiologists, except for high probabilities. Radiomics failed to improve clinical benefit as a prescreening tool., (© 2022. The Author(s) under exclusive licence to European Society of Radiology.)

Within the scope of the two NUM projects CODEX and RACOON we developed a preliminary technical concept for documenting clinical and radiological COVID-19 data in a collaborative approach and its preceding findings of a requirement analysis. At first, we provide an overview of NUM and its two projects CODEX and RACOON including the GECCO data set. Furthermore, we demonstrate the foundation for the increased collaboration of both projects, which was additionally supported by a survey conducted at University Hospital Frankfurt. Based on the survey results mint Lesion™, developed by Mint Medical and used at all project sites within RACOON, was selected as the "Electronic Data Capture" (EDC) system for CODEX. Moreover, to avoid duplicate entry of GECCO data into both EDC systems, an early effort was made to consider a collaborative and efficient technical approach to reduce the workload for the medical documentalists. As a first effort we present a preliminary technical concept representing the current and possible future data workflow of CODEX and RACOON. This concept includes a software component to synchronize GECCO data sets between the two EDC systems using the HL7 FHIR standard. Our first approach of a collaborative use of an EDC system and its medical documentalists could be beneficial in combination with the presented synchronization component for all participating project sites of CODEX and RACOON with regard to an overall reduced documentation workload.

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.