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Cui M, Bao S, Li J, Dong H, Xu Z, Yan F, Yang W. CT radiomic features reproducibility of virtual non-contrast series derived from photon-counting CCTA datasets using a novel calcium-preserving reconstruction algorithm compared with standard non-contrast series: focusing on epicardial adipose tissue. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2024; 40:1257-1267. [PMID: 38587689 DOI: 10.1007/s10554-024-03096-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 03/26/2024] [Indexed: 04/09/2024]
Abstract
PURPOSE We aimed to evaluate the reproducibility of computed tomography (CT) radiomic features (RFs) about Epicardial Adipose Tissue (EAT). The features derived from coronary photon-counting computed tomography (PCCT) angiography datasets using the PureCalcium (VNCPC) and conventional virtual non-contrast (VNCConv) algorithm were compared with true non-contrast (TNC) series. METHODS RFs of EAT from 52 patients who underwent PCCT were quantified using VNCPC, VNCConv, and TNC series. The agreement of EAT volume (EATV) and EAT density (EATD) was evaluated using Pearson's correlation coefficient and Bland-Altman analysis. A total of 1530 RFs were included. They are divided into 17 feature categories, each containing 90 RFs. The intraclass correlation coefficients (ICCs) and concordance correlation coefficients (CCCs) were calculated to assess the reproducibility of RFs. The cutoff value considered indicative of reproducible features was > 0.75. RESULTS the VNCPC and VNCConv tended to underestimate EATVs and overestimate EATDs. Both EATV and EATD of VNCPC series showed higher correlation and agreement with TNC than VNCConv series. All types of RFs from VNCPC series showed greater reproducibility than VNCConv series. Across all image filters, the Square filter exhibited the highest level of reproducibility (ICC = 67/90, 74.4%; CCC = 67/90, 74.4%). GLDM_GrayLevelNonUniformity feature had the highest reproducibility in the original image (ICC = 0.957, CCC = 0.958), exhibiting a high degree of reproducibility across all image filters. CONCLUSION The accuracy evaluation of EATV and EATD and the reproducibility of RFs from VNCPC series make it an excellent substitute for TNC series exceeding VNCConv series.
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Affiliation(s)
- MengXu Cui
- Department of Radiology, Ruijin Hospital affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - ShouYu Bao
- Department of Radiology, Ruijin Hospital affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - JiQiang Li
- Department of Radiology, Ruijin Hospital affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - HaiPeng Dong
- Department of Radiology, Ruijin Hospital affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - ZhiHan Xu
- Siemens Healthineers CT Collaboration, Erlangen, Germany
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wenjie Yang
- Department of Radiology, Ruijin Hospital affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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Zheng YL, Cai PY, Li J, Huang DH, Wang WD, Li MM, Du JR, Wang YG, Cai YL, Zhang RC, Wu CC, Lin S, Lin HL. A novel radiomics-based technique for identifying vulnerable coronary plaques: a follow-up study. Coron Artery Dis 2024:00019501-990000000-00232. [PMID: 38767051 DOI: 10.1097/mca.0000000000001389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
BACKGROUND Previous reports have suggested that coronary computed tomography angiography (CCTA)-based radiomics analysis is a potentially helpful tool for assessing vulnerable plaques. We aimed to investigate whether coronary radiomic analysis of CCTA images could identify vulnerable plaques in patients with stable angina pectoris. METHODS This retrospective study included patients initially diagnosed with stable angina pectoris. Patients were randomly divided into either the training or test dataset at an 8 : 2 ratio. Radiomics features were extracted from CCTA images. Radiomics models for predicting vulnerable plaques were developed using the support vector machine (SVM) algorithm. The model performance was assessed using the area under the curve (AUC); the accuracy, sensitivity, and specificity were calculated to compare the diagnostic performance using the two cohorts. RESULTS A total of 158 patients were included in the analysis. The SVM radiomics model performed well in predicting vulnerable plaques, with AUC values of 0.977 and 0.875 for the training and test cohorts, respectively. With optimal cutoff values, the radiomics model showed accuracies of 0.91 and 0.882 in the training and test cohorts, respectively. CONCLUSION Although further larger population studies are necessary, this novel CCTA radiomics model may identify vulnerable plaques in patients with stable angina pectoris.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Shu Lin
- Centre of Neurological and Metabolic Research, the Second Affiliated Hospital of Fujian Medical University, Quanzhou, China and
- Diabetes and Metabolism Division, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
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Du G, Zhang P, Guo J, Zhou X, Kan G, Jia J, Chen X, Liang J, Zhan Y. Exploring Radiomics Features Based on H&E Images as Potential Biomarkers for Evaluating Muscle Atrophy: A Preliminary Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01122-w. [PMID: 38653909 DOI: 10.1007/s10278-024-01122-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/11/2024] [Accepted: 04/12/2024] [Indexed: 04/25/2024]
Abstract
Radiomics features have been widely used as novel biomarkers in the diagnosis of various diseases, but whether radiomics features derived from hematoxylin and eosin (H&E) images can evaluate muscle atrophy has not been studied. Therefore, this study aims to establish a new biomarker based on H&E images using radiomics methods to quantitatively analyze H&E images, which is crucial for improving the accuracy of muscle atrophy assessment. Firstly, a weightless muscle atrophy model was established by laying macaques in bed, and H&E images of the shank muscle fibers of the control and bed rest (BR) macaques were collected. Muscle fibers were accurately segmented by designing a semi-supervised segmentation framework based on contrastive learning. Then, 77 radiomics features were extracted from the segmented muscle fibers, and a stable subset of features was selected through the LASSO method. Finally, the correlation between radiomics features and muscle atrophy was analyzed using a support vector machine (SVM) classifier. The semi-supervised segmentation results show that the proposed method had an average Spearman's and intra-class correlation coefficient (ICC) of 88% and 86% compared to manually extracted features, respectively. Radiomics analysis showed that the AUC of the muscle atrophy evaluation model based on H&E images was 96.87%. For individual features, GLSZM_SZE outperformed other features in terms of AUC (91.5%) and ACC (84.4%). In summary, the feature extraction based on the semi-supervised segmentation method is feasible and reliable for subsequent radiomics research. Texture features have greater advantages in evaluating muscle atrophy compared to other features. This study provides important biomarkers for accurate diagnosis of muscle atrophy.
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Affiliation(s)
- Getao Du
- School of Life Science and Technology, & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xidian University, Xi'an, Shaanxi, 710126, China
| | - Peng Zhang
- National Key Laboratory of Space Medicine, China Astronaut Research and Training Center, Beijing, 100094, People's Republic of China
| | - Jianzhong Guo
- Institute of Applied Acoustics, School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710062, China
| | - Xu Zhou
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, 100094, People's Republic of China
| | - Guanghan Kan
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, 100094, People's Republic of China
| | - Jiajie Jia
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, 100094, People's Republic of China
| | - Xiaoping Chen
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, 100094, People's Republic of China.
| | - Jimin Liang
- School of Electronic Engineering, Xidian University, Xi'an, Shaanxi, 710071, China.
| | - Yonghua Zhan
- School of Life Science and Technology, & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xidian University, Xi'an, Shaanxi, 710126, China.
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Ramli Z, Farizan A, Tamchek N, Haron Z, Abdul Karim MK. Impact of Image Enhancement on the Radiomics Stability of Diffusion-Weighted MRI Images of Cervical Cancer. Cureus 2024; 16:e52132. [PMID: 38347995 PMCID: PMC10859681 DOI: 10.7759/cureus.52132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/11/2024] [Indexed: 02/15/2024] Open
Abstract
The diffusion-weighted imaging (DWI) technique is known for its capability to differentiate the diffusion of water molecules between cancerous and non-cancerous cervix tissues, which enhances the accuracy of detection. Despite the potential of DWI-MRI, its accuracy is limited by technical factors influencing in vivo data acquisition, thus impacting the quantification of radiomics features. This study aimed to measure the radiomics stability of manual and semi-automated segmentation on contrast limited adaptive histogram equalization (CLAHE)-enhanced DWI-MRI cervical images. Eighty diffusion-weighted MRI images were obtained from patients diagnosed with cervical cancer, and an active contour model was used to analyze the data. Radiomics analysis was conducted to extract the first statistical order, shape, and textural features with intraclass correlation coefficient (ICC) measurement. The results of the CLAHE segmentation approach showed a marked improvement when compared to the manual and semi-automated segmentation methods, with an ICC value of 0.990 ± 0.005 (p<0.05), compared to 0.864 ± 0.033 (p<0.05) and 0.554 ± 0.185 (p>0.05), respectively. The CLAHE segmentation displayed a higher level of robustness than the manual groups in terms of the features present in both categories. Thus, CLAHE segmentation is owing to its potential to generate radiomics features that are more durable and consistent.
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Affiliation(s)
- Zarina Ramli
- Department of Radiology, National Cancer Institute, Putrajaya, MYS
| | - Aishah Farizan
- Department of Physics, Universiti Putra Malaysia, Serdang, MYS
| | - Nizam Tamchek
- Department of Physics, Universiti Putra Malaysia, Serdang, MYS
| | - Zaharudin Haron
- Department of Radiology, National Cancer Institute, Putrajaya, MYS
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Ramli Z, Karim MKA, Effendy N, Abd Rahman MA, Kechik MMA, Ibahim MJ, Haniff NSM. Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI. Diagnostics (Basel) 2022; 12:diagnostics12123125. [PMID: 36553132 PMCID: PMC9777485 DOI: 10.3390/diagnostics12123125] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/25/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Cervical cancer is the most common cancer and ranked as 4th in morbidity and mortality among Malaysian women. Currently, Magnetic Resonance Imaging (MRI) is considered as the gold standard imaging modality for tumours with a stage higher than IB2, due to its superiority in diagnostic assessment of tumour infiltration with excellent soft-tissue contrast. In this research, the robustness of semi-automatic segmentation has been evaluated using a flood-fill algorithm for quantitative feature extraction, using 30 diffusion weighted MRI images (DWI-MRI) of cervical cancer patients. The relevant features were extracted from DWI-MRI segmented images of cervical cancer. First order statistics, shape features, and textural features were extracted and analysed. The intra-class relation coefficient (ICC) was used to compare 662 radiomic features extracted from manual and semi-automatic segmentations. Notably, the features extracted from the semi-automatic segmentation and flood filling algorithm (average ICC = 0.952 0.009, p > 0.05) were significantly higher than the manual extracted features (average ICC = 0.897 0.011, p > 0.05). Henceforth, we demonstrate that the semi-automatic segmentation is slightly expanded to manual segmentation as it produces more robust and reproducible radiomic features.
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Affiliation(s)
- Zarina Ramli
- Department of Physics, Faculty of Science, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
- Department of Radiology, National Cancer Institute, Putrajaya 65000, Wilayah Persekutuan, Malaysia
| | - Muhammad Khalis Abdul Karim
- Department of Physics, Faculty of Science, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
- Department of Radiology, National Cancer Institute, Putrajaya 65000, Wilayah Persekutuan, Malaysia
- Correspondence:
| | - Nuraidayani Effendy
- Department of Physics, Faculty of Science, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Mohd Amiruddin Abd Rahman
- Department of Physics, Faculty of Science, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Mohd Mustafa Awang Kechik
- Department of Physics, Faculty of Science, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Mohamad Johari Ibahim
- Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh 47200, Selangor, Malaysia
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