1
|
Anari PY, Lay N, Zahergivar A, Firouzabadi FD, Chaurasia A, Golagha M, Singh S, Homayounieh F, Obiezu F, Harmon S, Turkbey E, Merino M, Jones EC, Ball MW, Linehan WM, Turkbey B, Malayeri AA. Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results. Abdom Radiol (NY) 2024; 49:1194-1201. [PMID: 38368481 DOI: 10.1007/s00261-023-04172-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/17/2023] [Accepted: 12/19/2023] [Indexed: 02/19/2024]
Abstract
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.
Collapse
Affiliation(s)
- Pouria Yazdian Anari
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Nathan Lay
- Artificial Intelligence Resource, National Institutes of Health, Bethesda, USA
| | - Aryan Zahergivar
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Fatemeh Dehghani Firouzabadi
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Aditi Chaurasia
- Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, USA
| | - Mahshid Golagha
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Shiva Singh
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | | | - Fiona Obiezu
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Stephanie Harmon
- Artificial Intelligence Resource, National Institutes of Health, Bethesda, USA
| | - Evrim Turkbey
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Maria Merino
- Pathology Department, National Cancer Institutes, National Institutes of Health, Bethesda, USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Mark W Ball
- Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, USA
| | - W Marston Linehan
- Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, USA
| | - Baris Turkbey
- Artificial Intelligence Resource, National Institutes of Health, Bethesda, USA
| | - Ashkan A Malayeri
- Radiology and Imaging Sciences, Clinical Center,, National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA.
| |
Collapse
|
2
|
Yazdian Anari P, Zahergivar A, Gopal N, Chaurasia A, Lay N, Ball MW, Turkbey B, Turkbey E, Jones EC, Linehan WM, Malayeri AA. Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI. Abdom Radiol (NY) 2024; 49:1202-1209. [PMID: 38347265 DOI: 10.1007/s00261-023-04162-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/11/2023] [Accepted: 12/14/2023] [Indexed: 03/22/2024]
Abstract
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.
Collapse
Affiliation(s)
- Pouria Yazdian Anari
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892-1109, USA
| | - Aryan Zahergivar
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892-1109, USA
| | - Nikhil Gopal
- Urology Oncology Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892-1109, USA
| | - Aditi Chaurasia
- Urology Oncology Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892-1109, USA
| | - Nathan Lay
- Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, USA
| | - Mark W Ball
- Urology Oncology Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892-1109, USA
| | - Baris Turkbey
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892-1109, USA
| | - Evrim Turkbey
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892-1109, USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892-1109, USA
| | - W Marston Linehan
- Urology Oncology Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892-1109, USA.
| | - Ashkan A Malayeri
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892-1109, USA.
| |
Collapse
|
3
|
Nikpanah M, Dehghani Firouzabadi F, Farhadi F, Mirmomen SM, Ahlman MA, Huda F, Millo C, Saboury B, Paschall AK, Gahl WA, Estrada-Veras JI, Turkbey E, Jones EC, O'Brien K, Malayeri AA. Skeletal involvement in Erdheim-Chester disease: Multimodality imaging features and association with the BRAF V600E mutation. Clin Imaging 2024; 106:110067. [PMID: 38128404 DOI: 10.1016/j.clinimag.2023.110067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 12/01/2023] [Accepted: 12/10/2023] [Indexed: 12/23/2023]
Abstract
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), 18F-FDG positron emission tomography/computed tomography (PET/CT), and bone scans, as well as looking for associations with the BRAFV600E mutation. MATERIAL AND METHODS Prospective study of 50 consecutive patients with biopsy-confirmed ECD who had radiographs, CT, 18F-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 BRAFV600E 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 BRAFV600E mutation, which was found in 24 patients, was correlated with femoral and tibial involvement on 18F-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.
Collapse
Affiliation(s)
- Moozhan Nikpanah
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA; Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Fatemeh Dehghani Firouzabadi
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA; Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Faraz Farhadi
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA; Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - S Mojdeh Mirmomen
- Department of Radiology, UC San Diego School of Medicine, San Diego, CA, USA
| | - Mark A Ahlman
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Fahimul Huda
- Department of Radiology, University of Louisville School of Medicine, KY, USA
| | - Corina Millo
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Babak Saboury
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Anna K Paschall
- Duke University Health System, School of Medicine, Durham, NC, USA
| | - William A Gahl
- National Human Genome Research Institute, Medical Genetics Branch, Office of the Clinical Director, National Institutes of Health, Bethesda, MD, USA
| | - Juvianee I Estrada-Veras
- National Human Genome Research Institute, Medical Genetics Branch, Office of the Clinical Director, National Institutes of Health, Bethesda, MD, USA
| | - Evrim Turkbey
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Kevin O'Brien
- National Human Genome Research Institute, Medical Genetics Branch, Office of the Clinical Director, National Institutes of Health, Bethesda, MD, USA.
| | - Ashkan A Malayeri
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
| |
Collapse
|
4
|
Zahergivar A, Yazdian Anari P, Mendhiratta N, Lay N, Singh S, Dehghani Firouzabadi F, Chaurasia A, Golagha M, Homayounieh F, Gautam R, Harmon S, Turkbey E, Merino M, Jones EC, Ball MW, Turkbey B, Linehan WM, Malayeri AA. Non-Invasive Tumor Grade Evaluation in Von Hippel-Lindau-Associated Clear Cell Renal Cell Carcinoma: A Magnetic Resonance Imaging-Based Study. J Magn Reson Imaging 2024. [PMID: 38299714 DOI: 10.1002/jmri.29222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/01/2023] [Accepted: 12/02/2023] [Indexed: 02/02/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Aryan Zahergivar
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Pouria Yazdian Anari
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Neil Mendhiratta
- Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, Maryland, USA
| | - Nathan Lay
- Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Shiva Singh
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Aditi Chaurasia
- Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, Maryland, USA
| | - Mahshid Golagha
- Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, Maryland, USA
| | - Fatemeh Homayounieh
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Rabindra Gautam
- Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, Maryland, USA
| | - Stephanie Harmon
- Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Evrim Turkbey
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Maria Merino
- Pathology Department, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Mark W Ball
- Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, Maryland, USA
| | - Baris Turkbey
- Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - W Marston Linehan
- Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, Maryland, USA
| | - Ashkan A Malayeri
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| |
Collapse
|
5
|
Herington J, McCradden MD, Creel K, Boellaard R, Jones EC, Jha AK, Rahmim A, Scott PJH, Sunderland JJ, Wahl RL, Zuehlsdorff S, Saboury B. Ethical Considerations for Artificial Intelligence in Medical Imaging: Data Collection, Development, and Evaluation. J Nucl Med 2023; 64:1848-1854. [PMID: 37827839 PMCID: PMC10690124 DOI: 10.2967/jnumed.123.266080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 09/12/2023] [Indexed: 10/14/2023] Open
Abstract
The development of artificial intelligence (AI) within nuclear imaging involves several ethically fraught components at different stages of the machine learning pipeline, including during data collection, model training and validation, and clinical use. Drawing on the traditional principles of medical and research ethics, and highlighting the need to ensure health justice, the AI task force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks: privacy of data subjects, data quality and model efficacy, fairness toward marginalized populations, and transparency of clinical performance. We provide preliminary recommendations to developers of AI-driven medical devices for mitigating the impact of these risks on patients and populations.
Collapse
Affiliation(s)
- Jonathan Herington
- Department of Health Humanities and Bioethics and Department of Philosophy, University of Rochester, Rochester, New York
| | - Melissa D McCradden
- Department of Bioethics, Hospital for Sick Children, Toronto and Dana Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Kathleen Creel
- Department of Philosophy and Religion and Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Elizabeth C Jones
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland
| | - Abhinav K Jha
- Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Peter J H Scott
- Department of Radiology, University of Michigan Medical School, Ann Arbor, Michigan
| | - John J Sunderland
- Departments of Radiology and Physics, University of Iowa, Iowa City, Iowa
| | - Richard L Wahl
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri; and
| | | | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland;
| |
Collapse
|
6
|
Farhadi F, Sahbaee P, Rajagopal JR, Nikpanah M, Saboury B, Gutjahr R, Biassou NM, Shah R, Flohr TG, Samei E, Pritchard WF, Malayeri AA, Bluemke DA, Jones EC. Virtual monoenergetic imaging in photon-counting CT of the head and neck. Clin Imaging 2023; 102:109-115. [PMID: 37672849 PMCID: PMC10838526 DOI: 10.1016/j.clinimag.2023.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 08/08/2023] [Indexed: 09/08/2023]
Abstract
PURPOSE Advantages of virtual monoenergetic images (VMI) have been reported for dual energy CT of the head and neck, and more recently VMIs derived from photon-counting (PCCT) angiography of the head and neck. We report image quality metrics of VMI in a PCCT angiography dataset, expanding the anatomical regions evaluated and extending observer-based qualitative methods further than previously reported. METHODS In a prospective study, asymptomatic subjects underwent contrast enhanced PCCT of the head and neck using an investigational scanner. Image sets of low, high, and full spectrum (Threshold-1) energies; linear mix of low and high energies (Mix); and 23 VMIs (40-150 keV, 5 keV increments) were generated. In 8 anatomical locations, SNR and radiologists' preferences for VMI energy levels were measured using a forced-choice rank method (4 observers) and ratings of image quality using visual grading characteristic (VGC) analysis (2 observers) comparing VMI to Mix and Threshold-1 images. RESULTS Fifteen subjects were included (7 men, 8 women, mean 57 years, range 46-75). Among all VMIs, SNRs varied by anatomic location. The highest SNRs were observed in VMIs. Radiologists preferred 50-60 keV VMIs for vascular structures and 75-85 keV for all other structures. Cumulative ratings of image quality averaged across all locations were higher for VMIs with areas under the curve of VMI vs Mix and VMI vs Threshold-1 of 0.67 and 0.68 for the first reader and 0.72 and 0.76 for the second, respectively. CONCLUSION Preferred keV level and quality ratings of VMI compared to mixed and Threshold-1 images varied by anatomical location.
Collapse
Affiliation(s)
- Faraz Farhadi
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | | | - Jayasai R Rajagopal
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA; Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Moozhan Nikpanah
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Babak Saboury
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | | | - Nadia M Biassou
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Ritu Shah
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | | | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - William F Pritchard
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA; Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Ashkan A Malayeri
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - David A Bluemke
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
| |
Collapse
|
7
|
Rajagopal JR, Farhadi F, Nikpanah M, Sahbaee P, Saboury B, Pritchard WF, Jones EC, Chen MY, Samei E. Impact of the confluence of cardiac motion and high spatial resolution on performance of ECG-gated imaging with an investigational photon-counting CT system: A phantom study. Phys Med 2023; 114:102683. [PMID: 37738807 PMCID: PMC10798551 DOI: 10.1016/j.ejmp.2023.102683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/06/2023] [Accepted: 09/11/2023] [Indexed: 09/24/2023] Open
Abstract
PURPOSE Photon-counting CT (PCCT) has higher spatial resolution that conventional EID CT which improves imaging of stationary coronary plaques and stents.. In this work, we evaluated the relationship between higher spatial resolution and motion acquisition on an investigational PCCT system. METHODS An investigational photon-counting CT scanner (Siemens CounT) with ECG gating was used to image a coronary tree phantom with models of healthy, stenotic, and stented arteries using a motion simulator. Images were acquired with matched clinical parameters at rest and 60 beats per minute. An additional set of high dose stationary images were averaged to generate a motion-free, reduced noise reference. Scans were completed at standard (0.5 mm2) and high-resolution (0.25 mm2). Motion images were reconstructed at multiple phases. Regions of interest were drawn around vessels and segmented. Percentage difference from the reference standard was evaluated for vessel diameter and circularity. Mutual information between the reference and stationary and motion datasets was used as a measure of volumetric similarity. RESULTS The stenotic vessel showed the most variation from the reference when compared to healthy or stented vessels. Compared to standard resolution, high-resolution images had lower bias for diameter (-0.012 ± 0.19% vs -0.052 ± 0.14%) and lower variability for circularity (-0.13 ± 0.138% vs -0.12 ± 0.144%). Both differences were found to be statistically significant. High-resolution images had a slightly lower mutual information (1.28) than standard resolution (1.31). CONCLUSION The higher spatial resolution enabled by photon-counting CT can be harnessed for cardiac imaging as the benefits of high spatial resolution acquisitions remain relevant in the presence of motion.
Collapse
Affiliation(s)
- Jayasai R Rajagopal
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, Duke University Medical Center, Durham, NC, 27705, USA; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Faraz Farhadi
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Moozhan Nikpanah
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA
| | | | - Babak Saboury
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - William F Pritchard
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Marcus Y Chen
- Cardiovascular Branch, National Institute of Heart, Lung, and Blood, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, NC 27705, USA
| |
Collapse
|
8
|
Herington J, McCradden MD, Creel K, Boellaard R, Jones EC, Jha AK, Rahmim A, Scott PJH, Sunderland JJ, Wahl RL, Zuehlsdorff S, Saboury B. Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance. J Nucl Med 2023; 64:1509-1515. [PMID: 37620051 DOI: 10.2967/jnumed.123.266110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 07/11/2023] [Indexed: 08/26/2023] Open
Abstract
The deployment of artificial intelligence (AI) has the potential to make nuclear medicine and medical imaging faster, cheaper, and both more effective and more accessible. This is possible, however, only if clinicians and patients feel that these AI medical devices (AIMDs) are trustworthy. Highlighting the need to ensure health justice by fairly distributing benefits and burdens while respecting individual patients' rights, the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks that arise during the deployment of AIMD: autonomy of patients and clinicians, transparency of clinical performance and limitations, fairness toward marginalized populations, and accountability of physicians and developers. We provide preliminary recommendations for governing these ethical risks to realize the promise of AIMD for patients and populations.
Collapse
Affiliation(s)
- Jonathan Herington
- Department of Health Humanities and Bioethics and Department of Philosophy, University of Rochester, Rochester, New York
| | - Melissa D McCradden
- Department of Bioethics, Hospital for Sick Children, and Dana Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Kathleen Creel
- Department of Philosophy and Religion and Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Elizabeth C Jones
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland
| | - Abhinav K Jha
- Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Peter J H Scott
- Department of Radiology, University of Michigan Medical School, Ann Arbor, Michigan
| | - John J Sunderland
- Departments of Radiology and Physics, University of Iowa, Iowa City, Iowa
| | - Richard L Wahl
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri; and
| | | | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland;
| |
Collapse
|
9
|
Dehghani Firouzabadi F, Gopal N, Hasani A, Homayounieh F, Li X, Jones EC, Yazdian Anari P, Turkbey E, Malayeri AA. CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis. PLoS One 2023; 18:e0287299. [PMID: 37498830 PMCID: PMC10374097 DOI: 10.1371/journal.pone.0287299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 06/03/2023] [Indexed: 07/29/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Fatemeh Dehghani Firouzabadi
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Nikhil Gopal
- Urology Department, National Cancer Institutes (NCI), Clinical Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Amir Hasani
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Fatemeh Homayounieh
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Xiaobai Li
- Biostatistics and Clinical Epidemiology Service, NIH Clinical Center, Bethesda, MD, United States of America
| | - Elizabeth C Jones
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Pouria Yazdian Anari
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Evrim Turkbey
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| | - Ashkan A Malayeri
- Radiology Department, National Institutes of Health, Clinical Center (CC), Bethesda, Maryland, United States of America
| |
Collapse
|
10
|
Chaurasia A, Singh S, Homayounieh F, Gopal N, Jones EC, Linehan WM, Shyn PB, Ball MW, Malayeri AA. Complications after Nephron-sparing Interventions for Renal Tumors: Imaging Findings and Management. Radiographics 2023; 43:e220196. [PMID: 37384546 PMCID: PMC10323228 DOI: 10.1148/rg.220196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 12/20/2022] [Accepted: 01/10/2023] [Indexed: 07/01/2023]
Abstract
The two primary nephron-sparing interventions for treating renal masses such as renal cell carcinoma are surgical partial nephrectomy (PN) and image-guided percutaneous thermal ablation. Nephron-sparing surgery, such as PN, has been the standard of care for treating many localized renal masses. Although uncommon, complications resulting from PN can range from asymptomatic and mild to symptomatic and life-threatening. These complications include vascular injuries such as hematoma, pseudoaneurysm, arteriovenous fistula, and/or renal ischemia; injury to the collecting system causing urinary leak; infection; and tumor recurrence. The incidence of complications after any nephron-sparing surgery depends on many factors, such as the proximity of the tumor to blood vessels or the collecting system, the skill or experience of the surgeon, and patient-specific factors. More recently, image-guided percutaneous renal ablation has emerged as a safe and effective treatment option for small renal tumors, with comparable oncologic outcomes to those of PN and a low incidence of major complications. Radiologists must be familiar with the imaging findings encountered after these surgical and image-guided procedures, especially those indicative of complications. The authors review cross-sectional imaging characteristics of complications after PN and image-guided thermal ablation of kidney tumors and highlight the respective management strategies, ranging from clinical observation to interventions such as angioembolization or repeat surgery. Work of the U.S. Government published under an exclusive license with the RSNA. Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. Quiz questions for this article are available in the Online Learning Center. See the invited commentary by Chung and Raman in this issue.
Collapse
Affiliation(s)
- Aditi Chaurasia
- From the Urologic Oncology Branch, National Cancer Institute,
National Institutes of Health, Bethesda, Md (A.C., N.G., W.M.L., M.W.B.);
Department of Radiology and Imaging Sciences, Clinical Center, National
Institutes of Health, 10 Center Dr 1C352, Bethesda, MD 20892 (S.S., F.H.,
E.C.J., A.A.M.); and Division of Abdominal Imaging and Intervention, Department
of Radiology, Brigham and Women’s Hospital, Harvard Medical School,
Boston, Mass (P.B.S.)
| | - Shiva Singh
- From the Urologic Oncology Branch, National Cancer Institute,
National Institutes of Health, Bethesda, Md (A.C., N.G., W.M.L., M.W.B.);
Department of Radiology and Imaging Sciences, Clinical Center, National
Institutes of Health, 10 Center Dr 1C352, Bethesda, MD 20892 (S.S., F.H.,
E.C.J., A.A.M.); and Division of Abdominal Imaging and Intervention, Department
of Radiology, Brigham and Women’s Hospital, Harvard Medical School,
Boston, Mass (P.B.S.)
| | - Fatemeh Homayounieh
- From the Urologic Oncology Branch, National Cancer Institute,
National Institutes of Health, Bethesda, Md (A.C., N.G., W.M.L., M.W.B.);
Department of Radiology and Imaging Sciences, Clinical Center, National
Institutes of Health, 10 Center Dr 1C352, Bethesda, MD 20892 (S.S., F.H.,
E.C.J., A.A.M.); and Division of Abdominal Imaging and Intervention, Department
of Radiology, Brigham and Women’s Hospital, Harvard Medical School,
Boston, Mass (P.B.S.)
| | - Nikhil Gopal
- From the Urologic Oncology Branch, National Cancer Institute,
National Institutes of Health, Bethesda, Md (A.C., N.G., W.M.L., M.W.B.);
Department of Radiology and Imaging Sciences, Clinical Center, National
Institutes of Health, 10 Center Dr 1C352, Bethesda, MD 20892 (S.S., F.H.,
E.C.J., A.A.M.); and Division of Abdominal Imaging and Intervention, Department
of Radiology, Brigham and Women’s Hospital, Harvard Medical School,
Boston, Mass (P.B.S.)
| | - Elizabeth C. Jones
- From the Urologic Oncology Branch, National Cancer Institute,
National Institutes of Health, Bethesda, Md (A.C., N.G., W.M.L., M.W.B.);
Department of Radiology and Imaging Sciences, Clinical Center, National
Institutes of Health, 10 Center Dr 1C352, Bethesda, MD 20892 (S.S., F.H.,
E.C.J., A.A.M.); and Division of Abdominal Imaging and Intervention, Department
of Radiology, Brigham and Women’s Hospital, Harvard Medical School,
Boston, Mass (P.B.S.)
| | - W. Marston Linehan
- From the Urologic Oncology Branch, National Cancer Institute,
National Institutes of Health, Bethesda, Md (A.C., N.G., W.M.L., M.W.B.);
Department of Radiology and Imaging Sciences, Clinical Center, National
Institutes of Health, 10 Center Dr 1C352, Bethesda, MD 20892 (S.S., F.H.,
E.C.J., A.A.M.); and Division of Abdominal Imaging and Intervention, Department
of Radiology, Brigham and Women’s Hospital, Harvard Medical School,
Boston, Mass (P.B.S.)
| | - Paul B. Shyn
- From the Urologic Oncology Branch, National Cancer Institute,
National Institutes of Health, Bethesda, Md (A.C., N.G., W.M.L., M.W.B.);
Department of Radiology and Imaging Sciences, Clinical Center, National
Institutes of Health, 10 Center Dr 1C352, Bethesda, MD 20892 (S.S., F.H.,
E.C.J., A.A.M.); and Division of Abdominal Imaging and Intervention, Department
of Radiology, Brigham and Women’s Hospital, Harvard Medical School,
Boston, Mass (P.B.S.)
| | - Mark W. Ball
- From the Urologic Oncology Branch, National Cancer Institute,
National Institutes of Health, Bethesda, Md (A.C., N.G., W.M.L., M.W.B.);
Department of Radiology and Imaging Sciences, Clinical Center, National
Institutes of Health, 10 Center Dr 1C352, Bethesda, MD 20892 (S.S., F.H.,
E.C.J., A.A.M.); and Division of Abdominal Imaging and Intervention, Department
of Radiology, Brigham and Women’s Hospital, Harvard Medical School,
Boston, Mass (P.B.S.)
| | - Ashkan A. Malayeri
- From the Urologic Oncology Branch, National Cancer Institute,
National Institutes of Health, Bethesda, Md (A.C., N.G., W.M.L., M.W.B.);
Department of Radiology and Imaging Sciences, Clinical Center, National
Institutes of Health, 10 Center Dr 1C352, Bethesda, MD 20892 (S.S., F.H.,
E.C.J., A.A.M.); and Division of Abdominal Imaging and Intervention, Department
of Radiology, Brigham and Women’s Hospital, Harvard Medical School,
Boston, Mass (P.B.S.)
| |
Collapse
|
11
|
Hsu LY, Ali Z, Bagheri H, Huda F, Redd BA, Jones EC. Comparison of CT and Dixon MR Abdominal Adipose Tissue Quantification Using a Unified Computer-Assisted Software Framework. Tomography 2023; 9:1041-1051. [PMID: 37218945 DOI: 10.3390/tomography9030085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/17/2023] [Accepted: 05/18/2023] [Indexed: 05/24/2023] Open
Abstract
PURPOSE Reliable and objective measures of abdominal fat distribution across imaging modalities are essential for various clinical and research scenarios, such as assessing cardiometabolic disease risk due to obesity. We aimed to compare quantitative measures of subcutaneous (SAT) and visceral (VAT) adipose tissues in the abdomen between computed tomography (CT) and Dixon-based magnetic resonance (MR) images using a unified computer-assisted software framework. MATERIALS AND METHODS This study included 21 subjects who underwent abdominal CT and Dixon MR imaging on the same day. For each subject, two matched axial CT and fat-only MR images at the L2-L3 and the L4-L5 intervertebral levels were selected for fat quantification. For each image, an outer and an inner abdominal wall regions as well as SAT and VAT pixel masks were automatically generated by our software. The computer-generated results were then inspected and corrected by an expert reader. RESULTS There were excellent agreements for both abdominal wall segmentation and adipose tissue quantification between matched CT and MR images. Pearson coefficients were 0.97 for both outer and inner region segmentation, 0.99 for SAT, and 0.97 for VAT quantification. Bland-Altman analyses indicated minimum biases in all comparisons. CONCLUSION We showed that abdominal adipose tissue can be reliably quantified from both CT and Dixon MR images using a unified computer-assisted software framework. This flexible framework has a simple-to-use workflow to measure SAT and VAT from both modalities to support various clinical research applications.
Collapse
Affiliation(s)
- Li-Yueh Hsu
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10, Room 1C370, 10 Center Drive, Bethesda, MA 20892, USA
| | - Zara Ali
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10, Room 1C370, 10 Center Drive, Bethesda, MA 20892, USA
| | - Hadi Bagheri
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10, Room 1C370, 10 Center Drive, Bethesda, MA 20892, USA
| | - Fahimul Huda
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10, Room 1C370, 10 Center Drive, Bethesda, MA 20892, USA
| | - Bernadette A Redd
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10, Room 1C370, 10 Center Drive, Bethesda, MA 20892, USA
| | - Elizabeth C Jones
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Building 10, Room 1C370, 10 Center Drive, Bethesda, MA 20892, USA
| |
Collapse
|
12
|
Dehghani Firouzabadi F, Gopal N, Homayounieh F, Anari PY, Li X, Ball MW, Jones EC, Samimi S, Turkbey E, Malayeri AA. CT radiomics for differentiating oncocytoma from renal cell carcinomas: Systematic review and meta-analysis. Clin Imaging 2023; 94:9-17. [PMID: 36459898 PMCID: PMC9812928 DOI: 10.1016/j.clinimag.2022.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/02/2022] [Indexed: 11/18/2022]
Abstract
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.
Collapse
Affiliation(s)
| | - Nikhil Gopal
- Urology Department, Clinical Center, National Cancer Institutes (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Fatemeh Homayounieh
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Pouria Yazdian Anari
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Xiaobai Li
- Biostatistics and Clinical Epidemiology Service, NIH Clinical Center, Bethesda, MD, USA
| | - Mark W Ball
- Urology Department, Clinical Center, National Cancer Institutes (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth C Jones
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Safa Samimi
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Evrim Turkbey
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Ashkan A Malayeri
- Radiology Department, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA.
| |
Collapse
|
13
|
Anari PY, Lay N, Chaurasia A, Gopal N, Samimi S, Harmon S, Gautam R, Ma K, Firouzabadi FD, Turkbey E, Merino M, Jones EC, Ball MW, Linehan WM, Turkbey B, Malayeri AA. Automatic segmentation of clear cell renal cell tumors, kidney, and cysts in patients with von Hippel-Lindau syndrome using U-net architecture on magnetic resonance images. ArXiv 2023:arXiv:2301.02538v1. [PMID: 36789136 PMCID: PMC9928055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
We demonstrate automated segmentation of clear cell renal cell carcinomas (ccRCC), cysts, and surrounding normal kidney parenchyma in patients with von Hippel-Lindau (VHL) syndrome using convolutional neural networks (CNN) on Magnetic Resonance Imaging (MRI). We queried 115 VHL patients and 117 scans (3 patients have two separate scans) with 504 ccRCCs and 1171 cysts from 2015 to 2021. Lesions were manually segmented on T1 excretory phase, co-registered on all contrast-enhanced T1 sequences and used to train 2D and 3D U-Net. The U-Net performance was evaluated on 10 randomized splits of the cohort. The models were evaluated using the dice similarity coefficient (DSC). Our 2D U-Net achieved an average ccRCC lesion detection Area under the curve (AUC) of 0.88 and DSC scores of 0.78, 0.40, and 0.46 for segmentation of the kidney, cysts, and tumors, respectively. Our 3D U-Net achieved an average ccRCC lesion detection AUC of 0.79 and DSC scores of 0.67, 0.32, and 0.34 for kidney, cysts, and tumors, respectively. We demonstrated good detection and moderate segmentation results using U-Net for ccRCC on MRI. Automatic detection and segmentation of normal renal parenchyma, cysts, and masses may assist radiologists in quantifying the burden of disease in patients with VHL.
Collapse
Affiliation(s)
- Pouria Yazdian Anari
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Nathan Lay
- Artificial Intelligence Resource, National Institutes of Health, USA
| | - Aditi Chaurasia
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Nikhil Gopal
- Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA
| | - Safa Samimi
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Stephanie Harmon
- Artificial Intelligence Resource, National Institutes of Health, USA
| | - Rabindra Gautam
- Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA
| | - Kevin Ma
- Artificial Intelligence Resource, National Institutes of Health, USA
| | | | - Evrim Turkbey
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Maria Merino
- Pathology Department, National Cancer Institutes, National Institutes of Health, USA
| | - Elizabeth C. Jones
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| | - Mark W. Ball
- Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA
| | - W. Marston Linehan
- Urology Oncology Branch, National cancer institutes, National Institutes of Health, USA
| | - Baris Turkbey
- Artificial Intelligence Resource, National Institutes of Health, USA
| | - Ashkan A. Malayeri
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, USA
| |
Collapse
|
14
|
Chaurasia A, Gopal N, Dehghani Firouzabadi F, Yazdian Anari P, Wakim P, Ball MW, Jones EC, Turkbey B, Huda F, Linehan WM, Turkbey EB, Malayeri AA. Role of ultra-high b-value DWI in the imaging of hereditary leiomyomatosis and renal cell carcinoma (HLRCC). Abdom Radiol (NY) 2023; 48:340-349. [PMID: 36207629 PMCID: PMC10681094 DOI: 10.1007/s00261-022-03689-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 01/21/2023]
Abstract
PURPOSE Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) syndrome is associated with an aggressive form of renal cell carcinoma with high risk of metastasis, even in small primary tumors with unequivocal imaging findings. In this study, we compare the performance of ultra-high b-value diffusion-weighted imaging (DWI) sequence (b = 2000 s/mm2) to standard DWI (b = 800 s/mm2) sequence in identifying malignant lesions in patients with HLRCC. METHODS Twenty-eight patients (n = 18 HLRCC patients with 22 lesions, n = 10 controls) were independently evaluated by three abdominal radiologists with different levels of experience using four combinations of MRI sequences in two separate sessions (session 1: DWI with b-800, session 2: DWI with b-2000). T1 precontrast, T2-weighted (T2WI), and apparent diffusion coefficient (ADC) sequences were similar in both sessions. Each identified lesion was subjectively assessed using a six-point cancer likelihood score based on individual sequences and overall impression. RESULTS The ability to distinguish benign versus malignant renal lesions improved with the use of b-2000 for more experienced radiologists (Reader 1 AUC: Session 1-0.649 and Session 2-0.938, p = 0.017; Reader 2 AUC: Session 1-0.781 and Session 2-0.921, p = 0.157); whereas no improvement was observed for the less experienced reader (AUC: Session 1-0.541 and Session 2-0.607, p = 0.699). CONCLUSION The inclusion of ultra-high b-value DWI sequence improved the ability of classification of renal lesions in patients with HLRCC for experienced radiologists. Consideration should be given toward incorporation of DWI with b-2000 s/mm2 into existing renal MRI protocols.
Collapse
Affiliation(s)
- Aditi Chaurasia
- Urologic Oncology Branch, National Cancer Institute (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Nikhil Gopal
- Urologic Oncology Branch, National Cancer Institute (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Fatemeh Dehghani Firouzabadi
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Pouria Yazdian Anari
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Paul Wakim
- Biostatistics and Clinical Epidemiology Service, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Mark W Ball
- Urologic Oncology Branch, National Cancer Institute (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Baris Turkbey
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Fahimul Huda
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - W Marston Linehan
- Urologic Oncology Branch, National Cancer Institute (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Evrim B Turkbey
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA
| | - Ashkan A Malayeri
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, 10 Center Drive, 1C352, Bethesda, MD, 20892, USA.
| |
Collapse
|
15
|
Anari PY, Lay N, Gopal N, Chaurasia A, Samimi S, Harmon S, Firouzabadi FD, Merino MJ, Wakim P, Turkbey E, Jones EC, Ball MW, Turkbey B, Linehan WM, Malayeri AA. An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome. Abdom Radiol (NY) 2022; 47:3554-3562. [PMID: 35869307 PMCID: PMC10645140 DOI: 10.1007/s00261-022-03610-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 06/28/2022] [Accepted: 07/03/2022] [Indexed: 01/18/2023]
Abstract
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.
Collapse
Affiliation(s)
- Pouria Yazdian Anari
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Nathan Lay
- Artificial Intelligence Resource, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Nikhil Gopal
- Urologic Oncology Branch, Clinical Center, National Cancer Institute (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Aditi Chaurasia
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Safa Samimi
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Stephanie Harmon
- Artificial Intelligence Resource, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | | | - Maria J Merino
- Pathology Department, Clinical Center, National Cancer Institutes (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Paul Wakim
- Biostatistics and Clinical Epidemiology Service, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Evrim Turkbey
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, Bethesda, MD, USA
| | - Mark W Ball
- Urologic Oncology Branch, Clinical Center, National Cancer Institute (NCI), National Institutes of Health, Bethesda, MD, USA
| | - Baris Turkbey
- Artificial Intelligence Resource, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - W Marston Linehan
- Urologic Oncology Branch, Clinical Center, National Cancer Institute (NCI), National Institutes of Health, Bldg. 10, Room 2 W-5940 and Room 1-5940, 10 Center Drive, Bethesda, MD, 20892, USA.
| | - Ashkan A Malayeri
- Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health, 10 Center Drive, Bethesda, MD, 20892-1109, USA.
| |
Collapse
|
16
|
Abstract
The goal of parathyroid imaging is to identify all sources of excess parathyroid hormone secretion pre-operatively. A variety of imaging approaches have been evaluated and utilized over the years for this purpose. Ultrasound relies solely on structural features and is without radiation, however is limited to superficial evaluation. 4DCT and 4DMRI provide enhancement characteristics in addition to structural features and dynamic enhancement has been investigated as a way to better distinguish parathyroid from adjacent structures. It is important to recognize that 4DCT provides valuable information however results in much higher radiation dose to the thyroid gland than the other available examinations, and therefore the optimal number of phases is an area of controversy. Single-photon scintigraphy with 99mTc-Sestamibi, or dual tracer 99mTc-pertechnetate and 99mTc-sestamibi with or without SPECT or SPECT/CT is part of the standard of care in many centers with availability and expertise in nuclear medicine. This molecular imaging approach detects cellular physiology such as mitochondria content found in parathyroid adenomas. Combining structural imaging such as CT or MRI with molecular imaging in a hybrid approach allows the ability to obtain robust structural and functional information in one examination. Hybrid PET/CT is widely available and provides improved imaging and quantification over SPECT or SPECT/CT. Emerging PET imaging techniques, such as 18F-Fluorocholine, have the exciting potential to reinvent parathyroid imaging. PET/MRI may be particularly well suited to parathyroid imaging, where available, because of the ability to perform dynamic contrast-enhanced imaging and co-registered 18F-Fluorocholine PET imaging simultaneously with low radiation dose to the thyroid. A targeted agent specific for a parathyroid tissue biomarker remains to be identified.
Collapse
Affiliation(s)
| | | | | | | | | | - Clara C. Chen
- National Institutes of Health (NIH) Clinical Center, Department of Radiology and Imaging Sciences, Bethesda, MD, United States
| | - Corina Millo
- National Institutes of Health (NIH) Clinical Center, Department of Radiology and Imaging Sciences, Bethesda, MD, United States
| |
Collapse
|
17
|
Rajagopal JR, Farhadi F, Solomon J, Sahbaee P, Saboury B, Pritchard WF, Jones EC, Samei E. Comparison of Low Dose Performance of Photon-Counting and Energy Integrating CT. Acad Radiol 2021; 28:1754-1760. [PMID: 32855051 PMCID: PMC7902731 DOI: 10.1016/j.acra.2020.07.033] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 07/23/2020] [Accepted: 07/26/2020] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES The purpose of this study was to investigate the potential of photon-counting CT (PCCT) to improve quantitative image quality for low dose imaging compared to energy-integrating detector CT (EID CT). MATERIALS AND METHODS An investigational scanner (Siemens, Germany) with PCCT and EID CT subsystems was used to compare image quality performance at four dose levels: 1.7, 2, 4, 6 mGy CTDIvol, all at or below current dose values used for conventional abdominal CT. A CT quality control phantom with a homogeneous section for noise measurements and a section with cylindrical inserts of air (-910 HU), polystyrene (50 HU), acrylic (205 HU), and Teflon (1000 HU) was imaged and characterized in terms of noise, resolution, contrast-to-noise ratio (CNR), and detectability index. A second phantom with a 30 cm diameter was also imaged containing iodine solutions ranging from 0.125 to 8 mg I/mL. CNR of the iodine vials was computed as a function of CT dose and iodine concentration. RESULTS With resolution unaffected by dose in both PCCT and EID CT, PCCT images exhibited 22.1-24.0% improvement in noise across dose levels evaluated. This noise improvement translated into a 29-41% improvement in CNR and 20-36% improvement in detectability index. For iodine contrast, PCCT images had a higher CNR for all combinations of iodine contrast and dose evaluated. CONCLUSION For the conditions studied, PCCT exhibited superior image quality compared to EID CT. For iodine detection, PCCT offered a notable advantage with improved CNR at all doses and iodine concentration levels.
Collapse
Affiliation(s)
- Jayasai R Rajagopal
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, Duke University Medical Center, Durham, North Carolina; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Room 1C351, Bethesda, MD 20892.
| | - Faraz Farhadi
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Room 1C351, Bethesda, MD 20892
| | - Justin Solomon
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, North Carolina
| | | | - Babak Saboury
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Room 1C351, Bethesda, MD 20892
| | - William F Pritchard
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Room 1C351, Bethesda, MD 20892
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, North Carolina.
| |
Collapse
|
18
|
Fuller SN, Shafiei A, Venzon DJ, Liewehr DJ, Mauda Havanuk M, Ilanchezhian MG, Edgerly M, Anderson VL, Levy EB, Hoang CD, Jones EC, Reilly KM, Widemann BC, Wood BJ, Bagheri H, Del Rivero J. Tumor Doubling Time Using CT Volumetric Segmentation in Metastatic Adrenocortical Carcinoma. Curr Oncol 2021; 28:4357-4366. [PMID: 34898541 PMCID: PMC8628706 DOI: 10.3390/curroncol28060370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 10/17/2021] [Accepted: 10/27/2021] [Indexed: 12/03/2022] Open
Abstract
Adrenocortical carcinoma (ACC) is a rare malignancy with an overall unfavorable prognosis. Clinicians treating patients with ACC have noted accelerated growth in metastatic liver lesions that requires rapid intervention compared to other metastatic locations. This study measured and compared the growth rates of metastatic ACC lesions in the lungs, liver, and lymph nodes using volumetric segmentation. A total of 12 patients with metastatic ACC (six male; six female) were selected based on their medical history. Computer tomography (CT) exams were retrospectively reviewed and a sampling of ≤5 metastatic lesions per organ were selected for evaluation. Lesions in the liver, lung, and lymph nodes were measured and evaluated by volumetric segmentation. Statistical analyses were performed to compare the volumetric growth rates of the lesions in each organ system. In this cohort, 5/12 had liver lesions, 7/12 had lung lesions, and 5/12 had lymph node lesions. A total of 92 lesions were evaluated and segmented for lesion volumetry. The volume doubling time per organ system was 27 days in the liver, 90 days in the lungs, and 95 days in the lymph nodes. In this series of 12 patients with metastatic ACC, liver lesions showed a faster growth rate than lung or lymph node lesions.
Collapse
Affiliation(s)
- Sarah N. Fuller
- Pediatric Oncology Branch, Rare Tumor Initiative, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; (S.N.F.); (M.G.I.); (K.M.R.); (B.C.W.)
| | - Ahmad Shafiei
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, USA; (A.S.); (E.C.J.); (H.B.)
| | - David J. Venzon
- Biostatistics and Data Management Section, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; (D.J.V.); (D.J.L.)
| | - David J. Liewehr
- Biostatistics and Data Management Section, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; (D.J.V.); (D.J.L.)
| | - Michal Mauda Havanuk
- Center for Interventional Oncology, National Institutes of Health, Bethesda, MD 20892, USA; (M.M.H.); (V.L.A.); (E.B.L.); (B.J.W.)
| | - Maran G. Ilanchezhian
- Pediatric Oncology Branch, Rare Tumor Initiative, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; (S.N.F.); (M.G.I.); (K.M.R.); (B.C.W.)
| | - Maureen Edgerly
- Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA;
| | - Victoria L. Anderson
- Center for Interventional Oncology, National Institutes of Health, Bethesda, MD 20892, USA; (M.M.H.); (V.L.A.); (E.B.L.); (B.J.W.)
| | - Elliot B. Levy
- Center for Interventional Oncology, National Institutes of Health, Bethesda, MD 20892, USA; (M.M.H.); (V.L.A.); (E.B.L.); (B.J.W.)
| | - Choung D. Hoang
- Thoracic Surgery Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA;
| | - Elizabeth C. Jones
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, USA; (A.S.); (E.C.J.); (H.B.)
| | - Karlyne M. Reilly
- Pediatric Oncology Branch, Rare Tumor Initiative, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; (S.N.F.); (M.G.I.); (K.M.R.); (B.C.W.)
| | - Brigitte C. Widemann
- Pediatric Oncology Branch, Rare Tumor Initiative, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; (S.N.F.); (M.G.I.); (K.M.R.); (B.C.W.)
| | - Bradford J. Wood
- Center for Interventional Oncology, National Institutes of Health, Bethesda, MD 20892, USA; (M.M.H.); (V.L.A.); (E.B.L.); (B.J.W.)
| | - Hadi Bagheri
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, USA; (A.S.); (E.C.J.); (H.B.)
| | - Jaydira Del Rivero
- Pediatric Oncology Branch, Rare Tumor Initiative, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; (S.N.F.); (M.G.I.); (K.M.R.); (B.C.W.)
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Correspondence:
| |
Collapse
|
19
|
Rajagopal JR, Farhadi F, Richards T, Nikpanah M, Sahbaee P, Shanbhag SM, Bandettini WP, Saboury B, Malayeri AA, Pritchard WF, Jones EC, Samei E, Chen MY. Evaluation of Coronary Plaques and Stents with Conventional and Photon-counting CT: Benefits of High-Resolution Photon-counting CT. Radiol Cardiothorac Imaging 2021; 3:e210102. [PMID: 34778782 PMCID: PMC8581588 DOI: 10.1148/ryct.2021210102] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 08/30/2021] [Accepted: 09/30/2021] [Indexed: 11/11/2022]
Abstract
PURPOSE To compare the performance of energy-integrating detector (EID) CT, photon-counting detector CT (PCCT), and high-resolution PCCT (HR-PCCT) for the visualization of coronary plaques and reduction of stent artifacts in a phantom model. MATERIALS AND METHODS An investigational scanner with EID and PCCT subsystems was used to image a coronary artery phantom containing cylindrical probes simulating different plaque compositions. The phantom was imaged with and without coronary stents using both subsystems. Images were reconstructed with a clinical cardiac kernel and an additional HR-PCCT kernel. Regions of interest were drawn around probes and evaluated for in-plane diameter and a qualitative comparison by expert readers. A linear mixed-effects model was used to compare the diameter results, and a Shrout-Fleiss intraclass correlation coefficient was used to assess consistency in the reader study. RESULTS Comparing in-plane diameter to the physical dimension for nonstented and stented phantoms, measurements of the HR-PCCT images were more accurate (nonstented: 4.4% ± 1.1 [standard deviation], stented: -9.4% ± 4.6) than EID (nonstented: 15.5% ± 4.0, stented: -19.5% ± 5.8) and PCCT (nonstented: 19.4% ± 2.5, stented: -18.3% ± 4.4). Our analysis of variance found diameter measurements to be different across image groups for both nonstented and stented cases (P < .001). HR-PCCT showed less change on average in percent stenosis due to the addition of a stent (-5.5%) than either EID (+90.5%) or PCCT (+313%). For both nonstented and stented phantoms, observers rated the HR-PCCT images as having higher plaque conspicuity and as being the image type that was least impacted by stent artifacts, with a high level of agreement (interclass correlation coefficient = 0.85). CONCLUSION Despite increased noise, HR-PCCT images were able to better visualize coronary plaques and reduce stent artifacts compared with EID or PCCT reconstructions.Keywords: CT-Spectral Imaging (Dual Energy), Phantom Studies, Cardiac, Physics, Technology Assessment© RSNA, 2021.
Collapse
Affiliation(s)
- Jayasai R Rajagopal
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, Duke University Medical Center, Durham, NC (J.R.R., T.R., E.S.); Department of Radiology and Imaging Sciences, Clinical Center (F.F., M.N., B.S., A.A.M., E.C.J.), Cardiovascular Branch, National Heart, Lung, and Blood Institute (S.M.S., W.P.B., M.Y.C.), and Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center (W.F.P.), National Institutes of Health, 10 Center Dr, Building 10, Room B1D417, Bethesda, MD 20892; and Siemens Medical Solutions USA, Malvern, Pa (P.S.)
| | - Faraz Farhadi
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, Duke University Medical Center, Durham, NC (J.R.R., T.R., E.S.); Department of Radiology and Imaging Sciences, Clinical Center (F.F., M.N., B.S., A.A.M., E.C.J.), Cardiovascular Branch, National Heart, Lung, and Blood Institute (S.M.S., W.P.B., M.Y.C.), and Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center (W.F.P.), National Institutes of Health, 10 Center Dr, Building 10, Room B1D417, Bethesda, MD 20892; and Siemens Medical Solutions USA, Malvern, Pa (P.S.)
| | - Taylor Richards
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, Duke University Medical Center, Durham, NC (J.R.R., T.R., E.S.); Department of Radiology and Imaging Sciences, Clinical Center (F.F., M.N., B.S., A.A.M., E.C.J.), Cardiovascular Branch, National Heart, Lung, and Blood Institute (S.M.S., W.P.B., M.Y.C.), and Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center (W.F.P.), National Institutes of Health, 10 Center Dr, Building 10, Room B1D417, Bethesda, MD 20892; and Siemens Medical Solutions USA, Malvern, Pa (P.S.)
| | - Moozhan Nikpanah
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, Duke University Medical Center, Durham, NC (J.R.R., T.R., E.S.); Department of Radiology and Imaging Sciences, Clinical Center (F.F., M.N., B.S., A.A.M., E.C.J.), Cardiovascular Branch, National Heart, Lung, and Blood Institute (S.M.S., W.P.B., M.Y.C.), and Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center (W.F.P.), National Institutes of Health, 10 Center Dr, Building 10, Room B1D417, Bethesda, MD 20892; and Siemens Medical Solutions USA, Malvern, Pa (P.S.)
| | - Pooyan Sahbaee
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, Duke University Medical Center, Durham, NC (J.R.R., T.R., E.S.); Department of Radiology and Imaging Sciences, Clinical Center (F.F., M.N., B.S., A.A.M., E.C.J.), Cardiovascular Branch, National Heart, Lung, and Blood Institute (S.M.S., W.P.B., M.Y.C.), and Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center (W.F.P.), National Institutes of Health, 10 Center Dr, Building 10, Room B1D417, Bethesda, MD 20892; and Siemens Medical Solutions USA, Malvern, Pa (P.S.)
| | - Sujata M Shanbhag
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, Duke University Medical Center, Durham, NC (J.R.R., T.R., E.S.); Department of Radiology and Imaging Sciences, Clinical Center (F.F., M.N., B.S., A.A.M., E.C.J.), Cardiovascular Branch, National Heart, Lung, and Blood Institute (S.M.S., W.P.B., M.Y.C.), and Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center (W.F.P.), National Institutes of Health, 10 Center Dr, Building 10, Room B1D417, Bethesda, MD 20892; and Siemens Medical Solutions USA, Malvern, Pa (P.S.)
| | - W Patricia Bandettini
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, Duke University Medical Center, Durham, NC (J.R.R., T.R., E.S.); Department of Radiology and Imaging Sciences, Clinical Center (F.F., M.N., B.S., A.A.M., E.C.J.), Cardiovascular Branch, National Heart, Lung, and Blood Institute (S.M.S., W.P.B., M.Y.C.), and Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center (W.F.P.), National Institutes of Health, 10 Center Dr, Building 10, Room B1D417, Bethesda, MD 20892; and Siemens Medical Solutions USA, Malvern, Pa (P.S.)
| | - Babak Saboury
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, Duke University Medical Center, Durham, NC (J.R.R., T.R., E.S.); Department of Radiology and Imaging Sciences, Clinical Center (F.F., M.N., B.S., A.A.M., E.C.J.), Cardiovascular Branch, National Heart, Lung, and Blood Institute (S.M.S., W.P.B., M.Y.C.), and Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center (W.F.P.), National Institutes of Health, 10 Center Dr, Building 10, Room B1D417, Bethesda, MD 20892; and Siemens Medical Solutions USA, Malvern, Pa (P.S.)
| | - Ashkan A Malayeri
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, Duke University Medical Center, Durham, NC (J.R.R., T.R., E.S.); Department of Radiology and Imaging Sciences, Clinical Center (F.F., M.N., B.S., A.A.M., E.C.J.), Cardiovascular Branch, National Heart, Lung, and Blood Institute (S.M.S., W.P.B., M.Y.C.), and Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center (W.F.P.), National Institutes of Health, 10 Center Dr, Building 10, Room B1D417, Bethesda, MD 20892; and Siemens Medical Solutions USA, Malvern, Pa (P.S.)
| | - William F Pritchard
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, Duke University Medical Center, Durham, NC (J.R.R., T.R., E.S.); Department of Radiology and Imaging Sciences, Clinical Center (F.F., M.N., B.S., A.A.M., E.C.J.), Cardiovascular Branch, National Heart, Lung, and Blood Institute (S.M.S., W.P.B., M.Y.C.), and Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center (W.F.P.), National Institutes of Health, 10 Center Dr, Building 10, Room B1D417, Bethesda, MD 20892; and Siemens Medical Solutions USA, Malvern, Pa (P.S.)
| | - Elizabeth C Jones
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, Duke University Medical Center, Durham, NC (J.R.R., T.R., E.S.); Department of Radiology and Imaging Sciences, Clinical Center (F.F., M.N., B.S., A.A.M., E.C.J.), Cardiovascular Branch, National Heart, Lung, and Blood Institute (S.M.S., W.P.B., M.Y.C.), and Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center (W.F.P.), National Institutes of Health, 10 Center Dr, Building 10, Room B1D417, Bethesda, MD 20892; and Siemens Medical Solutions USA, Malvern, Pa (P.S.)
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, Duke University Medical Center, Durham, NC (J.R.R., T.R., E.S.); Department of Radiology and Imaging Sciences, Clinical Center (F.F., M.N., B.S., A.A.M., E.C.J.), Cardiovascular Branch, National Heart, Lung, and Blood Institute (S.M.S., W.P.B., M.Y.C.), and Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center (W.F.P.), National Institutes of Health, 10 Center Dr, Building 10, Room B1D417, Bethesda, MD 20892; and Siemens Medical Solutions USA, Malvern, Pa (P.S.)
| | - Marcus Y Chen
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Department of Radiology, Duke University Medical Center, Durham, NC (J.R.R., T.R., E.S.); Department of Radiology and Imaging Sciences, Clinical Center (F.F., M.N., B.S., A.A.M., E.C.J.), Cardiovascular Branch, National Heart, Lung, and Blood Institute (S.M.S., W.P.B., M.Y.C.), and Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center (W.F.P.), National Institutes of Health, 10 Center Dr, Building 10, Room B1D417, Bethesda, MD 20892; and Siemens Medical Solutions USA, Malvern, Pa (P.S.)
| |
Collapse
|
20
|
Nikpanah M, Xu Z, Jin D, Farhadi F, Saboury B, Ball MW, Gautam R, Merino MJ, Wood BJ, Turkbey B, Jones EC, Linehan WM, Malayeri AA. A deep-learning based artificial intelligence (AI) approach for differentiation of clear cell renal cell carcinoma from oncocytoma on multi-phasic MRI. Clin Imaging 2021; 77:291-298. [PMID: 34171743 PMCID: PMC9990181 DOI: 10.1016/j.clinimag.2021.06.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 04/19/2021] [Accepted: 06/08/2021] [Indexed: 11/29/2022]
Abstract
PURPOSE To investigate the diagnostic performance of a deep convolutional neural network for differentiation of clear cell renal cell carcinoma (ccRCC) from renal oncocytoma. METHODS In this retrospective study, 74 patients (49 male, mean age 59.3) with 243 renal masses (203 ccRCC and 40 oncocytoma) that had undergone MR imaging 6 months prior to pathologic confirmation of the lesions were included. Segmentation using seed placement and bounding box selection was used to extract the lesion patches from T2-WI, and T1-WI pre-contrast, post-contrast arterial and venous phases. Then, a deep convolutional neural network (AlexNet) was fine-tuned to distinguish the ccRCC from oncocytoma. Five-fold cross validation was used to evaluate the AI algorithm performance. A subset of 80 lesions (40 ccRCC, 40 oncocytoma) were randomly selected to be classified by two radiologists and their performance was compared to the AI algorithm. Intra-class correlation coefficient was calculated using the Shrout-Fleiss method. RESULTS Overall accuracy of the AI system was 91% for differentiation of ccRCC from oncocytoma with an area under the curve of 0.9. For the observer study on 80 randomly selected lesions, there was moderate agreement between the two radiologists and AI algorithm. In the comparison sub-dataset, classification accuracies were 81%, 78%, and 70% for AI, radiologist 1, and radiologist 2, respectively. CONCLUSION The developed AI system in this study showed high diagnostic performance in differentiation of ccRCC versus oncocytoma on multi-phasic MRIs.
Collapse
Affiliation(s)
- Moozhan Nikpanah
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA. https://twitter.com/MoozhanNikpanah
| | - Ziyue Xu
- NVIDIA Corporation, Bethesda, MD, USA
| | | | - Faraz Farhadi
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA. https://twitter.com/Faraz_Farhadi
| | - Babak Saboury
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Mark W Ball
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. https://twitter.com/markballmd
| | - Rabindra Gautam
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Bradford J Wood
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA. https://twitter.com/BradWoodMD
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, USA. https://twitter.com/radiolobt
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - W Marston Linehan
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Ashkan A Malayeri
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
| |
Collapse
|
21
|
Nikpanah M, Paschall AK, Ahlman MA, Civelek AC, Farhadi F, Mirmomen SM, Li X, Saboury B, Ball MW, Merino MJ, Srinivasan R, Jones EC, Linehan WM, Malayeri AA. 18Fluorodeoxyglucose-positron emission tomography/computed tomography for differentiation of renal tumors in hereditary kidney cancer syndromes. Abdom Radiol (NY) 2021; 46:3301-3308. [PMID: 33688985 DOI: 10.1007/s00261-021-02999-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 02/03/2021] [Accepted: 02/11/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE To assess differences in FDG-PET/CT uptake among four subtypes of renal tumors: clear cell RCC (ccRCC), papillary type I and II RCC (pRCC), and oncocytoma. METHODS This retrospective study investigated 33 patients with 98 hereditary renal tumors. Lesions greater than 1 cm and patients with a timeframe of less than 18 months between preoperative imaging and surgery were considered. FDG-PET/CT images were independently reviewed by two nuclear medicine physicians, blinded to clinical information. Volumetric lesion SUVmean was measured and used to calculate a target-to-background ratio respective to liver (TBR). The Shrout-Fleiss intra-class correlation coefficient was used to assess reliability between readers. A linear mixed effects model, accounting for within-patient correlations, was used to compare TBR values of primary renal lesions with and without distant metastasis. RESULTS The time interval between imaging and surgery for all tumors had a median of 77 (Mean: 139; Range: 1-512) days. Intra-class reliability of mean TBR resulted in a mean κ score of 0.93, indicating strong agreement between the readers. The mixed model showed a significant difference in mean TBR among the subtypes (p < 0.0001). Pairwise comparison showed significant differences between pRCC type II and ccRCC (p < 0.0001), pRCC type II and pRCC type I (p = 0.0001), and pRCC type II and oncocytoma (p = 0.0016). Furthermore, a significant difference in FDG uptake was present between primary pRCC type II renal lesions with and without distant metastasis (p = 0.023). CONCLUSION pRCC type II lesions demonstrated significantly higher FDG activity than ccRCC, pRCC type I, or oncocytoma. These findings indicate that FDG may prove useful in studying the metabolic activity of renal neoplasms, identifying lesions of highest clinical concern, and ultimately optimizing active surveillance, and personalizing management plans.
Collapse
|
22
|
Campbell-Washburn AE, Malayeri AA, Jones EC, Moss J, Fennelly KP, Olivier KN, Chen MY. T2-weighted Lung Imaging Using a 0.55-T MRI System. Radiol Cardiothorac Imaging 2021; 3:e200611. [PMID: 34250492 DOI: 10.1148/ryct.2021200611] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 04/22/2021] [Accepted: 05/04/2021] [Indexed: 02/03/2023]
Abstract
Purpose To assess a 0.55-T MRI system for imaging lung disease and to compare image quality with clinical CT scans. Materials and Methods In this prospective study conducted between November 2018 and December 2019, respiratory-triggered T2-weighted turbo spin-echo MRI at 0.55 T was compared with clinical CT scans in 24 participants (mean age, 59 years ± 16 [standard deviation]; 18 women) with common lung abnormalities. MR images were reviewed and scored by experienced readers. Abnormal findings identified with MRI and CT were compared using the Cohen κ statistic. Results High-quality structural pulmonary MR images were attained with an average acquisition time of 11 minutes ± 3. MRI generated sufficient image quality to robustly detect bronchiectasis (κ = 0.61), consolidative opacities (κ = 1.00), cavitary lesions (κ = 1.00), effusion (κ = 0.64), mucus plug (κ = 0.68), and solid scattered nodularity (κ = 0.82). Diffuse disease, including ground-glass opacities (κ = 0.57) and tree-in-bud nodules (κ = 0.48), were the findings that were most difficult to discern using MRI, with false readings in four of 18 patients for each feature. Nodule size, which was measured independently at CT and MRI, was strongly correlated (R 2 = 0.99) for nodules with a measurement of 10 mm ± 5 (range, 5-23 mm). Conclusion This initial study indicates that high-performance 0.55-T MRI holds promise in the evaluation of common lung disease.Clinical trials registration no. NCT03331380Supplemental material is available for this article. Keywords: MRI, Pulmonary, Technology Assessment© RSNA, 2021.
Collapse
Affiliation(s)
- Adrienne E Campbell-Washburn
- Cardiovascular (A.E.C.W., M.Y.C.) and Pulmonary (J.M., K.P.F., K.N.O.) Branches, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, Building 10, Room BID-47, 10 Center Dr, Bethesda, MD 20892; and Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Department of Health and Human Services, Bethesda, Md (A.A.M., E.C.J.)
| | - Ashkan A Malayeri
- Cardiovascular (A.E.C.W., M.Y.C.) and Pulmonary (J.M., K.P.F., K.N.O.) Branches, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, Building 10, Room BID-47, 10 Center Dr, Bethesda, MD 20892; and Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Department of Health and Human Services, Bethesda, Md (A.A.M., E.C.J.)
| | - Elizabeth C Jones
- Cardiovascular (A.E.C.W., M.Y.C.) and Pulmonary (J.M., K.P.F., K.N.O.) Branches, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, Building 10, Room BID-47, 10 Center Dr, Bethesda, MD 20892; and Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Department of Health and Human Services, Bethesda, Md (A.A.M., E.C.J.)
| | - Joel Moss
- Cardiovascular (A.E.C.W., M.Y.C.) and Pulmonary (J.M., K.P.F., K.N.O.) Branches, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, Building 10, Room BID-47, 10 Center Dr, Bethesda, MD 20892; and Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Department of Health and Human Services, Bethesda, Md (A.A.M., E.C.J.)
| | - Kevin P Fennelly
- Cardiovascular (A.E.C.W., M.Y.C.) and Pulmonary (J.M., K.P.F., K.N.O.) Branches, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, Building 10, Room BID-47, 10 Center Dr, Bethesda, MD 20892; and Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Department of Health and Human Services, Bethesda, Md (A.A.M., E.C.J.)
| | - Kenneth N Olivier
- Cardiovascular (A.E.C.W., M.Y.C.) and Pulmonary (J.M., K.P.F., K.N.O.) Branches, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, Building 10, Room BID-47, 10 Center Dr, Bethesda, MD 20892; and Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Department of Health and Human Services, Bethesda, Md (A.A.M., E.C.J.)
| | - Marcus Y Chen
- Cardiovascular (A.E.C.W., M.Y.C.) and Pulmonary (J.M., K.P.F., K.N.O.) Branches, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, Building 10, Room BID-47, 10 Center Dr, Bethesda, MD 20892; and Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Department of Health and Human Services, Bethesda, Md (A.A.M., E.C.J.)
| |
Collapse
|
23
|
Shafiei A, Bagheri M, Farhadi F, Apolo AB, Biassou NM, Folio LR, Jones EC, Summers RM. CT Evaluation of Lymph Nodes That Merge or Split during the Course of a Clinical Trial: Limitations of RECIST 1.1. Radiol Imaging Cancer 2021; 3:e200090. [PMID: 33874734 PMCID: PMC8189184 DOI: 10.1148/rycan.2021200090] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 02/11/2021] [Accepted: 02/16/2021] [Indexed: 06/12/2023]
Abstract
Purpose To compare Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 with volumetric measurement in the setting of target lymph nodes that split into two or more nodes or merge into one conglomerate node. Materials and Methods In this retrospective study, target lymph nodes were evaluated on CT scans from 166 patients with different types of cancer; 158 of the scans came from The Cancer Imaging Archive. Each target node was measured using RECIST 1.1 criteria before and after merging or splitting, followed by volumetric segmentation. To compare RECIST 1.1 with volume, a single-dimension hypothetical diameter (HD) was determined from the nodal volume. The nodes were divided into three groups: (a) one-target merged (one target node merged with other nodes); (b) two-target merged (two neighboring target nodes merged); and (c) split node (a conglomerate node cleaved into smaller fragments). Bland-Altman analysis and t test were applied to compare RECIST 1.1 with HD. On the basis of the RECIST 1.1 concept, we compared response category changes between RECIST 1.1 and HD. Results The data set consisted of 30 merged nodes (19 one-target merged and 11 two-target merged) and 20 split nodes (mean age for all 50 included patients, 50 years ± 7 [standard deviation]; 38 men). RECIST 1.1, volumetric, and HD measurements indicated an increase in size in all one-target merged nodes. While volume and HD indicated an increase in size for nodes in the two-target merged group, RECIST 1.1 showed a decrease in size in all two-target merged nodes. Although volume and HD demonstrated a decrease in size of all split nodes, RECIST 1.1 indicated an increase in size in 60% (12 of 20) of the nodes. Discrepancy of the response categories between RECIST 1.1 and HD was observed in 5% (one of 19) in one-target merged, 82% (nine of 11) in two-target merged, and 55% (11 of 20) in split nodes. Conclusion RECIST 1.1 does not optimally reflect size changes when lymph nodes merge or split. Keywords: CT, Lymphatic, Tumor Response Supplemental material is available for this article. © RSNA, 2021.
Collapse
|
24
|
Mauda-Havakuk M, Mikhail AS, Starost MF, Jones EC, Karim B, Kleiner DE, Partanen A, Esparza-Trujillo JA, Bakhutashvili I, Wakim PG, Kassin MT, Lewis AL, Karanian JW, Wood BJ, Pritchard WF. Imaging, Pathology, and Immune Correlates in the Woodchuck Hepatic Tumor Model. J Hepatocell Carcinoma 2021; 8:71-83. [PMID: 33728278 PMCID: PMC7955744 DOI: 10.2147/jhc.s287800] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 01/25/2021] [Indexed: 12/30/2022] Open
Abstract
Background Woodchucks chronically infected with woodchuck hepatitis virus (WHV), which resembles human hepatitis B virus, develop spontaneous hepatic tumors and may be an important biological and immunological model for human HCC. Nonetheless, this model requires further validation to fully realize its translational potential. Methods Woodchucks infected at birth with WHV that had developed HCC (n=12) were studied. Computed tomography, ultrasound, and magnetic resonance imaging were performed under anesthesia. LI-RADS scoring and correlative histologic analysis of sectioned tissues were performed. For immune characterization of tumors, CD3 (T cells), CD4 (T helpers), NCAM (Natural killers), FOXP3 (T-regulatory), PDL-1 (inhibitory checkpoint protein), and the human hepatocellular carcinoma (HCC) biomarker alpha-fetoprotein (AFP) immunohistochemical stains were performed. Results Forty tumors were identified on imaging of which 29 were confirmed to be HCC with 26 categorized as LR-4 or 5. The remainder of the tumors had benign histology including basophilic foci, adenoma, and lipidosis as well as pre-malignant dysplastic foci. LR-4 and LR-5 lesions showed high sensitivity (90%) and specificity (100%) for malignant and pre-malignant tumors. Natural killers count was found to be 2–5 times lower in tumors relative to normal parenchyma while other immune cells were located in the periphery of tumors. Tumors expressed AFP and did not express PD-L1. Conclusion Woodchucks chronically infected with WHV developed diverse hepatic tumor types with diagnostic imaging, pathology, and immune patterns comparable to that in humans. This unique animal model may provide a valuable tool for translation and validation of novel image-guided and immune-therapeutic investigations.
Collapse
Affiliation(s)
- Michal Mauda-Havakuk
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Andrew S Mikhail
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Matthew F Starost
- Division of Veterinary Resources, National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Baktiar Karim
- National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - David E Kleiner
- Center for Cancer Research, Clinical Center, National Cancer Institute, Bethesda, MD, USA
| | - Ari Partanen
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Juan A Esparza-Trujillo
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Ivane Bakhutashvili
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Paul G Wakim
- Biostatistics and Clinical Epidemiology Service, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Michael T Kassin
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Andrew L Lewis
- Biocompatibles UK Ltd (a BTG International Group Company), Camberley, UK
| | - John W Karanian
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Bradford J Wood
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institute of Biomedical Imaging and Bioengineering and National Cancer Institute Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA
| | - William F Pritchard
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| |
Collapse
|
25
|
Abstract
Total-body PET scans will initiate a new era for the PET clinic. The benefits of 40-fold effective sensitivity improvement provide new capabilities to image with lower radiation dose, perform delayed imaging, and achieve improved temporal resolution. These technical features are detailed in the first of this 2-part series. In this part, the clinical impacts of the novel features of total-body PET scans are further explored. Applications of total-body PET scans focus on the real-time interrogation of systemic disease manifestations in a variety of practical clinical contexts. Total-body PET scans make clinical systems biology imaging a reality.
Collapse
Affiliation(s)
- Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA; Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104, USA
| | - Michael A Morris
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Moozhan Nikpanah
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
| | - Thomas J Werner
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104, USA
| | - Elizabeth C Jones
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA
| | - Abass Alavi
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104, USA.
| |
Collapse
|
26
|
Rajagopal JR, Sahbaee P, Farhadi F, Solomon JB, Ramirez-Giraldo JC, Pritchard WF, Wood BJ, Jones EC, Samei E. A Clinically Driven Task-Based Comparison of Photon Counting and Conventional Energy Integrating CT for Soft Tissue, Vascular, and High-Resolution Tasks. IEEE Trans Radiat Plasma Med Sci 2020; 5:588-595. [PMID: 34250326 DOI: 10.1109/trpms.2020.3019954] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Photon-counting CT detectors are the next step in advancing CT system development and will replace the current energy integrating detectors (EID) in CT systems in the near future. In this context, the performance of PCCT was compared to EID CT for three clinically relevant tasks: abdominal soft tissue imaging, where differentiating low contrast features is important; vascular imaging, where iodine detectability is critical; and, high-resolution skeletal and lung imaging. A multi-tiered phantom was imaged on an investigational clinical PCCT system (Siemens Healthineers) across different doses using three imaging modes: macro and ultra-high resolution (UHR) PCCT modes and EID CT. Images were reconstructed using filtered backprojection and soft tissue (B30f), vascular (B46f), or high-resolution (B70f; U70f for UHR) kernels. Noise power spectra, task transfer functions, and detectability index were evaluated. For a soft tissue task, PCCT modes showed comparable noise and resolution with improved contrast-to-noise ratio. For a vascular task, PCCT modes showed lower noise and improved iodine detectability. For a high resolution task, macro mode showed lower noise and comparable resolution while UHR mode showed higher noise but improved spatial resolution for both air and bone. PCCT offers competitive advantages to EID CT for clinical tasks.
Collapse
Affiliation(s)
- Jayasai R Rajagopal
- Carl E. Ravin Advanced Imaging Laboratories, and Medical Physics Graduate Program, Duke University, Durham, NC, 27705 USA
| | | | - Faraz Farhadi
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892 USA
| | - Justin B Solomon
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, and Department of Radiology, Duke University, Durham NC, 27705 USA
| | | | - William F Pritchard
- Center for Interventional Oncology, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda MD, 20892 USA
| | - Bradford J Wood
- Center for Interventional Oncology, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, 20892 USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892 USA
| | - Ehsan Samei
- Carl. E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, and Departments of Electrical and Computer Engineering, Radiology, Biomedical Engineering, and Physics, Duke University, Durham, NC, 27705 USA
| |
Collapse
|
27
|
Paschall AK, Nikpanah M, Farhadi F, Jones EC, Wakim PG, Dwyer AJ, Gautam R, Merino MJ, Srinivasan R, Linehan WM, Malayeri AA. Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) syndrome: Spectrum of imaging findings. Clin Imaging 2020; 68:14-19. [PMID: 32562921 DOI: 10.1016/j.clinimag.2020.06.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 04/30/2020] [Accepted: 06/07/2020] [Indexed: 01/16/2023]
Abstract
PURPOSE To retrospectively investigate the radiological presentations of HLRCC-associated renal tumors to facilitate accurate lesion characterization and compare these presentations with simple cysts and characteristics of other subtypes of renal cell carcinoma (RCC) as reported in the literature. METHODS The MRI and CT imaging characteristics of 39 pathologically confirmed lesions from 30 patients (20 male, 10 female) with HLRCC syndrome were evaluated by two radiologists. Patients had an average age at diagnosis of 43.8 ± 13.1 years. Lesion characteristics including laterality, homogeneity, diameter (cm), nodularity, septations, T1 and T2 signal intensity, enhancement, and restricted diffusion were recorded. Imaging characteristics of the lesions were further compared to characteristics of benign simple cysts surgically removed at the same time point. RESULTS The examined lesions had a mean diameter of 5.06 ± 3.80 cm, an average growth rate of 2.91 × 10-3 cm/day and an estimated annual growth rate of 1.06 cm/year. 50% of lesions demonstrated nodularity, 65% were mostly T2-hyperintense, 83% demonstrated restricted diffusion in solid portions of the lesions, and 65% had well-defined margins. 76% of patients demonstrated extra-renal manifestations, 53% lymphadenopathy, and 43% distant metastasis. CONCLUSIONS Our analysis confirmed that while HLRCC-associated renal lesions demonstrate diversity in imaging presentations, the majority are unilateral and solitary, T2-hyperintense, heterogeneous with well-defined margins, and frequently demonstrate restricted diffusion and nodularity.
Collapse
Affiliation(s)
- Anna K Paschall
- National Institutes of Health Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20814, United States of America; Duke University Health System, School of Medicine, 8 Searle Center Dr., Durham, NC 27710, United States of America
| | - Moozhan Nikpanah
- National Institutes of Health Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20814, United States of America
| | - Faraz Farhadi
- National Institutes of Health Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20814, United States of America
| | - Elizabeth C Jones
- National Institutes of Health Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20814, United States of America
| | - Paul G Wakim
- National Institutes of Health Clinical Center, Biostatistics and Clinical Epidemiology Service, 10 Center Drive, Bethesda, MD 20814, United States of America
| | - Andrew J Dwyer
- National Institutes of Health Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20814, United States of America
| | - Rabindra Gautam
- National Institutes of Health, National Cancer Institute- Urologic Oncology Branch, 10 Center Drive, Bethesda, MD 20814, United States of America
| | - Maria J Merino
- National Institutes of Health, National Cancer Institute- Laboratory of Pathology, 10 Center Drive, Bethesda, MD 20814, United States of America
| | - Ramaprasad Srinivasan
- National Institutes of Health, National Cancer Institute- Urologic Oncology Branch, 10 Center Drive, Bethesda, MD 20814, United States of America
| | - W Marston Linehan
- National Institutes of Health, National Cancer Institute- Urologic Oncology Branch, 10 Center Drive, Bethesda, MD 20814, United States of America
| | - Ashkan A Malayeri
- National Institutes of Health Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20814, United States of America.
| |
Collapse
|
28
|
Farhadi F, Nikpanah M, Paschall AK, Shafiei A, Tadayoni A, Ball MW, Linehan WM, Jones EC, Malayeri AA. Clear Cell Renal Cell Carcinoma Growth Correlates with Baseline Diffusion-weighted MRI in Von Hippel-Lindau Disease. Radiology 2020; 295:E10. [PMID: 32421466 DOI: 10.1148/radiol.2020204010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
29
|
Ierardi AM, Wood BJ, Gaudino C, Angileri SA, Jones EC, Hausegger K, Carrafiello G. How to Handle a COVID-19 Patient in the Angiographic Suite. Cardiovasc Intervent Radiol 2020; 43:820-826. [PMID: 32277272 PMCID: PMC7147145 DOI: 10.1007/s00270-020-02476-8] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 04/01/2020] [Indexed: 01/12/2023]
Abstract
This is a single-center report on coordination of staff and handling of patients during the outbreak of the COVID-19 (coronavirus disease 2019) in a region with high incidence and prevalence of disease. The selection of procedures for interventional radiology (IR), preparation of staff and interventional suite before the arrival of patients, the facility ventilation systems and intra- and post-procedural workflow optimization are described. The control measures described may increase the cost of the equipment, prolong procedural times and increase technical difficulties. However, these precautions may help control the spread of COVID-19 within the healthcare facility.
Collapse
Affiliation(s)
- Anna Maria Ierardi
- Radiology Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Bradford J Wood
- Center for Interventional Oncology, National Institutes of Health Clinical Center and National Cancer Institute, National Institutes of Health, 10 Center Dr., Room 3N320B, MSC 1182, Bethesda, MD, 20892, USA
| | - Chiara Gaudino
- Department of Neuroradiology, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy
| | | | | | - Klaus Hausegger
- Institut für Diagnostische und Interventionelle Radiologie, Klinikum Klagenfurt am Wörthersee, Feschnigstraße 11, 9020, Klagenfurt, Austria
| | - Gianpaolo Carrafiello
- Radiology Department, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy.,Department of Health Sciences, Università degli Studi di Milano, Milan, Italy
| |
Collapse
|
30
|
Farhadi F, Nikpanah M, Paschall AK, Shafiei A, Tadayoni A, Ball MW, Linehan WM, Jones EC, Malayeri AA. Clear Cell Renal Cell Carcinoma Growth Correlates with Baseline Diffusion-weighted MRI in Von Hippel-Lindau Disease. Radiology 2020; 295:583-590. [PMID: 32255415 DOI: 10.1148/radiol.2020191016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Background Identification of markers to aid in understanding the growth kinetics of Von Hippel-Lindau (VHL)-associated clear cell renal cell carcinoma (ccRCC) has the potential to allow individualization of patient care, thereby helping prevent unnecessary screening and optimizing intervention. Purpose To determine whether the degree of restricted diffusion at baseline MRI holds predictive potential for the growth rate of VHL-associated ccRCC. Materials and Methods Patients with VHL disease who underwent surgical resection of tumors between November 2014 and October 2017 were analyzed retrospectively in this HIPAA-compliant study. The change in ccRCC volume between two time points and apparent diffusion coefficient (ADC) at baseline was calculated by using segmentations by two readers at nephrographic-phase CT and diffusion-weighted MRI, respectively. Intraclass correlation coefficient was used to assess agreement between readers. Repeated-measures correlation was used to investigate relationships between ADC (histogram parameters) and tumor size at baseline with growth rate and volume doubling time (VDT). Predictive performance of the ADC parameter with highest correlation and tumor size at baseline was reviewed to differentiate tumors based on their VDT (≤1 year or >1 year). Results Forty-six patients (mean age, 46 years ± 7 [standard deviation]; 25 women) with 100 ccRCCs were evaluated. Interreader agreement resulted in mean κ scores of 0.89, 0.82, and 0.93 for mean ADC, baseline tumor volume, and follow-up tumor volume, respectively. ADC percentiles correlated negatively with tumor growth rate but correlated positively with VDT. Lower ADC values demonstrated stronger correlations. The 25th percentile ADC had the strongest correlation with growth rate (ρ = -0.52, P < .001) and VDT (ρ = 0.60, P < .001) and enabled prediction of VDT (≤1 year or >1 year) with an area under the receiver operating characteristic curve of 0.86 (sensitivity, 67%; specificity, 89%) (P < .001). Conclusion Apparent diffusion coefficient at baseline was negatively correlated with tumor growth rate. Diffusion-weighted MRI may be useful to identify clear cell renal cell carcinomas with higher growth rates. © RSNA, 2020See also the editorial by Goh and Prezzi in this issue.
Collapse
Affiliation(s)
- Faraz Farhadi
- From the Radiology and Imaging Sciences, NIH Clinical Center (F.F., M.N., A.K.P., A.S., A.T., E.C.J., A.A.M.), and Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute (M.W.B., W.M.L.), National Institutes of Health, 10 Center Dr, Bethesda, MD 20814
| | - Moozhan Nikpanah
- From the Radiology and Imaging Sciences, NIH Clinical Center (F.F., M.N., A.K.P., A.S., A.T., E.C.J., A.A.M.), and Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute (M.W.B., W.M.L.), National Institutes of Health, 10 Center Dr, Bethesda, MD 20814
| | - Anna K Paschall
- From the Radiology and Imaging Sciences, NIH Clinical Center (F.F., M.N., A.K.P., A.S., A.T., E.C.J., A.A.M.), and Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute (M.W.B., W.M.L.), National Institutes of Health, 10 Center Dr, Bethesda, MD 20814
| | - Ahmad Shafiei
- From the Radiology and Imaging Sciences, NIH Clinical Center (F.F., M.N., A.K.P., A.S., A.T., E.C.J., A.A.M.), and Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute (M.W.B., W.M.L.), National Institutes of Health, 10 Center Dr, Bethesda, MD 20814
| | - Ashkan Tadayoni
- From the Radiology and Imaging Sciences, NIH Clinical Center (F.F., M.N., A.K.P., A.S., A.T., E.C.J., A.A.M.), and Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute (M.W.B., W.M.L.), National Institutes of Health, 10 Center Dr, Bethesda, MD 20814
| | - Mark W Ball
- From the Radiology and Imaging Sciences, NIH Clinical Center (F.F., M.N., A.K.P., A.S., A.T., E.C.J., A.A.M.), and Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute (M.W.B., W.M.L.), National Institutes of Health, 10 Center Dr, Bethesda, MD 20814
| | - W Marston Linehan
- From the Radiology and Imaging Sciences, NIH Clinical Center (F.F., M.N., A.K.P., A.S., A.T., E.C.J., A.A.M.), and Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute (M.W.B., W.M.L.), National Institutes of Health, 10 Center Dr, Bethesda, MD 20814
| | - Elizabeth C Jones
- From the Radiology and Imaging Sciences, NIH Clinical Center (F.F., M.N., A.K.P., A.S., A.T., E.C.J., A.A.M.), and Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute (M.W.B., W.M.L.), National Institutes of Health, 10 Center Dr, Bethesda, MD 20814
| | - Ashkan A Malayeri
- From the Radiology and Imaging Sciences, NIH Clinical Center (F.F., M.N., A.K.P., A.S., A.T., E.C.J., A.A.M.), and Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute (M.W.B., W.M.L.), National Institutes of Health, 10 Center Dr, Bethesda, MD 20814
| |
Collapse
|
31
|
Pritchard WF, Woods DL, Esparza-Trujillo JA, Starost MF, Mauda-Havakuk M, Mikhail AS, Bakhutashvili I, Leonard S, Jones EC, Krishnasamy V, Karanian JW, Wood BJ. Transarterial Chemoembolization in a Woodchuck Model of Hepatocellular Carcinoma. J Vasc Interv Radiol 2020; 31:812-819.e1. [PMID: 32107125 DOI: 10.1016/j.jvir.2019.08.031] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 08/15/2019] [Accepted: 08/31/2019] [Indexed: 02/06/2023] Open
Abstract
PURPOSE To assess the feasibility of transarterial chemoembolization with drug-eluting embolic (DEE) microspheres in a woodchuck model of hepatocellular carcinoma (HCC). MATERIALS AND METHODS Nine woodchucks were studied: 4 normal animals and 5 animals infected with woodchuck hepatitis virus in which HCC had developed. Three animals with HCC underwent multidetector CT. A 3-F sheath was introduced into the femoral artery, and the hepatic arteries were selectively catheterized with 2.0-2.4-F microcatheters. Normal animals underwent diagnostic angiography and bland embolization. Animals with HCC underwent DEE transarterial chemoembolization with 70-150-μm radiopaque microspheres loaded with 37.5 mg doxorubicin per milliliter. Cone-beam CT and multidetector CT were performed. Following euthanasia, explanted livers underwent micro-CT, histopathologic examination, and fluorescence imaging of doxorubicin. RESULTS The tumors were hypervascular and supplied by large-caliber tortuous vessels, with arteriovenous shunts present in 2 animals. There was heterogeneous enhancement on multidetector CT with areas of necrosis. Six tumors were identified. The most common location was the right medial lobe (n = 3). Mean tumor volume was 30.7 cm3 ± 12.3. DEE chemoembolization of tumors was achieved. Excluding the 2 animals with arteriovenous shunts, the mean volume of DEE microspheres injected was 0.49 mL ± 0.17. Fluorescence imaging showed diffusion of doxorubicin from the DEE microspheres into the tumor. CONCLUSIONS Woodchuck HCC shares imaging appearances and biologic characteristics with human HCC. Selective catheterization and DEE chemoembolization may similarly be performed. Woodchucks may be used to model interventional therapies and possibly characterize radiologic-pathologic correlations.
Collapse
Affiliation(s)
- William F Pritchard
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr., Room 3N320B, MSC 1182, Bethesda, MD 20892.
| | - David L Woods
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr., Room 3N320B, MSC 1182, Bethesda, MD 20892
| | - Juan A Esparza-Trujillo
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr., Room 3N320B, MSC 1182, Bethesda, MD 20892
| | - Matthew F Starost
- Division of Veterinary Resources, National Institutes of Health, 10 Center Dr., Room 3N320B, MSC 1182, Bethesda, MD 20892
| | - Michal Mauda-Havakuk
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr., Room 3N320B, MSC 1182, Bethesda, MD 20892
| | - Andrew S Mikhail
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr., Room 3N320B, MSC 1182, Bethesda, MD 20892
| | - Ivane Bakhutashvili
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr., Room 3N320B, MSC 1182, Bethesda, MD 20892
| | - Shelby Leonard
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr., Room 3N320B, MSC 1182, Bethesda, MD 20892
| | - Elizabeth C Jones
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr., Room 3N320B, MSC 1182, Bethesda, MD 20892
| | - Venkatesh Krishnasamy
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr., Room 3N320B, MSC 1182, Bethesda, MD 20892
| | - John W Karanian
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr., Room 3N320B, MSC 1182, Bethesda, MD 20892
| | - Bradford J Wood
- Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr., Room 3N320B, MSC 1182, Bethesda, MD 20892; National Institute of Biomedical Imaging and Bioengineering and National Cancer Institute Center for Cancer Research, National Institutes of Health, 10 Center Dr., Room 3N320B, MSC 1182, Bethesda, MD 20892
| |
Collapse
|
32
|
Jung JW, Lee C, Mosher EG, Mille MM, Yeom YS, Jones EC, Choi M, Lee C. Automatic segmentation of cardiac structures for breast cancer radiotherapy. Phys Imaging Radiat Oncol 2019; 12:44-48. [PMID: 33458294 PMCID: PMC7807574 DOI: 10.1016/j.phro.2019.11.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 11/22/2019] [Accepted: 11/22/2019] [Indexed: 12/25/2022]
Abstract
We developed an automatic method to segment cardiac sub-structures for radiotherapy planning CTs. The Dice Similarity Coefficients and Average Surface Distance were up to 97% and < 11 mm, respectively. The whole heart showed the absolute dose difference < 0.3 Gy whereas the coronary arteries showed < 2.3 Gy in breast radiotherapy simulations. No notable improvement in our method beyond 10 atlases and using the manual guide points.
Background and purpose We developed an automatic method to segment cardiac substructures given a radiotherapy planning CT images to support epidemiological studies or clinical trials looking at cardiac disease endpoints after radiotherapy. Material and methods We used a most-similar atlas selection algorithm and 3D deformation combined with 30 detailed cardiac atlases. We cross-validated our method within the atlas library by evaluating geometric comparison metrics and by comparing cardiac doses for simulated breast radiotherapy between manual and automatic contours. We analyzed the impact of the number of cardiac atlas in the library and the use of manual guide points on the performance of our method. Results The Dice Similarity Coefficients from the cross-validation reached up to 97% (whole heart) and 80% (chambers). The Average Surface Distance for the coronary arteries was less than 10.3 mm on average, with the best agreement (7.3 mm) in the left anterior descending artery (LAD). The dose comparison for simulated breast radiotherapy showed differences less than 0.06 Gy for the whole heart and atria, and 0.3 Gy for the ventricles. For the coronary arteries, the dose differences were 2.3 Gy (LAD) and 0.3 Gy (other arteries). The sensitivity analysis showed no notable improvement beyond ten atlases and the manual guide points does not significantly improve performance. Conclusion We developed an automated method to contour cardiac substructures for radiotherapy CTs. When combined with accurate dose calculation techniques, our method should be useful for cardiac dose reconstruction of a large number of patients in epidemiological studies or clinical trials.
Collapse
Affiliation(s)
- Jae Won Jung
- Department of Physics, East Carolina University, Greenville, NC 27858, USA
| | - Choonik Lee
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Elizabeth G Mosher
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Matthew M Mille
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Yeon Soo Yeom
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20852, USA
| | - Minsoo Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Choonsik Lee
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| |
Collapse
|
33
|
Campbell-Washburn AE, Ramasawmy R, Restivo MC, Bhattacharya I, Basar B, Herzka DA, Hansen MS, Rogers T, Bandettini WP, McGuirt DR, Mancini C, Grodzki D, Schneider R, Majeed W, Bhat H, Xue H, Moss J, Malayeri AA, Jones EC, Koretsky AP, Kellman P, Chen MY, Lederman RJ, Balaban RS. Opportunities in Interventional and Diagnostic Imaging by Using High-Performance Low-Field-Strength MRI. Radiology 2019; 293:384-393. [PMID: 31573398 PMCID: PMC6823617 DOI: 10.1148/radiol.2019190452] [Citation(s) in RCA: 191] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 08/06/2019] [Accepted: 08/15/2019] [Indexed: 12/24/2022]
Abstract
Background Commercial low-field-strength MRI systems are generally not equipped with state-of-the-art MRI hardware, and are not suitable for demanding imaging techniques. An MRI system was developed that combines low field strength (0.55 T) with high-performance imaging technology. Purpose To evaluate applications of a high-performance low-field-strength MRI system, specifically MRI-guided cardiovascular catheterizations with metallic devices, diagnostic imaging in high-susceptibility regions, and efficient image acquisition strategies. Materials and Methods A commercial 1.5-T MRI system was modified to operate at 0.55 T while maintaining high-performance hardware, shielded gradients (45 mT/m; 200 T/m/sec), and advanced imaging methods. MRI was performed between January 2018 and April 2019. T1, T2, and T2* were measured at 0.55 T; relaxivity of exogenous contrast agents was measured; and clinical applications advantageous at low field were evaluated. Results There were 83 0.55-T MRI examinations performed in study participants (45 women; mean age, 34 years ± 13). On average, T1 was 32% shorter, T2 was 26% longer, and T2* was 40% longer at 0.55 T compared with 1.5 T. Nine metallic interventional devices were found to be intrinsically safe at 0.55 T (<1°C heating) and MRI-guided right heart catheterization was performed in seven study participants with commercial metallic guidewires. Compared with 1.5 T, reduced image distortion was shown in lungs, upper airway, cranial sinuses, and intestines because of improved field homogeneity. Oxygen inhalation generated lung signal enhancement of 19% ± 11 (standard deviation) at 0.55 T compared with 7.6% ± 6.3 at 1.5 T (P = .02; five participants) because of the increased T1 relaxivity of oxygen (4.7e-4 mmHg-1sec-1). Efficient spiral image acquisitions were amenable to low field strength and generated increased signal-to-noise ratio compared with Cartesian acquisitions (P < .02). Representative imaging of the brain, spine, abdomen, and heart generated good image quality with this system. Conclusion This initial study suggests that high-performance low-field-strength MRI offers advantages for MRI-guided catheterizations with metal devices, MRI in high-susceptibility regions, and efficient imaging. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Grist in this issue.
Collapse
Affiliation(s)
- Adrienne E. Campbell-Washburn
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Rajiv Ramasawmy
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Matthew C. Restivo
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Ipshita Bhattacharya
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Burcu Basar
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Daniel A. Herzka
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Michael S. Hansen
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Toby Rogers
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - W. Patricia Bandettini
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Delaney R. McGuirt
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Christine Mancini
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - David Grodzki
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Rainer Schneider
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Waqas Majeed
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Himanshu Bhat
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Hui Xue
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Joel Moss
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Ashkan A. Malayeri
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Elizabeth C. Jones
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Alan P. Koretsky
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Peter Kellman
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Marcus Y. Chen
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Robert J. Lederman
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| | - Robert S. Balaban
- From the Cardiovascular Branch, Division of Intramural Research,
National Heart, Lung, and Blood Institute, National Institutes of Health,
Bethesda, Md (A.E.C.W., R.R., M.C.R., I.B., B.B., D.A.H., M.S.H., T.R., W.P.B.,
D.R.M., C.M., M.Y.C., R.J.L.); Siemens Healthcare GmbH, Erlangen, Germany (D.G.,
R.S.); Siemens Medical Solutions Inc, Malvern Pa (W.M., H.B.); Systems Biology
Center, Division of Intramural Research, National Heart, Lung, and Blood
Institute, National Institutes of Health, 10 Center Dr, Building 10, Room
4C-1581, Bethesda, MD 20892-1458 (H.X., P.K., R.S.B.); Pulmonary Branch,
Division of Intramural Research, National Heart, Lung, and Blood Institute,
National Institutes of Health, Bethesda, MD (J.M.); Department of Radiology and
Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Md
(A.A.M., E.C.J.); and Laboratory of Functional and Molecular Imaging, Division
of Intramural Research, National Institute of Neurologic Disorders and Stroke,
National Institutes of Health, Bethesda, Md (A.P.K.)
| |
Collapse
|
34
|
Ben-Yakov G, Alao H, Haydek JP, Fryzek N, Cho MH, Hemmati M, Samala V, Shovlin M, Dunleavy K, Wilson W, Jones EC, Rotman Y. Development of Hepatic Steatosis After Chemotherapy for Non-Hodgkin Lymphoma. Hepatol Commun 2019; 3:220-226. [PMID: 30766960 PMCID: PMC6357828 DOI: 10.1002/hep4.1304] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 12/01/2018] [Indexed: 12/17/2022] Open
Abstract
Nonalcoholic fatty liver disease is the most common liver disorder in the developed world. Although typically reflecting caloric overload, it can also be secondary to drug toxicity. We aimed to describe the incidence and risk factors for de novo steatosis during chemotherapy for non‐Hodgkin lymphoma (NHL). In this retrospective case‐control study, adult patients with NHL were treated with rituximab, cyclophosphamide, doxorubicin, prednisone, and vincristine (R‐CHOP) or R‐CHOP + etoposide (EPOCH‐R). Patients with liver disease or steatosis were excluded. Abdominal computed tomography was performed pretreatment and at 3‐ to 6‐month intervals and reviewed for steatosis. Patients with de novo steatosis were matched 1:1 to controls by age, sex, and ethnicity. Of 251 treated patients (median follow‐up 53 months), 25 (10%) developed de novo steatosis, with the vast majority (23 of 25; 92%) developing it after chemotherapy. Of those, 14 (61%) developed steatosis within the first 18 months posttreatment and 20 (87%) within 36 months. Cases had higher baseline body mass index (BMI; mean ± SD, 29.0 ± 6.5 versus 26.0 ± 5.2 kg/m2; P = 0.014) and hyperlipidemia (12% versus 2%; P = 0.035). Although their weights did not change during chemotherapy, BMI in cases increased by 2.4 ± 2 kg/m2 (mean ± SD) from end of treatment to steatosis compared to 0.68 ± 1.4 in controls (P = 0.003). Etoposide‐containing regimens were associated with a shorter time to steatosis (median 34 weeks versus 154 weeks; P < 0.001) despite similar baseline risk factors. Conclusion: The recovery period from NHL chemotherapy appears to be a “hot spot” for development of fatty liver, driven by early posttreatment weight gain, especially in subjects with baseline risk factors.
Collapse
Affiliation(s)
- Gil Ben-Yakov
- Liver and Energy Metabolism Unit, Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health Bethesda MD
| | - Hawwa Alao
- Liver and Energy Metabolism Unit, Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health Bethesda MD.,Department of Gastroenterology Louis Stokes VA Medical Center Cleveland OH
| | - John P Haydek
- Liver and Energy Metabolism Unit, Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health Bethesda MD
| | - Nancy Fryzek
- Liver and Energy Metabolism Unit, Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health Bethesda MD
| | - Min Ho Cho
- Liver and Energy Metabolism Unit, Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health Bethesda MD.,Department of Medicine MedStar Washington Hospital Center Washington DC
| | - Mehdi Hemmati
- Liver and Energy Metabolism Unit, Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health Bethesda MD.,Department of Medicine MedStar Health Baltimore MD
| | - Vikram Samala
- Liver and Energy Metabolism Unit, Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health Bethesda MD
| | - Margaret Shovlin
- Lymphoid Malignancies Branch, Center for Cancer Research National Cancer Institute, National Institutes of Health Bethesda MD
| | - Kieron Dunleavy
- Lymphoid Malignancies Branch, Center for Cancer Research National Cancer Institute, National Institutes of Health Bethesda MD
| | - Wyndham Wilson
- Lymphoid Malignancies Branch, Center for Cancer Research National Cancer Institute, National Institutes of Health Bethesda MD
| | - Elizabeth C Jones
- Radiology and Imaging Sciences National Institutes of Health Clinical Center Bethesda MD
| | - Yaron Rotman
- Liver and Energy Metabolism Unit, Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases National Institutes of Health Bethesda MD
| |
Collapse
|
35
|
Choyke PL, Jones EC, Kim L. Andrew Joseph Dwyer, MD. Radiology 2018; 287:E1. [DOI: 10.1148/radiol.2018184009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
36
|
Shafiei A, Bagheri M, Farhadi F, Lay N, Yao J, Folio LR, Jones EC, Summers RM. Optimizing measurement when lymph nodes merge on serial CT in clinical trials. J Clin Oncol 2018. [DOI: 10.1200/jco.2018.36.15_suppl.e18590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
| | - Mohammadhadi Bagheri
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD
| | | | - Nathan Lay
- National Institutes of Health (NIH), Bethesda, MD
| | - Jianhua Yao
- National Institutes of Health (NIH), Bethesda, MD
| | - Les R. Folio
- National Institutes of Health (NIH), Bethesda, MD
| | | | | |
Collapse
|
37
|
Shafiei A, Bagheri M, Farhadi F, Lay N, Yao J, Folio LR, Jones EC, Summers RM. A proposed measurement scheme when lymph nodes split on serial CT in clinical trials. J Clin Oncol 2018. [DOI: 10.1200/jco.2018.36.15_suppl.e18591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
| | - Mohammadhadi Bagheri
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD
| | | | - Nathan Lay
- National Institutes of Health (NIH), Bethesda, MD
| | - Jianhua Yao
- National Institutes of Health (NIH), Bethesda, MD
| | - Les R. Folio
- National Institutes of Health (NIH), Bethesda, MD
| | | | | |
Collapse
|
38
|
Symons R, Pourmorteza A, Sandfort V, Ahlman MA, Cropper T, Mallek M, Kappler S, Ulzheimer S, Mahesh M, Jones EC, Malayeri AA, Folio LR, Bluemke DA. Feasibility of Dose-reduced Chest CT with Photon-counting Detectors: Initial Results in Humans. Radiology 2017; 285:980-989. [PMID: 28753389 DOI: 10.1148/radiol.2017162587] [Citation(s) in RCA: 110] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Purpose To investigate whether photon-counting detector (PCD) technology can improve dose-reduced chest computed tomography (CT) image quality compared with that attained with conventional energy-integrating detector (EID) technology in vivo. Materials and Methods This was a HIPAA-compliant institutional review board-approved study, with informed consent from patients. Dose-reduced spiral unenhanced lung EID and PCD CT examinations were performed in 30 asymptomatic volunteers in accordance with manufacturer-recommended guidelines for CT lung cancer screening (120-kVp tube voltage, 20-mAs reference tube current-time product for both detectors). Quantitative analysis of images included measurement of mean attenuation, noise power spectrum (NPS), and lung nodule contrast-to-noise ratio (CNR). Images were qualitatively analyzed by three radiologists blinded to detector type. Reproducibility was assessed with the intraclass correlation coefficient (ICC). McNemar, paired t, and Wilcoxon signed-rank tests were used to compare image quality. Results Thirty study subjects were evaluated (mean age, 55.0 years ± 8.7 [standard deviation]; 14 men). Of these patients, 10 had a normal body mass index (BMI) (BMI range, 18.5-24.9 kg/m2; group 1), 10 were overweight (BMI range, 25.0-29.9 kg/m2; group 2), and 10 were obese (BMI ≥30.0 kg/m2, group 3). PCD diagnostic quality was higher than EID diagnostic quality (P = .016, P = .016, and P = .013 for readers 1, 2, and 3, respectively), with significantly better NPS and image quality scores for lung, soft tissue, and bone and with fewer beam-hardening artifacts (all P < .001). Image noise was significantly lower for PCD images in all BMI groups (P < .001 for groups 1 and 3, P < .01 for group 2), with higher CNR for lung nodule detection (12.1 ± 1.7 vs 10.0 ± 1.8, P < .001). Inter- and intrareader reproducibility were good (all ICC > 0.800). Conclusion Initial human experience with dose-reduced PCD chest CT demonstrated lower image noise compared with conventional EID CT, with better diagnostic quality and lung nodule CNR. © RSNA, 2017 Online supplemental material is available for this article.
Collapse
Affiliation(s)
- Rolf Symons
- From the Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bethesda, MD 20892 (R.S., A.P., V.S., M.A.A., T.C., M. Mallek, E.C.J., A.A.M., L.R.F., D.A.B.); Siemens Healthcare, Forchheim, Germany (S.K., S.U.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (M. Mahesh)
| | - Amir Pourmorteza
- From the Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bethesda, MD 20892 (R.S., A.P., V.S., M.A.A., T.C., M. Mallek, E.C.J., A.A.M., L.R.F., D.A.B.); Siemens Healthcare, Forchheim, Germany (S.K., S.U.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (M. Mahesh)
| | - Veit Sandfort
- From the Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bethesda, MD 20892 (R.S., A.P., V.S., M.A.A., T.C., M. Mallek, E.C.J., A.A.M., L.R.F., D.A.B.); Siemens Healthcare, Forchheim, Germany (S.K., S.U.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (M. Mahesh)
| | - Mark A Ahlman
- From the Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bethesda, MD 20892 (R.S., A.P., V.S., M.A.A., T.C., M. Mallek, E.C.J., A.A.M., L.R.F., D.A.B.); Siemens Healthcare, Forchheim, Germany (S.K., S.U.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (M. Mahesh)
| | - Tracy Cropper
- From the Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bethesda, MD 20892 (R.S., A.P., V.S., M.A.A., T.C., M. Mallek, E.C.J., A.A.M., L.R.F., D.A.B.); Siemens Healthcare, Forchheim, Germany (S.K., S.U.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (M. Mahesh)
| | - Marissa Mallek
- From the Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bethesda, MD 20892 (R.S., A.P., V.S., M.A.A., T.C., M. Mallek, E.C.J., A.A.M., L.R.F., D.A.B.); Siemens Healthcare, Forchheim, Germany (S.K., S.U.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (M. Mahesh)
| | - Steffen Kappler
- From the Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bethesda, MD 20892 (R.S., A.P., V.S., M.A.A., T.C., M. Mallek, E.C.J., A.A.M., L.R.F., D.A.B.); Siemens Healthcare, Forchheim, Germany (S.K., S.U.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (M. Mahesh)
| | - Stefan Ulzheimer
- From the Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bethesda, MD 20892 (R.S., A.P., V.S., M.A.A., T.C., M. Mallek, E.C.J., A.A.M., L.R.F., D.A.B.); Siemens Healthcare, Forchheim, Germany (S.K., S.U.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (M. Mahesh)
| | - Mahadevappa Mahesh
- From the Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bethesda, MD 20892 (R.S., A.P., V.S., M.A.A., T.C., M. Mallek, E.C.J., A.A.M., L.R.F., D.A.B.); Siemens Healthcare, Forchheim, Germany (S.K., S.U.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (M. Mahesh)
| | - Elizabeth C Jones
- From the Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bethesda, MD 20892 (R.S., A.P., V.S., M.A.A., T.C., M. Mallek, E.C.J., A.A.M., L.R.F., D.A.B.); Siemens Healthcare, Forchheim, Germany (S.K., S.U.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (M. Mahesh)
| | - Ashkan A Malayeri
- From the Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bethesda, MD 20892 (R.S., A.P., V.S., M.A.A., T.C., M. Mallek, E.C.J., A.A.M., L.R.F., D.A.B.); Siemens Healthcare, Forchheim, Germany (S.K., S.U.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (M. Mahesh)
| | - Les R Folio
- From the Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bethesda, MD 20892 (R.S., A.P., V.S., M.A.A., T.C., M. Mallek, E.C.J., A.A.M., L.R.F., D.A.B.); Siemens Healthcare, Forchheim, Germany (S.K., S.U.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (M. Mahesh)
| | - David A Bluemke
- From the Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bethesda, MD 20892 (R.S., A.P., V.S., M.A.A., T.C., M. Mallek, E.C.J., A.A.M., L.R.F., D.A.B.); Siemens Healthcare, Forchheim, Germany (S.K., S.U.); and Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (M. Mahesh)
| |
Collapse
|
39
|
Yang N, Leung ELH, Liu C, Li L, Eguether T, Jun Yao XJ, Jones EC, Norris DA, Liu A, Clark RA, Roop DR, Pazour GJ, Shroyer KR, Chen J. INTU is essential for oncogenic Hh signaling through regulating primary cilia formation in basal cell carcinoma. Oncogene 2017; 36:4997-5005. [PMID: 28459465 PMCID: PMC5578876 DOI: 10.1038/onc.2017.117] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Revised: 12/29/2016] [Accepted: 03/23/2017] [Indexed: 12/30/2022]
Abstract
Inturned (INTU), a cilia and planar polarity effector (CPLANE), performs prominent ciliogenic functions during morphogenesis, such as in the skin. INTU is expressed in adult tissues but its role in tissue maintenance is unknown. Here, we report that the expression of the INTU gene is aberrantly elevated in human basal cell carcinoma (BCC), coinciding with increased primary cilia formation and activated hedgehog (Hh) signaling. Disrupting Intu in an oncogenic mutant Smo (SmoM2)-driven BCC mouse model prevented the formation of BCC through suppressing primary cilia formation and Hh signaling, suggesting that Intu performs a permissive role during BCC formation. INTU is essential for IFT-A complex assembly during ciliogenesis. To further determine whether Intu is directly involved in the activation of Hh signaling downstream of ciliogenesis, we examined the Hh signaling pathway in mouse embryonic fibroblasts, which readily respond to Hh pathway activation. Depleting Intu blocked SAG-induced Hh pathway activation, whereas the expression of Gli2ΔN, a constitutively active Gli2, restored Hh pathway activation in Intu-deficient cells, suggesting that INTU functions upstream of Gli2 activation. In contrast, overexpressing Intu did not promote ciliogenesis or Hh signaling. Taken together, data obtained from this study suggest that INTU is indispensable during BCC tumorigenesis and that its aberrant upregulation is likely a prerequisite for primary cilia formation during Hh-dependent tumorigenesis.
Collapse
Affiliation(s)
- N Yang
- Department of Pathology, Stony Brook University, Stony Brook, NY, USA
| | - E L-H Leung
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau, China
| | - C Liu
- Department of Pathology, Stony Brook University, Stony Brook, NY, USA
| | - L Li
- Department of Dermatology, Peking Union Medical College Hospital, Beijing, China
| | - T Eguether
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - X-J Jun Yao
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau, China
| | - E C Jones
- Department of Dermatology, Stony Brook University, Stony Brook, NY, USA
| | - D A Norris
- Charles C. Gates Center for Regenerative Medicine, University of Colorado Denver, Aurora, CO, USA
| | - A Liu
- Department of Biology, Eberly College of Science, Pennsylvania State University, University Park, PA, USA
| | - R A Clark
- Department of Dermatology, Stony Brook University, Stony Brook, NY, USA
| | - D R Roop
- Charles C. Gates Center for Regenerative Medicine, University of Colorado Denver, Aurora, CO, USA.,Department of Dermatology, University of Colorado Denver, Aurora, CO, USA
| | - G J Pazour
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - K R Shroyer
- Department of Pathology, Stony Brook University, Stony Brook, NY, USA
| | - J Chen
- Department of Pathology, Stony Brook University, Stony Brook, NY, USA.,State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau, China.,Department of Dermatology, Stony Brook University, Stony Brook, NY, USA
| |
Collapse
|
40
|
Sandfort V, Ahlman MA, Jones EC, Selwaness M, Y Chen M, R Folio L, Bluemke DA. High pitch third generation dual-source CT: Coronary and cardiac visualization on routine chest CT. J Cardiovasc Comput Tomogr 2016; 10:282-8. [PMID: 27133589 PMCID: PMC4958576 DOI: 10.1016/j.jcct.2016.03.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Revised: 03/21/2016] [Accepted: 03/25/2016] [Indexed: 12/21/2022]
Abstract
BACKGROUND Chest CT scans are frequently performed in radiology departments but have not previously contained detailed depiction of cardiac structures. OBJECTIVES To evaluate myocardial and coronary visualization on high-pitch non-gated CT of the chest using 3rd generation dual-source computed tomography (CT). METHODS Cardiac anatomy of patients who had 3rd generation, non-gated high pitch contrast enhanced chest CT and who also had prior conventional (low pitch) chest CT as part of a chest abdomen pelvis exam was evaluated. Cardiac image features were scored by reviewers blinded to diagnosis and pitch. Paired analysis was performed. RESULTS 3862 coronary segments and 2220 cardiac structures were evaluated by two readers in 222 CT scans. Most patients (97.2%) had chest CT for oncologic evaluation. The median pitch was 2.34 (IQR 2.05, 2.65) in high pitch and 0.8 (IQR 0.8, 0.8) in low pitch scans (p < 0.001). High pitch CT showed higher image visualization scores for all cardiovascular structures compared with conventional pitch scans (p < 0.0001). Coronary arteries were visualized in 9 coronary segments per exam in high pitch scans versus 2 segments for conventional pitch (p < 0.0001). Radiation exposure was lower in the high pitch group compared with the conventional pitch group (median CTDIvol 10.83 vs. 12.36 mGy and DLP 790 vs. 827 mGycm respectively, p < 0.01 for both) with comparable image noise (p = 0.43). CONCLUSION Myocardial structure and coronary arteries are frequently visualized on non-gated 3rd generation chest CT. These results raise the question of whether the heart and coronary arteries should be routinely interpreted on routine chest CT that is otherwise obtained for non-cardiac indications.
Collapse
Affiliation(s)
- Veit Sandfort
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Mark A Ahlman
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Mariana Selwaness
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Marcus Y Chen
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Les R Folio
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - David A Bluemke
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
| |
Collapse
|
41
|
Pourmorteza A, Symons R, Sandfort V, Mallek M, Fuld MK, Henderson G, Jones EC, Malayeri AA, Folio LR, Bluemke DA. Abdominal Imaging with Contrast-enhanced Photon-counting CT: First Human Experience. Radiology 2016; 279:239-45. [PMID: 26840654 PMCID: PMC4820083 DOI: 10.1148/radiol.2016152601] [Citation(s) in RCA: 139] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
PURPOSE To evaluate the performance of a prototype photon-counting detector (PCD) computed tomography (CT) system for abdominal CT in humans and to compare the results with a conventional energy-integrating detector (EID). MATERIALS AND METHODS The study was HIPAA-compliant and institutional review board-approved with informed consent. Fifteen asymptomatic volunteers (seven men; mean age, 58.2 years ± 9.8 [standard deviation]) were prospectively enrolled between September 2 and November 13, 2015. Radiation dose-matched delayed contrast agent-enhanced spiral and axial abdominal EID and PCD scans were acquired. Spiral images were scored for image quality (Wilcoxon signed-rank test) in five regions of interest by three radiologists blinded to the detector system, and the axial scans were used to assess Hounsfield unit accuracy in seven regions of interest (paired t test). Intraclass correlation coefficient (ICC) was used to assess reproducibility. PCD images were also used to calculate iodine concentration maps. Spatial resolution, noise-power spectrum, and Hounsfield unit accuracy of the systems were estimated by using a CT phantom. RESULTS In both systems, scores were similar for image quality (median score, 4; P = .19), noise (median score, 3; P = .30), and artifact (median score, 1; P = .17), with good interrater agreement (image quality, noise, and artifact ICC: 0.84, 0.88, and 0.74, respectively). Hounsfield unit values, spatial resolution, and noise-power spectrum were also similar with the exception of mean Hounsfield unit value in the spinal canal, which was lower in the PCD than the EID images because of beam hardening (20 HU vs 36.5 HU; P < .001). Contrast-to-noise ratio of enhanced kidney tissue was improved with PCD iodine mapping compared with EID (5.2 ± 1.3 vs 4.0 ± 1.3; P < .001). CONCLUSION The performance of PCD showed no statistically significant difference compared with EID when the abdomen was evaluated in a conventional scan mode. PCD provides spectral information, which may be used for material decomposition.
Collapse
Affiliation(s)
- Amir Pourmorteza
- From Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bldg 10, Bethesda, MD 20892 (A.P., R.S., V.S., M.M., G.H., E.C.J., A.A.M., L.R.F., D.A.B.); and Siemens Medical Solutions, Malvern, Pa (M.K.F.)
| | - Rolf Symons
- From Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bldg 10, Bethesda, MD 20892 (A.P., R.S., V.S., M.M., G.H., E.C.J., A.A.M., L.R.F., D.A.B.); and Siemens Medical Solutions, Malvern, Pa (M.K.F.)
| | - Veit Sandfort
- From Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bldg 10, Bethesda, MD 20892 (A.P., R.S., V.S., M.M., G.H., E.C.J., A.A.M., L.R.F., D.A.B.); and Siemens Medical Solutions, Malvern, Pa (M.K.F.)
| | - Marissa Mallek
- From Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bldg 10, Bethesda, MD 20892 (A.P., R.S., V.S., M.M., G.H., E.C.J., A.A.M., L.R.F., D.A.B.); and Siemens Medical Solutions, Malvern, Pa (M.K.F.)
| | - Matthew K. Fuld
- From Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bldg 10, Bethesda, MD 20892 (A.P., R.S., V.S., M.M., G.H., E.C.J., A.A.M., L.R.F., D.A.B.); and Siemens Medical Solutions, Malvern, Pa (M.K.F.)
| | - Gregory Henderson
- From Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bldg 10, Bethesda, MD 20892 (A.P., R.S., V.S., M.M., G.H., E.C.J., A.A.M., L.R.F., D.A.B.); and Siemens Medical Solutions, Malvern, Pa (M.K.F.)
| | - Elizabeth C. Jones
- From Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bldg 10, Bethesda, MD 20892 (A.P., R.S., V.S., M.M., G.H., E.C.J., A.A.M., L.R.F., D.A.B.); and Siemens Medical Solutions, Malvern, Pa (M.K.F.)
| | - Ashkan A. Malayeri
- From Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bldg 10, Bethesda, MD 20892 (A.P., R.S., V.S., M.M., G.H., E.C.J., A.A.M., L.R.F., D.A.B.); and Siemens Medical Solutions, Malvern, Pa (M.K.F.)
| | - Les R. Folio
- From Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bldg 10, Bethesda, MD 20892 (A.P., R.S., V.S., M.M., G.H., E.C.J., A.A.M., L.R.F., D.A.B.); and Siemens Medical Solutions, Malvern, Pa (M.K.F.)
| | - David A. Bluemke
- From Radiology and Imaging Sciences, National Institutes of Health Clinical Center, 10 Center Dr, Bldg 10, Bethesda, MD 20892 (A.P., R.S., V.S., M.M., G.H., E.C.J., A.A.M., L.R.F., D.A.B.); and Siemens Medical Solutions, Malvern, Pa (M.K.F.)
| |
Collapse
|
42
|
|
43
|
Linguraru MG, Sandberg JK, Jones EC, Summers RM. Assessing splenomegaly: automated volumetric analysis of the spleen. Acad Radiol 2013; 20:675-84. [PMID: 23535191 DOI: 10.1016/j.acra.2013.01.011] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2012] [Revised: 01/23/2013] [Accepted: 01/24/2013] [Indexed: 01/14/2023]
Abstract
RATIONALE AND OBJECTIVES To define systematic volumetric thresholds to identify and grade splenomegaly and retrospectively evaluate the performance of radiologists to assess splenomegaly in computed tomography (CT) image data. MATERIALS AND METHODS A clinical tool was developed to segment spleens from 172 contrast-enhanced clinical CT studies. There were 45 normal and 127 splenomegaly cases confirmed by radiological reports. Spleen volumes were compared to manual measurements using overlap/error. Volumetric thresholds for mild/massive splenomegaly were defined at 1/2.5 standard deviations above the average splenic volume of the healthy population. The thresholds were validated against consensus reports. The performance of radiologists in assessing splenomegaly was retrospectively evaluated. RESULTS The automated segmentation of spleens was robust with volume overlap/error of 95.2/3.3%. There were no significant differences (P > .2) between manual and automated segmentations for either normal/splenomegaly subgroups. Comparable correlations between interobserver and manual-automated measurements were found (r = 0.99 for all). The average volume of normal spleens was 236.89 ± 77.58 mL. For splenomegaly, average volume was 1004.75 ± 644.27 mL. Volumetric thresholds of 314.47/430.84 mL were used to define mild/massive splenomegaly (±18.86 mL, 95% CI). Radiologists disagreed in 23.25% (n = 40) of the diagnosed cases. The area under the receiver operating characteristic curve of the volumetric criterion for splenomegaly detection was 0.96. Using the volumetric thresholds as the reference standard, the sensitivity of radiologists in detecting all/mild/massive splenomegaly was 95.0/66.6/99.0% at 78.0% specificity, respectively. CONCLUSION Thresholds for the identification and grading of splenomegaly from automatic volumetric spleen assessment were introduced. The volumetric thresholds match well with clinical interpretations for splenomegaly and may improve splenomegaly detection compared with splenic cephalocaudal height measurements or visual inspection commonly used in current clinical practice.
Collapse
|
44
|
Abstract
PURPOSE To design and validate a computer system for automated detection and quantitative characterization of sclerotic metastases of the thoracolumbar spine on computed tomography (CT) images. MATERIALS AND METHODS This retrospective study was approved by the institutional review board and was HIPAA compliant; informed consent was waived. The data set consisted of CT examinations in 49 patients (14 female, 35 male patients; mean age, 57.0 years; range, 12-77 years), demonstrating a total of 532 sclerotic lesions of the spine of greater than 0.3 cm(3) in volume, and in 10 control case patients (four women, six men; mean age, 55.2 years; range, 19-70 years) without spinal lesions. CT examinations were divided into training and test sets, and images were analyzed according to prototypical fully-automated computer-aided detection (CAD) software. Free-response receiver operating characteristic analysis was performed. RESULTS Lesion detection sensitivity on images in the training set was 90%, relative to reference-standard marked lesions (95% confidence interval [CI]: 83%, 97%), at a false-positive rate (FPR) of 10.8 per patient (95% CI: 6.6, 15.0). For images in the testing set, sensitivity was 79% (95% CI: 74%, 84%), with an FPR of 10.9 per patient (95% CI: 8.5, 13.3). False-negative findings were most commonly (37 [40%] of 93) a result of endplate proximity, with 32 (34% of 93) caused by low CT attenuation. Marginal sclerosis caused by degenerative change (174 [28.1%] of 620 actual detections) was the most common cause of false-positive detections, followed by partial volume averaging with vertebral endplates (173 [27.9%] of 620) and pedicle cortex parallel to the axial imaging plane (121 [19.5%] 620). CONCLUSION This CAD system successfully identified and segmented sclerotic lesions in the thoracolumbar spine.
Collapse
Affiliation(s)
- Joseph E Burns
- Department of Radiological Sciences, University of California-Irvine, Orange, Calif, USA
| | | | | | | | | | | |
Collapse
|
45
|
Linguraru MG, Sandberg JK, Jones EC, Petrick N, Summers RM. Assessing hepatomegaly: automated volumetric analysis of the liver. Acad Radiol 2012; 19:588-98. [PMID: 22361033 DOI: 10.1016/j.acra.2012.01.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2011] [Revised: 01/26/2012] [Accepted: 01/28/2012] [Indexed: 12/12/2022]
Abstract
RATIONALE AND OBJECTIVES The aims of this study were to define volumetric nomograms for identifying hepatomegaly and to retrospectively evaluate the performance of radiologists in assessing hepatomegaly. MATERIALS AND METHODS Livers were automatically segmented from 148 abdominal contrast-enhanced computed tomographic scans: 77 normal livers and 71 cases of hepatomegaly (diagnosed by visual inspection and/or linear liver height by radiologists). Quantified liver volumes were compared to manual measurements using volume overlap and error. Liver volumes were normalized to body surface area, from which hepatomegaly nomograms were defined (H scores) by analyzing the distribution of liver sizes in the healthy population. H scores were validated against consensus reports. The performance of radiologists in diagnosing hepatomegaly was retrospectively evaluated. RESULTS The automated segmentation of livers was robust, with volume overlap and error of 96.2% and 2.2%, respectively. There were no significant differences (P > .10) between manual and automated segmentation for either the normal or the hepatomegaly subgroup. The average volumes of normal and enlarged livers were 1.51 ± 0.25 and 2.32 ± 0.75 L, respectively. One-way analysis of variance found that body surface area (P = .004) and gender (P = .02), but not age, significantly affected normal liver volume. No significant effects were observed for two-way and three-way interactions among the three variables (P > .18). H-score cutoffs of 0.92 and 1.08 L/m2 were used to define mild and massive hepatomegaly (95% confidence interval, ± 0.02 L/m2). Using the H score as the reference standard, the sensitivity of radiologists in detecting all, mild, and massive hepatomegaly was 84.4%, 56.7%, and 100.0% at 90.1% specificity, respectively. Radiologists disagreed on 20.9% of the diagnosed cases (n = 31). The area under the receiver-operating characteristic curve of the H-score criterion for hepatomegaly detection was 0.98. CONCLUSIONS Nomograms for the identification and grading of hepatomegaly from automatic volumetric liver assessment normalized to body surface area (H scores) are introduced. H scores match well with clinical interpretations for hepatomegaly and may improve hepatomegaly detection compared with height measurements or visual inspection, commonly used in current clinical practice.
Collapse
|
46
|
Mohebtash M, Tsang KY, Madan RA, Huen NY, Poole DJ, Jochems C, Jones J, Ferrara T, Heery CR, Arlen PM, Steinberg SM, Pazdur M, Rauckhorst M, Jones EC, Dahut WL, Schlom J, Gulley JL. A Pilot Study of MUC-1/CEA/TRICOM Poxviral-Based Vaccine in Patients with Metastatic Breast and Ovarian Cancer. Clin Cancer Res 2011; 17:7164-73. [DOI: 10.1158/1078-0432.ccr-11-0649] [Citation(s) in RCA: 95] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
|
47
|
Morse CG, Mican JM, Jones EC, Joe GO, Rick ME, Formentini E, Kovacs JA. The incidence and natural history of osteonecrosis in HIV-infected adults. Clin Infect Dis 2007; 44:739-48. [PMID: 17278070 DOI: 10.1086/511683] [Citation(s) in RCA: 86] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2006] [Accepted: 11/13/2006] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Osteonecrosis is increasingly recognized as a debilitating complication of human immunodeficiency virus (HIV) infection, but the natural history has not been well described. We previously documented a high prevalence (4.4%) of magnetic resonance imaging (MRI)-documented osteonecrosis of the hip in a cohort of 339 asymptomatic HIV-infected patients. The present study was designed to determine the incidence of newly diagnosed osteonecrosis in this cohort and to describe the natural history of osteonecrosis in HIV-infected patients. METHODS Asymptomatic HIV-infected patients with a previous hip MRI negative for osteonecrosis underwent follow-up MRI. Patients with asymptomatic or symptomatic osteonecrosis were enrolled in a natural history study, which included serial MRIs and a physiotherapy follow-up. RESULTS Two hundred thirty-nine patients underwent a second MRI a median of 23 months after the initial MRI. Osteonecrosis of the femoral head was diagnosed in 3 patients (incidence, 0.65 cases per 100 person-years). During the period of January 1999 through April 2006, symptomatic hip osteonecrosis developed in 13 clinic patients (incidence, 0.26 cases per 100 person-years). Among 22 patients enrolled with symptomatic hip osteonecrosis, 18 had bilateral involvement of the femoral heads, and 7 had osteonecrosis involving other bones. Two (11%) of 18 asymptomatic patients and 13 (59%) of 22 symptomatic patients underwent total hip replacement. The percentage of involvement of the weight-bearing surface of the femoral head and the rate of progression to total hip replacement was significantly greater (P<.001) in symptomatic patients than in asymptomatic patients. CONCLUSIONS HIV-infected patients are at approximately 100-fold greater risk of developing osteonecrosis than the general population. Disease progression is slower in asymptomatic patients than in symptomatic patients. Given the high frequency of total hip replacement in symptomatic patients, studies to assess preventive and treatment strategies are essential.
Collapse
Affiliation(s)
- Caryn G Morse
- Critical Care Medicine Department, NIH Clinical Center, Bethesda, Maryland 20892-1662, USA.
| | | | | | | | | | | | | |
Collapse
|
48
|
Morris GE, Parker LC, Ward JR, Jones EC, Whyte MKB, Brightling CE, Bradding P, Dower SK, Sabroe I. Cooperative molecular and cellular networks regulate Toll-like receptor-dependent inflammatory responses. FASEB J 2006; 20:2153-5. [PMID: 16935934 DOI: 10.1096/fj.06-5910fje] [Citation(s) in RCA: 66] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Viral and bacterial pathogens cause inflammation via Toll-like receptor (TLR) signaling. We have shown that effective responses to LPS may depend on cooperative interactions between TLR-expressing leukocytes and TLR-negative tissue cells. The aim of this work was to determine the roles of such networks in response to agonists of TLRs associated with antiviral and autoimmune responses. The TLR3 agonist poly(I:C) activated epithelial cells, primary endothelial cells, and two types of primary human smooth muscle cells (airway [ASMC] and vascular) directly, while the TLR7/8 agonist R848 required the presence of leukocytes to activate ASMC. In keeping with these data, ASMC expressed TLR3 but not TLR7 or TLR8. Activation of ASMC by poly(I:C) induced a specific cytokine repertoire characterized by induction of CXCL10 generation and the potential to recruit mast cells. We subsequently explored the ability of TLR agonists to cooperate in the induction of inflammation. Dual stimulation with LPS and poly(I:C) caused enhanced cytokine generation from epithelial and smooth muscle cells when in the presence of leukocytes. Thus, inflammatory responses to pathogens are regulated by networks in which patterns of TLR expression and colocalization of tissue cells and leukocytes are critical.
Collapse
Affiliation(s)
- Gavin E Morris
- Academic Unit of Respiratory Medicine, Division of Genomic Medicine, University of Sheffield, Sheffield, S10 2JF, UK
| | | | | | | | | | | | | | | | | |
Collapse
|
49
|
Affiliation(s)
- A Banks
- The Department of Industrial Chemistry, University of Liverpool
| | | | | |
Collapse
|
50
|
Affiliation(s)
- T P Hilditch
- The Department of Industrial Chemistry, University of Liverpool
| | | | | |
Collapse
|