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Cheng M, Zhang H, Huang W, Li F, Gao J. Deep Learning Radiomics Analysis of CT Imaging for Differentiating Between Crohn's Disease and Intestinal Tuberculosis. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1516-1528. [PMID: 38424279 PMCID: PMC11300798 DOI: 10.1007/s10278-024-01059-0] [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: 11/23/2023] [Revised: 02/17/2024] [Accepted: 02/21/2024] [Indexed: 03/02/2024]
Abstract
This study aimed to develop and evaluate a CT-based deep learning radiomics model for differentiating between Crohn's disease (CD) and intestinal tuberculosis (ITB). A total of 330 patients with pathologically confirmed as CD or ITB from the First Affiliated Hospital of Zhengzhou University were divided into the validation dataset one (CD: 167; ITB: 57) and validation dataset two (CD: 78; ITB: 28). Based on the validation dataset one, the synthetic minority oversampling technique (SMOTE) was adopted to create balanced dataset as training data for feature selection and model construction. The handcrafted and deep learning (DL) radiomics features were extracted from the arterial and venous phases images, respectively. The interobserver consistency analysis, Spearman's correlation, univariate analysis, and the least absolute shrinkage and selection operator (LASSO) regression were used to select features. Based on extracted multi-phase radiomics features, six logistic regression models were finally constructed. The diagnostic performances of different models were compared using ROC analysis and Delong test. The arterial-venous combined deep learning radiomics model for differentiating between CD and ITB showed a high prediction quality with AUCs of 0.885, 0.877, and 0.800 in SMOTE dataset, validation dataset one, and validation dataset two, respectively. Moreover, the deep learning radiomics model outperformed the handcrafted radiomics model in same phase images. In validation dataset one, the Delong test results indicated that there was a significant difference in the AUC of the arterial models (p = 0.037), while not in venous and arterial-venous combined models (p = 0.398 and p = 0.265) as comparing deep learning radiomics models and handcrafted radiomics models. In our study, the arterial-venous combined model based on deep learning radiomics analysis exhibited good performance in differentiating between CD and ITB.
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Affiliation(s)
- Ming Cheng
- Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
| | - Hanyue Zhang
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Wenpeng Huang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, 100034, China
| | - Fei Li
- School of Cyber Science and Engineering, Wuhan University, Wuhan, 430072, China
| | - Jianbo Gao
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
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Silverman AL, Shung D, Stidham RW, Kochhar GS, Iacucci M. How Artificial Intelligence Will Transform Clinical Care, Research, and Trials for Inflammatory Bowel Disease. Clin Gastroenterol Hepatol 2024:S1542-3565(24)00598-6. [PMID: 38992406 DOI: 10.1016/j.cgh.2024.05.048] [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] [Received: 03/08/2024] [Revised: 04/30/2024] [Accepted: 05/02/2024] [Indexed: 07/13/2024]
Abstract
Artificial intelligence (AI) refers to computer-based methodologies that use data to teach a computer to solve pre-defined tasks; these methods can be applied to identify patterns in large multi-modal data sources. AI applications in inflammatory bowel disease (IBD) includes predicting response to therapy, disease activity scoring of endoscopy, drug discovery, and identifying bowel damage in images. As a complex disease with entangled relationships between genomics, metabolomics, microbiome, and the environment, IBD stands to benefit greatly from methodologies that can handle this complexity. We describe current applications, critical challenges, and propose future directions of AI in IBD.
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Affiliation(s)
- Anna L Silverman
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Scottsdale, Arizona.
| | - Dennis Shung
- Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Ryan W Stidham
- Division of Gastroenterology, Department of Internal Medicine, Michigan Medicine, Ann Arbor, Michigan; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan; Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan
| | - Gursimran S Kochhar
- Division of Gastroenterology, Hepatology, and Nutrition, Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Marietta Iacucci
- University of Birmingham, Institute of Immunology and Immunotherapy, Birmingham, United Kingdom; College of Medicine and Health, University College Cork, and APC Microbiome Ireland, Cork, Ireland
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Huang L, Zhou G, Wang XT, Li GG, Li GY. Diagnostic accuracy of abdominal contrast-enhanced multi-slice spiral CT after oral diluted iodide in a time segment for gastrointestinal fistula in patients with severe acute pancreatitis. Jpn J Radiol 2024; 42:622-629. [PMID: 38381250 DOI: 10.1007/s11604-024-01540-4] [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: 10/09/2023] [Accepted: 01/25/2024] [Indexed: 02/22/2024]
Abstract
PURPOSE To evaluate the diagnostic accuracy of abdominal contrast-enhanced multi-slice spiral CT after oral diluted iodide in a time segment (post-ODI ACE-MSCT) for gastrointestinal fistula (GIF) in severe acute pancreatitis (SAP). MATERIALS AND METHODS Patients with SAP who underwent both post-ODI ACE-MSCT and endoscopy/surgery from 2017 to 2023 were continuously retrospectively involved. Their demographic information and clinical features were recorded prospectively in an in-hospital database. Using endoscopy/surgery results as the reference standard, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of post-ODI ACE-MSCT for diagnosing GIF in SAP were calculated by a four-cell table. The consistency of the two diagnostic methods was evaluated by the Kappa test and McNemar's test. RESULTS Using endoscopy/surgery as the reference standard, a total of 86 cases were divided into the GIF group (N = 52) and the non-GIF group (N = 34). Among the 52 cases of GIF, 88.5% (46/52) cases had a positive result and 11.5% (5/52) cases had a negative result of post-ODI ACE-MSCT for GIF. Among the 34 cases of non-GIF, 2.9% (1/34) case had a positive result and 97.1% (33/34) cases had a negative result of post-ODI ACE-MSCT for GIF. Post-ODI ACE-MSCT had a sensitivity of 88.5% (95% CI 75.9%-95.2%), a specificity of 97.1% (95% CI 82.9%-99.8%), a positive predictive value of 97.9% (95% CI 87.3%-99.9%), a negative predictive value of 84.6% (95% CI 68.8%-93.6%), and an accuracy of 91.9% (83.4%-96.4%). The kappa value was 0.834, and P < 0.001 by McNemar's test. There were no significant differences in diagnostic test characteristics between the two modalities. CONCLUSION Post-ODI ACE-MSCT can diagnose GIF in SAP in a simple, noninvasive, and accurate way, and can provide earlier imaging evidence for clinical diagnosis and treatment.
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Affiliation(s)
- Li Huang
- Department of Critical Care Medicine, Hunan Provincial People's Hospital (The First-Affiliated Hospital of Hunan Normal University), 61 Jiefang West Road, Changsha, 410005, Hunan, China
| | - Guang Zhou
- Department of Radiology, Hunan Provincial People's Hospital (The First-Affiliated Hospital of Hunan Normal University), 61 Jiefang West Road, Changsha, 410005, Hunan, China
| | - Xi-Tao Wang
- Department of Hepatobiliary Surgery, Hunan Provincial People's Hospital (The First-Affiliated Hospital of Hunan Normal University), 61 Jiefang West Road, Changsha, 410005, Hunan, China
| | - Guo-Guang Li
- Department of Hepatobiliary Surgery, Hunan Provincial People's Hospital (The First-Affiliated Hospital of Hunan Normal University), 61 Jiefang West Road, Changsha, 410005, Hunan, China.
| | - Guang-Yi Li
- Department of General Surgery, Hunan Provincial People's Hospital (The First-Affiliated Hospital of Hunan Normal University), 61 Jiefang West Road, Changsha, 410005, Hunan, China
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Gu P, Chang JH, Carter D, McGovern DPB, Moore J, Wang P, Huang X. Radiomics-Based Analysis of Intestinal Ultrasound Images for Inflammatory Bowel Disease: A Feasibility Study. CROHN'S & COLITIS 360 2024; 6:otae034. [PMID: 38903657 PMCID: PMC11187771 DOI: 10.1093/crocol/otae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Indexed: 06/22/2024] Open
Abstract
Background The increasing adoption of intestinal ultrasound (IUS) for monitoring inflammatory bowel diseases (IBD) by IBD providers has uncovered new challenges regarding standardized image interpretation and limitations as a research tool. Artificial intelligence approaches can help address these challenges. We aim to determine the feasibility of radiomic analysis of IUS images and to determine if a radiomics-based classification model can accurately differentiate between normal and abnormal IUS images. We will also compare the radiomic-based model's performance to a convolutional neural network (CNN)-based classification model to understand which method is more effective for extracting meaningful information from IUS images. Methods Retrospectively analyzing IUS images obtained during routine outpatient visits, we developed and tested radiomic-based and CNN-based models to distinguish between normal and abnormal images, with abnormal images defined as bowel wall thickness > 3 mm or bowel hyperemia with modified Limberg score ≥ 1 (both are surrogate markers for inflammation). Model performances were measured by area under the receiver operator curve (AUC). Results For this feasibility study, 125 images (33% abnormal) were analyzed. A radiomic-based model using XG boost yielded the best classifier model with average test AUC 0.98%, 93.8% sensitivity, 93.8% specificity, and 93.7% accuracy. The CNN-based classification model yielded an average testing AUC of 0.75. Conclusions Radiomic analysis of IUS images is feasible, and a radiomic-based classification model could accurately differentiate abnormal from normal images. Our findings establish methods to facilitate future radiomic-based IUS studies that can help standardize image interpretation and expand IUS research capabilities.
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Affiliation(s)
- Phillip Gu
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jui-Hsuan Chang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Dan Carter
- Department of Gastroenterology, Sheba Medical Center, Tel Aviv, Israel
| | - Dermot P B McGovern
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jason Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Wang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Xiuzhen Huang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Gu P, Mendonca O, Carter D, Dube S, Wang P, Huang X, Li D, Moore JH, McGovern DPB. AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD. Inflamm Bowel Dis 2024:izae030. [PMID: 38452040 DOI: 10.1093/ibd/izae030] [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] [Received: 10/17/2023] [Indexed: 03/09/2024]
Abstract
Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.
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Affiliation(s)
- Phillip Gu
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Dan Carter
- Department of Gastroenterology, Sheba Medical Center, Tel Aviv, Israel
| | - Shishir Dube
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Wang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Xiuzhen Huang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Debiao Li
- Biomedical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Dermot P B McGovern
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Liu X, Reigle J, Prasath VBS, Dhaliwal J. Artificial intelligence image-based prediction models in IBD exhibit high risk of bias: A systematic review. Comput Biol Med 2024; 171:108093. [PMID: 38354499 DOI: 10.1016/j.compbiomed.2024.108093] [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: 09/04/2023] [Revised: 01/04/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND There has been an increase in the development of both machine learning (ML) and deep learning (DL) prediction models in Inflammatory Bowel Disease. We aim in this systematic review to assess the methodological quality and risk of bias of ML and DL IBD image-based prediction studies. METHODS We searched three databases, PubMed, Scopus and Embase, to identify ML and DL diagnostic or prognostic predictive models using imaging data in IBD, to Dec 31, 2022. We restricted our search to include studies that primarily used conventional imaging data, were undertaken in human participants, and published in English. Two reviewers independently reviewed the abstracts. The methodological quality of the studies was determined, and risk of bias evaluated using the prediction risk of bias assessment tool (PROBAST). RESULTS Forty studies were included, thirty-nine developed diagnostic models. Seven studies utilized ML approaches, six were retrospective and none used multicenter data for model development. Thirty-three studies utilized DL approaches, ten were prospective, and twelve multicenter studies. Overall, all studies demonstrated high risk of bias. ML studies were evaluated in 4 domains all rated as high risk of bias: participants (6/7), predictors (1/7), outcome (3/7), and analysis (7/7), and DL studies evaluated in 3 domains: participants (24/33), outcome (10/33), and analysis (18/33). The majority of image-based studies used colonoscopy images. CONCLUSION The risk of bias was high in AI IBD image-based prediction models, owing to insufficient sample size, unreported missingness and lack of an external validation cohort. Models with a high risk of bias are unlikely to be generalizable and suitable for clinical implementation.
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Affiliation(s)
- Xiaoxuan Liu
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA
| | - James Reigle
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA
| | - V B Surya Prasath
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA
| | - Jasbir Dhaliwal
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA; Cincinnati Children's Hospital Medical Center, Division of Gastroenterology, Hepatology and Nutrition, USA.
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Liu J, Qi L, Wang Y, Li F, Chen J, Cui S, Cheng S, Zhou Z, Li L, Wang J. Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules. Eur Radiol Exp 2024; 8:8. [PMID: 38228868 DOI: 10.1186/s41747-023-00400-6] [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: 08/22/2023] [Accepted: 10/16/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND We aimed to develop a combined model based on radiomics and computed tomography (CT) imaging features for use in differential diagnosis of benign and malignant subcentimeter (≤ 10 mm) solid pulmonary nodules (SSPNs). METHODS A total of 324 patients with SSPNs were analyzed retrospectively between May 2016 and June 2022. Malignant nodules (n = 158) were confirmed by pathology, and benign nodules (n = 166) were confirmed by follow-up or pathology. SSPNs were divided into training (n = 226) and testing (n = 98) cohorts. A total of 2107 radiomics features were extracted from contrast-enhanced CT. The clinical and CT characteristics retained after univariate and multivariable logistic regression analyses were used to develop the clinical model. The combined model was established by associating radiomics features with CT imaging features using logistic regression. The performance of each model was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS Six CT imaging features were independent predictors of SSPNs, and four radiomics features were selected after a dimensionality reduction. The combined model constructed by the logistic regression method had the best performance in differentiating malignant from benign SSPNs, with an AUC of 0.942 (95% confidence interval 0.918-0.966) in the training group and an AUC of 0.930 (0.902-0.957) in the testing group. The decision curve analysis showed that the combined model had clinical application value. CONCLUSIONS The combined model incorporating radiomics and CT imaging features had excellent discriminative ability and can potentially aid radiologists in diagnosing malignant from benign SSPNs. RELEVANCE STATEMENT The model combined radiomics features and clinical features achieved good efficiency in predicting malignant from benign SSPNs, having the potential to assist in early diagnosis of lung cancer and improving follow-up strategies in clinical work. KEY POINTS • We developed a pulmonary nodule diagnostic model including radiomics and CT features. • The model yielded the best performance in differentiating malignant from benign nodules. • The combined model had clinical application value and excellent discriminative ability. • The model can assist radiologists in diagnosing malignant from benign pulmonary nodules.
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Affiliation(s)
- Jianing Liu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Linlin Qi
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yawen Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Fenglan Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Jiaqi Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Shulei Cui
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Sainan Cheng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Zhen Zhou
- Beijing Deepwise & League of PhD Technology Co. Ltd, Beijing, China
| | - Lin Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| | - Jianwei Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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Zeng X, Jiang H, Dai Y, Zhang J, Zhao S, Wu Q. A radiomics nomogram based on MSCT and clinical factors can stratify fibrosis in inflammatory bowel disease. Sci Rep 2024; 14:1176. [PMID: 38216597 PMCID: PMC10786819 DOI: 10.1038/s41598-023-51036-w] [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: 09/15/2023] [Accepted: 12/29/2023] [Indexed: 01/14/2024] Open
Abstract
Intestinal fibrosis is one of the major complications of inflammatory bowel disease (IBD) and a pathological process that significantly impacts patient prognosis and treatment selection. Although current imaging assessment and clinical markers are widely used for the diagnosis and stratification of fibrosis, these methods suffer from subjectivity and limitations. In this study, we aim to develop a radiomics diagnostic model based on multi-slice computed tomography (MSCT) and clinical factors. MSCT images and relevant clinical data were collected from 218 IBD patients, and a large number of quantitative image features were extracted. Using these features, we constructed a radiomics model and transformed it into a user-friendly diagnostic nomogram. A nomogram was developed to predict fibrosis in IBD by integrating multiple factors. The nomogram exhibited favorable discriminative ability, with an AUC of 0.865 in the validation sets, surpassing both the logistic regression (LR) model (AUC = 0.821) and the clinical model (AUC = 0.602) in the test set. In the train set, the LR model achieved an AUC of 0.975, while the clinical model had an AUC of 0.735. The nomogram demonstrated superior performance with an AUC of 0.971, suggesting its potential as a valuable tool for predicting fibrosis in IBD and improving clinical decision-making. The radiomics nomogram, incorporating MSCT and clinical factors, demonstrates promise in stratifying fibrosis in IBD. The nomogram outperforms traditional clinical models and offers personalized risk assessment. However, further validation and addressing identified limitations are necessary to enhance its applicability.
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Affiliation(s)
- Xu Zeng
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Helongjiang Province, People's Republic of China
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Helongjiang Province, People's Republic of China.
| | - Yanmei Dai
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Helongjiang Province, People's Republic of China
| | - Jin Zhang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Helongjiang Province, People's Republic of China
| | - Sheng Zhao
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Helongjiang Province, People's Republic of China
| | - Qiong Wu
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, No.246 Xuefu Road, Nangang District, Harbin, 150081, Helongjiang Province, People's Republic of China
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Liu RX, Li H, Towbin AJ, Ata NA, Smith EA, Tkach JA, Denson LA, He L, Dillman JR. Machine Learning Diagnosis of Small-Bowel Crohn Disease Using T2-Weighted MRI Radiomic and Clinical Data. AJR Am J Roentgenol 2024; 222:e2329812. [PMID: 37530398 DOI: 10.2214/ajr.23.29812] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
BACKGROUND. Radiologists have variable diagnostic performance and considerable interreader variability when interpreting MR enterography (MRE) examinations for suspected Crohn disease (CD). OBJECTIVE. The purposes of this study were to develop a machine learning method for predicting ileal CD by use of radiomic features of ileal wall and mesenteric fat from noncontrast T2-weighted MRI and to compare the performance of the method with that of expert radiologists. METHODS. This single-institution study included retrospectively identified patients who underwent MRE for suspected ileal CD from January 1, 2020, to January 31, 2021, and prospectively enrolled participants (patients with newly diagnosed ileal CD or healthy control participants) from December 2018 to October 2021. Using axial T2-weighted SSFSE images, a radiologist selected two slices showing greatest terminal ileal wall thickening. Four ROIs were segmented, and radiomic features were extracted from each ROI. After feature selection, support-vector machine models were trained to classify the presence of ileal CD. Three fellowship-trained pediatric abdominal radiologists independently classified the presence of ileal CD on SSFSE images. The reference standard was clinical diagnosis of ileal CD based on endoscopy and biopsy results. Radiomic-only, clinical-only, and radiomic-clinical ensemble models were trained and evaluated by nested cross-validation. RESULTS. The study included 135 participants (67 female, 68 male; mean age, 15.2 ± 3.2 years); 70 were diagnosed with ileal CD. The three radiologists had accuracies of 83.7% (113/135), 88.1% (119/135), and 86.7% (117/135) for diagnosing CD; consensus accuracy was 88.1%. Interradiologist agreement was substantial (κ = 0.78). The best-performing ROI was bowel core (AUC, 0.95; accuracy, 89.6%); other ROIs had worse performance (whole-bowel AUC, 0.86; fat-core AUC, 0.70; whole-fat AUC, 0.73). For the clinical-only model, AUC was 0.85 and accuracy was 80.0%. The ensemble model combining bowel-core radiomic and clinical models had AUC of 0.98 and accuracy of 93.5%. The bowel-core radiomic-only model had significantly greater accuracy than radiologist 1 (p = .009) and radiologist 2 (p = .02) but not radiologist 3 (p > .99) or the radiologists in consensus (p = .05). The ensemble model had greater accuracy than the radiologists in consensus (p = .02). CONCLUSION. A radiomic machine learning model predicted CD diagnosis with better performance than two of three expert radiologists. Model performance improved when radiomic data were ensembled with clinical data. CLINICAL IMPACT. Deployment of a radiomic-based model including T2-weighted MRI data could decrease interradiologist variability and increase diagnostic accuracy for pediatric CD.
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Affiliation(s)
- Richard X Liu
- Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Ave, Cincinnati, OH 45229
| | - Hailong Li
- Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Ave, Cincinnati, OH 45229
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Alexander J Towbin
- Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Ave, Cincinnati, OH 45229
| | - Nadeen Abu Ata
- Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Ave, Cincinnati, OH 45229
| | - Ethan A Smith
- Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Ave, Cincinnati, OH 45229
| | - Jean A Tkach
- Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Ave, Cincinnati, OH 45229
| | - Lee A Denson
- Department of Pediatrics, Division of Gastroenterology, Hepatology, and Nutrition, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Lili He
- Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Ave, Cincinnati, OH 45229
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Jonathan R Dillman
- Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Ave, Cincinnati, OH 45229
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
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Gao Y, Zhang B, Zhao D, Li S, Rong C, Sun M, Wu X. Automatic Segmentation and Radiomics for Identification and Activity Assessment of CTE Lesions in Crohn's Disease. Inflamm Bowel Dis 2023:izad285. [PMID: 38011673 DOI: 10.1093/ibd/izad285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND The purpose of this article is to develop a deep learning automatic segmentation model for the segmentation of Crohn's disease (CD) lesions in computed tomography enterography (CTE) images. Additionally, the radiomics features extracted from the segmented CD lesions will be analyzed and multiple machine learning classifiers will be built to distinguish CD activity. METHODS This was a retrospective study with 2 sets of CTE image data. Segmentation datasets were used to establish nnU-Net neural network's automatic segmentation model. The classification dataset was processed using the automatic segmentation model to obtain segmentation results and extract radiomics features. The most optimal features were then selected to build 5 machine learning classifiers to distinguish CD activity. The performance of the automatic segmentation model was evaluated using the Dice similarity coefficient, while the performance of the machine learning classifier was evaluated using the area under the curve, sensitivity, specificity, and accuracy. RESULTS The segmentation dataset had 84 CTE examinations of CD patients (mean age 31 ± 13 years , 60 males), and the classification dataset had 193 (mean age 31 ± 12 years , 136 males). The deep learning segmentation model achieved a Dice similarity coefficient of 0.824 on the testing set. The logistic regression model showed the best performance among the 5 classifiers in the testing set, with an area under the curve, sensitivity, specificity, and accuracy of 0.862, 0.697, 0.840, and 0.759, respectively. CONCLUSION The automated segmentation model accurately segments CD lesions, and machine learning classifier distinguishes CD activity well. This method can assist radiologists in promptly and precisely evaluating CD activity.
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Affiliation(s)
- Yankun Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Bo Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Dehan Zhao
- Department of Precision Machinery and Precision Instruments, University of Science and Technology of China, Hefei, China
| | - Shuai Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Chang Rong
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Mingzhai Sun
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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Ruiqing L, Jing Y, Shunli L, Jia K, Zhibo W, Hongping Z, Keyu R, Xiaoming Z, Zhiming W, Weiming Z, Tianye N, Yun L. A Novel Radiomics Model Integrating Luminal and Mesenteric Features to Predict Mucosal Activity and Surgery Risk in Crohn's Disease Patients: A Multicenter Study. Acad Radiol 2023; 30 Suppl 1:S207-S219. [PMID: 37149448 DOI: 10.1016/j.acra.2023.03.023] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/16/2023] [Accepted: 03/18/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND To investigate the feasibility of integrating radiomics and morphological features based on computed tomography enterography (CTE) for developing a noninvasive grading model for mucosal activity and surgery risk of Crohn's disease (CD) patients. METHODS A total of 167 patients from three centers were enrolled. Radiomics and image morphological features were extracted to quantify segmental and global simple endoscopic score for Crohn's disease (SES-CD). An image-fusion-based support vector machine (SVM) classifier was used for grading SES-CD and identifying moderate-to-severe SES-CD. The performance of the predictive model was assessed using the area under the receiver operating characteristic curve (AUC). A multiparametric model was developed to predict surgical progression in CD patients by combining sum-image scores and clinical data. RESULTS The AUC values of the multicategorical segmental SES-CD fusion radiomic model based on a combination of luminal and mesenteric radiomics were 0.828 and 0.709 in training and validation cohorts. The image fusion model integrating the fusion radiomics and morphological features could accurately distinguish bowel segments with moderate-to-severe SES-CD in both the training cohort (AUC = 0.847, 95% confidence interval (CI): 0.784-0.902) and the validation cohort (AUC = 0.896, 95% CI: 0.812-0.960). A predictive nomogram for interval surgery was developed based on multivariable cox analysis. CONCLUSIONS This study demonstrated the feasibility of integrating lumen and mesentery radiomic features to develop a promising noninvasive grading model for mucosal activity of CD. In combination with clinical data, the fusion-image score may yield an accurate prognostic model for time to surgery.
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Affiliation(s)
- Liu Ruiqing
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Jiangsu Road 16#, Qingdao, Shandong 266400, People's Republic of China
| | - Yang Jing
- Institute of Translational Medicine, Zhejiang University, Hangzhou, ZJ, China
| | - Liu Shunli
- Department of Radiology, The Affiliated Hospital of Qingdao University Qingdao, Qingdao, SD, China
| | - Ke Jia
- Department of Colorectal Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, GD, China
| | - Wang Zhibo
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Jiangsu Road 16#, Qingdao, Shandong 266400, People's Republic of China
| | - Zhu Hongping
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Jiangsu Road 16#, Qingdao, Shandong 266400, People's Republic of China
| | - Ren Keyu
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, SD, China
| | - Zhou Xiaoming
- Department of Radiology, The Affiliated Hospital of Qingdao University Qingdao, Qingdao, SD, China
| | - Wang Zhiming
- Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, JS, China
| | - Zhu Weiming
- Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, JS, China
| | - Niu Tianye
- Shenzhen Bay Laboratory, Shenzhen, GD, China
| | - Lu Yun
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Jiangsu Road 16#, Qingdao, Shandong 266400, People's Republic of China.
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Pang W, Zhang B, Jin L, Yao Y, Han Q, Zheng X. Serological Biomarker-Based Machine Learning Models for Predicting the Relapse of Ulcerative Colitis. J Inflamm Res 2023; 16:3531-3545. [PMID: 37636275 PMCID: PMC10455884 DOI: 10.2147/jir.s423086] [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: 05/26/2023] [Accepted: 08/11/2023] [Indexed: 08/29/2023] Open
Abstract
Purpose To explore whether machine learning models using serological markers can predict the relapse of Ulcerative colitis (UC). Patients and Methods This clinical cohort study included 292 UC patients, and serological markers were obtained when patients were discharged from the hospital. Subsequently, four machine learning models including the random forest (RF) model, the logistic regression model, the decision tree, and the neural network were compared to predict the relapse of UC. A nomogram was constructed, and the performance of these models was evaluated by accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Results Based on the patients' characteristics and serological markers, we selected the relevant variables associated with relapse and developed a LR model. The novel model including gender, white blood cell count, percentage of leukomonocyte, percentage of monocyte, absolute value of neutrophilic granulocyte, and erythrocyte sedimentation rate was established for predicting the relapse. In addition, the average AUC of the four machine learning models was 0.828, of which the RF model was the best. The AUC of the test group was 0.889, the accuracy was 76.4%, the sensitivity was 78.5%, and the specificity was 76.4%. There were 45 variables in the RF models, and the relative weight coefficients of these variables were determined. Age has the greatest impact on classification results, followed by hemoglobin concentration, white blood cell count, and platelet distribution width. Conclusion Machine learning models based on serological markers had high accuracy in predicting the relapse of UC. The model can be used to noninvasively predict patient outcomes and can be an effective tool for determining personalized treatment plans.
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Affiliation(s)
- Wenwen Pang
- Department of Clinical Laboratory, Tianjin Union Medical Center, Nankai University, Tianjin, People’s Republic of China
| | - Bowei Zhang
- School of Medicine, Nankai University, Tianjin, People’s Republic of China
| | - Leixin Jin
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China
| | - Yao Yao
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China
| | - Qiurong Han
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China
| | - Xiaoli Zheng
- Department of Clinical Laboratory, Tianjin Union Medical Center, Nankai University, Tianjin, People’s Republic of China
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Laterza L, Boldrini L, Tran HE, Votta C, Larosa L, Minordi LM, Maresca R, Pugliese D, Zocco MA, Ainora ME, Lopetuso LR, Papa A, Armuzzi A, Gasbarrini A, Scaldaferri F. Radiomics could predict surgery at 10 years in Crohn's disease. Dig Liver Dis 2023; 55:1042-1048. [PMID: 36435716 DOI: 10.1016/j.dld.2022.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 11/04/2022] [Accepted: 11/07/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Predicting clinical outcomes represents a major challenge in Crohn's disease (CD). Radiomics provides a method to extract quantitative features from medical images and may successfully predict clinical course. AIMS The aim of this pilot study is to evaluate the use of radiomics to predict 10-year surgery for CD patients. METHODS We selected a cohort of CD patients with CT scan enterographies and a 10-year follow up. The R library Moddicom was used to extract radiomic features from each lesion of CD, segmented in the CT scans. A logistic regression model based on selected radiomic features was developed to predict 10-year surgery. The model was evaluated by computing the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity, positive and negative predictive values (PPV, NPV). RESULTS We enroled 30 patients, with 44 CT scans and 93 lesions. We extracted 217 radiomic features from each lesion. The developed model was based on two radiomic features and presented an AUC (95% CI) of 0.83 (0.73-0.91) in predicting 10-year surgery. Sensitivity, specificity, PPV, NPV of the radiomic model were equal to 0.72, 0.90, 0.79, 0.86, respectively. CONCLUSION Radiomics could be a helpful tool to identify patients with high risk for surgery and needing a stricter monitoring.
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Affiliation(s)
- Lucrezia Laterza
- IBD Unit -UOS Malattie Infiammatorie Croniche Intestinali, CEMAD, Digestive Diseases Center, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Università Cattolica del Sacro Cuore, L.go A. Gemelli 8, Roma 00168, Italy.
| | - Luca Boldrini
- Department of Diagnostic Imaging, Oncological Radiotherapy, and Hematology - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, L. go A. Gemelli 8, Rome 00168, Italy.
| | - Huong Elena Tran
- Department of Diagnostic Imaging, Oncological Radiotherapy, and Hematology - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, L. go A. Gemelli 8, Rome 00168, Italy.
| | - Claudio Votta
- Department of Diagnostic Imaging, Oncological Radiotherapy, and Hematology - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, L. go A. Gemelli 8, Rome 00168, Italy.
| | - Luigi Larosa
- Department of Diagnostic Imaging, Oncological Radiotherapy, and Hematology - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, L. go A. Gemelli 8, Rome 00168, Italy.
| | - Laura Maria Minordi
- Department of Diagnostic Imaging, Oncological Radiotherapy, and Hematology - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, L. go A. Gemelli 8, Rome 00168, Italy.
| | - Rossella Maresca
- IBD Unit -UOS Malattie Infiammatorie Croniche Intestinali, CEMAD, Digestive Diseases Center, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Università Cattolica del Sacro Cuore, L.go A. Gemelli 8, Roma 00168, Italy.
| | - Daniela Pugliese
- IBD Unit -UOS Malattie Infiammatorie Croniche Intestinali, CEMAD, Digestive Diseases Center, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Università Cattolica del Sacro Cuore, L.go A. Gemelli 8, Roma 00168, Italy.
| | - Maria Assunta Zocco
- IBD Unit -UOS Malattie Infiammatorie Croniche Intestinali, CEMAD, Digestive Diseases Center, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Università Cattolica del Sacro Cuore, L.go A. Gemelli 8, Roma 00168, Italy.
| | - Maria Elena Ainora
- IBD Unit -UOS Malattie Infiammatorie Croniche Intestinali, CEMAD, Digestive Diseases Center, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Università Cattolica del Sacro Cuore, L.go A. Gemelli 8, Roma 00168, Italy.
| | - Loris Riccardo Lopetuso
- IBD Unit -UOS Malattie Infiammatorie Croniche Intestinali, CEMAD, Digestive Diseases Center, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Università Cattolica del Sacro Cuore, L.go A. Gemelli 8, Roma 00168, Italy; Department of Medicine and Ageing Sciences,"G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy; Center for Advanced Studies and Technology (CAST), "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.
| | - Alfredo Papa
- IBD Unit -UOS Malattie Infiammatorie Croniche Intestinali, CEMAD, Digestive Diseases Center, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Università Cattolica del Sacro Cuore, L.go A. Gemelli 8, Roma 00168, Italy.
| | | | - Antonio Gasbarrini
- IBD Unit -UOS Malattie Infiammatorie Croniche Intestinali, CEMAD, Digestive Diseases Center, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Università Cattolica del Sacro Cuore, L.go A. Gemelli 8, Roma 00168, Italy; Dipartimento di Medicina e Chirurgia traslazionale, Università Cattolica del Sacro Cuore, L. go F. Vito 1, Rome 00168, Italy.
| | - Franco Scaldaferri
- IBD Unit -UOS Malattie Infiammatorie Croniche Intestinali, CEMAD, Digestive Diseases Center, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Università Cattolica del Sacro Cuore, L.go A. Gemelli 8, Roma 00168, Italy; Dipartimento di Medicina e Chirurgia traslazionale, Università Cattolica del Sacro Cuore, L. go F. Vito 1, Rome 00168, Italy.
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Alyami AS. The Role of Radiomics in Fibrosis Crohn's Disease: A Review. Diagnostics (Basel) 2023; 13:diagnostics13091623. [PMID: 37175014 PMCID: PMC10178496 DOI: 10.3390/diagnostics13091623] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 04/22/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
Inflammatory bowel disease (IBD) is a global health concern that has been on the rise in recent years. In addition, imaging is the established method of care for detecting, diagnosing, planning treatment, and monitoring the progression of IBD. While conventional imaging techniques are limited in their ability to provide comprehensive information, cross-sectional imaging plays a crucial role in the clinical management of IBD. However, accurately characterizing, detecting, and monitoring fibrosis in Crohn's disease remains a challenging task for clinicians. Recent advances in artificial intelligence technology, machine learning, computational power, and radiomic emergence have enabled the automated evaluation of medical images to generate prognostic biomarkers and quantitative diagnostics. Radiomics analysis can be achieved via deep learning algorithms or by extracting handcrafted radiomics features. As radiomic features capture pathophysiological and biological data, these quantitative radiomic features have been shown to offer accurate and rapid non-invasive tools for IBD diagnostics, treatment response monitoring, and prognosis. For these reasons, the present review aims to provide a comprehensive review of the emerging radiomics methods in intestinal fibrosis research that are highlighted and discussed in terms of challenges and advantages.
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Affiliation(s)
- Ali S Alyami
- Department of Diagnostic Radiography Technology, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia
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15
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Gong T, Li M, Pu H, Yin LL, Peng SK, Zhou Z, Zhou M, Li H. Computed tomography enterography-based multiregional radiomics model for differential diagnosis of Crohn's disease from intestinal tuberculosis. Abdom Radiol (NY) 2023; 48:1900-1910. [PMID: 37004555 DOI: 10.1007/s00261-023-03889-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/13/2023] [Accepted: 03/14/2023] [Indexed: 04/04/2023]
Abstract
PURPOSE To build computed tomography enterography (CTE)-based multiregional radiomics model for distinguishing Crohn's disease (CD) from intestinal tuberculosis (ITB). MATERIALS AND METHODS A total of 105 patients with CD and ITB who underwent CTE were retrospectively enrolled. Volume of interest segmentation were performed on CTE and radiomic features were obtained separately from the intestinal wall of lesion, the largest lymph node (LN), and region surrounding the lesion in the ileocecal region. The most valuable radiomic features was selected by the selection operator and least absolute shrinkage. We established nomogram combining clinical factors, endoscopy results, CTE features, and radiomic score through multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the prediction performance. DeLong test was applied to compare the performance of the models. RESULTS The clinical-radiomic combined model comprised of four variables including one radiomic signature from intestinal wall, one radiomic signature from LN, involved bowel segments on CTE, and longitudinal ulcer on endoscopy. The combined model showed good diagnostic performance with an area under the ROC curve (AUC) of 0.975 (95% CI 0.953-0.998) in the training cohort and 0.958 (95% CI 0.925-0.991) in the validation cohort. The combined model showed higher AUC than that of the clinical model in cross-validation set (0.958 vs. 0.878, P = 0.004). The DCA showed the highest benefit for the combined model. CONCLUSION Clinical-radiomic combined model constructed by combining CTE-based radiomics from the intestinal wall of lesion and LN, endoscopy results, and CTE features can accurately distinguish CD from ITB.
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Affiliation(s)
- Tong Gong
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610072, Sichuan, China
- Institute of Radiation Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Mou Li
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610072, Sichuan, China
| | - Hong Pu
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610072, Sichuan, China
| | - Long-Lin Yin
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610072, Sichuan, China
| | - Sheng-Kun Peng
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610072, Sichuan, China
| | - Zhou Zhou
- Department of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610072, Sichuan, China
| | - Mi Zhou
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610072, Sichuan, China
| | - Hang Li
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610072, Sichuan, China.
- Institute of Radiation Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
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16
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Volumetric visceral fat machine learning phenotype on CT for differential diagnosis of inflammatory bowel disease. Eur Radiol 2023; 33:1862-1872. [PMID: 36255487 DOI: 10.1007/s00330-022-09171-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 08/17/2022] [Accepted: 09/15/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To investigate whether volumetric visceral adipose tissue (VAT) features extracted using radiomics and three-dimensional convolutional neural network (3D-CNN) approach are effective in differentiating Crohn's disease (CD) and ulcerative colitis (UC). METHODS This retrospective study enrolled 316 patients (mean age, 36.25 ± 13.58 [standard deviation]; 219 men) with confirmed diagnosis of CD and UC who underwent CT enterography between 2012 and 2021. Volumetric VAT was semi-automatically segmented on the arterial phase images. Radiomics analysis was performed using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. We developed a 3D-CNN model using VAT imaging data from the training cohort. Clinical covariates including age, sex, modified body mass index, and disease duration that impact VAT were added to the machine learning model for adjustment. The model's performance was evaluated on the testing cohort separating from the model's development process by its discrimination and clinical utility. RESULTS Volumetric VAT radiomics analysis with LASSO had the highest AUC value of 0.717 (95% CI, 0.614-0.820), though difference of diagnostic performance among the 3D-CNN model (AUC = 0.693; 95% CI, 0.587-0.798) and radiomics analysis with PCA (AUC = 0.662; 95% CI, 0.548-0.776) and LASSO have not reached statistical significance (all p > 0.05). The radiomics score was higher in UC than in CD on the testing cohort (mean ± SD, UC 0.29 ± 1.05 versus CD -0.60 ± 1.25; p < 0.001). The LASSO model with adjustment of clinical covariates reached an AUC of 0.775 (95%CI, 0.683-0.868). CONCLUSION The developed volumetric VAT-based radiomics and 3D-CNN models provided comparable and effective performance for the characterization of CD from UC. KEY POINTS • High-output feature data extracted from volumetric visceral adipose tissue on CT enterography had an effective diagnostic performance for differentiating Crohn's disease from ulcerative colitis. • With adjustment of clinical covariates that cause difference in volumetric visceral adipose tissue, adjusted clinical machine learning model reached stronger performance when distinguishing Crohn's disease patients from ulcerative colitis patients.
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McCague C, Ramlee S, Reinius M, Selby I, Hulse D, Piyatissa P, Bura V, Crispin-Ortuzar M, Sala E, Woitek R. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78:83-98. [PMID: 36639175 DOI: 10.1016/j.crad.2022.08.149] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/31/2022] [Indexed: 01/12/2023]
Abstract
Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.
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Affiliation(s)
- C McCague
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S Ramlee
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - M Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - I Selby
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - D Hulse
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - P Piyatissa
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - V Bura
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - M Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Oncology, University of Cambridge, Cambridge, UK
| | - E Sala
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - R Woitek
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
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Cheng X, Ji Y, Li X, Wang Z, Wang B, He F, Xue S. The beneficial effects of Fomitopsis pinicola chloroform extract on a dextran sulfate sodium-induced ulcerative colitis mice model. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:35. [PMID: 36819509 PMCID: PMC9929819 DOI: 10.21037/atm-22-5143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 11/14/2022] [Indexed: 11/30/2022]
Abstract
Background As an intestinal non-specific inflammatory lesion, ulcerative colitis (UC) affects the health of many individuals. This study examined the possible beneficial effects of the chloroform extract of Fomitopsis pinicola (Swartz.: Fr) Karst (FPKc) on UC. Methods The mice were given free access to drink with 4% dextran sulfate sodium (DSS) for 1 week to establish acute UC model. Next, 35 mg of FPKc or sulfasalazine (SASP) was given to the mice via gavage for 3 weeks. The disease activity index (DAI) and colonic mucosa damage index (CMDI) scores were calculated. The colon tissues of the mice were collected to measure the length and perform hematoxylin and eosin staining. The thymus and spleen indexes were determined. Interleukin (IL)-6, IL-8, tumor necrosis factor-α, aminotransferase (AST) and alanine aminotransferase (ALT) levels in the serum were determined. Results FPKc or SASP treatment alleviated hematochezia and weight loss, ameliorated DAI and CMDI scores, and improved the crypt structure and length of the colon tissues. Relative to the UC model group, the spleen index in the FPKc group was reduced, which was accompanied by decreases of the IL-6 and IL-8 levels in the serum. FPKc also lowered the AST and ALT levels in the serum of the UC mice. Conclusions FPKc protected the mice from DSS-induced UC injury. It may be that FPKc activates immune regulation and downregulates the expression of pro-inflammatory cytokines.
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Affiliation(s)
- Xiaoxia Cheng
- Key Laboratory of Natural Product Development and Anti-Cancer Innovative Drug Research in Qinling, Xi’an, China;,Genetic Engineering Laboratory, College of Biological and Environmental Engineering, Xi’an University, Xi’an, China
| | - Yifan Ji
- Key Laboratory of Natural Product Development and Anti-Cancer Innovative Drug Research in Qinling, Xi’an, China;,Genetic Engineering Laboratory, College of Biological and Environmental Engineering, Xi’an University, Xi’an, China
| | - Xinyi Li
- Key Laboratory of Natural Product Development and Anti-Cancer Innovative Drug Research in Qinling, Xi’an, China;,Genetic Engineering Laboratory, College of Biological and Environmental Engineering, Xi’an University, Xi’an, China
| | - Zijian Wang
- Key Laboratory of Natural Product Development and Anti-Cancer Innovative Drug Research in Qinling, Xi’an, China;,Genetic Engineering Laboratory, College of Biological and Environmental Engineering, Xi’an University, Xi’an, China
| | - Bo Wang
- Key Laboratory of Natural Product Development and Anti-Cancer Innovative Drug Research in Qinling, Xi’an, China;,Genetic Engineering Laboratory, College of Biological and Environmental Engineering, Xi’an University, Xi’an, China
| | - Fengqin He
- Key Laboratory of Natural Product Development and Anti-Cancer Innovative Drug Research in Qinling, Xi’an, China;,Genetic Engineering Laboratory, College of Biological and Environmental Engineering, Xi’an University, Xi’an, China
| | - Shaoan Xue
- Key Laboratory of Natural Product Development and Anti-Cancer Innovative Drug Research in Qinling, Xi’an, China;,Genetic Engineering Laboratory, College of Biological and Environmental Engineering, Xi’an University, Xi’an, China
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Grassi G, Laino ME, Fantini MC, Argiolas GM, Cherchi MV, Nicola R, Gerosa C, Cerrone G, Mannelli L, Balestrieri A, Suri JS, Carriero A, Saba L. Advanced imaging and Crohn’s disease: An overview of clinical application and the added value of artificial intelligence. Eur J Radiol 2022; 157:110551. [DOI: 10.1016/j.ejrad.2022.110551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/23/2022] [Accepted: 09/27/2022] [Indexed: 11/03/2022]
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Lovinfosse P, Hustinx R. The role of PET imaging in inflammatory bowel diseases: state-of-the-art review. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF... 2022; 66:206-217. [PMID: 35708600 DOI: 10.23736/s1824-4785.22.03467-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Inflammatory bowel diseases (IBD), i.e. Crohn disease and ulcerative colitis, are autoimmune processes of undetermined origin characterized by the chronic inflammation of the digestive tract. There is no single gold-standard to diagnose IBD which is therefore carried out through the combination of endoscopy, biopsy, radiological and biological investigations; and the development of non-invasive technique allowing the assessment and monitoring of these diseases is necessary. In this state-of-the-art review of the literature, we present the results of PET imaging studies for the diagnosis and staging of IBD (suspected or known), response evaluation to treatment and evaluation of one the main complication, i.e. strictures; explain the reasons why this examination is currently not considered in the IBD guidelines, e.g. radiation exposure, lack of standardization and not validated performances; and finally discuss the perspectives that could possibly allow it to find a place in the future, e.g. digital PET-CT, dynamic PET images acquisition, new radiopharmaceuticals, use of radiomics and use of artificial intelligence for automatically characterize and quantify digestive [18F]FDG uptake.
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Affiliation(s)
- Pierre Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, University Hospital CHU of Liège, Liège, Belgium -
- GIGA-CRC in vivo Imaging, University of Liège, Liège, Belgium -
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, University Hospital CHU of Liège, Liège, Belgium
- GIGA-CRC in vivo Imaging, University of Liège, Liège, Belgium
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The Role of Magnetic Resonance Enterography in Crohn’s Disease: A Review of Recent Literature. Diagnostics (Basel) 2022; 12:diagnostics12051236. [PMID: 35626391 PMCID: PMC9140029 DOI: 10.3390/diagnostics12051236] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/06/2022] [Accepted: 05/13/2022] [Indexed: 11/17/2022] Open
Abstract
Inflammatory bowel disease (IBD) is the term used to identify a form of chronic inflammation of the gastrointestinal tract that primarily contemplates two major entities: ulcerative colitis (UC) and Crohn’s disease (CD). The classic signs are abdominal pain and diarrhoea that correlate with the localization of gastro-enteric disease, although in this pathology extraintestinal symptoms may coexist. The diagnosis of CD relies on a synergistic combination of clinical, laboratory (stool and biochemical), cross-sectional imaging evaluation, as well as endoscopic and histologic assessments. The purpose of this paper is to prove the role of imaging in the diagnosis and follow-up of patients with CD with particular focus on recent innovations of magnetic resonance enterography (MRE) as a pivotal diagnostic tool, analysing the MRE study protocol and imaging features during the various phases of disease activity and its complications.
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Li T, Liu Y, Guo J, Wang Y. Prediction of the activity of Crohn's disease based on CT radiomics combined with machine learning models. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:1155-1168. [PMID: 35988261 DOI: 10.3233/xst-221224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
PURPOSE To investigate the value of a CT-based radiomics model in identification of Crohn's disease (CD) active phase and remission phase. METHODS CT images of 101 patients diagnosed with CD were retrospectively collected, which included 60 patients in active phase and 41 patients in remission phase. These patients were randomly divided into training group and test group at a ratio of 7 : 3. First, the lesion areas were manually delineated by the physician. Meanwhile, radiomics features were extracted from each lesion. Next, the features were selected by t-test and the least absolute shrinkage and selection operator regression algorithm. Then, several machine learning models including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to construct CD activity classification models respectively. Finally, the soft-voting mechanism was used to integrate algorithms with better effects to perform two classifications of data, and the receiver operating characteristic curves were applied to evaluate the diagnostic value of the models. RESULTS Both on the training set and the test set, AUC of the five machine learning classification models reached 0.85 or more. The ensemble soft-voting classifier obtained by using the combination of SVM, LR and KNN could better distinguish active CD from CD remission. For the test set, AUC was 0.938, and accuracy, sensitivity, and specificity were 0.903, 0.911, and 0.892, respectively. CONCLUSION This study demonstrated that the established radiomics model could objectively and effectively diagnose CD activity. The integrated approach has better diagnostic performance.
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Affiliation(s)
- Tingting Li
- Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yu Liu
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School ofMedicine, Shanghai 200011, China
| | - Jiuhong Guo
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School ofMedicine, Shanghai 200011, China
| | - Yuanjun Wang
- Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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Zhu C, Yu Y, Wang S, Wang X, Gao Y, Li C, Li J, Ge Y, Wu X. A Novel Clinical Radiomics Nomogram to Identify Crohn's Disease from Intestinal Tuberculosis. J Inflamm Res 2021; 14:6511-6521. [PMID: 34887674 PMCID: PMC8651213 DOI: 10.2147/jir.s344563] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 11/17/2021] [Indexed: 12/22/2022] Open
Abstract
Purpose To establish a clinical radiomics nomogram to differentiate Crohn’s disease (CD) from intestinal tuberculosis (ITB). Patients and Methods Ninety-three patients with CD and 67 patients with ITB were recruited (111 in training cohort and 49 in test cohort). The region of interest (ROI) for the lesions in the ileocecal region was delineated on computed tomography enterography and radiomics features extracted. Radiomics features were filtered by the gradient boosting decision tree (GBDT), and a radiomics score was calculated by using the radiomics signature-based formula. We constructed a clinical radiomics model and nomogram combining clinical factors and radiomics score through multivariate logistic regression analysis, and the internal validation was undertaken by ten-fold cross validation. Analyses of receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the prediction performance. DeLong test was applied to evaluate the performance of the clinical, radiomics and combined model. Results The clinical radiomics nomogram, which was based on the 9 radiomics signature and two clinical factors, indicated that the clinical radiomics model had an area under the ROC curve (AUC) value of 0.96 (95% confidence interval [CI]: 0.93–0.99) in the training cohort and 0.93 (95% CI: 0.86–1.00) in validation cohort. The clinical radiomics model was superior to the clinical model and radiomics model, and the difference was significant (P = 0.006, 0.004) in the training cohort. DCA confirmed the clinical utility of clinical radiomics nomogram. Conclusion CTE-based radiomics model has a good performance in distinguishing CD from ITB. A nomogram constructed by combining radiomics and clinical factors can help clinicians accurately diagnose and select appropriate treatment strategies between CD and ITB.
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Affiliation(s)
- Chao Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People's Republic of China
| | - Yongmei Yu
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People's Republic of China
| | - Shihui Wang
- Department of Radiology, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People's Republic of China
| | - Xia Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People's Republic of China
| | - Yankun Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People's Republic of China
| | - Cuiping Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People's Republic of China
| | - Jianying Li
- GE Healthcare China, Shanghai, 210000, People's Republic of China
| | - Yaqiong Ge
- GE Healthcare China, Shanghai, 210000, People's Republic of China
| | - Xingwang Wu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People's Republic of China
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