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Nakajo M, Hirahara D, Jinguji M, Ojima S, Hirahara M, Tani A, Takumi K, Kamimura K, Ohishi M, Yoshiura T. Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis. Jpn J Radiol 2024; 42:744-752. [PMID: 38491333 PMCID: PMC11217075 DOI: 10.1007/s11604-024-01546-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/05/2024] [Indexed: 03/18/2024]
Abstract
OBJECTIVES To investigate the usefulness of machine learning (ML) models using pretreatment 18F-FDG-PET-based radiomic features for predicting adverse clinical events (ACEs) in patients with cardiac sarcoidosis (CS). MATERIALS AND METHODS This retrospective study included 47 patients with CS who underwent 18F-FDG-PET/CT scan before treatment. The lesions were assigned to the training (n = 38) and testing (n = 9) cohorts. In total, 49 18F-FDG-PET-based radiomic features and the visibility of right ventricle 18F-FDG uptake were used to predict ACEs using seven different ML algorithms (namely, decision tree, random forest [RF], neural network, k-nearest neighbors, Naïve Bayes, logistic regression, and support vector machine [SVM]) with tenfold cross-validation and the synthetic minority over-sampling technique. The ML models were constructed using the top four features ranked by the decrease in Gini impurity. The AUCs and accuracies were used to compare predictive performances. RESULTS Patients who developed ACEs presented with a significantly higher surface area and gray level run length matrix run length non-uniformity (GLRLM_RLNU), and lower neighborhood gray-tone difference matrix_coarseness and sphericity than those without ACEs (each, p < 0.05). In the training cohort, all seven ML algorithms had a good classification performance with AUC values of > 0.80 (range: 0.841-0.944). In the testing cohort, the RF algorithm had the highest AUC and accuracy (88.9% [8/9]) with a similar classification performance between training and testing cohorts (AUC: 0.945 vs 0.889). GLRLM_RLNU was the most important feature of the modeling process of this RF algorithm. CONCLUSION ML analyses using 18F-FDG-PET-based radiomic features may be useful for predicting ACEs in patients with CS.
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Affiliation(s)
- Masatoyo Nakajo
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
| | - Daisuke Hirahara
- Department of Management Planning Division, Harada Academy, 2-54-4 Higashitaniyama, Kagoshima, 890-0113, Japan
| | - Megumi Jinguji
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Satoko Ojima
- Department of Cardiovascular Medicine and Hypertension, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Mitsuho Hirahara
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Atsushi Tani
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Koji Takumi
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Kiyohisa Kamimura
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Mitsuru Ohishi
- Department of Cardiovascular Medicine and Hypertension, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Takashi Yoshiura
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
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Awais M, Khan N, Khan AK, Rehman A. CT texture analysis for differentiating between peritoneal carcinomatosis and peritoneal tuberculosis: a cross-sectional study. Abdom Radiol (NY) 2024; 49:857-867. [PMID: 37996544 DOI: 10.1007/s00261-023-04103-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 11/25/2023]
Abstract
PURPOSE Peritoneal carcinomatosis (PC) and peritoneal tuberculosis (PTB) have similar clinical and radiologic imaging features, which make it very difficult to differentiate between the two entities clinically. Our aim was to determine if the CT textural parameters of omental lesions among patients with PC were different from those with PTB. METHODS All patients who had undergone omental biopsy at our institution from January 2010 to December 2018 and had a tissue diagnosis of PC or PTB were eligible for inclusion. Patients who did not have a contrast-enhanced CT abdomen within one month of the omental biopsy were excluded. A region of interest (ROI) was manually drawn over omental lesions and radiomic features were extracted using open-source LIFEx software. Statistical analysis was performed to compare mean differences in CT texture parameters between the PC and PTB groups. RESULTS A total of 66 patients were included in the study of which 38 and 28 had PC and PTB, respectively. Omental lesions in patients with PC had higher mean radiodensity (mean difference: +32.4; p = 0.001), higher mean entropy (mean difference: +0.11; p < 0.001), and lower mean energy (mean difference: -0.024; p = 0.001) compared to those in PTB. Additionally, omental lesions in the PC group had lower gray-level co-occurrence matrix (GLCM) homogeneity (mean difference: -0.073; p < 0.001) and higher GLCM dissimilarity (mean difference: +0.480; p < 0.001) as compared to the PTB group. CONCLUSION CT texture parameters of omental lesions differed significantly between patients with PTB and those with PC, which may help clinicians in differentiating between the two entities.
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Affiliation(s)
- Muhammad Awais
- Department of Radiology, Aga Khan University Hospital, Stadium Road, P.O. Box 3500, Karachi, 74800, Sindh, Pakistan.
| | - Noman Khan
- Department of Radiology, Aga Khan University Hospital, Stadium Road, P.O. Box 3500, Karachi, 74800, Sindh, Pakistan
| | - Ayimen Khalid Khan
- Department of Radiology, Aga Khan University Hospital, Stadium Road, P.O. Box 3500, Karachi, 74800, Sindh, Pakistan
| | - Abdul Rehman
- Department of Medicine, Rutgers-New Jersey Medical School, Newark, NJ, 07103, USA
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Hanaoka K, Watanabe S, Morimoto-Ishikawa D, Kaida H, Md, Yamada T, Yasuda M, Md, Iwanaga Y, Md, Nakazawa G, Md, Ishii K. Impact of respiratory gating and ECG gating on 18F-FDG PET/CT for cardiac sarcoidosis. J Nucl Cardiol 2023; 30:1879-1885. [PMID: 36918460 DOI: 10.1007/s12350-023-03236-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 02/10/2023] [Indexed: 03/15/2023]
Abstract
BACKGROUND The aim of this study was to estimate the impact of respiratory and electrocardiogram (ECG)-gated FDG positron emission tomography (PET)/computed tomography (CT) on the diagnosis of cardiac sarcoidosis (CS). METHODS AND RESULTS Imaging from thirty-one patients was acquired on a PET/CT scanner equipped with a respiratory- and ECG-gating system. Non-gated PET images and three kinds of gated PET/CT images were created from identical list-mode clinical PET data: respiratory-gated PET during expiration (EX), ECG-gated PET at end diastole (ED), and ECG-gated PET at end systole (ES). The maximum standardized uptake value (SUVmax) and cardiac metabolic volume (CMV) were measured, and the locations of FDG accumulation were analyzed using a polar map. The mean SUVmax of the subjects was significantly higher after application of either respiratory-gated or ECG-gated reconstruction. Conversely, the mean CMV was significantly lower following the application of respiratory-gated or ECG-gated reconstruction. The segment showing maximum accumulation was shifted to the adjacent segment in 25.8%, 38.7%, and 41.9% of cases in EX, ED, and ES images, respectively. CONCLUSION In FDG PET/CT scanning for the diagnosis of CS, gated scanning is likely to increase quantitative accuracy, but the effect depends on the location and synchronization method.
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Affiliation(s)
- Kohei Hanaoka
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University Hospital, 377-2 Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan.
| | - Shota Watanabe
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University Hospital, 377-2 Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan
| | - Daisuke Morimoto-Ishikawa
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University Hospital, 377-2 Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan
| | | | - Md
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University Hospital, 377-2 Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan
- Department of Radiology, Kindai University Faculty of Medicine, Osakasayama, Japan
| | - Takahiro Yamada
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University Hospital, 377-2 Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan
| | | | - Md
- Division of Cardiology, Department of Internal Medicine, Kindai University Faculty of Medicine, Osakasayama, Japan
| | | | - Md
- Department of Medical and Health Information Management, National Cerebral and Cardiovascular Center, Suita, Japan
| | | | - Md
- Division of Cardiology, Department of Internal Medicine, Kindai University Faculty of Medicine, Osakasayama, Japan
| | - Kazunari Ishii
- Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University Hospital, 377-2 Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan
- Department of Radiology, Kindai University Faculty of Medicine, Osakasayama, Japan
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Wang C, Ma Y, Liu Y, Li L, Cui C, Qin H, Zhao Z, Li C, Ju W, Chen M, Li D, Zhou W. Texture analysis of SPECT myocardial perfusion provides prognostic value for dilated cardiomyopathy. J Nucl Cardiol 2023; 30:504-515. [PMID: 35676551 DOI: 10.1007/s12350-022-03006-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 05/03/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Texture analysis (TA) has demonstrated clinical values in extracting information, quantifying inhomogeneity, evaluating treatment outcomes, and predicting long-term prognosis for cardiac diseases. The aim of this study was to explore whether TA of SPECT myocardial perfusion could contribute to improving the prognosis of dilated cardiomyopathy (DCM) patients. METHODS Eighty-eight patients were recruited in our study between 2009 and 2020 who were diagnosed with DCM and underwent single-photon emission tomography myocardial perfusion imaging (SPECT MPI). Forty TA features were obtained from quantitative analysis of SPECT imaging in subjects with myocardial perfusion at rest. All patients were divided into two groups: the all-cause death group and the survival group. The prognostic value of texture parameters was assessed by Cox regression and Kaplan-Meier analysis. RESULTS Twenty-five all-cause deaths (28.4%) were observed during the follow-up (39.2±28.7 months). Compared with the survival group, NT-proBNP and total perfusion deficit (TPD) were higher and left ventricular ejection fraction (LVEF) was lower in the all-cause death group. In addition, 26 out of 40 texture parameters were significantly different between the two groups. Univariate Cox regression analysis revealed that NT-proBNP, LVEF, and 25 texture parameters were significantly associated with all-cause death. The multivariate Cox regression analysis showed that low gray-level emphasis (LGLE) (P = 0.010, HR = 4.698, 95% CI 1.457-15.145) and long-run low gray-level emphasis (LRLGE) (P =0.002, HR = 6.085, 95% CI 1.906-19.422) were independent predictors of the survival outcome. When added to clinical parameters, LVEF, TPD, and TA parameters, including LGLE and LRLGE, were incrementally associated with all-cause death (global chi-square statistic of 26.246 vs. 33.521; P = 0.028, global chi-square statistic of 26.246 vs. 34.711; P = 0.004). CONCLUSION TA based on gated SPECT MPI could discover independent prognostic predictors of all-cause death in medically treated patients with DCM. Moreover, TA parameters, including LGLE and LRLGE, independent of the total perfusion deficit of the cardiac myocardium, appeared to provide incremental prognostic value for DCM patients.
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Affiliation(s)
- Cheng Wang
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Ying Ma
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Yanyun Liu
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Shaanxi, 710126, China
| | - Longxi Li
- School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Chang Cui
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Huiyuan Qin
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Zhongqiang Zhao
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Chunxiang Li
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Weizhu Ju
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Minglong Chen
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China
| | - Dianfu Li
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China.
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA.
- Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, USA.
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Régis C, Benali K, Rouzet F. FDG PET/CT Imaging of Sarcoidosis. Semin Nucl Med 2023; 53:258-272. [PMID: 36870707 DOI: 10.1053/j.semnuclmed.2022.08.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 08/18/2022] [Accepted: 08/22/2022] [Indexed: 11/11/2022]
Abstract
Sarcoidosis is a multisystemic granulomatous disease of unknown etiology. The diagnostic can be made by histological identification of non-caseous granuloma or by a combination of clinical criteria. Active inflammatory granuloma can lead to fibrotic damage. Although 50% of cases resolve spontaneously, systemic treatments are often necessary to decrease symptoms and avoid permanent organ dysfunction, notably in cardiac sarcoidosis. The course of the disease can be punctuated by exacerbations and relapses and the prognostic depends mainly on affected sites and patient management. FDG-PET/CT along with newer FDG-PET/MR have emerged as key imaging modalities in sarcoidosis, namely for certain diagnostic purposes, staging and biopsy guiding. By identifying with a high sensitivity inflammatory active granuloma, FDG hybrid imaging is a main prognostic tool and therapeutic ally in sarcoidosis. This review aims to highlight the actual critical roles of hybrid PET imaging in sarcoidosis and display a brief perspective for the future which appears to include other radiotracers and artificial intelligence applications.
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Affiliation(s)
- Claudine Régis
- Nuclear medicine department, Hôpital Bichat-Claude Bernard, AP-HP, Paris, France.; Department of Medical Imaging, Institut de Cardiologie de Montréal, Université de Montréal, Montréal, Québec, Canada
| | - Khadija Benali
- Nuclear medicine department, Hôpital Bichat-Claude Bernard, AP-HP, Paris, France.; Université Paris Cité and Inserm U1148, Paris, France
| | - François Rouzet
- Nuclear medicine department, Hôpital Bichat-Claude Bernard, AP-HP, Paris, France.; Université Paris Cité and Inserm U1148, Paris, France..
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Lovinfosse P, Ferreira M, Withofs N, Jadoul A, Derwael C, Frix AN, Guiot J, Bernard C, Diep AN, Donneau AF, Lejeune M, Bonnet C, Vos W, Meyer PE, Hustinx R. Distinction of Lymphoma from Sarcoidosis on 18F-FDG PET/CT: Evaluation of Radiomics-Feature-Guided Machine Learning Versus Human Reader Performance. J Nucl Med 2022; 63:1933-1940. [PMID: 35589406 PMCID: PMC9730930 DOI: 10.2967/jnumed.121.263598] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 05/10/2022] [Indexed: 01/11/2023] Open
Abstract
Sarcoidosis and lymphoma often share common features on 18F-FDG PET/CT, such as intense hypermetabolic lesions in lymph nodes and multiple organs. We aimed at developing and validating radiomics signatures to differentiate sarcoidosis from Hodgkin lymphoma (HL) and diffuse large B-cell lymphoma (DLBCL). Methods: We retrospectively collected 420 patients (169 sarcoidosis, 140 HL, and 111 DLBCL) who underwent pretreatment 18F-FDG PET/CT at the University Hospital of Liege. The studies were randomly distributed to 4 physicians, who gave their diagnostic suggestion among the 3 diseases. The individual and pooled performance of the physicians was then calculated. Interobserver variability was evaluated using a sample of 34 studies interpreted by all physicians. Volumes of interest were delineated over the lesions and the liver using MIM software, and 215 radiomics features were extracted using the RadiomiX Toolbox. Models were developed combining clinical data (age, sex, and weight) and radiomics (original and tumor-to-liver TLR radiomics), with 7 different feature selection approaches and 4 different machine-learning (ML) classifiers, to differentiate sarcoidosis and lymphomas on both lesion-based and patient-based approaches. Results: For identifying lymphoma versus sarcoidosis, physicians' pooled sensitivity, specificity, area under the receiver-operating-characteristic curve (AUC), and accuracy were 0.99 (95% CI, 0.97-1.00), 0.75 (95% CI, 0.68-0.81), 0.87 (95% CI, 0.84-0.90), and 89.3%, respectively, whereas for identifying HL in the tumor population, it was 0.58 (95% CI, 0.49-0.66), 0.82 (95% CI, 0.74-0.89), 0.70 (95% CI, 0.64-0.75) and 68.5%, respectively. Moderate agreement was found among observers for the diagnosis of lymphoma versus sarcoidosis and HL versus DLBCL, with Fleiss κ-values of 0.66 (95% CI, 0.45-0.87) and 0.69 (95% CI, 0.45-0.93), respectively. The best ML models for identifying lymphoma versus sarcoidosis showed an AUC of 0.94 (95% CI, 0.93-0.95) and 0.85 (95% CI, 0.82-0.88) in lesion- and patient-based approaches, respectively, using TLR radiomics (plus age for the second). To differentiate HL from DLBCL, we obtained an AUC of 0.95 (95% CI, 0.93-0.96) in the lesion-based approach using TLR radiomics and 0.86 (95% CI, 0.80-0.91) in the patient-based approach using original radiomics and age. Conclusion: Characterization of sarcoidosis and lymphoma lesions is feasible using ML and radiomics, with very good to excellent performance, equivalent to or better than that of physicians, who showed significant interobserver variability in their assessment.
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Affiliation(s)
- Pierre Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, CHU of Liège, Liège, Belgium
| | - Marta Ferreira
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
| | - Nadia Withofs
- Division of Nuclear Medicine and Oncological Imaging, CHU of Liège, Liège, Belgium
| | - Alexandre Jadoul
- Division of Nuclear Medicine and Oncological Imaging, CHU of Liège, Liège, Belgium
| | - Céline Derwael
- Division of Nuclear Medicine and Oncological Imaging, CHU of Liège, Liège, Belgium
| | - Anne-Noelle Frix
- Department of Respiratory Medicine, CHU of Liège, Liège, Belgium
| | - Julien Guiot
- Department of Respiratory Medicine, CHU of Liège, Liège, Belgium
| | - Claire Bernard
- Division of Nuclear Medicine and Oncological Imaging, CHU of Liège, Liège, Belgium
| | - Anh Nguyet Diep
- Biostatistics Unit, Department of Public Health, University of Liège, Liège, Belgium
| | | | - Marie Lejeune
- Department of Hematology, CHU of Liège, Liège, Belgium
| | | | - Wim Vos
- Radiomics SA, Liège, Belgium; and
| | - Patrick E. Meyer
- Bioinformatics and Systems Biology Lab, University of Liège, Liège, Belgium
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, CHU of Liège, Liège, Belgium
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Zhang X, Sun T, Liu E, Xu W, Wang S, Wang Q. Development and evaluation of a radiomics model of resting 13N-ammonia positron emission tomography myocardial perfusion imaging to predict coronary artery stenosis in patients with suspected coronary heart disease. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1167. [PMID: 36467349 PMCID: PMC9708489 DOI: 10.21037/atm-22-4692] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/21/2022] [Indexed: 11/09/2023]
Abstract
BACKGROUND Coronary angiography (CAG) is usually performed in patients with coronary heart disease (CHD) to evaluate the coronary artery stenosis. However, patients with iodine allergy and renal dysfunction are not suitable for CAG. We try to develop a radiomics machine learning model based on rest 13N-ammonia (13N-NH3) positron emission tomography (PET) myocardial perfusion imaging (MPI) to predict coronary stenosis. METHODS Eighty-four patients were included with the inclusion criteria: adult patients; suspected CHD; resting MPI and CAG were performed; and complete data. Coronary artery stenosis >75% were considered to be significant stenosis. Patients were randomly divided into a training group and a testing group with a ratio of 1:1. Myocardial blood flow (MBF), perfusion defect extent (EXT), total perfusion deficit (TPD), and summed rest score (SRS) were obtained. Myocardial static images of the left ventricular (LV) coronary segments were segmented, and radiomics features were extracted. In the training set, the conventional parameter (MPI model) and radiomics (Rad model) models were constructed using the machine learning method and were combined to construct a nomogram. The models' performance was evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, decision analysis curve (DCA), and calibration curves. Testing and subgroup analysis were performed. RESULTS MPI model was composed of MBF and EXT, and Rad model was composed of 12 radiomics features. In the training set, the AUC/accuracy/sensitivity/specificity of the MPI model, Rad model, and the nomogram were 0.795/0.778/0.937/0.511, 0.912/0.825/0.760/0.936 and 0.911/0.865/0.924/0.766 respectively. In the testing set, the AUC/accuracy/sensitivity/specificity of the MPI model, Rad model, and the nomogram were 0.798/0.722/0.659/0.841, 0.887/0.810/0.744/0.932 and 0.900/0.849/0.854/0.841 respectively. The AUC of Rad model and nomogram were significantly higher than that of MPI model. The DCA curve also showed that the clinical net benefit of the Rad model and nomogram was similar but greater than that of MPI model. The calibration curve showed good agreement between the observed and predicted values of the Rad model. In the subgroup analysis of Rad model, there was no significant difference in AUC between subgroups. CONCLUSIONS The Rad model is more accurate than the MPI model in predicting coronary stenosis. This noninvasive technique could help improve risk stratification and had good generalization ability.
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Affiliation(s)
- Xiaochun Zhang
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Taotao Sun
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Entao Liu
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Weiping Xu
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Shuxia Wang
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Quanshi Wang
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Park J, Young BD, Miller EJ. Potential novel imaging targets of inflammation in cardiac sarcoidosis. J Nucl Cardiol 2022; 29:2171-2187. [PMID: 34734365 DOI: 10.1007/s12350-021-02838-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 09/26/2021] [Indexed: 10/19/2022]
Abstract
Cardiac sarcoidosis (CS) is an inflammatory disease with high morbidity and mortality, with a pathognomonic feature of non-caseating granulomatous inflammation. While 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is a well-established modality to image inflammation and diagnose CS, there are limitations to its specificity and reproducibility. Imaging focused on the molecular processes of inflammation including the receptors and cellular microenvironments present in sarcoid granulomas provides opportunities to improve upon FDG-PET imaging for CS. This review will highlight the current limitations of FDG-PET imaging for CS while discussing emerging new nuclear imaging molecular targets for the imaging of cardiac sarcoidosis.
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Affiliation(s)
- Jakob Park
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Bryan D Young
- Section of Cardiovascular Medicine, Department of Medicine, Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Medicine, Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06520, USA.
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Comparison of the Classification Results Accuracy for CT Soft Tissue and Bone Reconstructions in Detecting the Porosity of a Spongy Tissue. J Clin Med 2022; 11:jcm11154526. [PMID: 35956142 PMCID: PMC9369728 DOI: 10.3390/jcm11154526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 07/09/2022] [Accepted: 07/31/2022] [Indexed: 02/04/2023] Open
Abstract
The aim of the study was to compare the accuracy of the classification pertaining to the results of two types of soft tissue and bone reconstructions of the spinal CT in detecting the porosity of L1 vertebral body spongy tissue. The dataset for each type of reconstruction (high-resolution bone reconstruction and soft tissue reconstruction) included 400 sponge tissue images from 50 healthy patients and 50 patients with osteoporosis. Texture feature descriptors were calculated based on the statistical analysis of the grey image histogram, autoregression model, and wavelet transform. The data dimensional reduction was applied by feature selection using nine methods representing various approaches (filter, wrapper, and embedded methods). Eleven methods were used to build the classifier models. In the learning process, hyperparametric optimization based on the grid search method was applied. On this basis, the most effective model and the optimal subset of features for each selection method used were determined. In the case of bone reconstruction images, four models achieved a maximum accuracy of 92%, one of which had the highest sensitivity of 95%, with a specificity of 89%. For soft tissue reconstruction images, five models achieved the highest testing accuracy of 95%, whereas the other quality indices (TPR and TNR) were also equal to 95%. The research showed that the images derived from soft tissue reconstruction allow for obtaining more accurate values of texture parameters, which increases the accuracy of the classification and offers better possibilities for diagnosing osteoporosis.
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Brunken RC. Is quantitative fluorine-18 fluorodeoxyglucose PET image analysis the key to Identify cardiac sarcoidosis? J Nucl Cardiol 2022; 29:97-100. [PMID: 32676908 DOI: 10.1007/s12350-020-02272-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 06/25/2020] [Indexed: 10/23/2022]
Affiliation(s)
- Richard C Brunken
- Department of Radiology, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA.
- Department of Nuclear Medicine/Jb3, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44195, USA.
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11
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Tsuruta C, Hirata K, Kudo K, Masumori N, Hatakenaka M. DWI-related texture analysis for prostate cancer: differences in correlation with histological aggressiveness and data repeatability between peripheral and transition zones. Eur Radiol Exp 2022; 6:1. [PMID: 35018507 PMCID: PMC8752657 DOI: 10.1186/s41747-021-00252-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 10/25/2021] [Indexed: 11/10/2022] Open
Abstract
Background We investigated the correlation between texture features extracted from apparent diffusion coefficient (ADC) maps or diffusion-weighted images (DWIs), and grade group (GG) in the prostate peripheral zone (PZ) and transition zone (TZ), and assessed reliability in repeated examinations. Methods Patients underwent 3-T pelvic magnetic resonance imaging (MRI) before radical prostatectomy with repeated DWI using b-values of 0, 100, 1,000, and 1,500 s/mm2. Region of interest (ROI) for cancer was assigned to the first and second DWI acquisition separately. Texture features of ROIs were extracted from comma-separated values (CSV) data of ADC maps generated from several sets of two b-value combinations and DWIs, and correlation with GG, discrimination ability between GG of 1–2 versus 3–5, and data repeatability were evaluated in PZ and TZ. Results Forty-four patients with 49 prostate cancers met the eligibility criteria. In PZ, ADC 10% and 25% based on ADC map of two b-value combinations of 100 and 1,500 s/mm2 and 10% based on ADC map with b-value of 0 and 1,500 s/mm2 showed significant correlation with GG, acceptable discrimination ability, and good repeatability. In TZ, higher-order texture feature of busyness extracted from ADC map of 100 and 1,500 s/mm2, and high gray-level run emphasis, short-run high gray-level emphasis, and high gray-level zone emphasis from DWI with b-value of 100 s/mm2 demonstrated significant correlation, excellent discrimination ability, but moderate repeatability. Conclusions Some DWI-related features showed significant correlation with GG, acceptable to excellent discrimination ability, and moderate to good data repeatability in prostate cancer, and differed between PZ and TZ. Supplementary Information The online version contains supplementary material available at 10.1186/s41747-021-00252-y.
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Affiliation(s)
- Chie Tsuruta
- Department of Diagnostic Radiology, School of Medicine, Sapporo Medical University, South 1, West 17, Chuo-ku, Sapporo, 060-8556, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Japan.,Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | - Naoya Masumori
- Department of Urology, School of Medicine, Sapporo Medical University, Sapporo, Japan
| | - Masamitsu Hatakenaka
- Department of Diagnostic Radiology, School of Medicine, Sapporo Medical University, South 1, West 17, Chuo-ku, Sapporo, 060-8556, Japan.
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12
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Manabe O, Oyama-Manabe N, Aikawa T, Tsuneta S, Tamaki N. Advances in Diagnostic Imaging for Cardiac Sarcoidosis. J Clin Med 2021; 10:jcm10245808. [PMID: 34945105 PMCID: PMC8704832 DOI: 10.3390/jcm10245808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/05/2021] [Accepted: 12/09/2021] [Indexed: 11/16/2022] Open
Abstract
Sarcoidosis is a systemic granulomatous disease of unknown etiology, and its clinical presentation depends on the affected organ. Cardiac sarcoidosis (CS) is one of the leading causes of death among patients with sarcoidosis. The clinical manifestations of CS are heterogeneous, and range from asymptomatic to life-threatening arrhythmias and progressive heart failure due to the extent and location of granulomatous inflammation in the myocardium. Advances in imaging techniques have played a pivotal role in the evaluation of CS because histological diagnoses obtained by myocardial biopsy tend to have lower sensitivity. The diagnosis of CS is challenging, and several approaches, notably those using positron emission tomography and cardiac magnetic resonance imaging (MRI), have been reported. Delayed-enhanced computed tomography (CT) may also be used for diagnosing CS in patients with MRI-incompatible devices and allows acceptable evaluation of myocardial hyperenhancement in such patients. This article reviews the advances in imaging techniques for the evaluation of CS.
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Affiliation(s)
- Osamu Manabe
- Department of Radiology, Jichi Medical University Saitama Medical Center, Saitama 330-8503, Japan; (O.M.); (T.A.)
| | - Noriko Oyama-Manabe
- Department of Radiology, Jichi Medical University Saitama Medical Center, Saitama 330-8503, Japan; (O.M.); (T.A.)
- Correspondence: ; Tel.: +81-48-647-2111
| | - Tadao Aikawa
- Department of Radiology, Jichi Medical University Saitama Medical Center, Saitama 330-8503, Japan; (O.M.); (T.A.)
| | - Satonori Tsuneta
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo 060-8648, Japan;
| | - Nagara Tamaki
- Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan;
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13
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Xue C, Yuan J, Lo GG, Chang ATY, Poon DMC, Wong OL, Zhou Y, Chu WCW. Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review. Quant Imaging Med Surg 2021; 11:4431-4460. [PMID: 34603997 DOI: 10.21037/qims-21-86] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 05/17/2021] [Indexed: 12/13/2022]
Abstract
Radiomics research is rapidly growing in recent years, but more concerns on radiomics reliability are also raised. This review attempts to update and overview the current status of radiomics reliability research in the ever expanding medical literature from the perspective of a single reliability metric of intraclass correlation coefficient (ICC). To conduct this systematic review, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. After literature search and selection, a total of 481 radiomics studies using CT, PET, or MRI, covering a wide range of subject and disease types, were included for review. In these highly heterogeneous studies, feature reliability to image segmentation was much more investigated than reliability to other factors, such as image acquisition, reconstruction, post-processing, and feature quantification. The reported ICCs also suggested high radiomics feature reliability to image segmentation. Image acquisition was found to introduce much more feature variability than image segmentation, in particular for MRI, based on the reported ICC values. Image post-processing and feature quantification yielded different levels of radiomics reliability and might be used to mitigate image acquisition-induced variability. Some common flaws and pitfalls in ICC use were identified, and suggestions on better ICC use were given. Due to the extremely high study heterogeneities and possible risks of bias, the degree of radiomics feature reliability that has been achieved could not yet be safely synthesized or derived in this review. More future researches on radiomics reliability are warranted.
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Affiliation(s)
- Cindy Xue
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China.,Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Jing Yuan
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Gladys G Lo
- Department of Diagnostic & Interventional Radiology, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Amy T Y Chang
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Darren M C Poon
- Comprehensive Oncology Centre, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Oi Lei Wong
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Yihang Zhou
- Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, China
| | - Winnie C W Chu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
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14
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Guo J, Li Z, Qu X, Xian J. Value of MRI-based radiomics analysis for differentiation of benign and malignant epithelial neoplasms in the lacrimal gland: a retrospective study. Acta Radiol 2021; 62:743-751. [PMID: 32660315 DOI: 10.1177/0284185120940258] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND It is essential to distinguish malignant from benign epithelial neoplasms in the lacrimal gland for different treatment options and prognosis. PURPOSE To retrospectively assess the performance of magnetic resonance imaging (MRI)-based radiomics features in the differentiation of benign and malignant epithelial neoplasms in the lacrimal gland. MATERIAL AND METHODS Seventy-six consecutive patients with histopathology-proven epithelial neoplasms of the lacrimal gland were enrolled in the study, including 41 benign and 35 malignant neoplasms. Radiomics features were extracted from T2-weighted and post-contrast T1-weighted imaging. The least absolute shrinkage and selection operator method was used to select imaging features and reduce data dimension to discriminate malignant from benign neoplasms in the lacrimal gland. Diagnostic performance of the radiomics model was assessed by receive operation characteristic (ROC) curve and compared with that of radiologists. RESULTS Four quantitative image features including inverse difference moment normalized (IDMN), mean deviation (MD), standard deviation (SD), and long-run emphasis (LRE) were selected to distinguish malignant from benign epithelial neoplasms in the lacrimal gland. Area under the curve (AUC) of these four features were 0.88, 086, 0.88, and 0.86, respectively, with 0.93 for the combination model. The model identified malignant epithelial neoplasms from benign group with 89% sensitivity, 93% specificity, and 89% accuracy. There was a significant difference in the diagnostic performance of radiomics model and the radiologists, with AUC of 0.70 for radiologists. The diagnostic performance of radiomics is superior to that of radiologists. CONCLUSION MRI-based radiomics analysis has potential for differentiation of benign and malignant epithelial neoplasms in the lacrimal gland.
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Affiliation(s)
- Jian Guo
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, PR China
- Clinical Center for Eye Tumors, Capital Medical University, Beijing, PR China
| | - Zheng Li
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, PR China
- Clinical Center for Eye Tumors, Capital Medical University, Beijing, PR China
| | - Xiaoxia Qu
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, PR China
- Clinical Center for Eye Tumors, Capital Medical University, Beijing, PR China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, PR China
- Clinical Center for Eye Tumors, Capital Medical University, Beijing, PR China
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15
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Manabe O, Tamaki N. Potential roles of 123I-BMIPP SPECT to assess cardiac sarcoidosis. J Nucl Cardiol 2021; 28:936-938. [PMID: 33988810 DOI: 10.1007/s12350-021-02654-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 01/09/2023]
Affiliation(s)
- Osamu Manabe
- Department of Radiology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Nagara Tamaki
- Department of Radiology, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566, Japan.
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16
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Oyama-Manabe N, Manabe O, Aikawa T, Tsuneta S. The Role of Multimodality Imaging in Cardiac Sarcoidosis. Korean Circ J 2021; 51:561-578. [PMID: 34085435 PMCID: PMC8263295 DOI: 10.4070/kcj.2021.0104] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 04/21/2021] [Indexed: 12/19/2022] Open
Abstract
The etiology and the progression of sarcoidosis remain unknown. However, cardiac sarcoidosis (CS) is significantly associated with a poor prognosis due to the associated congestive heart failure, arrhythmias (such as an advanced atrioventricular block), and ventricular tachyarrhythmia. Novel imaging modalities are now available to detect CS lesions secondary to active inflammation, granuloma formation, and fibrotic changes. 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) and cardiac magnetic resonance imaging (CMR) play essential roles in diagnosing and monitoring patients with confirmed or suspected CS. The following focused review will highlight the emerging role of non-invasive cardiac imaging techniques, including FDG PET/CT and CMR.
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Affiliation(s)
- Noriko Oyama-Manabe
- Department of Radiology, Jichi Medical University Saitama Medical Center, Saitama, Japan.
| | - Osamu Manabe
- Department of Radiology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Tadao Aikawa
- Department of Radiology, Jichi Medical University Saitama Medical Center, Saitama, Japan.,Department of Cardiology, Hokkaido Cardiovascular Hospital, Sapporo, Japan
| | - Satonori Tsuneta
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
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17
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Tana C, Mantini C, Donatiello I, Mucci L, Tana M, Ricci F, Cipollone F, Giamberardino MA. Clinical Features and Diagnosis of Cardiac Sarcoidosis. J Clin Med 2021; 10:1941. [PMID: 34062709 PMCID: PMC8124502 DOI: 10.3390/jcm10091941] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/09/2021] [Accepted: 04/13/2021] [Indexed: 12/12/2022] Open
Abstract
Cardiac sarcoidosis (CS) is an unusual, but potentially harmful, manifestation of systemic sarcoidosis (SA), a chronic disease characterized by organ involvement from noncaseating and nonnecrotizing granulomas. Lungs and intrathoracic lymph nodes are usually the sites that are most frequently affected, but no organ is spared and CS can affect a variable portion of SA patients, up to 25% from post-mortem studies. The cardiovascular involvement is usually associated with a bad prognosis and is responsible for the major cause of death and complications, particularly in African American patients. Furthermore, the diagnosis is often complicated by the occurrence of non-specific clinical manifestations, which can mimic the effect of more common heart disorders, and imaging and biopsies are the most valid approach to avoid misdiagnosis. This narrative review summarizes the main clinical features of CS and imaging findings, particularly of CMR and 18-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) that can give the best cost/benefit ratio in terms of the diagnostic approach. Imaging can be very useful in replacing the endomyocardial biopsy in selected cases, to avoid unnecessary, and potentially dangerous, invasive maneuvers.
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Affiliation(s)
- Claudio Tana
- COVID-19 Medicine Unit and Geriatrics Clinic, SS Annunziata Hospital of Chieti, 66100 Chieti, Italy; (F.C.); (M.A.G.)
| | - Cesare Mantini
- Department of Neuroscience, Imaging and Clinical Sciences, Institute of Radiology, “SS Annunziata” Hospital, “G. d’Annunzio” University, 66100 Chieti, Italy; (C.M.); (F.R.)
| | - Iginio Donatiello
- Internal Medicine Unit, University Hospital of Salerno, 84121 Salerno, Italy;
| | - Luciano Mucci
- Internal Medicine Unit, Hospital of Fano, Azienda Ospedaliera Ospedali Riuniti Marche, 61032 Fano, Italy;
| | - Marco Tana
- 2nd Medicine Unit and Department of Vascular Medicine and Cardiovascular Ultrasound, SS Annunziata Hospital of Chieti, 66100 Chieti, Italy;
| | - Fabrizio Ricci
- Department of Neuroscience, Imaging and Clinical Sciences, Institute of Radiology, “SS Annunziata” Hospital, “G. d’Annunzio” University, 66100 Chieti, Italy; (C.M.); (F.R.)
| | - Francesco Cipollone
- COVID-19 Medicine Unit and Geriatrics Clinic, SS Annunziata Hospital of Chieti, 66100 Chieti, Italy; (F.C.); (M.A.G.)
- Department of Medicine and Science of Aging, and CAST, G D’Annunzio University of Chieti, 66100 Chieti, Italy;
| | - Maria Adele Giamberardino
- COVID-19 Medicine Unit and Geriatrics Clinic, SS Annunziata Hospital of Chieti, 66100 Chieti, Italy; (F.C.); (M.A.G.)
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18
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Josselyn N, MacLean MT, Jean C, Fuchs B, Moon BF, Hwuang E, Iyer SK, Litt H, Han Y, Kaghazchi F, Bravo PE, Witschey WR. Classification of Myocardial 18F-FDG PET Uptake Patterns Using Deep Learning. Radiol Artif Intell 2021; 3:e200148. [PMID: 34350405 DOI: 10.1148/ryai.2021200148] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 02/17/2021] [Accepted: 03/11/2021] [Indexed: 11/11/2022]
Abstract
Purpose To perform automated myocardial segmentation and uptake classification from whole-body fluorine 18 fluorodeoxyglucose (FDG) PET. Materials and Methods In this retrospective study, consecutive patients who underwent FDG PET imaging for oncologic indications were included (July-August 2018). The left ventricle (LV) on whole-body FDG PET images was manually segmented and classified as showing no myocardial uptake, diffuse uptake, or partial uptake. A total of 609 patients (mean age, 64 years ± 14 [standard deviation]; 309 women) were included and split between training (60%, 365 patients), validation (20%, 122 patients), and testing (20%, 122 patients) datasets. Two sequential neural networks were developed to automatically segment the LV and classify the myocardial uptake pattern using segmentation and classification training data provided by human experts. Linear regression was performed to correlate findings from human experts and deep learning. Classification performance was evaluated using receiver operating characteristic (ROC) analysis. Results There was moderate agreement of uptake pattern between experts and deep learning (as a fraction of correctly categorized images) with 78% (36 of 46) for no uptake, 71% (34 of 48) for diffuse uptake, and 71% (20 of 28) for partial uptake. There was no bias in LV volume for partial or diffuse uptake categories (P = .56); however, deep learning underestimated LV volumes in the no uptake category. There was good correlation for LV volume (R 2 = 0.35, b = .71). ROC analysis showed the area under the curve for classifying no uptake and diffuse uptake was high (> 0.90) but lower for partial uptake (0.77). The feasibility of a myocardial uptake index (MUI) for quantifying the degree of myocardial activity patterns was shown, and there was excellent visual agreement between MUI and uptake patterns. Conclusion Deep learning was able to segment and classify myocardial uptake patterns on FDG PET images.Keywords: PET, Heart, Computer Aided Diagnosis, Computer Application-Detection/DiagnosisSupplemental material is available for this article.©RSNA, 2021.
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Affiliation(s)
- Nicholas Josselyn
- Departments of Radiology (N.J., M.T.M., C.J., B.F., H.L., Y.H., F.K., P.E.B., W.R.W.), Bioengineering (B.F.M., E.H., S.K.I.), and Medicine (Y.H., P.B.), Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104
| | - Matthew T MacLean
- Departments of Radiology (N.J., M.T.M., C.J., B.F., H.L., Y.H., F.K., P.E.B., W.R.W.), Bioengineering (B.F.M., E.H., S.K.I.), and Medicine (Y.H., P.B.), Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104
| | - Christopher Jean
- Departments of Radiology (N.J., M.T.M., C.J., B.F., H.L., Y.H., F.K., P.E.B., W.R.W.), Bioengineering (B.F.M., E.H., S.K.I.), and Medicine (Y.H., P.B.), Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104
| | - Ben Fuchs
- Departments of Radiology (N.J., M.T.M., C.J., B.F., H.L., Y.H., F.K., P.E.B., W.R.W.), Bioengineering (B.F.M., E.H., S.K.I.), and Medicine (Y.H., P.B.), Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104
| | - Brianna F Moon
- Departments of Radiology (N.J., M.T.M., C.J., B.F., H.L., Y.H., F.K., P.E.B., W.R.W.), Bioengineering (B.F.M., E.H., S.K.I.), and Medicine (Y.H., P.B.), Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104
| | - Eileen Hwuang
- Departments of Radiology (N.J., M.T.M., C.J., B.F., H.L., Y.H., F.K., P.E.B., W.R.W.), Bioengineering (B.F.M., E.H., S.K.I.), and Medicine (Y.H., P.B.), Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104
| | - Srikant Kamesh Iyer
- Departments of Radiology (N.J., M.T.M., C.J., B.F., H.L., Y.H., F.K., P.E.B., W.R.W.), Bioengineering (B.F.M., E.H., S.K.I.), and Medicine (Y.H., P.B.), Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104
| | - Harold Litt
- Departments of Radiology (N.J., M.T.M., C.J., B.F., H.L., Y.H., F.K., P.E.B., W.R.W.), Bioengineering (B.F.M., E.H., S.K.I.), and Medicine (Y.H., P.B.), Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104
| | - Yuchi Han
- Departments of Radiology (N.J., M.T.M., C.J., B.F., H.L., Y.H., F.K., P.E.B., W.R.W.), Bioengineering (B.F.M., E.H., S.K.I.), and Medicine (Y.H., P.B.), Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104
| | - Fatemeh Kaghazchi
- Departments of Radiology (N.J., M.T.M., C.J., B.F., H.L., Y.H., F.K., P.E.B., W.R.W.), Bioengineering (B.F.M., E.H., S.K.I.), and Medicine (Y.H., P.B.), Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104
| | - Paco E Bravo
- Departments of Radiology (N.J., M.T.M., C.J., B.F., H.L., Y.H., F.K., P.E.B., W.R.W.), Bioengineering (B.F.M., E.H., S.K.I.), and Medicine (Y.H., P.B.), Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104
| | - Walter R Witschey
- Departments of Radiology (N.J., M.T.M., C.J., B.F., H.L., Y.H., F.K., P.E.B., W.R.W.), Bioengineering (B.F.M., E.H., S.K.I.), and Medicine (Y.H., P.B.), Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, South Pavilion, Room 11-155, Philadelphia, PA 19104
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19
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Shiiba T. [7. Applications of Machine Learning on Nuclear Medicine]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2021; 77:193-199. [PMID: 33612697 DOI: 10.6009/jjrt.2021_jsrt_77.2.193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Takuro Shiiba
- Department of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University
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20
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Preoperative Texture Analysis Using 11C-Methionine Positron Emission Tomography Predicts Survival after Surgery for Glioma. Diagnostics (Basel) 2021; 11:diagnostics11020189. [PMID: 33525709 PMCID: PMC7911154 DOI: 10.3390/diagnostics11020189] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 01/26/2021] [Accepted: 01/26/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Positron emission tomography with 11C-methionine (MET) is well established in the diagnostic work-up of malignant brain tumors. Texture analysis is a novel technique for extracting information regarding relationships among surrounding voxels, in order to quantify their inhomogeneity. This study evaluated whether the texture analysis of MET uptake has prognostic value for patients with glioma. METHODS We retrospectively analyzed adults with glioma who had undergone preoperative metabolic imaging at a single center. Tumors were delineated using a threshold of 1.3-fold of the mean standardized uptake value for the contralateral cortex, and then processed to calculate the texture features in glioma. RESULTS The study included 42 patients (median age: 56 years). The World Health Organization classifications were grade II (7 patients), grade III (17 patients), and grade IV (18 patients). Sixteen (16.1%) all-cause deaths were recorded during the median follow-up of 18.8 months. The univariate analyses revealed that overall survival (OS) was associated with age (hazard ratio (HR) 1.04, 95% confidence interval (CI) 1.01-1.08, p = 0.0093), tumor grade (HR 3.64, 95% CI 1.63-9.63, p = 0.0010), genetic status (p < 0.0001), low gray-level run emphasis (LGRE, calculated from the gray-level run-length matrix) (HR 2.30 × 1011, 95% CI 737.11-4.23 × 1019, p = 0.0096), and correlation (calculated from the gray-level co-occurrence matrix) (HR 5.17, 95% CI 1.07-20.93, p = 0.041). The multivariate analyses revealed OS was independently associated with LGRE and correlation. The survival curves were also significantly different (both log-rank p < 0.05). CONCLUSION Textural features obtained using preoperative MET positron emission tomography may compliment the semi-quantitative assessment for prognostication in glioma cases.
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21
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Texture analysis of delayed contrast-enhanced computed tomography to diagnose cardiac sarcoidosis. Jpn J Radiol 2021; 39:442-450. [PMID: 33483941 DOI: 10.1007/s11604-020-01086-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 12/27/2020] [Indexed: 01/09/2023]
Abstract
PURPOSE To investigate the diagnostic value of texture analysis to differentiate cardiac sarcoidosis (CS) from other non-ischemic cardiomyopathies (non-CS). MATERIALS AND METHODS Twenty CS patients and 15 non-CS patients who had undergone myocardial CT delayed enhancement (CTDE) were included. A total of 36 texture features were calculated according to the CT attenuation of CTDE. We investigated the diagnostic value to differentiate CS from non-CS. We also assessed the intra- and inter-rater reproducibility for each feature and inter-observer agreement for visual assessment. RESULTS Seven extracted features had significantly higher run length non-uniformity (RLNU) values (5.4 × 102 ± 6.2 × 102 vs. 11.2 × 102 ± 4.9 × 102, p = 0.037) and significantly lower low gray-level zone emphasis (LGZE) values (7.1 × 10-3 ± 8.6 × 10-3 vs. 18.1 × 10-3 ± 16.9 × 10-3, p = 0.017) in CS than in non-CS. Intra- and inter-rater reproducibility of RLNU and LGZE were excellent (ICCs > 0.8), while inter-observer agreement of visual assessment was poor (kappa = 0.19). The accuracies of texture analysis were 69% with RLNU and 71% with LGZE, which were better than that of visual assessment. CONCLUSION Texture analysis of CTDE could differentiate CS from non-CS with high reproducibility.
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Satoh Y, Hirata K, Tamada D, Funayama S, Onishi H. Texture Analysis in the Diagnosis of Primary Breast Cancer: Comparison of High-Resolution Dedicated Breast Positron Emission Tomography (dbPET) and Whole-Body PET/CT. Front Med (Lausanne) 2021; 7:603303. [PMID: 33425949 PMCID: PMC7793660 DOI: 10.3389/fmed.2020.603303] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Accepted: 12/02/2020] [Indexed: 12/02/2022] Open
Abstract
Objective: This retrospective study aimed to compare the ability to classify tumor characteristics of breast cancer (BC) of positron emission tomography (PET)-derived texture features between dedicated breast PET (dbPET) and whole-body PET/computed tomography (CT). Methods: Forty-four BCs scanned by both high-resolution ring-shaped dbPET and whole-body PET/CT were analyzed. The primary BC was extracted with a standardized uptake value (SUV) threshold segmentation method. On both dbPET and PET/CT images, 38 texture features were computed; their ability to classify tumor characteristics such as tumor (T)-category, lymph node (N)-category, molecular subtype, and Ki67 levels was compared. The texture features were evaluated using univariate and multivariate analyses following principal component analysis (PCA). AUC values were used to evaluate the diagnostic power of the computed texture features to classify BC characteristics. Results: Some texture features of dbPET and PET/CT were different between Tis-1 and T2-4 and between Luminal A and other groups, respectively. No association with texture features was found in the N-category or Ki67 level. In contrast, receiver-operating characteristic analysis using texture features' principal components showed that the AUC for classification of any BC characteristics were equally good for both dbPET and whole-body PET/CT. Conclusions: PET-based texture analysis of dbPET and whole-body PET/CT may have equally good classification power for BC.
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Affiliation(s)
- Yoko Satoh
- Yamanashi PET Imaging Clinic, Yamanashi, Japan.,Department of Radiology, University of Yamanashi, Yamanashi, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, School of Medicine, Hokkaido University, Sapporo, Japan
| | - Daiki Tamada
- Department of Radiology, University of Yamanashi, Yamanashi, Japan
| | - Satoshi Funayama
- Department of Radiology, University of Yamanashi, Yamanashi, Japan
| | - Hiroshi Onishi
- Department of Radiology, University of Yamanashi, Yamanashi, Japan
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Manabe O, Koyanagawa K, Hirata K, Oyama-Manabe N, Ohira H, Aikawa T, Furuya S, Naya M, Tsujino I, Tomiyama Y, Otaki Y, Anzai T, Tamaki N. Prognostic Value of 18F-FDG PET Using Texture Analysis in Cardiac Sarcoidosis. JACC Cardiovasc Imaging 2020; 13:1096-1097. [PMID: 31954654 DOI: 10.1016/j.jcmg.2019.11.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 11/06/2019] [Accepted: 11/12/2019] [Indexed: 01/01/2023]
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24
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Naya M, Manabe O. Nuclear Medicine Image Interpretation Progress in the Assessment of Cardiac Sarcoidosis: July 2019 ASNC/JSNC Joint Session. ANNALS OF NUCLEAR CARDIOLOGY 2020; 6:49-52. [PMID: 37123489 PMCID: PMC10133950 DOI: 10.17996/anc.20-00113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 12/06/2020] [Accepted: 01/28/2020] [Indexed: 05/02/2023]
Abstract
Sarcoidosis is a significant disease affecting the heart. 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is a well-validated method for identifying significant focal inflammatory sarcoid lesions. The recent progress in image interpretation in nuclear medicine improves the diagnosis and the risk stratification in patients with cardiac sarcoidosis. Especially, metabolic activity, texture analysis, phase analysis, right ventricle assessment, and digital PET/CT are promising methods to assess cardiac sarcoidosis. This review focuses on the latest data analyses and image interpretation used in nuclear medicine to assess cardiac sarcoidosis.
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Affiliation(s)
- Masanao Naya
- Department of Cardiovascular Medicine, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Osamu Manabe
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
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25
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Travin MI. Radionuclide Imaging for Cardiac Sarcoidosis: Much Potential Benefit, but Still Much to Do. ANNALS OF NUCLEAR CARDIOLOGY 2020; 6:53-60. [PMID: 37123488 PMCID: PMC10133935 DOI: 10.17996/anc.20-00115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 04/09/2020] [Accepted: 04/13/2020] [Indexed: 05/02/2023]
Abstract
Sarcoidosis, a multi-organ inflammatory condition commonly involving the heart and leading to high morbidity and mortality, is increasingly prevalent. PET imaging with 18F-FDG in conjunction with perfusion imaging is increasingly used for diagnosis, disease characterization, and to guide and follow treatment. However, various challenges remain with regard to protocols, interpretation of image findings, and how best to use test results to guide and monitor therapy. Further investigations of the testing technique, as well as better understanding of disease pathophysiology, are needed for better image utility in order to effectively improve patient outcome.
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Affiliation(s)
- Mark I. Travin
- Department of Radiology/Division of Nuclear Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
- Reprint requests and correspondence: Mark I. Travin, MD, Department of Radiology, Division of Nuclear Medicine, Montefiore Medical Center, 111 East-210th Street, Bronx, NY 10467-2490 USA / E-mail:
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Bennett D, Bargagli E, Refini RM, Rottoli P. New concepts in the pathogenesis of sarcoidosis. Expert Rev Respir Med 2019; 13:981-991. [DOI: 10.1080/17476348.2019.1655401] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- David Bennett
- Respiratory Diseases and Lung Transplantation Unit, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Elena Bargagli
- Respiratory Diseases and Lung Transplantation Unit, Azienda Ospedaliera Universitaria Senese, Siena, Italy
- Department of Medical and Surgical Sciences & Neurosciences, University of Siena, Siena, Italy
| | - Rosa Metella Refini
- Respiratory Diseases and Lung Transplantation Unit, Azienda Ospedaliera Universitaria Senese, Siena, Italy
- Department of Medical and Surgical Sciences & Neurosciences, University of Siena, Siena, Italy
| | - Paola Rottoli
- Department of Medical and Surgical Sciences & Neurosciences, University of Siena, Siena, Italy
- Regional Coordinator for Rare Respiratory Diseases for Tuscany, Azienda Ospedaliera Universitaria Senese, Siena, Italy
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Zwanenburg A. Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol Imaging 2019; 46:2638-2655. [PMID: 31240330 DOI: 10.1007/s00259-019-04391-8] [Citation(s) in RCA: 169] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 06/04/2019] [Indexed: 12/16/2022]
Abstract
Radiomics in nuclear medicine is rapidly expanding. Reproducibility of radiomics studies in multicentre settings is an important criterion for clinical translation. We therefore performed a meta-analysis to investigate reproducibility of radiomics biomarkers in PET imaging and to obtain quantitative information regarding their sensitivity to variations in various imaging and radiomics-related factors as well as their inherent sensitivity. Additionally, we identify and describe data analysis pitfalls that affect the reproducibility and generalizability of radiomics studies. After a systematic literature search, 42 studies were included in the qualitative synthesis, and data from 21 were used for the quantitative meta-analysis. Data concerning measurement agreement and reliability were collected for 21 of 38 different factors associated with image acquisition, reconstruction, segmentation and radiomics-specific processing steps. Variations in voxel size, segmentation and several reconstruction parameters strongly affected reproducibility, but the level of evidence remained weak. Based on the meta-analysis, we also assessed inherent sensitivity to variations of 110 PET image biomarkers. SUVmean and SUVmax were found to be reliable, whereas image biomarkers based on the neighbourhood grey tone difference matrix and most biomarkers based on the size zone matrix were found to be highly sensitive to variations, and should be used with care in multicentre settings. Lastly, we identify 11 data analysis pitfalls. These pitfalls concern model validation and information leakage during model development, but also relate to reporting and the software used for data analysis. Avoiding such pitfalls is essential for minimizing bias in the results and to enable reproduction and validation of radiomics studies.
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Affiliation(s)
- Alex Zwanenburg
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Helmholtz-Zentrum Dresden - Rossendorf, Technische Universität Dresden, Dresden, Germany.
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.
- German Cancer Research Center (DKFZ), Heidelberg, Germany.
- German Cancer Consortium (DKTK), Partner Site Dresden, Dresden, Germany.
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AlJaroudi WA, Hage FG. Review of cardiovascular imaging in the Journal of Nuclear Cardiology 2018. Part 1 of 2: Positron emission tomography, computed tomography, and magnetic resonance. J Nucl Cardiol 2019; 26:524-535. [PMID: 30603892 DOI: 10.1007/s12350-018-01558-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 11/28/2018] [Indexed: 12/26/2022]
Abstract
In this review, we summarize key articles that have been published in the Journal of Nuclear Cardiology in 2018 pertaining to nuclear cardiology with advanced multi-modality and hybrid imaging including positron emission tomography, cardiac-computed tomography, and magnetic resonance. In an upcoming review, we will summarize key articles that relate to the progress made in the field of single-photon emission computed tomography. We hope that these sister reviews will be useful to the reader to navigate the literature in our field.
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Affiliation(s)
- Wael A AlJaroudi
- Division of Cardiovascular Medicine, Clemenceau Medical Center, Beirut, Lebanon
| | - Fadi G Hage
- Division of Cardiovascular Disease, Department of Medicine, University of Alabama at Birmingham, 306 Lyons-Harrison Research Building, 701 19th Street South, Birmingham, AL, 35294-0007, USA.
- Section of Cardiology, Birmingham Veterans Affairs Medical Center, Birmingham, AL, USA.
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Kroenke M, Hirata K, Gafita A, Watanabe S, Okamoto S, Magota K, Shiga T, Kuge Y, Tamaki N. Voxel based comparison and texture analysis of 18F-FDG and 18F-FMISO PET of patients with head-and-neck cancer. PLoS One 2019; 14:e0213111. [PMID: 30818360 PMCID: PMC6394953 DOI: 10.1371/journal.pone.0213111] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 02/14/2019] [Indexed: 12/22/2022] Open
Abstract
Background Hypoxia can induce radiation resistance and is an independent prognostic marker for outcome in head and neck cancer. As 18F-FMISO (FMISO), a hypoxia tracer for PET, is far less common than 18F-FDG (FDG) and two separate PET scans result in doubled cost and radiation exposure to the patient, we aimed to predict hypoxia from FDG PET with new techniques of voxel based analysis and texture analysis. Methods Thirty-eight patients with head-and-neck cancer underwent consecutive FDG and FMISO PET scans before any treatment. ROIs enclosing the primary cancer were compared in a voxel-by-voxel manner between FDG and FMISO PET. Tumour hypoxia was defined as the volume with a tumour-to-muscle ratio (TMR) > 1.25 in the FMISO PET and hypermetabolic volume was defined as >50% SUVmax in the FDG PET. The concordance rate was defined as percentage of voxels within the tumour which were both hypermetabolic and hypoxic. 38 different texture analysis (TA) parameters were computed based on the ROIs and correlated with presence of hypoxia. Results Within the hypoxic tumour regions, the FDG uptake was twice as high as in the non-hypoxic tumour regions (SUVmean 10.9 vs. 5.4; p<0.001). A moderate correlation between FDG and FMISO uptake was found by a voxel-by-voxel comparison (r = 0.664 p<0.001). The average concordance rate was 25% (± 22%). Entropy was the TA parameter showing the highest correlation with hypoxia (r = 0.524 p<0.001). Conclusion FDG uptake was higher in hypoxic tumour regions than in non-hypoxic regions as expected by tumour biology. A moderate correlation between FDG and FMISO PET was found by voxel-based analysis. TA yielded similar results in FDG and FMISO PET. However, it may not be possible to predict tumour hypoxia even with the help of texture analysis.
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Affiliation(s)
- Markus Kroenke
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany.,Department of Nuclear Medicine, Graduate School of Medicine of Hokkaido University, Sapporo, Japan
| | - Kenji Hirata
- Department of Nuclear Medicine, Graduate School of Medicine of Hokkaido University, Sapporo, Japan
| | - Andrei Gafita
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
| | - Shiro Watanabe
- Department of Nuclear Medicine, Graduate School of Medicine of Hokkaido University, Sapporo, Japan
| | - Shozo Okamoto
- Department of Nuclear Medicine, Graduate School of Medicine of Hokkaido University, Sapporo, Japan
| | - Keiichi Magota
- Department of Nuclear Medicine, Graduate School of Medicine of Hokkaido University, Sapporo, Japan
| | - Tohru Shiga
- Department of Nuclear Medicine, Graduate School of Medicine of Hokkaido University, Sapporo, Japan
| | - Yuji Kuge
- Central Institute of Isotope Science, of Hokkaido University, Sapporo, Japan
| | - Nagara Tamaki
- Department of Nuclear Medicine, Graduate School of Medicine of Hokkaido University, Sapporo, Japan
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