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Background: Pathology grading is an essential step for the treatment and evaluation of the prognosis in patients with clear cell renal cell carcinoma (ccRCC)., Purpose: To investigate the utility of texture analysis in evaluating Fuhrman grades of renal tumors in patients with Von Hippel-Lindau (VHL)-associated ccRCC, aiming to improve non-invasive diagnosis and personalized treatment., Study Type: Retrospective analysis of a prospectively maintained cohort., Population: One hundred and thirty-six patients, 84 (61%) males and 52 (39%) females with pathology-proven ccRCC with a mean age of 52.8 ± 12.7 from 2010 to 2023., Field Strength and Sequences: 1.5 and 3 T MRIs. Segmentations were performed on the T1-weighted 3-minute delayed sequence and then registered on pre-contrast, T1-weighted arterial and venous sequences., Assessment: A total of 404 lesions, 345 low-grade tumors, and 59 high-grade tumors were segmented using ITK-SNAP on a T1-weighted 3-minute delayed sequence of MRI. Radiomics features were extracted from pre-contrast, T1-weighted arterial, venous, and delayed post-contrast sequences. Preprocessing techniques were employed to address class imbalances. Features were then rescaled to normalize the numeric values. We developed a stacked model combining random forest and XGBoost to assess tumor grades using radiomics signatures., Statistical Tests: The model's performance was evaluated using positive predictive value (PPV), sensitivity, F1 score, area under the curve of receiver operating characteristic curve, and Matthews correlation coefficient. Using Monte Carlo technique, the average performance of 100 benchmarks of 85% train and 15% test was reported., Results: The best model displayed an accuracy of 0.79. For low-grade tumor detection, a sensitivity of 0.79, a PPV of 0.95, and an F1 score of 0.86 were obtained. For high-grade tumor detection, a sensitivity of 0.78, PPV of 0.39, and F1 score of 0.52 were reported., Data Conclusion: Radiomics analysis shows promise in classifying pathology grades non-invasively for patients with VHL-associated ccRCC, potentially leading to better diagnosis and personalized treatment., Level of Evidence: 1 TECHNICAL EFFICACY: Stage 2., (Published 2024. This article is a U.S. Government work and is in the public domain in the USA.)

In order to assess homeostatic mechanisms in the lung after COVID-19, changes in the protein signature of bronchoalveolar lavage from 45 patients with mild to moderate disease at three phases (acute, recovery, and convalescent) are evaluated over a year. During the acute phase, inflamed and uninflamed phenotypes are characterized by the expression of tissue repair and host defense response molecules. With recovery, inflammatory and fibrogenic mediators decline and clinical symptoms abate. However, at 9 months, quantified radiographic abnormalities resolve in the majority of patients, and yet compared to healthy persons, all showed ongoing activation of cellular repair processes and depression of the renin-kallikrein-kinin, coagulation, and complement systems. This dissociation of prolonged reparative processes from symptom and radiographic resolution suggests that occult ongoing disruption of the lung proteome is underrecognized and may be relevant to recovery from other serious viral pneumonias., Competing Interests: Declaration of interests The authors declare no competing interests., (Published by Elsevier Inc.)

Rationale and Objectives: Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists., Materials and Methods: This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36)., Results: In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1-31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0-0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0-3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%)., Conclusion: Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Bradford J. Wood: Principal investigator on cooperative research and development agreement (CRADA) between National Institutes of Health (NIH) and Philips and CRADAs with industry partners unrelated to this work; travel support related to CRADAs; royalties from NIH related to Philips licensing agreement; patents planned, issued, or pending. Peter L. Choyke: Receives payment from royalties paid to the U.S. government for patents on MRI US fusion biopsy licensed to Philips Medical. Peter A. Pinto: Institutional CRADA with Philips; royalties from NIH related to Philips licensing agreement; NIH-related patents planned, issued, or pending (U.S. patent nos. 8 447 384 and 10 215 830). Baris Turkbey: CRADAs with NVIDIA and Philips; royalties from NIH; patents planned, issued, or pending in the field of artificial intelligence. Dong Yang, Ziyue Xu, Jesse Tetreault, Daguang Xu: employee of NVIDIA Corporation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)

Rationale and Objectives: Prostate MRI quality is essential in guiding prostate biopsies. However, assessment of MRI quality is subjective with variation. Quality degradation sources exert varying impacts based on the sequence under consideration, such as T2W versus DWI. As a result, employing sequence-specific techniques for quality assessment could yield more advantageous outcomes. This study aims to develop an AI tool that offers a more consistent evaluation of T2W prostate MRI quality, efficiently identifying suboptimal scans while minimizing user bias., Materials and Methods: This retrospective study included 1046 patients from three cohorts (ProstateX [n = 347], All-comer in-house [n = 602], enriched bad-quality MRI in-house [n = 97]) scanned between January 2011 and May 2022. An expert reader assigned T2W MRIs a quality score. A train-validation-test split of 70:15:15 was applied, ensuring equal distribution of MRI scanners and protocols across all partitions. T2W quality AI classification model was based on 3D DenseNet121 architecture using MONAI framework. In addition to multiclassification, binary classification was utilized (Classes 0/1 vs. 2). A score of 0 was given to scans considered non-diagnostic or unusable, a score of 1 was given to those with acceptable diagnostic quality with some usability but with some quality distortions present, and a score of 2 was given to those considered optimal diagnostic quality and usability. Partial occlusion sensitivity maps were generated for anatomical correlation. Three body radiologists assessed reproducibility within a subgroup of 60 test cases using weighted Cohen Kappa., Results: The best validation multiclass accuracy of 77.1% (121/157) was achieved during training. In the test dataset, multiclassification accuracy was 73.9% (116/157), whereas binary accuracy was 84.7% (133/157). Sub-class sensitivity for binary quality distortion classification for class 0 was 100% (18/18), and sub-class specificity for T2W classification of absence/minimal quality distortions for class 2 was 90.5% (95/105). All three readers showed moderate to substantial agreement with ground truth (R1-R3 κ = 0.588, κ = 0.649, κ = 0.487, respectively), moderate to substantial agreement with each other (R1-R2 κ = 0.599, R1-R3 κ = 0.612, R2-R3 κ = 0.685), fair to moderate agreement with AI (R1-R3 κ = 0.445, κ = 0.410, κ = 0.292, respectively). AI showed substantial agreement with ground truth (κ = 0.704). 3D quality heatmap evaluation revealed that the most critical non-diagnostic quality imaging features from an AI perspective related to obscuration of the rectoprostatic space (94.4%, 17/18)., Conclusion: The 3D AI model can assess T2W prostate MRI quality with moderate accuracy and translate whole sequence-level classification labels into 3D voxel-level quality heatmaps for interpretation. Image quality has a significant downstream impact on ruling out clinically significant cancers. AI may be able to help with reproducible identification of MRI sequences requiring re-acquisition with explainability., 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., (Published by Elsevier Inc.)

Introduction: Accurate diagnosis and treatment of kidney tumors greatly benefit from automated solutions for detection and classification on MRI. In this study, we explore the application of a deep learning algorithm, YOLOv7, for detecting kidney tumors on contrast-enhanced MRI., Material and Methods: We assessed the performance of YOLOv7 tumor detection on excretory phase MRIs in a large institutional cohort of patients with RCC. Tumors were segmented on MRI using ITK-SNAP and converted to bounding boxes. The cohort was randomly divided into ten benchmarks for training and testing the YOLOv7 algorithm. The model was evaluated using both 2-dimensional and a novel in-house developed 2.5-dimensional approach. Performance measures included F1, Positive Predictive Value (PPV), Sensitivity, F1 curve, PPV-Sensitivity curve, Intersection over Union (IoU), and mean average PPV (mAP)., Results: A total of 326 patients with 1034 tumors with 7 different pathologies were analyzed across ten benchmarks. The average 2D evaluation results were as follows: Positive Predictive Value (PPV) of 0.69 ± 0.05, sensitivity of 0.39 ± 0.02, and F1 score of 0.43 ± 0.03. For the 2.5D evaluation, the average results included a PPV of 0.72 ± 0.06, sensitivity of 0.61 ± 0.06, and F1 score of 0.66 ± 0.04. The best model performance demonstrated a 2.5D PPV of 0.75, sensitivity of 0.69, and F1 score of 0.72., Conclusion: Using computer vision for tumor identification is a cutting-edge and rapidly expanding subject. In this work, we showed that YOLOv7 can be utilized in the detection of kidney cancers., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Introduction: Classification of clear cell renal cell carcinoma (ccRCC) growth rates in patients with Von Hippel-Lindau (VHL) syndrome has several ramifications for tumor monitoring and surgical planning. Using two separate machine-learning algorithms, we sought to produce models to predict ccRCC growth rate classes based on qualitative MRI-derived characteristics., Material and Methods: We used a prospectively maintained database of patients with VHL who underwent surgical resection for ccRCC between January 2015 and June 2022. We employed a threshold growth rate of 0.5 cm per year to categorize ccRCC tumors into two distinct groups-'slow-growing' and 'fast-growing'. Utilizing a questionnaire of qualitative imaging features, two radiologists assessed each lesion on different MRI sequences. Two machine-learning models, a stacked ensemble technique and a decision tree algorithm, were used to predict the tumor growth rate classes. Positive predictive value (PPV), sensitivity, and F1-score were used to evaluate the performance of the models., Results: This study comprises 55 patients with VHL with 128 ccRCC tumors. Patients' median age was 48 years, and 28 patients were males. Each patient had an average of two tumors, with a median size of 2.1 cm and a median growth rate of 0.35 cm/year. The overall performance of the stacked and DT model had 0.77 ± 0.05 and 0.71 ± 0.06 accuracies, respectively. The best stacked model achieved a PPV of 0.92, a sensitivity of 0.91, and an F1-score of 0.90., Conclusion: This study provides valuable insight into the potential of machine-learning analysis for the determination of renal tumor growth rate in patients with VHL. This finding could be utilized as an assistive tool for the individualized screening and follow-up of this population., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Introduction: This study explores the use of the latest You Only Look Once (YOLO V7) object detection method to enhance kidney detection in medical imaging by training and testing a modified YOLO V7 on medical image formats., Methods: Study includes 878 patients with various subtypes of renal cell carcinoma (RCC) and 206 patients with normal kidneys. A total of 5657 MRI scans for 1084 patients were retrieved. 326 patients with 1034 tumors recruited from a retrospective maintained database, and bounding boxes were drawn around their tumors. A primary model was trained on 80% of annotated cases, with 20% saved for testing (primary test set). The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model. Ten benchmark training sets were created with generated coordinates on not-segmented patients. The final model used to predict the kidney in the primary test set. We reported the positive predictive value (PPV), sensitivity, and mean average precision (mAP)., Results: The primary training set showed an average PPV of 0.94 ± 0.01, sensitivity of 0.87 ± 0.04, and mAP of 0.91 ± 0.02. The best primary model yielded a PPV of 0.97, sensitivity of 0.92, and mAP of 0.95. The final model demonstrated an average PPV of 0.95 ± 0.03, sensitivity of 0.98 ± 0.004, and mAP of 0.95 ± 0.01., Conclusion: Using a semi-supervised approach with a medical image library, we developed a high-performing model for kidney detection. Further external validation is required to assess the model's generalizability.

Objective: The aim of this study was to characterize the distribution of skeletal involvement in Erdheim-Chester disease (ECD) by using radiography, computed tomography (CT), 18 F-FDG positron emission tomography/computed tomography (PET/CT), and bone scans, as well as looking for associations with the BRAF V600E mutation., Material and Methods: Prospective study of 50 consecutive patients with biopsy-confirmed ECD who had radiographs, CT, 18 F-FDG PET/CT, and Tc-99m MDP bone scans. At least two experienced radiologists with expertise in the relevant imaging studies analyzed the images. Summary statistics were expressed as the frequency with percentages for categorical data. Fisher's exact test, as well as odds ratios (OR) with 95 % confidence intervals (CI), were used to link imaging findings to BRAF V600E mutation. The probability for co-occurrence of bone involvement at different locations was calculated and graphed as a heat map., Results: All 50 cases revealed skeletal involvement at different regions of the skeleton. The BRAF V600E mutation, which was found in 24 patients, was correlated with femoral and tibial involvement on 18 F-FDG PET/CT and bone scan. The appearance of changes on the femoral, tibial, fibular, and humeral involvement showed correlation with each other based on heat maps of skeletal involvement on CT., Conclusion: This study reports the distribution of skeletal involvement in a cohort of patients with ECD. CT is able to detect the majority of ECD skeletal involvement. Considering the complementary nature of information from different modalities, imaging of ECD skeletal involvement is optimized by using a multi-modality strategy., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2023. Published by Elsevier Inc.)

Objective: To determine the reliability of an MRI-based qualitative kidney imaging surveillance scoring system (KISSS) and assess which imaging features predict growth rate (GR) of renal tumors in patients with VHL., Materials and Methods: We identified 55 patients with VHL with 128 renal tumors who underwent intervention from 2015 to 2020 at the National Cancer Institute. All patients had 2 preoperative MRIs at least 3 months apart. Two fellowship-trained radiologists scored each tumor on location and MR-sequence-specific imaging parameters from the earlier MRI. Weighted kappa was used to determine the degree of agreement between radiologists for each parameter. GR was calculated as the difference in maximum tumor dimension over time (cm/year). Differences in mean growth rate (MGR) within categories of each imaging variable were assessed by ANOVA., Results: Apart from tumor margin and renal sinus, reliability was at least moderate (K > 0.40) for imaging parameters. Median initial tumor size was 2.1 cm, with average follow-up of 1.2 years. Tumor MGR was 0.42 cm/year. T2 hypointense, mixed/predominantly solid, and high restricted diffusion tumors grew faster. When comparing different combinations of these variables, the model with the lowest mean error among both radiologists utilized only solid/cystic and restricted diffusion features., Conclusions: We demonstrate a novel MR-based scoring system (KISSS) that has good precision with minimal training and can be applied to other qualitative radiology studies. A subset of imaging variables (T2 intensity; restricted diffusion; and solid/cystic) were independently associated with growth rate in VHL renal tumors, with the combination of the latter two most optimal. Additional validation, including in sporadic RCC population, is warranted., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Rationale and Objectives: Metastatic epidural masses are an important radiological finding. The purpose of this study is to determine factors associated with non-reporting of thoracolumbar epidural metastases on body CT., Materials and Methods: In a study population of 166 patients from a single institution over a 12-year period, 293 body CT examinations were identified which were performed within 30 days before or after a spine MRI diagnosis of epidural metastasis. Associations were sought between patient diagnosis, CT examination characteristics, reporting radiologist (n = 17), and lesion characteristics with respect to whether an epidural metastasis was reported on CT., Results: In retrospective consensus review comprised of 3 radiologists, epidural metastases reported on spine MRI were clearly visible in 80.5% (236/293) of body CT examinations, however 65.3% (154/236) of the body CT reports omitted reporting their presence, even in cases where there was a preceding MRI diagnosis within 30 days (65.4%, 74/113). The identity of the reporting radiologist was statistically significantly associated with the accurate diagnostic reporting of epidural metastasis on body CT (p = 0.04). The only lesion features which were statistically significantly associated with CT reporting were lesion volume (p = 0.03) on noncontrast CT, and lesion volume (p = 0.006) and percentage of spinal canal stenosis (p = 0.001) on intravenous contrast-enhanced CT. The presence or absence of intravenous contrast was not significantly associated with CT reporting (p = 1.0)., Conclusion: Using spine MRI as the reference standard for the presence of epidural tumor, the majority of body CT reports omit describing thoracolumbar epidural metastases which are clearly visible in retrospect., Competing Interests: Declaration of competing interest Author RMS receives patent royalties from iCAD, ScanMed, Philips, Translation Holdings and PingAn. His lab received research support from PingAn through a CRADA., (Published by Elsevier Inc.)

Purpose: Differentiation of fat-poor angiomyolipoma (fp-AMLs) from renal cell carcinoma (RCC) is often not possible from just visual interpretation of conventional cross-sectional imaging, typically requiring biopsy or surgery for diagnostic confirmation. However, radiomics has the potential to characterize renal masses without the need for invasive procedures. Here, we conducted a systematic review on the accuracy of CT radiomics in distinguishing fp-AMLs from RCCs., Methods: We conducted a search using PubMed/MEDLINE, Google Scholar, Cochrane Library, Embase, and Web of Science for studies published from January 2011-2022 that utilized CT radiomics to discriminate between fp-AMLs and RCCs. A random-effects model was applied for the meta-analysis according to the heterogeneity level. Furthermore, subgroup analyses (group 1: RCCs vs. fp-AML, and group 2: ccRCC vs. fp-AML), and quality assessment were also conducted to explore the possible effect of interstudy differences. To evaluate CT radiomics performance, the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were assessed. This study is registered with PROSPERO (CRD42022311034)., Results: Our literature search identified 10 studies with 1456 lesions in 1437 patients. Pooled sensitivity was 0.779 [95% CI: 0.562-0.907] and 0.817 [95% CI: 0.663-0.910] for groups 1 and 2, respectively. Pooled specificity was 0.933 [95% CI: 0.814-0.978]and 0.926 [95% CI: 0.854-0.964] for groups 1 and 2, respectively. Also, our findings showed higher sensitivity and specificity of 0.858 [95% CI: 0.742-0.927] and 0.886 [95% CI: 0.819-0.930] for detecting ccRCC from fp-AML in the unenhanced phase of CT scan as compared to the corticomedullary and nephrogenic phases of CT scan., Conclusion: This study suggested that radiomic features derived from CT has high sensitivity and specificity in differentiating RCCs vs. fp-AML, particularly in detecting ccRCCs vs. fp-AML. Also, an unenhanced CT scan showed the highest specificity and sensitivity as compared to contrast CT scan phases. Differentiating between fp-AML and RCC often is not possible without biopsy or surgery; radiomics has the potential to obviate these invasive procedures due to its high diagnostic accuracy., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Background: von Hippel-Lindau syndrome (VHL) is an autosomal dominant hereditary syndrome with an increased predisposition of developing numerous cysts and tumors, almost exclusively clear cell renal cell carcinoma (ccRCC). Considering the lifelong surveillance in such patients to monitor the disease, patients with VHL are preferentially imaged using MRI to eliminate radiation exposure., Purpose: Segmentation of kidney and tumor structures on MRI in VHL patients is useful in lesion characterization (e.g. cyst vs. tumor), volumetric lesion analysis and tumor growth prediction. However, automated tasks such as ccRCC segmentation on MRI is sparsely studied. We develop segmentation methodology for ccRCC on T1 weighted pre-contrast, corticomedullary, nephrogenic and excretory contrast phase MRI., Methods: We applied a new neural network approach using a novel differentiable decision forest, called hinge forest (HF), to segment kidney parenchyma, cyst and ccRCC tumors in 117 images from 115 patients. This data set represented an unprecedented 504 ccRCCs with 1171 cystic lesions obtained at 5 different MRI scanners. The hinge forest architecture was compared with U-Net on 10 randomized splits with 75% used for training and 25% used for testing. Both methods were trained with Adam using default parameters (ɑ = 0.001, β 1 = 0.9, β 2 = 0.999) over 1000 epochs. We further demonstrated some interpretability of our HF method by exploiting decision tree structure., Results: The HF achieved an average kidney, cyst and tumor Dice similarity coefficient (DSC) of 0.75 ± 0.03, 0.44 ± 0.05, 0.53 ± 0.04 respectively while U-Net achieved an average kidney, cyst and tumor DSC of 0.78 ± 0.02, 0.41 ± 0.04, 0.46 ± 0.05 respectively. The HF significantly outperformed U-Net on tumors while U-Net significantly outperformed HF when segmenting kidney parenchymas (ɑ < 0.01)., Conclusions: For the task of ccRCC segmentation, the HF can offer better segmentation performance compared to the traditional U-Net architecture. The leaf maps can glean hints about deep learning features that might prove to be useful in other automated tasks such as tumor characterization., (This article is protected by copyright. All rights reserved.)

Background: Radiomics is a type of quantitative analysis that provides a more objective approach to detecting tumor subtypes using medical imaging. The goal of this paper is to conduct a comprehensive assessment of the literature on computed tomography (CT) radiomics for distinguishing renal cell carcinomas (RCCs) from oncocytoma., Methods: From February 15th 2012 to 2022, we conducted a broad search of the current literature using the PubMed/MEDLINE, Google scholar, Cochrane Library, Embase, and Web of Science. A meta-analysis of radiomics studies concentrating on discriminating between oncocytoma and RCCs was performed, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies method. The pooled sensitivity, specificity, and diagnostic odds ratio were evaluated via a random-effects model, which was applied for the meta-analysis. This study is registered with PROSPERO (CRD42022311575)., Results: After screening the search results, we identified 6 studies that utilized radiomics to distinguish oncocytoma from other renal tumors; there were a total of 1064 lesions in 1049 patients (288 oncocytoma lesions vs 776 RCCs lesions). The meta-analysis found substantial heterogeneity among the included studies, with pooled sensitivity and specificity of 0.818 [0.619-0.926] and 0.808 [0.537-0.938], for detecting different subtypes of RCCs (clear cell RCC, chromophobe RCC, and papillary RCC) from oncocytoma. Also, a pooled sensitivity and specificity of 0.83 [0.498-0.960] and 0.92 [0.825-0.965], respectively, was found in detecting oncocytoma from chromophobe RCC specifically., Conclusions: According to this study, CT radiomics has a high degree of accuracy in distinguishing RCCs from RO, including chromophobe RCCs from RO. Radiomics algorithms have the potential to improve diagnosis in scenarios that have traditionally been ambiguous. However, in order for this modality to be implemented in the clinical setting, standardization of image acquisition and segmentation protocols as well as inter-institutional sharing of software is warranted., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Published by Elsevier Inc.)

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Purpose: Upfront knowledge of tumor growth rates of clear cell renal cell carcinoma in von Hippel-Lindau syndrome (VHL) patients can allow for a more personalized approach to either surveillance imaging frequency or surgical planning. In this study, we implement a machine learning algorithm utilizing radiomic features of renal tumors identified on baseline magnetic resonance imaging (MRI) in VHL patients to predict the volumetric growth rate category of these tumors., Materials and Methods: A total of 73 VHL patients with 173 pathologically confirmed Clear Cell Renal Cell Carcinoma (ccRCCs) underwent MRI at least at two different time points between 2015 and 2021. Each tumor was manually segmented in excretory phase contrast T1 weighed MRI and co-registered on pre-contrast, corticomedullary and nephrographic phases. Radiomic features and volumetric data from each tumor were extracted using the PyRadiomics library in Python (4544 total features). Tumor doubling time (DT) was calculated and patients were divided into two groups: DT <  = 1 year and DT > 1 year. Random forest classifier (RFC) was used to predict the DT category. To measure prediction performance, the cohort was randomly divided into 100 training and test sets (80% and 20%). Model performance was evaluated using area under curve of receiver operating characteristic curve (AUC-ROC), as well as accuracy, F1, precision and recall, reported as percentages with 95% confidence intervals (CIs)., Results: The average age of patients was 47.2 ± 10.3 years. Mean interval between MRIs for each patient was 1.3 years. Tumors included in this study were categorized into 155 Grade 2; 16 Grade 3; and 2 Grade 4. Mean accuracy of RFC model was 79.0% [67.4-90.6] and mean AUC-ROC of 0.795 [0.608-0.988]. The accuracy for predicting DT classes was not different among the MRI sequences (P-value = 0.56)., Conclusion: Here we demonstrate the utility of machine learning in accurately predicting the renal tumor growth rate category of VHL patients based on radiomic features extracted from different T1-weighted pre- and post-contrast MRI sequences., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

BACKGROUNDHead and neck squamous cell carcinoma not associated with HPV (HPV-unrelated HNSCC) is associated with a high rate of recurrence and poor survival.METHODSWe conducted a clinical trial in 14 patients with newly diagnosed HPV-unrelated HNSCC to evaluate the safety and efficacy of neoadjuvant bintrafusp alfa, a bifunctional fusion protein that blocks programmed death ligand 1 (PD-L1) and neutralizes TGF-β.RESULTSBintrafusp alfa was well tolerated, and no treatment-associated surgical delays or complications occurred. Objective pathologic responses (PRs) were observed, and 12 of the 14 (86%) patients were alive and disease free at 1 year. Alterations in Treg infiltration and spatial distribution relative to proliferating CD8+ T cells indicated a reversal of Treg immunosuppression in the primary tumor. Detection of neoepitope-specific tumor T cell responses, but not virus-specific responses, correlated with the development of a PR. Detection of neoepitope-specific responses and PRs in tumors was not correlated with genomic features or tumor antigenicity but was associated with reduced pretreatment myeloid cell tumor infiltration. These results indicate that dual PD-L1 and TGF-β blockade can safely enhance tumor antigen-specific immunity and highlight the feasibility of multimechanism neoadjuvant immunotherapy for patients with HPV-unrelated HNSCC.CONCLUSIONOur studies provide insight into the ability of neoadjuvant immunotherapy to induce polyclonal neoadjuvant-specific T cell responses in tumors and suggest that features of the tumor microenvironment, such as myeloid cell infiltration, may be a major determinant of enhanced antitumor immunity following such treatment.TRIAL REGISTRATIONClinicalTrials.gov NCT04247282.FUNDINGThis work was funded by the Center for Cancer Research, the NCI, and the Intramural Research Program of the NIDCD, NIH. Bintrafusp alfa was provided by the health care business of Merck KGaA (Darmstadt, Germany), through a Cooperative Research and Development Agreement with the NCI. Additional funding was provided by ImmunityBio through a Cooperative Research and Development Agreement with the NIDCD.

With improved molecular characterization of clear cell renal cancer and advances in texture analysis as well as machine learning, diagnostic radiology is primed to enter personalized medicine with radiogenomics: the identification of relationships between tumor image features and underlying genomic expression. By developing surrogate image biomarkers, clinicians can augment their ability to non-invasively characterize a tumor and predict clinically relevant outcomes (i.e., overall survival; metastasis-free survival; or complete/partial response to treatment). It is thus important for clinicians to have a basic understanding of this nascent field, which can be difficult due to the technical complexity of many of the studies. We conducted a review of the existing literature for radiogenomics in clear cell kidney cancer, including original full-text articles until September 2021. We provide a basic description of radiogenomics in diagnostic radiology; summarize existing literature on relationships between image features and gene expression patterns, either computationally or by radiologists; and propose future directions to facilitate integration of this field into the clinical setting.

Visual explanation methods have an important role in the prognosis of the patients where the annotated data is limited or unavailable. There have been several attempts to use gradient-based attribution methods to localize pathology from medical scans without using segmentation labels. This research direction has been impeded by the lack of robustness and reliability. These methods are highly sensitive to the network parameters. In this study, we introduce a robust visual explanation method to address this problem for medical applications. We provide an innovative visual explanation algorithm for general purpose and as an example application we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels. This approach overcomes the drawbacks of commonly used Grad-CAM and its extended versions. The premise behind our proposed strategy is that the information flow is minimized while ensuring the classifier prediction stays similar. Our findings indicate that the bottleneck condition provides a more stable severity estimation than the similar attribution methods. The source code will be publicly available upon publication.

The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using RT-PCR for testing. As a complimentary tool with diagnostic imaging, chest Computed Tomography (CT) has been shown to be able to reveal visual patterns characteristic for COVID-19, which has definite value at several stages during the disease course. To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis with chest CT scan, which has shown promising results. However, domain shift of data across clinical data centers poses a serious challenge when deploying learning-based models. A common way to alleviate this issue is to fine-tune the model locally with the target domains local data and annotations. Unfortunately, the availability and quality of local annotations usually varies due to heterogeneity in equipment and distribution of medical resources across the globe. This impact may be pronounced in the detection of COVID-19, since the relevant patterns vary in size, shape, and texture. In this work, we attempt to find a solution for this challenge via federated and semi-supervised learning. A multi-national database consisting of 1704 scans from three countries is adopted to study the performance gap, when training a model with one dataset and applying it to another. Expert radiologists manually delineated 945 scans for COVID-19 findings. In handling the variability in both the data and annotations, a novel federated semi-supervised learning technique is proposed to fully utilize all available data (with or without annotations). Federated learning avoids the need for sensitive data-sharing, which makes it favorable for institutions and nations with strict regulatory policy on data privacy. Moreover, semi-supervision potentially reduces the annotation burden under a distributed setting. The proposed framework is shown to be effective compared to fully supervised scenarios with conventional data sharing instead of model weight sharing., 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 © 2021 Elsevier B.V. All rights reserved.)

Rationale and Objectives: The use of chest computed tomography (CT) in the era of the COVID-19 pandemic raises concern regarding the transmission risks to patients and staff caused by CT room contamination. Meanwhile the Center for Disease Control guidance for air exchange in between patients may heavily impact workflows. To design a portable custom isolation device to reduce imaging equipment contamination during a pandemic., Materials and Methods: Center for Disease Control air exchange guidelines and requirements were reviewed. Device functional requirements were outlined and designed. Engineering requirements were reviewed. Methods of practice and risk mitigation plans were outlined including donning and doffing procedures and failure modes. Cost impact was assessed in terms of CT patient throughput., Results: CT air exchange solutions and alternatives were reviewed. Multiple isolation bag device designs were considered. Several designs were custom fabricated, prototyped and reduced to practice. A final design was tested on volunteers for comfort, test-fit, air seal, and breathability. Less than 14 times enhanced patient throughput was estimated, in an ideal setting, which could more than counterbalance the cost of the device itself., Conclusion: A novel isolation bag device is feasible for use in CT and might facilitate containment and reduce contamination in radiology departments during the COVID Pandemic., (Published by Elsevier Inc.)

Background: Alström syndrome (AS), a monogenic form of obesity, is caused by recessive mutations in the centrosome- and basal body-associated gene ALMS1. AS is characterized by retinal dystrophy, sensory hearing loss, cardiomyopathy, childhood obesity, and metabolic derangements., Objective: We sought to characterize the endocrine and metabolic features of AS while accounting for obesity as a confounder by comparing patients with AS to body mass index (BMI)-matched controls., Methods: We evaluated 38 patients with AS (age 2 to 38 years) who were matched with 76 controls (age 2 to 48 years) by age, sex, race, and BMI. Fasting biochemistries, mixed meal test (MMT), indirect calorimetry, dual-energy X-ray absorptiometry, and MRI/magnetic resonance spectroscopy were performed., Results: Frequent abnormalities in AS included 76% obesity, 37% type 2 diabetes mellitus (T2DM), 29% hypothyroidism (one-third central, two-thirds primary), 3% central adrenal insufficiency, 57% adult hypogonadism (one-third central, two-thirds primary), and 25% female hyperandrogenism. Patients with AS and controls had similar BMI z scores, body fat, waist circumference, abdominal visceral fat, muscle fat, resting energy expenditure (adjusted for lean mass), free fatty acids, glucagon, prolactin, ACTH, and cortisol. Compared with controls, patients with AS were shorter and had lower IGF-1 concentrations (Ps ≤ 0.001). Patients with AS had significantly greater fasting and MMT insulin resistance indices, higher MMT glucose, insulin, and C-peptide values, higher HbA1c, and higher prevalence of T2DM (Ps < 0.001). Patients with AS had significantly higher triglycerides, lower high-density lipoprotein cholesterol, and a 10-fold greater prevalence of metabolic syndrome (Ps < 0.001). Patients with AS demonstrated significantly greater liver triglyceride accumulation and higher transaminases (P < 0.001)., Conclusion: Severe insulin resistance and T2DM are the hallmarks of AS. However, patients with AS may present with multiple other endocrinopathies affecting growth and development.

Objective: We investigated the association of cardiovascular risk factors and myocardial fibrosis with early cardiac dysfunction in type 1 diabetes., Research Design and Methods: Participants with type 1 diabetes aged 13-39 years without a known history of cardiovascular disease (CVD) ( n = 1,441) were recruited into the Diabetes Control and Complications Trial (1983-1993) and subsequently followed in the Epidemiology of Diabetes Interventions and Complications study (1994 to present). Seven hundred fourteen participants underwent cardiac magnetic resonance (CMR) imaging (2007-2009) with late gadolinium enhancement sequences to assess ischemic and nonischemic scars and tagging sequences to evaluate circumferential strain. CMR-derived T1 mapping also was used to assess interstitial fibrosis. The influence of cardiovascular risk factors and myocardial scar on circumferential strain was assessed using linear regression., Results: Circumferential dysfunction was consistently associated with older age, male sex, smoking history, obesity, higher blood pressure, lower HDL cholesterol, and higher mean HbA 1c . Participants with nonischemic scars ( n = 16) had the worst circumferential function compared with those without scars (β ± SE 1.32 ± 0.60; P = 0.03). In sex-adjusted models, the correlation between T1 times and circumferential strain was not significant. In the fully adjusted models, a trend toward circumferential dysfunction in participants with nonischemic scars was found. Left ventricular ejection fraction was not associated with risk factors but was significantly lower if a myocardial scar was present., Conclusions: Traditional CVD risk factors and elevated HbA 1c levels are major factors related to early cardiac dysfunction in type 1 diabetes. Nonischemic myocardial scar, possibly as a marker of chronic exposure to known risk factors, may predict early cardiac dysfunction mediated by diffuse myocardial fibrosis as seen in diabetic cardiomyopathy., (© 2017 by the American Diabetes Association.)

Robust organ segmentation is a prerequisite for computer-aided diagnosis, quantitative imaging analysis, pathology detection, and surgical assistance. For organs with high anatomical variability (e.g., the pancreas), previous segmentation approaches report low accuracies, compared with well-studied organs, such as the liver or heart. We present an automated bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans. The method generates a hierarchical cascade of information propagation by classifying image patches at different resolutions and cascading (segments) superpixels. The system contains four steps: 1) decomposition of CT slice images into a set of disjoint boundary-preserving superpixels; 2) computation of pancreas class probability maps via dense patch labeling; 3) superpixel classification by pooling both intensity and probability features to form empirical statistics in cascaded random forest frameworks; and 4) simple connectivity based post-processing. Dense image patch labeling is conducted using two methods: efficient random forest classification on image histogram, location and texture features; and more expensive (but more accurate) deep convolutional neural network classification, on larger image windows (i.e., with more spatial contexts). Over-segmented 2-D CT slices by the simple linear iterative clustering approach are adopted through model/parameter calibration and labeled at the superpixel level for positive (pancreas) or negative (non-pancreas or background) classes. The proposed method is evaluated on a data set of 80 manually segmented CT volumes, using six-fold cross-validation. Its performance equals or surpasses other state-of-the-art methods (evaluated by "leave-one-patient-out"), with a dice coefficient of 70.7% and Jaccard index of 57.9%. In addition, the computational efficiency has improved significantly, requiring a mere 6 ~ 8 min per testing case, versus ≥ 10 h for other methods. The segmentation framework using deep patch labeling confidences is also more numerically stable, as reflected in the smaller performance metric standard deviations. Finally, we implement a multi-atlas label fusion (MALF) approach for pancreas segmentation using the same data set. Under six-fold cross-validation, our bottom-up segmentation method significantly outperforms its MALF counterpart: 70.7±13.0% versus 52.51±20.84% in dice coefficients.

Context: Androgen excess may be adrenal and/or ovarian in origin; we hypothesized that a subgroup of patients with polycystic ovarian syndrome (PCOS) may have some degree of abnormal adrenocortical function., Objective: The objective of the study was to evaluate the pituitary adrenal axis with an oral low- and high-dose dexamethasone-suppression test (Liddle's test) in women with PCOS., Design: This was a case-control study., Setting: The study was conducted at the National Institutes of Health Clinical Center., Participants: A total of 38 women with PCOS and 20 healthy volunteers (HV) aged 16-29 years participated in the study., Main Outcome Measures: Urinary free cortisol (UFC) and 17-hydroxysteroids (17OHS) before and after low- and high-dose dexamethasone and assessment of adrenal volume by computed tomography scan were measured., Results: Twenty-four-hour urinary 17OHS and UFC were measured during day 1 to day 6 of the Liddle's test. Baseline UFC levels were not different between PCOS and HVs; on the day after the completion of high-dose dexamethasone administration (d 6), UFC was higher in the PCOS group (2.0 ± 0.7 μg/m(2)·d) than the HV group (1.5 ± 0.5) (P = .038). On day 5, 17OHS and UFC were negatively correlated with adrenal volumes (left side, rp = -0.47, P = .009, and rp = -0.61, P < .001, respectively). PCOS patients above the 75th percentile for UFC and/or 17OHS after high-dose dexamethasone (n = 15) had a significantly smaller total adrenal volume (6.9 ± 1.9 cm(3) vs 9.2 ± 1.8 cm(3), P = .003) when compared with the remaining PCOS patients (n = 22), but they did not have worse insulin resistance or hyperandrogenism., Conclusions: In a subset of young women with PCOS, we detected a pattern of glucocorticoid secretion that mimicked that of patients with micronodular adrenocortical hyperplasia: they had smaller adrenal volumes and higher steroid hormone secretion after dexamethasone compared with the group of PCOS with appropriate response to dexamethasone.

The widespread use of CT imaging and the critical importance of early detection of epidural masses of the spinal canal generate a scenario ideal for the implementation of a computer-aided detection (CAD) system. Epidural masses can lead to paralysis, incontinence and loss of neurological function if not promptly detected. We present, to our knowledge, the first CAD system to detect epidural masses on CT scans. In this paper, spatially constrained Gaussian mixture model (GMM) and supervoxel-based method are proposed for epidural mass detection. The detection is performed on the Gaussian level or the supervoxel level rather than the voxel level. Cross-validation on 40 patients with epidural masses on body CT showed that the supervoxel-based method yielded a significant improvement of performance (82% at 3 false positives per patient) over the spatially constrained GMM method (55% at 3 false positives per patient)., (Published by Elsevier Ltd.)

Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1 FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this paper, we first operate a preliminary candidate generation stage, towards -100% sensitivity at the cost of high FP levels (-40 per patient), to harvest volumes of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. These random views are then used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign LN probabilities for all N random views that can be simply averaged (as a set) to compute the final classification probability per VOI. We validate the approach on two datasets: 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in mediastinum and abdomen respectively, which drastically improves over the previous state-of-the-art work.

Objective: Controversy exists about the coronary artery disease (CAD) risk conveyed by diabetes in young and middle-aged women. We investigated sex differences in CAD by diabetes status among healthy individuals with different underlying risks of heart disease., Research Design and Methods: We examined subjects aged <60 years without CAD at enrollment in the high-risk GeneSTAR Study (n = 1,448; follow-up ∼12 years), Multi-Ethnic Study of Atherosclerosis (MESA; n = 3,072; follow-up ∼7 years), and National Health and Nutrition Examination Survey III (NHANES III) Mortality Follow-up Study (n = 6,997; follow-up ∼15 years). Diabetes was defined by report, hypoglycemic use, and/or fasting glucose ≥126 mg/dL. The outcome was any CAD event during follow-up (fatal CAD in NHANES)., Results: In the absence of diabetes, CAD rates were lower among women in GeneSTAR, MESA, and NHANES (4.27, 1.66, and 0.40/1,000 person-years, respectively) versus men (11.22, 5.64, and 0.88/1,000 person-years); log-rank P < 0.001 (GeneSTAR/MESA) and P = 0.07 (NHANES). In the presence of diabetes, CAD event rates were similar among women (17.65, 7.34, and 2.37/1,000 person-years) versus men (12.86, 9.71, and 1.83/1,000 person-years); all log-rank P values > 0.05. Adjusting for demographics, diabetes was associated with a significant four- to fivefold higher CAD rate among women in each cohort, without differences in men. In meta-analyses of three cohorts, additionally adjusted for BMI, smoking, hypertension, HDL, and non-HDL cholesterol, antihypertensive and cholesterol-lowering medication use, the hazard ratio of CAD in men versus women among nondiabetes was 2.43 (1.76-3.35) and diabetes was 0.89 (0.43-1.83); P = 0.013 interaction by diabetes status., Conclusions: Though young and middle-aged women are less likely to develop CAD in the absence of diabetes, the presence of diabetes equalizes the risk by sex. Our findings support aggressive CAD prevention strategies in women with diabetes and at similar levels to those that exist in men.

Segmentation of positron emission tomography (PET) images is an important objective because accurate measurement of signal from radio-tracer activity in a region of interest is critical for disease treatment and diagnosis. In this study, we present the use of a graph based method for providing robust, accurate, and reliable segmentation of functional volumes on PET images from standardized uptake values (SUVs). We validated the success of the segmentation method on different PET phantoms including ground truth CT simulation, and compared it to two well-known threshold based segmentation methods. Furthermore, we assessed intra-and inter-observer variation in delineation accuracy as well as reproducibility of delineations using real clinical data. Experimental results indicate that the presented segmentation method is superior to the commonly used threshold based methods in terms of accuracy, robustness, repeatability, and computational efficiency.

Current noninvasive surrogates of cardiac involvement in myotonic muscular dystrophy have low positive predictive value for sudden death. We hypothesized that the cardiac MR signal-to-noise ratio variance (SNRV) is a surrogate of the spatial heterogeneity of myocardial fibrosis and correlates with electrocardiography changes in myotonic muscular dystrophy. The SNRV for contrast enhanced cardiac MR images was calculated over the entire left ventricle in 43 patients with myotonic muscular dystrophy. All patients underwent standard electrocardiography, and a subset of 23 patients underwent signal averaged electrocardiography. After correcting for body mass index, age, and ejection fraction, SNRV was predictive of QRS duration on standard electrocardiography (1.35-msec increased QRS duration/unit increase in SNRV, P < 0.001). SNRV was also predictive of the low-amplitude late-potential duration (1.49-msec increased low-amplitude late-potential duration/unit increase in SNRV, P < 0.001). Ten-fold cross-validation yielded an area under the receiver operating characteristic curve of 0.87 for the predictive value of SNRV for QRS duration greater than 120 msec. The SNRV of the left ventricle is associated with QRS prolongation, likely due to late depolarization of tissue within islands of patchy fibrosis. The association of SNRV with future clinical events warrants further study., ((c) 2010 Wiley-Liss, Inc.)