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Physicians and scientists around the world are aggressively attempting to develop effective treatment strategies. The treatment goal is to reduce the fatality rate in 15% to 20% of individuals infected with SARS-CoV-2 who develop severe inflammatory conditions that can lead to pneumonia, and acute respiratory distress syndrome. These conditions are major causes of death in these patients. Convalescent plasma (CP) collected from patients recovered from the novel corona virus disease (COVID-19) has been considered as an effective treatment method for COVID-19. Moreover, low-dose radiation therapy (LDRT) for COVID-19 pneumonia was historically used to treat pneumonia during the first half of the 20th century. The concept of LDRT for COVID-19 pneumonia was first introduced in March 2020. Later scientists from Canada, Spain, United States, Germany and France also confirmed the potential efficacy of LDRT for treatment of COVID-19 pneumonia. The rationale behind introducing LDRT as an effective treatment method for pneumonia in COVID-19 patients is not only due to its anti-inflammatory effect, but also in optimization of the activity of the immune system. Moreover, LDRT, unlike other treatment methods such as antiviral drugs, does not have the key disadvantage of exerting a significant selective pressure on the SARS-CoV-2 virus and hence does not lead to evolution of the virus through mutations. Given these considerations, we believe that a hybrid treatment including both CP and LDRT can trigger synergistic responses that will help healthcare providers in mitigating today's COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Background: Selection of the best treatment modalities for lung cancer depends on many factors, like survival time, which are usually determined by imaging. Objectives: To predict the survival time of lung cancer patients using the advantages of both radiomics and logistic regression-based classification models. Material and Methods: Fifty-nine patients with primary lung adenocarcinoma were included in this retrospective study and pre-treatment contrast-enhanced CT images were acquired. The patients lived more than 2 years were classified as the 'Alive' class and otherwise as the 'Dead' class. In our proposed quantitative radiomic framework, we first extracted the associated regions of each lung lesion from pretreatment CT images for each patient via grow cut segmentation algorithm. Then, 40 radiomic features were extracted from the segmented lung lesions. In order to enhance the generalizability of the classification models, the mutual information-based feature selection method was applied to each feature vector. We investigated the performance of six logistic regression-based classification models. Results: It was observed that the mutual information feature selection method can help the classifier to achieve better predictive results. In our study, the Logistic regression (LR) and Dual Coordinate Descent method for Logistic Regression (DCD-LR) models achieved the best results indicating that these classification models have strong potential for classifying the more important class (i.e., the 'Alive' class). Conclusion: The proposed quantitative radiomic framework yielded promising results, which can guide physicians to make better and more precise decisions and increase the chance of treatment success. [ABSTRACT FROM AUTHOR]

Introduction: Providing tools for comprehensively evaluating scintigraphy images could enhance transthyretin amyloid cardiomyopathy (ATTR-CM) diagnosis. This study aims to automatically detect and score ATTR-CM in total body scintigraphy images using deep learning on multi-tracer, multi-scanner, and multi-center datasets., Methods: In the current study, we employed six datasets (from 12 cameras) for various tasks and purposes. Dataset #1 (93 patients, 99m Tc-MDP) was used to develop the 2D-planar segmentation and localization models. Dataset #2 (216 patients, 99m Tc-DPD) was used for the detection (grade 0 vs. grades 1, 2, and 3) and scoring (0 and 1 vs. grades 2 and 3) of ATTR-CM. Datasets #3 (41 patients, 99m Tc-HDP), #4 (53 patients, 99m Tc-PYP), and #5 (129 patients, 99m Tc-DPD) were used as external centers. ATTR-CM detection and scouring were performed by two physicians in each center. Moreover, Dataset #6 consisting of 3215 patients without labels, was employed for retrospective model performance evaluation. Different regions of interest were cropped and fed into the classification model for the detection and scoring of ATTR-CM. Ensembling was performed on the outputs of different models to improve their performance. Model performance was measured by classification accuracy, sensitivity, specificity, and AUC. Grad-CAM and saliency maps were generated to explain the models' decision-making process., Results: In the internal test set, all models for detection and scoring achieved an AUC of more than 0.95 and an F1 score of more than 0.90. For detection in the external dataset, AUCs of 0.93, 0.95, and 1 were achieved for datasets 3, 4, and 5, respectively. For the scoring task, AUCs of 0.95, 0.83, and 0.96 were achieved for these datasets, respectively. In dataset #6, we found ten cases flagged as ATTR-CM by the network. Out of these, four cases were confirmed by a nuclear medicine specialist as possibly having ATTR-CM. GradCam and saliency maps showed that the deep-learning models focused on clinically relevant cardiac areas., Conclusion: In the current study, we developed and evaluated a fully automated pipeline to detect and score ATTR-CM using large multi-tracer, multi-scanner, and multi-center datasets, achieving high performance on total body images. This fully automated pipeline could lead to more timely and accurate diagnoses, ultimately improving patient outcomes., Competing Interests: Declarations. Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study was approved by the Ethics committee of the Canton of Geneva, Switzerland. Consent forms were waived owing to the retrospective nature of the study. Consent to participate: Informed consent was obtained from all individual participants included in the study. Consent to publish: All authors approved the final version of the manuscript and consent to give the Publisher the permission to publish the work. Competing interests: The University Hospital Bern receives funding from AstraZeneca and Pfizer for the Bern Amyloidosis Registry (B-CARE) (NCT04776824). Dr Caobelli is currently supported by a research grant by Siemens Healthineers and receives speakers Honoraria by Bracco AG and Pfizer AG for matters not related to the present manuscript. Prof. Zaidi received funding from General Electric Healthcare not related to the present manuscript. Dr Federico Caobelli is an Editor (Guest Editor) of EJNMMI., (© 2025. The Author(s).)

Purpose: This study aimed to develop a deep-learning framework to generate multi-organ masks from CT images in adult and pediatric patients., Methods: A dataset consisting of 4082 CT images and ground-truth manual segmentation from various databases, including 300 pediatric cases, were collected. In strategy#1, the manual segmentation masks provided by public databases were split into training (90%) and testing (10% of each database named subset #1) cohort. The training set was used to train multiple nnU-Net networks in five-fold cross-validation (CV) for 26 separate organs. In the next step, the trained models from strategy #1 were used to generate missing organs for the entire dataset. This generated data was then used to train a multi-organ nnU-Net segmentation model in a five-fold CV (strategy#2). Models' performance were evaluated in terms of Dice coefficient (DSC) and other well-established image segmentation metrics., Results: The lowest CV DSC for strategy#1 was 0.804 ± 0.094 for adrenal glands while average DSC > 0.90 were achieved for 17/26 organs. The lowest DSC for strategy#2 (0.833 ± 0.177) was obtained for the pancreas, whereas DSC > 0.90 was achieved for 13/19 of the organs. For all mutual organs included in subset #1 and subset #2, our model outperformed the TotalSegmentator models in both strategies. In addition, our models outperformed the TotalSegmentator models on subset #3., Conclusions: Our model was trained on images with significant variability from different databases, producing acceptable results on both pediatric and adult cases, making it well-suited for implementation in clinical setting., 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 © 2025 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Background: Coronary artery disease (CAD) has one of the highest mortality rates in humans worldwide. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) provides clinicians with myocardial metabolic information non-invasively. However, there are some limitations to interpreting SPECT images performed by physicians or automatic quantitative approaches. Radiomics analyzes images objectively by extracting quantitative features and can potentially reveal biological characteristics that the human eye cannot detect. However, the reproducibility and repeatability of some radiomic features can be highly susceptible to segmentation and imaging conditions., Purpose: We aimed to assess the reproducibility of radiomic features extracted from uncorrected MPI-SPECT images reconstructed with 15 different settings before and after ComBat harmonization, along with evaluating the effectiveness of ComBat in realigning feature distributions., Materials and Methods: A total of 200 patients (50% normal and 50% abnormal) including rest and stress (without attenuation and scatter corrections) MPI-SPECT images were included. Images were reconstructed using 15 combinations of filter cut-off frequencies, filter orders, filter types, reconstruction algorithms, number of iterations and subsets resulting in 6000 images. Image segmentation was performed on the left ventricle in the first reconstruction for each patient and applied to 14 others. A total of 93 radiomic features were extracted from the segmented area, and ComBat was used to harmonize them. The intraclass correlation coefficient (ICC) and overall concordance correlation coefficient (OCCC) tests were performed before and after ComBat to examine the impact of each parameter on feature robustness and to assess harmonization efficiency. The ANOVA and the Kruskal-Wallis tests were performed to evaluate the effectiveness of ComBat in correcting feature distributions. In addition, the Student's t-test, Wilcoxon rank-sum, and signed-rank tests were implemented to assess the significance level of the impacts made by each parameter of different batches and patient groups (normal vs. abnormal) on radiomic features., Results: Before applying ComBat, the majority of features (ICC: 82, OCCC: 61) achieved high reproducibility (ICC/OCCC ≥ 0.900) under every batch except Reconstruction. The largest and smallest number of poor features (ICC/OCCC < 0.500) were obtained by IterationSubset and Order batches, respectively. The most reliable features were from the first-order (FO) and gray-level co-occurrence matrix (GLCM) families. Following harmonization, the minimum number of robust features increased (ICC: 84, OCCC: 78). Applying ComBat showed that Order and Reconstruction were the least and the most responsive batches, respectively. The most robust families, in a descending order, were found to be FO, neighborhood gray-tone difference matrix (NGTDM), GLCM, gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), and gray-level dependence matrix (GLDM) under Cut-off, Filter, and Order batches. The Wilcoxon rank-sum test showed that the number of robust features significantly differed under most batches in the Normal and Abnormal groups., Conclusion: The majority of radiomic features show high levels of robustness across different OSEM reconstruction parameters in uncorrected MPI-SPECT. ComBat is effective in realigning feature distributions and enhancing radiomic features reproducibility., (© 2024 The Author(s). Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: We aim to perform radiogenomic profiling of breast cancer tumors using dynamic contrast magnetic resonance imaging (MRI) for the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) genes., Methods: The dataset used in the current study consists of imaging data of 922 biopsy-confirmed invasive breast cancer patients with ER, PR, and HER2 gene mutation status. Breast MR images, including a T1-weighted pre-contrast sequence and three post-contrast sequences, were enrolled for analysis. All images were corrected using N4 bias correction algorithms. Based on all images and tumor masks, a bounding box of 128 × 128 × 68 was chosen to include all tumor regions. All networks were implemented in 3D fashion with input sizes of 128 × 128 × 68, and four images were input to each network for multi-channel analysis. Data were randomly split into train/validation (80%) and test set (20%) with stratification in class (patient-wise), and all metrics were reported in 20% of the untouched test dataset., Results: For ER prediction, SEResNet50 achieved an AUC mean of 0.695 (CI95%: 0.610-0.775), a sensitivity of 0.564, and a specificity of 0.787. For PR prediction, ResNet34 achieved an AUC mean of 0.658 (95% CI: 0.573-0.741), a sensitivity of 0.593, and a specificity of 0.734. For HER2 prediction, SEResNext101 achieved an AUC mean of 0.698 (95% CI: 0.560-0.822), a sensitivity of 0.750, and a specificity of 0.625., Conclusion: The current study demonstrated the feasibility of imaging gene-phenotype decoding in breast tumors using MR images and deep learning algorithms with moderate performance., Competing Interests: Declarations. Conflict of Interest: The authors declare no competing interests., (© 2025. The Author(s).)

Purpose: The common approach for organ segmentation in hybrid imaging relies on coregistered CT (CTAC) images. This method, however, presents several limitations in real clinical workflows where mismatch between PET and CT images are very common. Moreover, low-dose CTAC images have poor quality, thus challenging the segmentation task. Recent advances in CT-less PET imaging further highlight the necessity for an effective PET organ segmentation pipeline that does not rely on CT images. Therefore, the goal of this study was to develop a CT-less multitracer PET segmentation framework., Patients and Methods: We collected 2062 PET/CT images from multiple scanners. The patients were injected with either 18F-FDG (1487) or 68Ga-PSMA (575). PET/CT images with any kind of mismatch between PET and CT images were detected through visual assessment and excluded from our study. Multiple organs were delineated on CT components using previously trained in-house developed nnU-Net models. The segmentation masks were resampled to coregistered PET images and used to train 4 different deep learning models using different images as input, including noncorrected PET (PET-NC) and attenuation and scatter-corrected PET (PET-ASC) for 18F-FDG (tasks 1 and 2, respectively using 22 organs) and PET-NC and PET-ASC for 68Ga tracers (tasks 3 and 4, respectively, using 15 organs). The models' performance was evaluated in terms of Dice coefficient, Jaccard index, and segment volume difference., Results: The average Dice coefficient over all organs was 0.81 ± 0.15, 0.82 ± 0.14, 0.77 ± 0.17, and 0.79 ± 0.16 for tasks 1, 2, 3, and 4, respectively. PET-ASC models outperformed PET-NC models (P < 0.05) for most of organs. The highest Dice values were achieved for the brain (0.93 to 0.96 in all 4 tasks), whereas the lowest values were achieved for small organs, such as the adrenal glands. The trained models showed robust performance on dynamic noisy images as well., Conclusions: Deep learning models allow high-performance multiorgan segmentation for 2 popular PET tracers without the use of CT information. These models may tackle the limitations of using CT segmentation in PET/CT image quantification, kinetic modeling, radiomics analysis, dosimetry, or any other tasks that require organ segmentation masks., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.)

Aim: In the clinical setting of acute myocarditis, existing guidelines recommend refraining from moderate-intensity to high-intensity sports for 3-6 months, yet the extent to which these recommendations are implemented by clinicians and followed by patients remains unclear., Methods: From January 2020 to December 2023, consecutive patients with myocarditis according to European Society of Cardiology criteria were prospectively enrolled. Myocarditis was categorised into acute, subacute and non-acute myocarditis. Patients completed a sports questionnaire and sports behaviour was categorised into no sports (NSP), recreational (REC) or competitive sports (COMP)., Results: A total of 165 patients with myocarditis (mean age 50±17 years, 35% women) completed the questionnaire. Overall 73 (44%) patients received sports counselling. A total of 44 (72%) patients engaged in sports (REC+COMP) with acute or subacute myocarditis, received sports counselling with 38 (87%) adhering. Overall COMP patients (all male) received more counselling (11/11; 100%) compared with REC (53/105; 50%) and NSP (9/49; 18%). Of 39 women in the REC group, 14 (36%) received recommendations, whereas of 66 men 39 (59%) received recommendations (p<0.001). Of all patients engaged in sports, 55% received recommendations. Self-reported adherence to recommendations was significantly lower in COMP (73%) compared with REC (92%, p<0.001)., Conclusion: Although only half of the myocarditis patients received counselling regarding sports activity, adherence to these recommendations was generally high but varied by activity level. Women received fewer recommendations overall compared with men. While competitive athletes were counselled more frequently than recreational athletes, they were less likely to adhere to the recommendations., Competing Interests: BB reports a career development grant from the Swiss National Science Foundation. CGebhard was supported by grants from the Swiss National Science Foundation (SNSF), the Olga Mayenfisch Foundation, Switzerland, the OPO Foundation, Switzerland, the Novartis Foundation, Switzerland, the Swissheart Foundation, the Helmut Horten Foundation, Switzerland, the EMDO Foundation, the Iten-Kohaut Foundation, Switzerland, the University Hospital Zurich Foundation, the University of Zurich (UZH) Foundation and the LOOP, Zurich. CGebhard received funding from the Swiss National Science Foundation, Swissheart Foundation, InnoSuisse, CAIM foundation and GAMBIT foundation, outside of the submitted work. CGräni is Editor-in Chief of the International Journal of Cardiovascular Imaging. All other authors report no conflicts., (Copyright © Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.)

Background: Signs and symptoms of myocarditis may vary among men and women., Objectives: This study aimed to analyze sex-specific differences in the presentation and outcomes of patients with suspected myocarditis., Methods: Patients meeting clinical ESC criteria for suspected myocarditis were included from two tertiary centers between 2002 and 2021. Baseline characteristics, cardiac magnetic resonance (CMR), and outcomes (i.e. major adverse cardiovascular events (MACE), including all-cause death, ventricular tachycardia, hospitalization for heart failure, and recurrent myocarditis) in women and men were compared., Results: 776 consecutive patients (mean age 48 ± 16 years, 286 [36.9 %] women) were followed for a median of 3.7 years. Compared to men, women presented more often with severe dyspnea (NYHA III-IV: 25.9 % versus 19.2 % of men; p = 0.029), while chest pain was more frequent in men (39.8 % versus 32.2 % in women; p = 0.037). There was no difference in left ventricular ejection fraction at the time of presentation (women: 48.5 ± 15.4 % versus men: 48.6 ± 15.1 %;p = 0.954). Further, no sex-specific difference in the occurrence of MACE was noted; however, women were more often hospitalized for heart failure than men (women: 9.8 % versus men: 5.3 %, p = 0.018). Accordingly, female sex was independently associated with heart failure hospitalization in an adjusted model (HR: 2.31, 95 % CI:1.25-4.26; p = 0.007). The prognostic value of CMR markers was similar in both sex., Conclusion: Significant sex-specific differences in presentations and imaging findings are found in patients with suspected myocarditis. Female sex is associated with a twofold increase in the risk of heart failure hospitalization, which should be considered in risk stratification., Competing Interests: Declaration of competing interest B. Bernhard report a career development grant from the Swiss National Science Foundation. C. Gebhard was supported by grants from the Swiss National Science Foundation (SNSF), the Olga Mayenfisch Foundation, Switzerland, the OPO Foundation, Switzerland, the Novartis Foundation, Switzerland, the Swissheart Foundation, the Helmut Horten Foundation, Switzerland, the EMDO Foundation, the Iten-Kohaut Foundation, Switzerland, the University Hospital Zurich Foundation, the University of Zurich (UZH) Foundation, and the LOOP, Zurich. R. Kwong has received research support from Bristol Myers Squibb, Alnylam Pharmaceuticals, Epirium Bio, and Bayer AG, outside of the submitted work. C. Gräni has no conflict of interest with regard to the current study. C. Gräni received funding from the Swiss National Science Foundation, Swissheart Foundation, InnoSuisse, CAIM foundation and GAMBIT foundation, outside of the submitted work. C. Gräni is Editor-in Chief of the International Journal of Cardiovascular Imaging. All other authors report no relationships that could be construed as a conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Purpose: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequent concomitant condition in patients with severe aortic stenosis (AS), yet it often remains undetected. This study aims to comprehensively evaluate artificial intelligence-based models developed based on preprocedural and routinely collected data to detect ATTR-CM in patients with severe AS planned for transcatheter aortic valve implantation (TAVI)., Methods: In this prospective, single-center study, consecutive patients with AS were screened with [ 99m Tc]-3,3-diphosphono-1,2-propanodicarboxylic acid ([ 99m Tc]-DPD) for the presence of ATTR-CM. Clinical, laboratory, electrocardiogram, echocardiography, invasive measurements, 4-dimensional cardiac CT (4D-CCT) strain data, and CT-radiomic features were used for machine learning modeling of ATTR-CM detection and for outcome prediction. Feature selection and classifier algorithms were applied in single- and multi-modality classification scenarios. We split the dataset into training (70%) and testing (30%) samples. Performance was assessed using various metrics across 100 random seeds., Results: Out of 263 patients with severe AS (57% males, age 83 ± 4.6years) enrolled, ATTR-CM was confirmed in 27 (10.3%). The lowest performances for detection of concomitant ATTR-CM were observed in invasive measurements and ECG data with area under the curve (AUC) < 0.68. Individual clinical, laboratory, interventional imaging, and CT-radiomics-based features showed moderate performances (AUC 0.70-0.76, sensitivity 0.79-0.82, specificity 0.63-0.72), echocardiography demonstrated good performance (AUC 0.79, sensitivity 0.80, specificity 0.78), and 4D-CT-strain showed the highest performance (AUC 0.85, sensitivity 0.90, specificity 0.74). The multi-modality model (AUC 0.84, sensitivity 0.87, specificity 0.76) did not outperform the model performance based on 4D-CT-strain only data (p-value > 0.05). The multi-modality model adequately discriminated low and high-risk individuals for all-cause mortality at a mean follow-up of 13 months., Conclusion: Artificial intelligence-based models using collected pre-TAVI evaluation data can effectively detect ATTR-CM in patients with severe AS, offering an alternative diagnostic strategy to scintigraphy and myocardial biopsy., Competing Interests: Declarations. Informed consent: Informed consent was obtained from all individual participants included in the study. Consent to participate: All procedures performed in studies involving human participants were in accordance with the ethical standard of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study design was approved by the Bern cantonal ethics committee (ClinicalTrials.gov: NCT04061213), conducted in accordance with the Declaration of Helsinki, and study participants provided written informed consent before any data collection. Competing interest: Dr. Bernhard reports a career development grant from the Swiss National Science Foundation. Dr. Pilgrim reports research grants to the institution from Biotronik, Boston Scientific and Edwards Lifesciences; speaker fees from Biotronik, Boston Scientific, Abbott, and Metronic; Clinical event committee for study sponsored by HighLifeSAS. Dr. Federico Caobelli reports ongoing Grants supports from Siemens Healthineers and from the University of Bern, as well as speaker honoraria from Bracco AG, Siemens AG and Pfizer AG, all for matters not related to the present study. Dr. Dobner reports a research grant for the Bern amyloidosis registry (B-CARE) (NCT04776824) and the ATTR Amyloidosis in Elderly Patients With Aortic Stenosis study (NCT04061213) on behalf of Inselspital Bern from Pfizer, and acknowledges speaker fees and travel grants unrelated to the submitted work from Boehringer Ingelheim, Alnylam and Pfizer. Dr. Windecker reports research, travel or educational grants to the institution from Abbott, Abiomed, Amgen, Astra Zeneca, Bayer, Biotronik, Boehringer Ingelheim, Boston Scientific, Bristol Myers Squibb, Cardinal Health, CardioValve, Corflow Therapeutics, CSL Behring, Daiichi Sankyo, Edwards Lifesciences, Guerbet, InfraRedx, Janssen-Cilag, Johnson & Johnson, Medicure, Medtronic, Merck Sharp & Dohm, Miracor Medical, Novartis, Novo Nordisk, Organon, OrPha Suisse, Pfizer, Polares, Regeneron, Sanofi-Aventis, Servier, Sinomed, Terumo, Vifor, V-Wave. Dr. Windecker serves as advisory board member and/or member of the steering/executive group of trials funded by Abbott, Abiomed, Amgen, Astra Zeneca, Bayer, Boston Scientific, Biotronik, Bristol Myers Squibb, Edwards Lifesciences, Janssen, MedAlliance, Medtronic, Novartis, Polares, Recardio, Sinomed, Terumo, V-Wave and Xeltis with payments to the institution but no personal payments. He is also member of the steering/executive committee group of several investigator-initiated trials that receive funding by industry without impact on his personal remuneration. Dr. Stortecky reports research grants to the institution from Edwards Lifesciences, Medtronic, Boston Scientific and Abbott, as well as personal fees from Boston Scientific, Teleflex and BTG. Dr. Gräni received research funding from the GAMBIT foundation for this work. Dr. Stortecky reports research grants to the institution from Edwards Lifesciences, Medtronic, Boston Scientific and Abbott, as well as personal fees from Boston Scientific, Teleflex and BTG. Dr. Gräni further received funding from the Swiss National Science Foundation and Innosuisse, from the Center for Artificial Intelligence in Medicine Research Project Fund University Bern, outside of the submitted work. Dr. Bakula reports speaker fees and travel grants from Pfizer. Dr. Shiri reports speaker fees and travel grants from Alnylam Pharmaceuticals. Dr. Rominger and Dr. Caobelli are editors of European Journal of Nuclear Medicine and Molecular Imaging. All other authors report no conflicts. The remaining authors have nothing to disclose., (© 2024. The Author(s).)

Anomalous aortic origin of coronary artery can lead to ischemia. Due to the limitations of invasive catheterization dobutamine stress testing, an alternative noninvasive approach is desired. A 65-year-old woman with atypical chest pain was referred for coronary computed tomography angiography. Although coronary artery disease was excluded, a right anomalous aortic origin of coronary artery with an interarterial and intramural course was discovered. The patient underwent invasive coronary angiography with a dobutamine stress test, which revealed a pathologic fractional flow reserve (ie, dobutamine fractional flow reserve) of 0.76 (normal >0.8) and lateral ostial compression in dobutamine intravascular ultrasound. A physical replication, using a patient-specific 3-dimensional-printed phantom was created based on coronary computed tomography angiography and evaluated in a flow loop under the same hemodynamic rest and stress conditions. The 3-dimensional-printed phantom fractional flow reserve was similar with 0.78, and dobutamine intravascular ultrasound showed comparable lateral compression., Competing Interests: This work was supported by the Swiss National Science Foundation (grant number 200871) to Dr Gräni. Dr Gräni has received funding from InnoSuisse, Center for Artificial Intelligence in Medicine University Bern, GAMBIT Foundation, Novartis Foundation for Medical-Biological Research, and Swiss Heart Foundation, outside of the submitted work; and serves as editor-in-chief of The International Journal of Cardiovascular Imaging (Springer). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2025 The Authors.)

Introduction: We propose a fully automated framework to conduct a region-wise image quality assessment (IQA) on whole-body 18 F-FDG PET scans. This framework (1) can be valuable in daily clinical image acquisition procedures to instantly recognize low-quality scans for potential rescanning and/or image reconstruction, and (2) can make a significant impact in dataset collection for the development of artificial intelligence-driven 18 F-FDG PET analysis models by rejecting low-quality images and those presenting with artifacts, toward building clean datasets., Patients and Methods: Two experienced nuclear medicine physicians separately evaluated the quality of 174 18 F-FDG PET images from 87 patients, for each body region, based on a 5-point Likert scale. The body regisons included the following: (1) the head and neck, including the brain, (2) the chest, (3) the chest-abdomen interval (diaphragmatic region), (4) the abdomen, and (5) the pelvis. Intrareader and interreader reproducibility of the quality scores were calculated using 39 randomly selected scans from the dataset. Utilizing a binarized classification, images were dichotomized into low-quality versus high-quality for physician quality scores ≤3 versus >3, respectively. Inputting the 18 F-FDG PET/CT scans, our proposed fully automated framework applies 2 deep learning (DL) models on CT images to perform region identification and whole-body contour extraction (excluding extremities), then classifies PET regions as low and high quality. For classification, 2 mainstream artificial intelligence-driven approaches, including machine learning (ML) from radiomic features and DL, were investigated. All models were trained and evaluated on scores attributed by each physician, and the average of the scores reported. DL and radiomics-ML models were evaluated on the same test dataset. The performance evaluation was carried out on the same test dataset for radiomics-ML and DL models using the area under the curve, accuracy, sensitivity, and specificity and compared using the Delong test with P values <0.05 regarded as statistically significant., Results: In the head and neck, chest, chest-abdomen interval, abdomen, and pelvis regions, the best models achieved area under the curve, accuracy, sensitivity, and specificity of [0.97, 0.95, 0.96, and 0.95], [0.85, 0.82, 0.87, and 0.76], [0.83, 0.76, 0.68, and 0.80], [0.73, 0.72, 0.64, and 0.77], and [0.72, 0.68, 0.70, and 0.67], respectively. In all regions, models revealed highest performance, when developed on the quality scores with higher intrareader reproducibility. Comparison of DL and radiomics-ML models did not show any statistically significant differences, though DL models showed overall improved trends., Conclusions: We developed a fully automated and human-perceptive equivalent model to conduct region-wise IQA over 18 F-FDG PET images. Our analysis emphasizes the necessity of developing separate models for body regions and performing data annotation based on multiple experts' consensus in IQA studies., Competing Interests: Conflicts of interest and sources of funding: none declared. This work was supported by the Swiss National Science Foundation under grant SNSF 320030-231742 and the Private Foundation of Geneva University Hospitals under grant RC-06-01., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Anomalous aortic origin of a coronary artery (AAOCA) is a rare congenital heart disease, potentially leading to myocardial ischemia and adverse cardiac events. As the sole presence of AAOCA does not always imply a revascularization, a detailed anatomical and functional analysis is crucial for clinical decision-making. Currently, invasive coronary angiography is the gold-standard method for a thorough hemodynamic assessment of AAOCA. However, due to its invasive nature, the development of noninvasive diagnostic alternatives is desired., Methods: In the NARCO trial, patients with AAOCA will undergo coronary computed tomography angiography (CCTA) to assess anatomical high-risk features followed by a vessel-based (i.e. invasive measurement with fractional flow reserve and intravascular imaging under a dobutamine-volume challenge) and a myocardium-based (i.e. nuclear imaging) ischemia testing. Comparison of noninvasive and invasive imaging will be performed. Additionally, explorative analysis of post-processing advanced computational fluid dynamics (CFD) and 3D printing will be performed to unravel the pathophysiologic mechanism of myocardial ischemia in AAOCA., Aims: Our primary aim is to define characteristics of anatomical high-risk features (using CCTA) to rule out noninvasively hemodynamically relevant anomalous vessels in AAOCA patients. The secondary aim is to investigate the underlying pathophysiology of AAOCA-related hemodynamic relevance using advanced techniques such as CFD and 3D printing., Conclusions: The NARCO trial will help to optimize AAOCA patient selection for revascularization by improving risk stratification and ruling out hemodynamic relevance noninvasively and, therefore, preventing unnecessary downstream testing and/or costly interventions in patients with AAOCA., Competing Interests: Dr Caobelli has received academic grant support from Siemens Healthineers, Mallinckrodt AG and Tillots AG; and has received speaker honoraria from Siemens Healthineers, Pfizer and Bracco. Dr. Giannopoulos receives grant support from the Promedica Stiftung and the Iten-Kohaut Foundation in collaboration with the USZ Foundation. Dr. Haeberlin has received travel fees/educational grants from Medtronic, Biotronik, Abbott, and Philips/Spectranetics without impact on his personal remuneration. He serves as a proctor for Medtronic. He has received research grants from the Swiss National Science Foundation, the Swiss Innovation agency Innosuisse, the Swiss Heart Foundation, the University of Bern, the University Hospital Bern, the Velux Foundation, the Hasler Foundation, the Swiss Heart Rhythm Foundation, and the Novartis Research Foundation. He is Co-founder and CEO of Act-Inno AG. Dr. Obrist is a member of the advisory board of Novostia.Dr. Räber received research grants to the institution by Abbott Vascular, Biotronik, Boston Scientific, Medis, Sanofi, and Regeneron and consultation/speaker fees by Abbott Vacular, Amgen, AstraZeneca, Canon, Occlutech, and Vifor. Dr. Gräni received funding from the Swiss National Science Foundation, InnoSuisse, Center for Artificial Intelligence in Medicine University Bern, GAMBIT foundation, outside of the submitted work., (© 2024 The Authors.)

Aims: Tafamidis improves clinical outcomes in transthyretin amyloid cardiomyopathy (ATTR-CM), yet how tafamidis affects cardiac structure and function remains poorly described. This study prospectively analysed the effect of tafamidis on 12-month longitudinal changes in cardiac structure and function by cardiac magnetic resonance (CMR) compared with the natural course of disease in an untreated historic control cohort., Methods and Results: ATTR-CM patients underwent CMR at tafamidis initiation and at 12 months. Untreated patients with serial CMRs served as reference to compare biventricular function, global longitudinal strain (GLS), LV mass and extracellular volume fraction (ECV). Thirty-six tafamidis-treated (n = 35; 97.1% male) and 15 untreated patients (n = 14; 93.3% male) with a mean age of 78.3 ± 6.5 and 76.9 ± 6.5, respectively, and comparable baseline characteristics were included. Tafamidis was associated with preserving biventricular function (LVEF (%): 50.5 ± 12 to 50.7 ± 11.5, P = 0.87; RVEF (%): 48.2 ± 10.4 to 48.2 ± 9.4, P = 0.99) and LV-GLS (-9.6 ± 3.2 to -9.9 ± 2.4%; P = 0.595) at 12 months, while a significantly reduced RV-function (50.8 ± 7.3 to 44.2 ± 11.6%, P = 0.028; P (change over time between groups) = 0.032) and numerically worsening LVGLS (-10.9 ± 3.3 to -9.1 ± 2.9%, P = 0.097; P (change over time between groups) = 0.048) was observed without treatment. LV mass significantly declined with tafamidis (184.7 ± 47.7 to 176.5 ± 44.3 g; P = 0.011), yet remained unchanged in untreated patients (163.8 ± 47.5 to 171.2 ± 39.7 g P = 0.356, P (change over time between groups) = 0.027). Irrespective of tafamidis, ECV and native T1-mapping did not change significantly from baseline to 12-month follow-up (P > 0.05)., Conclusions: Compared with untreated ATTR-CM patients, initiation of tafamidis preserved CMR-measured biventricular function and reduced LV mass at 12 months. ECV and native T1-mapping did not change significantly comparable to baseline in both groups., (© 2024 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Purpose: Non-small cell lung cancer is the most common subtype of lung cancer. Patient survival prediction using machine learning (ML) and radiomics analysis proved to provide promising outcomes. However, most studies reported in the literature focused on information extracted from malignant lesions. This study aims to explore the relevance and additional value of information extracted from healthy organs in addition to tumoral tissue using ML algorithms., Patients and Methods: This study included PET/CT images of 154 patients collected from available online databases. The gross tumor volume and 33 volumes of interest defined on healthy organs were segmented using nnU-Net deep learning-based segmentation. Subsequently, 107 radiomic features were extracted from PET and CT images (Organomics). Clinical information was combined with PET and CT radiomics from organs and gross tumor volumes considering 19 different combinations of inputs. Finally, different feature selection (FS; 5 methods) and ML (6 algorithms) algorithms were tested in a 3-fold data split cross-validation scheme. The performance of the models was quantified in terms of the concordance index (C-index) metric., Results: For an input combination of all radiomics information, most of the selected features belonged to PET Organomics and CT Organomics. The highest C-index (0.68) was achieved using univariate C-index FS method and random survival forest ML model using CT Organomics + PET Organomics as input as well as minimum depth FS method and CoxPH ML model using PET Organomics as input. Considering all 17 combinations with C-index higher than 0.65, Organomics from PET or CT images were used as input in 16 of them., Conclusions: The selected features and C-indices demonstrated that the additional information extracted from healthy organs of both PET and CT imaging modalities improved the ML performance. Organomics could be a step toward exploiting the whole information available from multimodality medical images, contributing to the emerging field of digital twins in health care., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Importance: Accurate risk stratification of nonischemic dilated cardiomyopathy (NIDCM) remains challenging., Objective: To evaluate the association of cardiac magnetic resonance (CMR) imaging-derived measurements with clinical outcomes in NIDCM., Data Sources: MEDLINE, Embase, Cochrane Library, and Web of Science Core Collection databases were systematically searched for articles from January 2005 to April 2023., Study Selection: Prospective and retrospective nonrandomized diagnostic studies reporting on the association between CMR imaging-derived measurements and adverse clinical outcomes in NIDCM were deemed eligible., Data Extraction and Synthesis: Prespecified items related to patient population, CMR imaging measurements, and clinical outcomes were extracted at the study level by 2 independent reviewers. Random-effects models were fitted using restricted maximum likelihood estimation and the method of Hartung, Knapp, Sidik, and Jonkman., Main Outcomes and Measures: All-cause mortality, cardiovascular mortality, arrhythmic events, heart failure events, and major adverse cardiac events (MACE)., Results: A total of 103 studies including 29 687 patients with NIDCM were analyzed. Late gadolinium enhancement (LGE) presence and extent (per 1%) were associated with higher all-cause mortality (hazard ratio [HR], 1.81 [95% CI, 1.60-2.04]; P < .001 and HR, 1.07 [95% CI, 1.02-1.12]; P = .02, respectively), cardiovascular mortality (HR, 2.43 [95% CI, 2.13-2.78]; P < .001 and HR, 1.15 [95% CI, 1.07-1.24]; P = .01), arrhythmic events (HR, 2.69 [95% CI, 2.20-3.30]; P < .001 and HR, 1.07 [95% CI, 1.03-1.12]; P = .004) and heart failure events (HR, 1.98 [95% CI, 1.73-2.27]; P < .001 and HR, 1.06 [95% CI, 1.01-1.10]; P = .02). Left ventricular ejection fraction (LVEF) (per 1%) was not associated with all-cause mortality (HR, 0.99 [95% CI, 0.97-1.02]; P = .47), cardiovascular mortality (HR, 0.97 [95% CI, 0.94-1.00]; P = .05), or arrhythmic outcomes (HR, 0.99 [95% CI, 0.97-1.01]; P = .34). Lower risks for heart failure events (HR, 0.97 [95% CI, 0.95-0.98]; P = .002) and MACE (HR, 0.98 [95% CI, 0.96-0.99]; P < .001) were observed with higher LVEF. Higher native T1 relaxation times (per 10 ms) were associated with arrhythmic events (HR, 1.07 [95% CI, 1.01-1.14]; P = .04) and MACE (HR, 1.06 [95% CI, 1.01-1.11]; P = .03). Global longitudinal strain (GLS) (per 1%) was not associated with heart failure events (HR, 1.06 [95% CI, 0.95-1.18]; P = .15) or MACE (HR, 1.03 [95% CI, 0.94-1.14]; P = .43). Limited data precluded definitive analysis for native T1 relaxation times, GLS, and extracellular volume fraction (ECV) with respect to mortality outcomes., Conclusion: The presence and extent of LGE were associated with various adverse clinical outcomes, whereas LVEF was not significantly associated with mortality and arrhythmic end points in NIDCM. Risk stratification using native T1 relaxation times, extracellular volume fraction, and global longitudinal strain requires further evaluation.

Manual segmentation poses a time-consuming challenge for disease quantification, therapy evaluation, treatment planning, and outcome prediction. Convolutional neural networks (CNNs) hold promise in accurately identifying tumor locations and boundaries in PET scans. However, a major hurdle is the extensive amount of supervised and annotated data necessary for training. To overcome this limitation, this study explores semi-supervised approaches utilizing unlabeled data, specifically focusing on PET images of diffuse large B-cell lymphoma (DLBCL) and primary mediastinal large B-cell lymphoma (PMBCL) obtained from two centers. We considered 2-[ 18 F]FDG PET images of 292 patients PMBCL (n = 104) and DLBCL (n = 188) (n = 232 for training and validation, and n = 60 for external testing). We harnessed classical wisdom embedded in traditional segmentation methods, such as the fuzzy clustering loss function (FCM), to tailor the training strategy for a 3D U-Net model, incorporating both supervised and unsupervised learning approaches. Various supervision levels were explored, including fully supervised methods with labeled FCM and unified focal/Dice loss, unsupervised methods with robust FCM (RFCM) and Mumford-Shah (MS) loss, and semi-supervised methods combining FCM with supervised Dice loss (MS + Dice) or labeled FCM (RFCM + FCM). The unified loss function yielded higher Dice scores (0.73 ± 0.11; 95% CI 0.67-0.8) than Dice loss (p value < 0.01). Among the semi-supervised approaches, RFCM + αFCM (α = 0.3) showed the best performance, with Dice score of 0.68 ± 0.10 (95% CI 0.45-0.77), outperforming MS + αDice for any supervision level (any α) (p < 0.01). Another semi-supervised approach with MS + αDice (α = 0.2) achieved Dice score of 0.59 ± 0.09 (95% CI 0.44-0.76) surpassing other supervision levels (p < 0.01). Given the time-consuming nature of manual delineations and the inconsistencies they may introduce, semi-supervised approaches hold promise for automating medical imaging segmentation workflows., (© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)

This study investigated the impact of ComBat harmonization on the reproducibility of radiomic features extracted from magnetic resonance images (MRI) acquired on different scanners, using various data acquisition parameters and multiple image pre-processing techniques using a dedicated MRI phantom. Four scanners were used to acquire an MRI of a nonanatomic phantom as part of the TCIA RIDER database. In fast spin-echo inversion recovery (IR) sequences, several inversion durations were employed, including 50, 100, 250, 500, 750, 1000, 1500, 2000, 2500, and 3000 ms. In addition, a 3D fast spoiled gradient recalled echo (FSPGR) sequence was used to investigate several flip angles (FA): 2, 5, 10, 15, 20, 25, and 30 degrees. Nineteen phantom compartments were manually segmented. Different approaches were used to pre-process each image: Bin discretization, Wavelet filter, Laplacian of Gaussian, logarithm, square, square root, and gradient. Overall, 92 first-, second-, and higher-order statistical radiomic features were extracted. ComBat harmonization was also applied to the extracted radiomic features. Finally, the Intraclass Correlation Coefficient (ICC) and Kruskal-Wallis's (KW) tests were implemented to assess the robustness of radiomic features. The number of non-significant features in the KW test ranged between 0-5 and 29-74 for various scanners, 31-91 and 37-92 for three times tests, 0-33 to 34-90 for FAs, and 3-68 to 65-89 for IRs before and after ComBat harmonization, with different image pre-processing techniques, respectively. The number of features with ICC over 90% ranged between 0-8 and 6-60 for various scanners, 11-75 and 17-80 for three times tests, 3-83 to 9-84 for FAs, and 3-49 to 3-63 for IRs before and after ComBat harmonization, with different image pre-processing techniques, respectively. The use of various scanners, IRs, and FAs has a great impact on radiomic features. However, the majority of scanner-robust features is also robust to IR and FA. Among the effective parameters in MR images, several tests in one scanner have a negligible impact on radiomic features. Different scanners and acquisition parameters using various image pre-processing might affect radiomic features to a large extent. ComBat harmonization might significantly impact the reproducibility of MRI radiomic features., (© 2024. The Author(s).)

We introduce an innovative, simple, effective segmentation-free approach for survival analysis of head and neck cancer (HNC) patients from PET/CT images. By harnessing deep learning-based feature extraction techniques and multi-angle maximum intensity projections (MA-MIPs) applied to Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images, our proposed method eliminates the need for manual segmentations of regions-of-interest (ROIs) such as primary tumors and involved lymph nodes. Instead, a state-of-the-art object detection model is trained utilizing the CT images to perform automatic cropping of the head and neck anatomical area, instead of only the lesions or involved lymph nodes on the PET volumes. A pre-trained deep convolutional neural network backbone is then utilized to extract deep features from MA-MIPs obtained from 72 multi-angel axial rotations of the cropped PET volumes. These deep features extracted from multiple projection views of the PET volumes are then aggregated and fused, and employed to perform recurrence-free survival analysis on a cohort of 489 HNC patients. The proposed approach outperforms the best performing method on the target dataset for the task of recurrence-free survival analysis. By circumventing the manual delineation of the malignancies on the FDG PET-CT images, our approach eliminates the dependency on subjective interpretations and highly enhances the reproducibility of the proposed survival analysis method. The code for this work is publicly released.

Background: Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi-institutional cohort of patients with COVID-19 using a DL-based model., Purpose: This study aimed to evaluate the performance of deep privacy-preserving federated learning (DPFL) in predicting COVID-19 outcomes using chest CT images., Methods: After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold-out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold-out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences., Results: The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79-0.85) and (95% CI: 0.77-0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models (p-value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501., Conclusion: The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi-institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process., (© 2024 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: This study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC)., Methods: We included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC. Nine segmentation methods were applied to each image, including manual delineation, K-means (KM), watershed, fuzzy-C-mean, region-growing, local active contour (LAC), and iterative thresholding (IT) with 40, 45, and 50% thresholds. Diverse image discretizations, both without a filter and with different wavelet decompositions, were applied to PET images. Overall, 6741 radiomic features were extracted from each image (749 radiomic features from each segmented area). Non-parametric empirical Bayes (NPEB) ComBat harmonization was used to harmonize the features. Linear Support Vector Classifier (LinearSVC) with L1 regularization For feature selection and Support Vector Machine classifier (SVM) with fivefold nested cross-validation was performed using StratifiedKFold with 'n&#95;splits' set to 5 to predict recurrence in NSCLC patients and assess the impact of ComBat harmonization on the outcome., Results: From 749 extracted radiomic features, 206 (27%) and 389 (51%) features showed excellent reliability (ICC ≥ 0.90) against segmentation method variation before and after NPEB ComBat harmonization, respectively. Among all, 39 features demonstrated poor reliability, which declined to 10 after ComBat harmonization. The 64 fixed bin widths (without any filter) and wavelets (LLL)-based radiomic features set achieved the best performance in terms of robustness against diverse segmentation techniques before and after ComBat harmonization. The first-order and GLRLM and also first-order and NGTDM feature families showed the largest number of robust features before and after ComBat harmonization, respectively. In terms of predicting recurrence in NSCLC, our findings indicate that using ComBat harmonization can significantly enhance machine learning outcomes, particularly improving the accuracy of watershed segmentation, which initially had fewer reliable features than manual contouring. Following the application of ComBat harmonization, the majority of cases saw substantial increase in sensitivity and specificity., Conclusion: Radiomic features are vulnerable to different segmentation methods. ComBat harmonization might be considered a solution to overcome the poor reliability of radiomic features., (© 2024. The Author(s).)

Purpose: Our study aimed to determine whether 4D cardiac computed tomography (4DCCT) based quantitative myocardial analysis may improve risk stratification and can predict reverse remodeling (RRM) and mortality after transcatheter aortic valve implantation (TAVI)., Methods: Consecutive patients undergoing clinically indicated 4DCCT prior to TAVI were prospectively enrolled. 4DCCT-derived left- (LV) and right ventricular (RV), and left atrial (LA) dimensions, mass, ejection fraction (EF) and myocardial strain were evaluated to predict RRM and survival. RRM was defined by either relative increase in LVEF by 5% or relative decline in LV end diastolic diameter (LVEDD) by 5% assessed by transthoracic echocardiography prior TAVI, at discharge, and at 12-month follow-up compared to baseline prior to TAVI., Results: Among 608 patients included in this study (55 % males, age 81 ± 6.6 years), RRM was observed in 279 (54 %) of 519 patients at discharge and in 218 (48 %) of 453 patients at 12-month echocardiography. While no CCT based measurements predicted RRM at discharge, CCT based LV mass index and LVEF independently predicted RRM at 12-month (OR adj  = 1.012; 95 %CI:1.001-1.024; p = 0.046 and OR adj  = 0.969; 95 %CI:0.943-0.996; p = 0.024, respectively). The most pronounced changes in LVEF and LVEDD were observed in patients with impaired LV function at baseline. In multivariable analysis age (HR adj  = 1.037; 95 %CI:1.005-1.070; p = 0.022) and CCT-based LVEF (HR adj  = 0.972; 95 %CI:0.945-0.999; p = 0.048) and LAEF (HR adj  = 0.982; 95 %CI:0.968-0.996; p = 0.011) independently predicted survival., Conclusion: Comprehensive myocardial functional information derived from routine 4DCCT in patients with severe aortic stenosis undergoing TAVI could predict reverse remodeling and clinical outcomes at 12-month following TAVI., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Bernhard reports a career development grant from the Swiss National Science Foundation. Dr. Dobner reports travel grants from Pfizer and Alnylam, speaking fees from Boehringer Ingelheim. D. Heg is employed by the CTU Bern, University of Bern, which has a staff policy of not accepting honoraria or consultancy fees. However, CTU Bern is involved in design, conduct, or analysis of clinical studies funded by not-for-profit and for-profit organizations. In particular, pharmaceutical and medical device companies provide direct funding to some of these studies. Dr. Stortecky reports research grants to the institution from Edwards Lifesciences, Medtronic, Boston Scientific and Abbott, as well as personal fees from Boston Scientific, Teleflex and BTG. Dr. Pilgrim reports research grants to the institution from Biotronik, Boston Scientific and Edwards Lifesciences; speaker fees from Biotronik, Boston Scientific, Abbott, and Medtronic; Clinical event committee for study sponsored by HighLifeSAS. Stephan Windecker reports research, travel or educational grants to the institution without personal remuneration from Abbott, Abiomed, Amgen, Astra Zeneca, Bayer, Braun, Biotronik, Boehringer Ingelheim, Boston Scientific, Bristol Myers Squibb, Cardinal Health, CardioValve, Cordis Medical, Corflow Therapeutics, CSL Behring, Daiichi Sankyo, Edwards Lifesciences, Farapulse Inc. Fumedica, Guerbet, Idorsia, Inari Medical, InfraRedx, Janssen-Cilag, Johnson & Johnson, Medalliance, Medicure, Medtronic, Merck Sharp & Dohm, Miracor Medical, Novartis, Novo Nordisk, Organon, OrPha Suisse, Pharming Tech. Pfizer, Polares, Regeneron, Sanofi-Aventis, Servier, Sinomed, Terumo, Vifor, V-Wave. Stephan Windecker served as advisory board member and/or member of the steering/executive group of trials funded by Abbott, Abiomed, Amgen, Astra Zeneca, Bayer, Boston Scientific, Biotronik, Bristol Myers Squibb, Edwards Lifesciences, MedAlliance, Medtronic, Novartis, Polares, Recardio, Sinomed, Terumo, and V-Wave with payments to the institution but no personal payments. He is also member of the steering/executive committee group of several investigator-initiated trials that receive funding by industry without impact on his personal remuneration. Dr. Gräni further received funding from the Swiss National Science Foundation, InnoSuisse, from the Center for Artificial Intelligence in Medicine Research Project Fund University Bern and Gambit foundation, outside of the submitted work. All other authors report no conflicts., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is usually missing on public datasets and not standardized in the clinic even in the same region and language. This is a barrier to effective use of available CECT images in clinical research., Purpose: The aim of this study is to detect contrast media injection phase from CT images by means of organ segmentation and machine learning algorithms., Methods: A total number of 2509 CT images split into four subsets of non-contrast (class #0), arterial (class #1), venous (class #2), and delayed (class #3) after contrast media injection were collected from two CT scanners. Seven organs including the liver, spleen, heart, kidneys, lungs, urinary bladder, and aorta along with body contour masks were generated by pre-trained deep learning algorithms. Subsequently, five first-order statistical features including average, standard deviation, 10, 50, and 90 percentiles extracted from the above-mentioned masks were fed to machine learning models after feature selection and reduction to classify the CT images in one of four above mentioned classes. A 10-fold data split strategy was followed. The performance of our methodology was evaluated in terms of classification accuracy metrics., Results: The best performance was achieved by Boruta feature selection and RF model with average area under the curve of more than 0.999 and accuracy of 0.9936 averaged over four classes and 10 folds. Boruta feature selection selected all predictor features. The lowest classification was observed for class #2 (0.9888), which is already an excellent result. In the 10-fold strategy, only 33 cases from 2509 cases (∼1.4%) were misclassified. The performance over all folds was consistent., Conclusions: We developed a fast, accurate, reliable, and explainable methodology to classify contrast media phases which may be useful in data curation and annotation in big online datasets or local datasets with non-standard or no series description. Our model containing two steps of deep learning and machine learning may help to exploit available datasets more effectively., (© 2024 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: Total metabolic tumor volume (TMTV) segmentation has significant value enabling quantitative imaging biomarkers for lymphoma management. In this work, we tackle the challenging task of automated tumor delineation in lymphoma from PET/CT scans using a cascaded approach., Methods: Our study included 1418 2-[ 18 F]FDG PET/CT scans from four different centers. The dataset was divided into 900 scans for development/validation/testing phases and 518 for multi-center external testing. The former consisted of 450 lymphoma, lung cancer, and melanoma scans, along with 450 negative scans, while the latter consisted of lymphoma patients from different centers with diffuse large B cell, primary mediastinal large B cell, and classic Hodgkin lymphoma cases. Our approach involves resampling PET/CT images into different voxel sizes in the first step, followed by training multi-resolution 3D U-Nets on each resampled dataset using a fivefold cross-validation scheme. The models trained on different data splits were ensemble. After applying soft voting to the predicted masks, in the second step, we input the probability-averaged predictions, along with the input imaging data, into another 3D U-Net. Models were trained with semi-supervised loss. We additionally considered the effectiveness of using test time augmentation (TTA) to improve the segmentation performance after training. In addition to quantitative analysis including Dice score (DSC) and TMTV comparisons, the qualitative evaluation was also conducted by nuclear medicine physicians., Results: Our cascaded soft-voting guided approach resulted in performance with an average DSC of 0.68 ± 0.12 for the internal test data from developmental dataset, and an average DSC of 0.66 ± 0.18 on the multi-site external data (n = 518), significantly outperforming (p < 0.001) state-of-the-art (SOTA) approaches including nnU-Net and SWIN UNETR. While TTA yielded enhanced performance gains for some of the comparator methods, its impact on our cascaded approach was found to be negligible (DSC: 0.66 ± 0.16). Our approach reliably quantified TMTV, with a correlation of 0.89 with the ground truth (p < 0.001). Furthermore, in terms of visual assessment, concordance between quantitative evaluations and clinician feedback was observed in the majority of cases. The average relative error (ARE) and the absolute error (AE) in TMTV prediction on external multi-centric dataset were ARE = 0.43 ± 0.54 and AE = 157.32 ± 378.12 (mL) for all the external test data (n = 518), and ARE = 0.30 ± 0.22 and AE = 82.05 ± 99.78 (mL) when the 10% outliers (n = 53) were excluded., Conclusion: TMTV-Net demonstrates strong performance and generalizability in TMTV segmentation across multi-site external datasets, encompassing various lymphoma subtypes. A negligible reduction of 2% in overall performance during testing on external data highlights robust model generalizability across different centers and cancer types, likely attributable to its training with resampled inputs. Our model is publicly available, allowing easy multi-site evaluation and generalizability analysis on datasets from different institutions., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)