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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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Song F, Ma M, Zeng S, Shao F, Huang W, Feng Z, Rong P. CT enterography-based radiomics combined with body composition to predict infliximab treatment failure in Crohn's disease. LA RADIOLOGIA MEDICA 2024; 129:175-187. [PMID: 37982937 DOI: 10.1007/s11547-023-01748-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 10/27/2023] [Indexed: 11/21/2023]
Abstract
PURPOSE Accurately predicting the treatment response in patients with Crohn's disease (CD) receiving infliximab therapy is crucial for clinical decision-making. We aimed to construct a prediction model incorporating radiomics and body composition features derived from computed tomography (CT) enterography for identifying individuals at high risk for infliximab treatment failure. METHODS This retrospective study included 137 patients with CD between 2015 and 2021, who were divided into a training cohort and a validation cohort with a ratio of 7:3. Patients underwent CT enterography examinations within 1 month before infliximab initiation. Radiomic features of the intestinal segments involved were extracted, and body composition features were measured at the level of the L3 lumbar vertebra. A model that combined radiomics with body composition was constructed. The primary outcome was the occurrence of infliximab treatment failure within 1 year. The model performance was evaluated using discrimination, calibration, and decision curves. RESULTS Fifty-two patients (38.0%) showed infliximab treatment failure. Eight significant radiomic features were used to develop the radiomics model. The model incorporating radiomics model score, skeletal muscle index (SMI), and creeping fat showed good discrimination for predicting infliximab treatment failure, with an area under the curve (AUC) of 0.88 (95% CI 0.81, 0.95) in the training cohort and 0.83 (95% CI 0.66, 1.00) in the validation cohort. The favorable clinical application was observed using decision curve analysis. CONCLUSIONS We constructed a comprehensive model incorporating radiomics and muscle volume, which could potentially be used to facilitate the individualized prediction of infliximab treatment response in patients with CD.
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Affiliation(s)
- Fulong Song
- Department of Radiology, Third Xiangya Hospital, Central South University, No. 138 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Mengtian Ma
- Department of Radiology, Third Xiangya Hospital, Central South University, No. 138 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Shumin Zeng
- Department of Radiology, Third Xiangya Hospital, Central South University, No. 138 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Fang Shao
- Department of Radiology, Third Xiangya Hospital, Central South University, No. 138 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Weiyan Huang
- Department of Radiology, Third Xiangya Hospital, Central South University, No. 138 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Zhichao Feng
- Department of Radiology, Third Xiangya Hospital, Central South University, No. 138 Tongzipo Road, Changsha, 410013, Hunan, China.
| | - Pengfei Rong
- Department of Radiology, Third Xiangya Hospital, Central South University, No. 138 Tongzipo Road, Changsha, 410013, Hunan, China.
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Dave M, Dev A, Somoza RA, Zhao N, Viswanath S, Mina PR, Chirra P, Obmann VC, Mahabeleshwar GH, Menghini P, Durbin-Johnson B, Nolta J, Soto C, Osme A, Khuat LT, Murphy WJ, Caplan AI, Cominelli F. MSCs mediate long-term efficacy in a Crohn's disease model by sustained anti-inflammatory macrophage programming via efferocytosis. NPJ Regen Med 2024; 9:6. [PMID: 38245543 PMCID: PMC10799947 DOI: 10.1038/s41536-024-00347-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/04/2024] [Indexed: 01/22/2024] Open
Abstract
Mesenchymal stem cells (MSCs) are novel therapeutics for the treatment of Crohn's disease. However, their mechanism of action is unclear, especially in disease-relevant chronic models of inflammation. Thus, we used SAMP-1/YitFc (SAMP), a chronic and spontaneous murine model of small intestinal inflammation, to study the therapeutic effects and mechanism of action of human bone marrow-derived MSCs (hMSC). hMSC dose-dependently inhibited naïve T lymphocyte proliferation via prostaglandin E2 (PGE2) secretion and reprogrammed macrophages to an anti-inflammatory phenotype. We found that the hMSCs promoted mucosal healing and immunologic response early after administration in SAMP when live hMSCs are present (until day 9) and resulted in a complete response characterized by mucosal, histological, immunologic, and radiological healing by day 28 when no live hMSCs are present. hMSCs mediate their effect via modulation of T cells and macrophages in the mesentery and mesenteric lymph nodes (mLN). Sc-RNAseq confirmed the anti-inflammatory phenotype of macrophages and identified macrophage efferocytosis of apoptotic hMSCs as a mechanism that explains their long-term efficacy. Taken together, our findings show that hMSCs result in healing and tissue regeneration in a chronic model of small intestinal inflammation and despite being short-lived, exert long-term effects via sustained anti-inflammatory programming of macrophages via efferocytosis.
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Affiliation(s)
- Maneesh Dave
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, UC Davis Medical Center, University of California Davis School of Medicine, Sacramento, CA, USA.
- Institute for Regenerative Cures, University of California Davis School of Medicine, Sacramento, CA, USA.
| | - Atul Dev
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, UC Davis Medical Center, University of California Davis School of Medicine, Sacramento, CA, USA
| | - Rodrigo A Somoza
- Skeletal Research Center, Department of Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Nan Zhao
- Division of Gastroenterology and Liver Disease, University Hospitals, Case Western Reserve University, Cleveland, OH, USA
| | - Satish Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Pooja Rani Mina
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, UC Davis Medical Center, University of California Davis School of Medicine, Sacramento, CA, USA
| | - Prathyush Chirra
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Verena Carola Obmann
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Ganapati H Mahabeleshwar
- Department of Pathology, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Paola Menghini
- Division of Gastroenterology and Liver Disease, University Hospitals, Case Western Reserve University, Cleveland, OH, USA
| | - Blythe Durbin-Johnson
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Sacramento, CA, USA
| | - Jan Nolta
- Institute for Regenerative Cures, University of California Davis School of Medicine, Sacramento, CA, USA
- Division of Malignant Hematology/Cell and Marrow Transplantation, Department of Internal Medicine, University of California Davis School of Medicine, Sacramento, USA
| | - Christopher Soto
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, UC Davis Medical Center, University of California Davis School of Medicine, Sacramento, CA, USA
| | - Abdullah Osme
- Department of Pathology, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Lam T Khuat
- Department of Dermatology, University of California Davis School of Medicine, Sacramento, CA, USA
| | - William J Murphy
- Division of Malignant Hematology/Cell and Marrow Transplantation, Department of Internal Medicine, University of California Davis School of Medicine, Sacramento, USA
- Department of Dermatology, University of California Davis School of Medicine, Sacramento, CA, USA
| | - Arnold I Caplan
- Skeletal Research Center, Department of Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Fabio Cominelli
- Division of Gastroenterology and Liver Disease, University Hospitals, Case Western Reserve University, Cleveland, OH, USA
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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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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: 5] [Impact Index Per Article: 5.0] [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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Dave M, Dev A, Somoza RA, Zhao N, Viswanath S, Mina PR, Chirra P, Obmann VC, Mahabeleshwar GH, Menghini P, Johnson BD, Nolta J, Soto C, Osme A, Khuat LT, Murphy W, Caplan AI, Cominelli F. Mesenchymal stem cells ameliorate inflammation in an experimental model of Crohn's disease via the mesentery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.22.541829. [PMID: 37292753 PMCID: PMC10245893 DOI: 10.1101/2023.05.22.541829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Objective Mesenchymal stem cells (MSCs) are novel therapeutics for treatment of Crohn's disease. However, their mechanism of action is unclear, especially in disease-relevant chronic models of inflammation. Thus, we used SAMP-1/YitFc, a chronic and spontaneous murine model of small intestinal inflammation, to study the therapeutic effect and mechanism of human bone marrow-derived MSCs (hMSC). Design hMSC immunosuppressive potential was evaluated through in vitro mixed lymphocyte reaction, ELISA, macrophage co-culture, and RT-qPCR. Therapeutic efficacy and mechanism in SAMP were studied by stereomicroscopy, histopathology, MRI radiomics, flow cytometry, RT-qPCR, small animal imaging, and single-cell RNA sequencing (Sc-RNAseq). Results hMSC dose-dependently inhibited naïve T lymphocyte proliferation in MLR via PGE 2 secretion and reprogrammed macrophages to an anti-inflammatory phenotype. hMSC promoted mucosal healing and immunologic response early after administration in SAMP model of chronic small intestinal inflammation when live hMSCs are present (until day 9) and resulted in complete response characterized by mucosal, histological, immunologic, and radiological healing by day 28 when no live hMSCs are present. hMSC mediate their effect via modulation of T cells and macrophages in the mesentery and mesenteric lymph nodes (mLN). Sc-RNAseq confirmed the anti-inflammatory phenotype of macrophages and identified macrophage efferocytosis of apoptotic hMSCs as a mechanism of action that explains their long-term efficacy. Conclusion hMSCs result in healing and tissue regeneration in a chronic model of small intestinal inflammation. Despite being short-lived, exert long-term effects via macrophage reprogramming to an anti-inflammatory phenotype. Data Transparency Statement Single-cell RNA transcriptome datasets are deposited in an online open access repository 'Figshare' (DOI: https://doi.org/10.6084/m9.figshare.21453936.v1 ).
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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: 3] [Impact Index Per Article: 3.0] [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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