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Tüdös Z, Veverková L, Baxa J, Hartmann I, Čtvrtlík F. The current and upcoming era of radiomics in phaeochromocytoma and paraganglioma. Best Pract Res Clin Endocrinol Metab 2024:101923. [PMID: 39227277 DOI: 10.1016/j.beem.2024.101923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
The topic of the diagnosis of phaeochromocytomas remains highly relevant because of advances in laboratory diagnostics, genetics, and therapeutic options and also the development of imaging methods. Computed tomography still represents an essential tool in clinical practice, especially in incidentally discovered adrenal masses; it allows morphological evaluation, including size, shape, necrosis, and unenhanced attenuation. More advanced post-processing tools to analyse digital images, such as texture analysis and radiomics, are currently being studied. Radiomic features utilise digital image pixels to calculate parameters and relations undetectable by the human eye. On the other hand, the amount of radiomic data requires massive computer capacity. Radiomics, together with machine learning and artificial intelligence in general, has the potential to improve not only the differential diagnosis but also the prediction of complications and therapy outcomes of phaeochromocytomas in the future. Currently, the potential of radiomics and machine learning does not match expectations and awaits its fulfilment.
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Affiliation(s)
- Zbyněk Tüdös
- Department of Radiology, University Hospital and Faculty of Medicine and Dentistry, Palacky University, Olomouc, Czech Republic
| | - Lucia Veverková
- Department of Radiology, University Hospital and Faculty of Medicine and Dentistry, Palacky University, Olomouc, Czech Republic
| | - Jan Baxa
- Department of Imaging Methods, Faculty Hospital Pilsen and Faculty of Medicine in Pilsen, Charles University, Czech Republic
| | - Igor Hartmann
- Department of Urology, University Hospital and Faculty of Medicine and Dentistry, Palacky University, Olomouc, Czech Republic
| | - Filip Čtvrtlík
- Department of Radiology, University Hospital and Faculty of Medicine and Dentistry, Palacky University, Olomouc, Czech Republic.
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Zhang J, Xia L, Tang J, Xia J, Liu Y, Zhang W, Liu J, Liang Z, Zhang X, Zhang L, Tang G. Constructing a Deep Learning Radiomics Model Based on X-ray Images and Clinical Data for Predicting and Distinguishing Acute and Chronic Osteoporotic Vertebral Fractures: A Multicenter Study. Acad Radiol 2024; 31:2011-2026. [PMID: 38016821 DOI: 10.1016/j.acra.2023.10.061] [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: 08/28/2023] [Revised: 09/13/2023] [Accepted: 10/31/2023] [Indexed: 11/30/2023]
Abstract
RATIONALE AND OBJECTIVES To construct and validate a deep learning radiomics (DLR) model based on X-ray images for predicting and distinguishing acute and chronic osteoporotic vertebral fractures (OVFs). METHODS A total of 942 cases (1076 vertebral bodies) with both vertebral X-ray examination and MRI scans were included in this study from three hospitals. They were divided into a training cohort (n = 712), an internal validation cohort (n = 178), an external validation cohort (n = 111), and a prospective validation cohort (n = 75). The ResNet-50 model architecture was used for deep transfer learning (DTL), with pre-training performed on RadImageNet and ImageNet datasets. DTL features and radiomics features were extracted from lateral X-ray images of OVFs patients and fused together. A logistic regression model with the least absolute shrinkage and selection operator was established, with MRI showing bone marrow edema as the gold standard for acute OVFs. The performance of the model was evaluated using receiver operating characteristic curves. Eight machine learning classification models were evaluated for their ability to distinguish between acute and chronic OVFs. The Nomogram was constructed by combining clinical baseline data to achieve visualized classification assessment. The predictive performance of the best RadImageNet model and ImageNet model was compared using the Delong test. The clinical value of the Nomogram was evaluated using decision curve analysis (DCA). RESULTS Pre-training resulted in 34 and 39 fused features after feature selection and fusion. The most effective machine learning algorithm in both DLR models was Light Gradient Boosting Machine. Using the Delong test, the area under the curve (AUC) for distinguishing between acute and chronic OVFs in the training cohort was 0.979 and 0.972 for the RadImageNet and ImageNet models, respectively, with no statistically significant difference between them (P = 0.235). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.629, 0.886 vs 0.817, and 0.933 vs 0.661, respectively, with statistically significant differences in all comparisons (P < 0.05). The deep learning radiomics nomogram (DLRN) was constructed by combining the predictive model of RadImageNet with clinical baseline features, resulting in AUCs of 0.981, 0.974, 0.895, and 0.902 in the training cohort, internal validation cohort, external validation cohort, and prospective validation cohort, respectively. Using the Delong test, the AUCs for the fused feature model and the DLRN in the training cohort were 0.979 and 0.981, respectively, with no statistically significant difference between them (P = 0.169). In the internal validation cohort, external validation cohort, and prospective validation cohort, the AUCs for the two models were 0.967 vs 0.974, 0.886 vs 0.895, and 0.933 vs 0.902, respectively, with statistically significant differences in all comparisons (P < 0.05). The Nomogram showed a slight improvement in predictive performance in the internal and external validation cohort, but a slight decrease in the prospective validation cohort (0.933 vs 0.902). DCA showed that the Nomogram provided more benefits to patients compared to the DLR models. CONCLUSION Compared to the ImageNet model, the RadImageNet model has higher diagnostic value in distinguishing between acute and chronic OVFs. Furthermore, the diagnostic performance of the model is further improved when combined with clinical baseline features to construct the Nomogram.
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Affiliation(s)
- Jun Zhang
- Department of Radiology, Shanghai Tenth People's Hospital, Clinical Medical College of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, PR China (J.Z., G.T.); Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, PR China (J.Z., L.X., W.Z., J.L., Z.L.)
| | - Liang Xia
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, PR China (J.Z., L.X., W.Z., J.L., Z.L.)
| | - Jun Tang
- Department of Radiology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, 366 Taihu Road, Taizhou, Jiangsu, 225300, PR China (J.T., J.X.)
| | - Jianguo Xia
- Department of Radiology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, 366 Taihu Road, Taizhou, Jiangsu, 225300, PR China (J.T., J.X.)
| | - Yongkang Liu
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong Road, Nanjing, Jiangsu, 210004, PR China (Y.L.)
| | - Weixiao Zhang
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, PR China (J.Z., L.X., W.Z., J.L., Z.L.)
| | - Jiayi Liu
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, PR China (J.Z., L.X., W.Z., J.L., Z.L.)
| | - Zhipeng Liang
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, PR China (J.Z., L.X., W.Z., J.L., Z.L.)
| | - Xueli Zhang
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, PR China (X.Z., L.Z., G.T.)
| | - Lin Zhang
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, PR China (X.Z., L.Z., G.T.).
| | - Guangyu Tang
- Department of Radiology, Shanghai Tenth People's Hospital, Clinical Medical College of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, PR China (J.Z., G.T.); Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, PR China (X.Z., L.Z., G.T.)
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Zhang J, Xia L, Liu J, Niu X, Tang J, Xia J, Liu Y, Zhang W, Liang Z, Zhang X, Tang G, Zhang L. Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images. Front Endocrinol (Lausanne) 2024; 15:1370838. [PMID: 38606087 PMCID: PMC11007145 DOI: 10.3389/fendo.2024.1370838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 03/15/2024] [Indexed: 04/13/2024] Open
Abstract
Purpose To develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs). Material and methods The study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the "One-vs-Rest" strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model. Results Following pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P<0.05). Utilizing the binary "One-vs-Rest" strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. Predicting Class 1 yielded an AUC of 0.945 and accuracy of 0.875, while for Class 2, the AUC and accuracy were 0.809 and 0.692, respectively. Conclusion The DLR model, based on the RadImageNet dataset, outperformed the ImageNet model in predicting the classification of OVFs, with generalizability confirmed in the prospective validation set.
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Affiliation(s)
- Jun Zhang
- Department of Radiology, Shanghai Tenth People’s Hospital, Clinical Medical College of Nanjing Medical University, Shanghai, China
- Department of Radiology, Sir RunRun Hospital, Nanjing Medical University, Nanjing, China
| | - Liang Xia
- Department of Radiology, Sir RunRun Hospital, Nanjing Medical University, Nanjing, China
| | - Jiayi Liu
- Department of Radiology, Sir RunRun Hospital, Nanjing Medical University, Nanjing, China
| | - Xiaoying Niu
- Department of Neonates, Dongfeng General Hospital of National Medicine, Hubei University of Medicine, Shiyan, China
| | - Jun Tang
- Department of Radiology, The Affiliated Taizhou People’s Hospital of Nanjing Medical University, Taizhou, China
| | - Jianguo Xia
- Department of Radiology, The Affiliated Taizhou People’s Hospital of Nanjing Medical University, Taizhou, China
| | - Yongkang Liu
- Department of Radiology, Jiangsu Provincial Hospital of Traditional Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Weixiao Zhang
- Department of Radiology, Sir RunRun Hospital, Nanjing Medical University, Nanjing, China
| | - Zhipeng Liang
- Department of Radiology, Sir RunRun Hospital, Nanjing Medical University, Nanjing, China
| | - Xueli Zhang
- Department of Radiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Guangyu Tang
- Department of Radiology, Shanghai Tenth People’s Hospital, Clinical Medical College of Nanjing Medical University, Shanghai, China
- Department of Radiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Lin Zhang
- Department of Radiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
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Lee K, Goh J, Jang J, Hwang J, Kwak J, Kim J, Eom K. Feasibility study of computed tomography texture analysis for evaluation of canine primary adrenal gland tumors. Front Vet Sci 2023; 10:1126165. [PMID: 37711438 PMCID: PMC10499047 DOI: 10.3389/fvets.2023.1126165] [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] [Received: 12/17/2022] [Accepted: 08/01/2023] [Indexed: 09/16/2023] Open
Abstract
Objective This study aimed to investigate the feasibility of computed tomography (CT) texture analysis for distinguishing canine adrenal gland tumors and its usefulness in clinical decision-making. Materials and methods The medical records of 25 dogs with primary adrenal masses who underwent contrast CT and a histopathological examination were retrospectively reviewed, of which 12 had adenomas (AAs), 7 had adenocarcinomas (ACCs), and 6 had pheochromocytomas (PHEOs). Conventional CT evaluation of each adrenal gland tumor included the mean, maximum, and minimum attenuation values in Hounsfield units (HU), heterogeneity of the tumor parenchyma, and contrast enhancement (type, pattern, and degree), respectively, in each phase. In CT texture analysis, precontrast and delayed-phase images of 18 adrenal gland tumors, which could be applied for ComBat harmonization were used, and 93 radiomic features (18 first-order and 75 second-order statistics) were extracted. Then, ComBat harmonization was applied to compensate for the batch effect created by the different CT protocols. The area under the receiver operating characteristic curve (AUC) for each significant feature was used to evaluate the diagnostic performance of CT texture analysis. Results Among the conventional features, PHEO showed significantly higher mean and maximum precontrast HU values than ACC (p < 0.05). Eight second-order features on the precontrast images showed significant differences between the adrenal gland tumors (p < 0.05). However, none of them were significantly different between AA and PHEO, or between precontrast images and delayed-phase images. This result indicates that ACC exhibited more heterogeneous and complex textures and more variable intensities with lower gray-level values than AA and PHEO. The correlation, maximal correlation coefficient, and gray level non-uniformity normalized were significantly different between AA and ACC, and between ACC and PHEO. These features showed high AUCs in discriminating ACC and PHEO, which were comparable or higher than the precontrast mean and maximum HU (AUC = 0.865 and 0.860, respectively). Conclusion Canine primary adrenal gland tumor differentiation can be achieved with CT texture analysis on precontrast images and may have a potential role in clinical decision-making. Further prospective studies with larger populations and cross-validation are warranted.
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Affiliation(s)
- Kyungsoo Lee
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Konkuk University, Seoul, Republic of Korea
| | - Jinhyong Goh
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Konkuk University, Seoul, Republic of Korea
| | - Jaeyoung Jang
- Jang Jae Young Veterinary Surgery Center, Seong-nam, Gyunggi-do, Republic of Korea
| | | | - Jungmin Kwak
- Saram and Animal Medical Center, Yongin-si, Gyunggi-do, Republic of Korea
| | - Jaehwan Kim
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Konkuk University, Seoul, Republic of Korea
| | - Kidong Eom
- Department of Veterinary Medical Imaging, College of Veterinary Medicine, Konkuk University, Seoul, Republic of Korea
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Yueying C, Jing F, Qi F, Jun S. Infliximab response associates with radiologic findings in bio-naïve Crohn's disease. Eur Radiol 2023; 33:5247-5257. [PMID: 36928565 PMCID: PMC10326128 DOI: 10.1007/s00330-023-09542-y] [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: 06/09/2022] [Revised: 02/11/2023] [Accepted: 02/26/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVES Since a reliable model for predicting infliximab (IFX) benefits in bio-naïve Crohn's disease (CD) is still lacking, we constructed a magnetic resonance enterography (MRE)-based model to predict the risk of loss of response to IFX in bio-naïve patients with CD. METHODS This retrospective multicenter study enrolled 188 bio-naïve patients with CD who underwent MRE before IFX therapy. Therapeutic outcomes were determined based on clinical symptoms and endoscopic findings within 52 weeks. The areas of bowel wall segmentation were decided by two experienced radiologists in consensus. Texture features were extracted using the least absolute shrinkage and selection operator, and a radiomic model was built using multivariate logistic regression. The model performance was validated by receiver operating characteristic, calibration curve, and decision curve analysis. RESULTS The area under the curve of radiomic model was 0.88 (95% confidence interval: 0.82-0.95), and the model provided clinical net benefit in identifying the loss of response to IFX and exhibited remarkable robustness among centers, scanners, and disease characteristics. The high-risk patients defined by the radiomic model were more likely to develop IFX nonresponse than low-risk patients (all p < 0.05). CONCLUSIONS This novel pretreatment MRE-based model could act as an effective tool for the early estimation of loss of response to IFX in bio-naïve patients with CD. KEY POINTS • Magnetic resonance enterography model guides infliximab therapy in Crohn's disease. • The model presented significant discrimination and provided net clinical benefit. • Model divided patients into low- and high-risk groups for infliximab failure.
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Affiliation(s)
- Chen Yueying
- State Key Laboratory for Oncogenes and Related Genes, Key Laboratory of Gastroenterology & Hepatology, Ministry of Health, Division of Gastroenterology and Hepatology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, Shanghai Institute of Digestive Disease, Shanghai, China
- Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Feng Jing
- State Key Laboratory for Oncogenes and Related Genes, Key Laboratory of Gastroenterology & Hepatology, Ministry of Health, Division of Gastroenterology and Hepatology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, Shanghai Institute of Digestive Disease, Shanghai, China
| | - Feng Qi
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pu Jian Road, Shanghai, China.
| | - Shen Jun
- State Key Laboratory for Oncogenes and Related Genes, Key Laboratory of Gastroenterology & Hepatology, Ministry of Health, Division of Gastroenterology and Hepatology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, Shanghai Institute of Digestive Disease, Shanghai, China.
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Yu X, Gao L, Zhang S, Sun C, Zhang J, Kang B, Wang X. Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma. Front Oncol 2023; 12:1016583. [PMID: 36686790 PMCID: PMC9846314 DOI: 10.3389/fonc.2022.1016583] [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] [Received: 08/11/2022] [Accepted: 12/12/2022] [Indexed: 01/06/2023] Open
Abstract
Background Early identification of synchronous distant metastasis (SDM) in patients with clear cell Renal cell carcinoma (ccRCC) can certify the reasonable diagnostic examinations. Methods This retrospective study recruited 463 ccRCC patients who were divided into two cohorts (training and internal validation) at a 7:3 ratio. Besides, 115 patients from other hospital were assigned external validation cohort. A radiomics signature was developed based on features by means of the least absolute shrinkage and selection operator method. Demographics, laboratory variables and CT findings were combined to develop clinical factors model. Integrating radiomics signature and clinical factors model, a radiomics nomogram was developed. Results Ten features were used to build radiomics signature, which yielded an area under the curve (AUC) 0.882 in the external validation cohort. By incorporating the clinical independent predictors, the clinical model was developed with AUC of 0.920 in the external validation cohort. Radiomics nomogram (external validation, 0.925) had better performance than clinical factors model or radiomics signature. Decision curve analysis demonstrated the superiority of the radiomics nomogram in terms of clinical usefulness. Conclusions The CT-based nomogram could help in predicting SDM status in patients with ccRCC, which might provide assistance for clinicians in making diagnostic examinations.
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Affiliation(s)
- Xinxin Yu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China,School of Medicine, Shandong University, Jinan, China
| | - Lin Gao
- Department of Nuclear Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China,School of Medicine, Shandong First Medical University, Jinan, China
| | - Shuai Zhang
- School of Medicine, Shandong First Medical University, Jinan, China
| | - Cong Sun
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Juntao Zhang
- GE Healthcare, PDx GMS Advanced Analytics, Shanghai, China,*Correspondence: Ximing Wang, ; Bing Kang, ; Juntao Zhang,
| | - Bing Kang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China,*Correspondence: Ximing Wang, ; Bing Kang, ; Juntao Zhang,
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China,School of Medicine, Shandong University, Jinan, China,*Correspondence: Ximing Wang, ; Bing Kang, ; Juntao Zhang,
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Cui Z, Ren G, Cai R, Wu C, Shi H, Wang X, Zhu M. MRI-based texture analysis for differentiate between pediatric posterior fossa ependymoma type A and B. Eur J Radiol 2022; 152:110288. [DOI: 10.1016/j.ejrad.2022.110288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 01/01/2022] [Accepted: 03/28/2022] [Indexed: 11/03/2022]
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Predictive value of major adverse cardiac events by T2-mapping texture analysis of the myocardial remote zone in patients with acute myocardial infarction. Clin Radiol 2022; 77:e241-e249. [DOI: 10.1016/j.crad.2021.12.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 12/16/2021] [Indexed: 01/16/2023]
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Abstract
Artificial intelligence (AI) algorithms, particularly deep learning, have developed to the point that they can be applied in image recognition tasks. The use of AI in medical imaging can guide radiologists to more accurate image interpretation and diagnosis in radiology. The software will provide data that we cannot extract from the images. The rapid development in computational capabilities supports the wide applications of AI in a range of cancers. Among those are its widespread applications in head and neck cancer.
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