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Pugashetti JV, Khanna D, Kazerooni EA, Oldham J. Clinically Relevant Biomarkers in Connective Tissue Disease-Associated Interstitial Lung Disease. Rheum Dis Clin North Am 2024; 50:439-461. [PMID: 38942579 DOI: 10.1016/j.rdc.2024.03.007] [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] [Indexed: 06/30/2024]
Abstract
Interstitial lung disease (ILD) complicates connective tissue disease (CTD) with variable incidence and is a leading cause of death in these patients. To improve CTD-ILD outcomes, early recognition and management of ILD is critical. Blood-based and radiologic biomarkers that assist in the diagnosis CTD-ILD have long been studied. Recent studies, including -omic investigations, have also begun to identify biomarkers that may help prognosticate such patients. This review provides an overview of clinically relevant biomarkers in patients with CTD-ILD, highlighting recent advances to assist in the diagnosis and prognostication of CTD-ILD.
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Affiliation(s)
- Janelle Vu Pugashetti
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan.
| | - Dinesh Khanna
- Scleroderma Program, Division of Rheumatology, Department of Internal Medicine, University of Michigan
| | - Ella A Kazerooni
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan; Division of Cardiothoracic Radiology, Department of Radiology, University of Michigan
| | - Justin Oldham
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan; Department of Epidemiology, University of Michigan
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Yu F, Yang M, He C, Yang Y, Peng Y, Yang H, Lu H, Liu H. CT radiomics combined with clinical and radiological factors predict hematoma expansion in hypertensive intracerebral hemorrhage. Eur Radiol 2024:10.1007/s00330-024-10921-2. [PMID: 38990325 DOI: 10.1007/s00330-024-10921-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/24/2024] [Accepted: 05/19/2024] [Indexed: 07/12/2024]
Abstract
OBJECTIVES This study aimed to establish a hematoma expansion (HE) prediction model for hypertensive intracerebral hemorrhage (HICH) patients by combining CT radiomics, clinical information, and conventional imaging signs. METHODS A retrospective continuous collection of HICH patients from three medical centers was divided into a training set (n = 555), a validation set (n = 239), and a test set (n = 77). Extract radiomics features from baseline CT plain scan images and combine them with clinical information and conventional imaging signs to construct radiomics models, clinical imaging sign models, and hybrid models, respectively. The models will be evaluated using the area under the curve (AUC), clinical decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI). RESULTS In the training, validation, and testing sets, the radiomics model predicts an AUC of HE of 0.885, 0.827, and 0.894, respectively, while the clinical imaging sign model predicts an AUC of HE of 0.759, 0.725, and 0.765, respectively. Glasgow coma scale score at admission, first CT hematoma volume, irregular hematoma shape, and radiomics score were used to construct a hybrid model, with AUCs of 0.901, 0.838, and 0.917, respectively. The DCA shows that the hybrid model had the highest net profit rate. Compared with the radiomics model and the clinical imaging sign model, the hybrid model showed an increase in NRI and IDI. CONCLUSION The hybrid model based on CT radiomics combined with clinical and radiological factors can effectively individualize the evaluation of the risk of HE in patients with HICH. CLINICAL RELEVANCE STATEMENT CT radiomics combined with clinical information and conventional imaging signs can identify HICH patients with a high risk of HE and provide a basis for clinical-targeted treatment. KEY POINTS HE is an important prognostic factor in patients with HICH. The hybrid model predicted HE with training, validation, and test AUCs of 0.901, 0.838, and 0.917, respectively. This model provides a tool for a personalized clinical assessment of early HE risk.
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Affiliation(s)
- Fei Yu
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi, China
- Department of Medical Imaging, Chongqing Emergency Medical Center, Chongqing University Central Hospital, The Fourth People's Hospital of Chongqing, Chongqing, China
| | - Mingguang Yang
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi, China
- Department of Medical Imaging, Chongqing Emergency Medical Center, Chongqing University Central Hospital, The Fourth People's Hospital of Chongqing, Chongqing, China
| | - Cheng He
- Department of Medical Imaging, Chongqing Emergency Medical Center, Chongqing University Central Hospital, The Fourth People's Hospital of Chongqing, Chongqing, China
| | - Yanli Yang
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi, China
| | - Ying Peng
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi, China
| | - Hua Yang
- Department of Medical Imaging, Chongqing Traditional Chinese Medicine Hospital, Chongqing, China
| | - Hong Lu
- Department of Radiology, The Seventh People's Hospital of Chongqing, The Central Hospital Affiliated to Chongqing University of Technology, Chongqing, China
| | - Heng Liu
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Engineering Research Center of Intelligent Medical Imaging in Guizhou Higher Education Institutions, Zunyi, China.
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Haga A, Iwasawa T, Misumi T, Okudela K, Oda T, Kitamura H, Saka T, Matsushita S, Baba T, Natsume-Kitatani Y, Utsunomiya D, Ogura T. Correlation of CT-based radiomics analysis with pathological cellular infiltration in fibrosing interstitial lung diseases. Jpn J Radiol 2024:10.1007/s11604-024-01607-2. [PMID: 38888852 DOI: 10.1007/s11604-024-01607-2] [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: 02/08/2024] [Accepted: 05/28/2024] [Indexed: 06/20/2024]
Abstract
PURPOSE We aimed to identify computed tomography (CT) radiomics features that are associated with cellular infiltration and construct CT radiomics models predictive of cellular infiltration in patients with fibrotic ILD. MATERIALS AND METHODS CT images of patients with ILD who underwent surgical lung biopsy (SLB) were analyzed. Radiomics features were extracted using artificial intelligence-based software and PyRadiomics. We constructed a model predicting cell counts in histological specimens, and another model predicting two classifications of higher or lower cellularity. We tested these models using external validation. RESULTS Overall, 100 patients (mean age: 62 ± 8.9 [standard deviation] years; 61 men) were included. The CT radiomics model used to predict cell count in 140 histological specimens predicted the actual cell count in 59 external validation specimens (root-mean-square error: 0.797). The two-classification model's accuracy was 70% and the F1 score was 0.73 in the external validation dataset including 30 patients. CONCLUSION The CT radiomics-based model developed in this study provided useful information regarding the cellular infiltration in the ILD with good correlation with SLB specimens.
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Affiliation(s)
- Akira Haga
- Dept. of Radiology, Kanagawa Cardiovascular & Respiratory Center, Yokohama, Japan
- Dept. of Radiology, Yokohama City Univ. School of Medicine, Yokohama, Japan
| | - Tae Iwasawa
- Dept. of Radiology, Kanagawa Cardiovascular & Respiratory Center, Yokohama, Japan.
| | - Toshihiro Misumi
- Department of Data Science, National Cancer Center Hospital East, Kashiwa, Japan
| | - Koji Okudela
- Department of Pathology, Saitama Medical University, Moroyama, Japan
- Dept. of Pathology, Kanagawa Cardiovascular & Respiratory Center, Yokohama, Japan
| | - Tsuneyuki Oda
- Dept. of Respiratory Medicine, Kanagawa Cardiovascular & Respiratory Center, Yokohama, Japan
| | - Hideya Kitamura
- Dept. of Respiratory Medicine, Kanagawa Cardiovascular & Respiratory Center, Yokohama, Japan
| | | | | | - Tomohisa Baba
- Dept. of Respiratory Medicine, Kanagawa Cardiovascular & Respiratory Center, Yokohama, Japan
| | - Yayoi Natsume-Kitatani
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
- Institute of Advanced Medical Sciences, Tokushima University, Tokushima, Japan
| | - Daisuke Utsunomiya
- Dept. of Radiology, Yokohama City Univ. School of Medicine, Yokohama, Japan
| | - Takashi Ogura
- Dept. of Respiratory Medicine, Kanagawa Cardiovascular & Respiratory Center, Yokohama, Japan
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Wang Y, Zhou M, Ding Y, Li X, Xie T, Zhou Z, Fu W, Shi Z. Unsupervised machine learning cluster analysis to identification EVAR patients clinical phenotypes based on radiomics. Vascular 2024:17085381241262575. [PMID: 38885967 DOI: 10.1177/17085381241262575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
OBJECTIVE This study used unsupervised machine learning (UML) cluster analysis to explore clinical phenotypes of endovascular aortic repair (EVAR) for abdominal aortic aneurysm (AAA) patients based on radiomics. METHOD We retrospectively reviewed 1785 patients with infra-renal AAA who underwent elective EVAR procedures between January 2010 and December 2020. Pyradiomics was used to extract the radiomics features. Statistical analysis was applied to determine the radiomics features that related to severe adverse events (SAEs) after EVAR. The selected features were used for UML cluster analysis in training set and validation in test set. Comparison of basic characteristics and radiomics features of different clusters. The Kaplan-Meier analysis was conducted to generate the cumulative incidence of freedom from SAEs rate. RESULT A total of 1180 patients were enrolled. During the follow-up, 353 patients experienced EVAR-related SAEs. In total, 1223 radiomics features were extracted from each patient, of which 23 radiomics features were finally preserved to identify different clinical phenotypes. 944 patients were allocated to the training set. Three clusters were identified in training set, in which patients had identical clinical characteristics and morphological features, while varied considerably of selected radiomics features. This encouraging performance was further approved in the test set. In addition, each cluster was well differentiated from other clusters and Kaplan-Meier analysis showed significant differences of freedom from SAEs rate between different clusters both in the training (p = .0216) and test sets (p = .0253). CONCLUSION Based on radiomics, UML cluster analysis can identify clinical phenotypes in EVAR patients with distinct long-term outcomes.
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Affiliation(s)
- Yonggang Wang
- Department of Vascular Surgery, The First Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Min Zhou
- Department of Vascular Surgery, Zhongshan Hospital, Institute of Vascular Surgery, Fudan University, National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Yong Ding
- Department of Vascular Surgery, Zhongshan Hospital, Institute of Vascular Surgery, Fudan University, National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Xu Li
- Department of Vascular Surgery, Zhongshan Hospital, Institute of Vascular Surgery, Fudan University, National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Tianchen Xie
- Department of Vascular Surgery, Zhongshan Hospital, Institute of Vascular Surgery, Fudan University, National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Zhenyu Zhou
- Department of Vascular Surgery, Zhongshan Hospital, Institute of Vascular Surgery, Fudan University, National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Weiguo Fu
- Department of Vascular Surgery, Zhongshan Hospital, Institute of Vascular Surgery, Fudan University, National Clinical Research Center for Interventional Medicine, Shanghai, China
| | - Zhenyu Shi
- Department of Vascular Surgery, Zhongshan Hospital, Institute of Vascular Surgery, Fudan University, National Clinical Research Center for Interventional Medicine, Shanghai, China
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Mismetti V, Si-Mohamed S, Cottin V. Interstitial Lung Disease Associated with Systemic Sclerosis. Semin Respir Crit Care Med 2024; 45:342-364. [PMID: 38714203 DOI: 10.1055/s-0044-1786698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2024]
Abstract
Systemic sclerosis (SSc) is a rare autoimmune disease characterized by a tripod combining vasculopathy, fibrosis, and immune-mediated inflammatory processes. The prevalence of interstitial lung disease (ILD) in SSc varies according to the methods used to detect it, ranging from 25 to 95%. The fibrotic and vascular pulmonary manifestations of SSc, particularly ILD, are the main causes of morbidity and mortality, contributing to 35% of deaths. Although early trials were conducted with cyclophosphamide, more recent randomized controlled trials have been performed to assess the efficacy and tolerability of several medications, mostly mycophenolate, rituximab, tocilizumab, and nintedanib. Although many uncertainties remain, expert consensus is emerging to optimize the therapeutic management and to provide clinicians with evidence-based clinical practice guidelines for patients with SSc-ILD. This article provides an overview, in the light of the latest advances, of the available evidence for the diagnosis and management of SSc-ILD.
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Affiliation(s)
- Valentine Mismetti
- Department of Respiratory Medicine, National Coordinating Reference Centre for Rare Pulmonary Diseases, ERN-LUNG, Louis Pradel Hospital, Hospices Civils de Lyon, Lyon, France
| | - Salim Si-Mohamed
- INSA-Lyon, University of Lyon, University Claude-Bernard Lyon 1, Lyon, France
- Radiology Department, Hospices Civils de Lyon, Lyon, France
| | - Vincent Cottin
- Department of Respiratory Medicine, National Coordinating Reference Centre for Rare Pulmonary Diseases, ERN-LUNG, Louis Pradel Hospital, Hospices Civils de Lyon, Lyon, France
- UMR 754, INRAE, Claude Bernard University Lyon 1, Lyon, France
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Huang YS, Chen JLY, Ko WC, Chang YH, Chang CH, Chang YC. Clinical Variables and Radiomics Features for Predicting Pneumothorax in Patients Undergoing CT-guided Transthoracic Core Needle Biopsy. Radiol Cardiothorac Imaging 2024; 6:e230278. [PMID: 38780426 PMCID: PMC11211933 DOI: 10.1148/ryct.230278] [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: 04/30/2023] [Revised: 02/02/2024] [Accepted: 03/27/2024] [Indexed: 05/25/2024]
Abstract
Purpose To develop a prediction model combining both clinical and CT texture analysis radiomics features for predicting pneumothorax complications in patients undergoing CT-guided core needle biopsy. Materials and Methods A total of 424 patients (mean age, 65.6 years ± 12.7 [SD]; 232 male, 192 female) who underwent CT-guided core needle biopsy between January 2021 and October 2022 were retrospectively included as the training data set. Clinical and procedure-related characteristics were documented. Texture analysis radiomics features were extracted from the subpleural lung parenchyma traversed by needle. Moderate pneumothorax was defined as a postprocedure air rim of 2 cm or greater. The prediction model was developed using logistic regression with backward elimination, presented by linear fusion of the selected features weighted by their coefficients. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Validation was conducted in an external cohort (n = 45; mean age, 58.2 years ± 12.7; 19 male, 26 female) from a different hospital. Results Moderate pneumothorax occurred in 12.0% (51 of 424) of the training cohort and 8.9% (four of 45) of the external test cohort. Patients with emphysema (P < .001) or a longer needle path length (P = .01) exhibited a higher incidence of moderate pneumothorax in the training cohort. Texture analysis features, including gray-level co-occurrence matrix cluster shade (P < .001), gray-level run-length matrix low gray-level run emphasis (P = .049), gray-level run-length matrix run entropy (P = .003), gray-level size-zone matrix gray-level variance (P < .001), and neighboring gray-tone difference matrix complexity (P < .001), showed higher values in patients with moderate pneumothorax. The combined clinical-radiomics model demonstrated satisfactory performance in both the training (AUC 0.78, accuracy = 71.9%) and external test cohorts (AUC 0.86, accuracy 73.3%). Conclusion The model integrating both clinical and radiomics features offered practical diagnostic performance and accuracy for predicting moderate pneumothorax in patients undergoing CT-guided core needle biopsy. Keywords: Biopsy/Needle Aspiration, Thorax, CT, Pneumothorax, Core Needle Biopsy, Texture Analysis, Radiomics, CT Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Yu-Sen Huang
- From the Department of Medical Imaging (Y.S.H., W.C.K., Y.C.C.) and
Statistical Consulting Unit (Y.H.C., C.H.C.), National Taiwan University
Hospital, No. 7 Chung-Shan S. Rd, Taipei 100, Taiwan; Department of Radiology,
National Taiwan University College of Medicine, Taipei, Taiwan (Y.S.H.,
J.L.Y.C., Y.C.C.); and Department of Radiation Oncology, National Taiwan
University Cancer Center, Taipei, Taiwan (J.L.Y.C.)
| | - Jenny Ling-Yu Chen
- From the Department of Medical Imaging (Y.S.H., W.C.K., Y.C.C.) and
Statistical Consulting Unit (Y.H.C., C.H.C.), National Taiwan University
Hospital, No. 7 Chung-Shan S. Rd, Taipei 100, Taiwan; Department of Radiology,
National Taiwan University College of Medicine, Taipei, Taiwan (Y.S.H.,
J.L.Y.C., Y.C.C.); and Department of Radiation Oncology, National Taiwan
University Cancer Center, Taipei, Taiwan (J.L.Y.C.)
| | - Wei-Chun Ko
- From the Department of Medical Imaging (Y.S.H., W.C.K., Y.C.C.) and
Statistical Consulting Unit (Y.H.C., C.H.C.), National Taiwan University
Hospital, No. 7 Chung-Shan S. Rd, Taipei 100, Taiwan; Department of Radiology,
National Taiwan University College of Medicine, Taipei, Taiwan (Y.S.H.,
J.L.Y.C., Y.C.C.); and Department of Radiation Oncology, National Taiwan
University Cancer Center, Taipei, Taiwan (J.L.Y.C.)
| | - Yu-Han Chang
- From the Department of Medical Imaging (Y.S.H., W.C.K., Y.C.C.) and
Statistical Consulting Unit (Y.H.C., C.H.C.), National Taiwan University
Hospital, No. 7 Chung-Shan S. Rd, Taipei 100, Taiwan; Department of Radiology,
National Taiwan University College of Medicine, Taipei, Taiwan (Y.S.H.,
J.L.Y.C., Y.C.C.); and Department of Radiation Oncology, National Taiwan
University Cancer Center, Taipei, Taiwan (J.L.Y.C.)
| | - Chin-Hao Chang
- From the Department of Medical Imaging (Y.S.H., W.C.K., Y.C.C.) and
Statistical Consulting Unit (Y.H.C., C.H.C.), National Taiwan University
Hospital, No. 7 Chung-Shan S. Rd, Taipei 100, Taiwan; Department of Radiology,
National Taiwan University College of Medicine, Taipei, Taiwan (Y.S.H.,
J.L.Y.C., Y.C.C.); and Department of Radiation Oncology, National Taiwan
University Cancer Center, Taipei, Taiwan (J.L.Y.C.)
| | - Yeun-Chung Chang
- From the Department of Medical Imaging (Y.S.H., W.C.K., Y.C.C.) and
Statistical Consulting Unit (Y.H.C., C.H.C.), National Taiwan University
Hospital, No. 7 Chung-Shan S. Rd, Taipei 100, Taiwan; Department of Radiology,
National Taiwan University College of Medicine, Taipei, Taiwan (Y.S.H.,
J.L.Y.C., Y.C.C.); and Department of Radiation Oncology, National Taiwan
University Cancer Center, Taipei, Taiwan (J.L.Y.C.)
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Li Z, Huang H, Zhao Z, Ma W, Mao H, Liu F, Yang Y, Wang D, Lu Z. Development and Validation of a Nomogram Based on DCE-MRI Radiomics for Predicting Hypoxia-Inducible Factor 1α Expression in Locally Advanced Rectal Cancer. Acad Radiol 2024:S1076-6332(24)00300-3. [PMID: 38816315 DOI: 10.1016/j.acra.2024.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 06/01/2024]
Abstract
RATIONALE AND OBJECTIVES The expression levels of hypoxia-inducible factor 1 alpha (HIF-1α) have been identified as a pivotal marker, correlating with treatment response in patients with locally advanced rectal cancer (LARC). This study aimed to develop and validate a nomogram based on dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinical features for predicting the expression of HIF-1α in patients with LARC. MATERIALS AND METHODS A total of 102 patients diagnosed with locally advanced rectal cancer were divided into training (n = 71) and validation (n = 31) cohorts. The expression statuses of HIF-1α were histopathologically classified, categorizing patients into high and low expression groups. The intraclass correlation coefficient (ICC), minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) were employed for feature selection to construct a radiomics signature and calculate the radiomics score (Rad-score). Univariate and multivariate analyses of clinical features and Rad-score were applied, and the clinical model and the nomogram were constructed. The predictive performance of the nomogram incorporating clinical features and Rad-score was assessed using Receiver Operating Characteristics (ROC) curves, decision curve analysis (DCA), and calibration curves. RESULTS Seven radiomics features from DCE-MRI were used to build the radiomics signature. The nomogram incorporating CEA, Ki-67 and Rad-score had the highest AUC values in the training cohort and in the validation cohort (AUC: 0.918 and 0.920). Decision curve analysis showed that the nomogram outperformed the clinical model and radiomics signature in terms of clinical utility. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation. CONCLUSION The nomogram based on DCE-MRI radiomics and clinical features showed favorable predictive efficacy and might be useful for preoperatively discriminating the expression of HIF-1α.
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Affiliation(s)
- Zhiheng Li
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Huizhen Huang
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Weili Ma
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Haijia Mao
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Fang Liu
- Department of Pathology, Shaoxing People's Hospital, Shaoxing, China
| | - Ye Yang
- Department of Pathology, Shaoxing People's Hospital, Shaoxing, China
| | - Dandan Wang
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China
| | - Zengxin Lu
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, China.
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Lippitt WL, Maier LA, Fingerlin TE, Lynch DA, Yadav R, Rieck J, Hill AC, Liao SY, Mroz MM, Barkes BQ, Chae KJ, Hwang HJ, Carlson NE. The textures of sarcoidosis: quantifying lung disease through variograms. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.20.24307618. [PMID: 38826353 PMCID: PMC11142277 DOI: 10.1101/2024.05.20.24307618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Objective Sarcoidosis is a granulomatous disease affecting the lungs in over 90% of patients. Qualitative assessment of chest CT by radiologists is standard clinical practice and reliable quantification of disease from CT would support ongoing efforts to identify sarcoidosis phenotypes. Standard imaging feature engineering techniques such as radiomics suffer from extreme sensitivity to image acquisition and processing, potentially impeding generalizability of research to clinical populations. In this work, we instead investigate approaches to engineering variogram-based features with the intent to identify a robust, generalizable pipeline for image quantification in the study of sarcoidosis. Approach For a cohort of more than 300 individuals with sarcoidosis, we investigated 24 feature engineering pipelines differing by decisions for image registration to a template lung, empirical and model variogram estimation methods, and feature harmonization for CT scanner model, and subsequently 48 sets of phenotypes produced through unsupervised clustering. We then assessed sensitivity of engineered features, phenotypes produced through unsupervised clustering, and sarcoidosis disease signal strength to pipeline. Main results We found that variogram features had low to mild association with scanner model and associations were reduced by image registration. For each feature type, features were also typically robust to all pipeline decisions except image registration. Strength of disease signal as measured by association with pulmonary function testing and some radiologist visual assessments was strong (optimistic AUC ≈ 0.9, p ≪ 0.0001 in models for architectural distortion, conglomerate mass, fibrotic abnormality, and traction bronchiectasis) and fairly consistent across engineering approaches regardless of registration and harmonization for CT scanner. Significance Variogram-based features appear to be a suitable approach to image quantification in support of generalizable research in pulmonary sarcoidosis.
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Affiliation(s)
- William L Lippitt
- Dept of Biostatistics and Informatics, Uni. of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Lisa A Maier
- Dept of Medicine, National Jewish Health, Denver, CO, USA
- Dept of Medicine, Uni. of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Dept of Environmental and Occupational Health, Uni. of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Tasha E Fingerlin
- Dept of Biostatistics and Informatics, Uni. of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Dept of Immunology and Genomic Medicine, National Jewish Health, Denver, CO, USA
| | - David A Lynch
- Dept of Radiology, National Jewish Health, Denver, CO, USA
| | - Ruchi Yadav
- Dept of Diagnostic Radiology, Cleveland Clinic, Cleveland, OH, USA
| | - Jared Rieck
- Dept of Biostatistics and Informatics, Uni. of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Andrew C Hill
- Dept of Biostatistics and Informatics, Uni. of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Shu-Yi Liao
- Dept of Medicine, National Jewish Health, Denver, CO, USA
- Dept of Medicine, Uni. of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | | | - Kum Ju Chae
- Dept of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Jeollabuk-do, Korea
| | - Hye Jeon Hwang
- Dept of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, Korea
| | - Nichole E Carlson
- Dept of Biostatistics and Informatics, Uni. of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Yin XN, Wang ZH, Zou L, Yang CW, Shen CY, Liu BK, Yin Y, Liu XJ, Zhang B. Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor. World J Gastrointest Oncol 2024; 16:1296-1308. [PMID: 38660646 PMCID: PMC11037038 DOI: 10.4251/wjgo.v16.i4.1296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 01/23/2024] [Accepted: 02/25/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Preoperative knowledge of mutational status of gastrointestinal stromal tumors (GISTs) is essential to guide the individualized precision therapy. AIM To develop a combined model that integrates clinical and contrast-enhanced computed tomography (CE-CT) features to predict gastric GISTs with specific genetic mutations, namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions. METHODS A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio. The models were constructed using selected clinical features, conventional CT features, and radiomics features extracted from abdominal CE-CT images. Three models were developed: ModelCT sign, modelCT sign + rad, and model CTsign + rad + clinic. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis and the Delong test. RESULTS The ROC analyses revealed that in the training cohort, the area under the curve (AUC) values for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic for predicting KIT exon 11 mutation were 0.743, 0.818, and 0.915, respectively. In the validation cohort, the AUC values for the same models were 0.670, 0.781, and 0.811, respectively. For predicting KIT exon 11 codons 557-558 deletions, the AUC values in the training cohort were 0.667, 0.842, and 0.720 for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic, respectively. In the validation cohort, the AUC values for the same models were 0.610, 0.782, and 0.795, respectively. Based on the decision curve analysis, it was determined that the modelCT sign + rad + clinic had clinical significance and utility. CONCLUSION Our findings demonstrate that the combined modelCT sign + rad + clinic effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions. This combined model has the potential to be valuable in assessing the genotype of GISTs.
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Affiliation(s)
- Xiao-Nan Yin
- Gastric Cancer Research Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zi-Hao Wang
- Gastric Cancer Research Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Li Zou
- Department of Paediatric Surgery, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Cai-Wei Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Chao-Yong Shen
- Gastric Cancer Research Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bai-Ke Liu
- Gastric Cancer Research Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yuan Yin
- Gastric Cancer Research Center, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Xi-Jiao Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bo Zhang
- Department of Gastrointestinal Surgery, Sichuan University West China Hospital, Chengdu 610041, Sichuan Province, China
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Rubio-Rivas M, Pestaña-Fernández M. Prevalence of the limited vs. extensive scleroderma-related interstitial lung disease at the time of diagnosis of SSc-ILD based on Goh et al. criteria. Systematic review and meta-analysis. Rev Clin Esp 2024; 224:189-196. [PMID: 38387499 DOI: 10.1016/j.rceng.2024.02.008] [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] [Indexed: 02/24/2024]
Abstract
INTRODUCTION Goh et al. proposed in 2008 a classificatory algorithm of limited or extensive SSc-ILD. The prevalence of both at the time of diagnosis of SSc-ILD is not known with exactitude. METHODS The review was undertaken by means of MEDLINE and SCOPUS from 2008 to 2023 and using the terms: "systemic", "scleroderma" or "interstitial lung disease" [MesH]. The Newcastle-Ottawa Scale was used for the qualifying assessment for observational studies and the Jadad scale for clinical trials. The inverse variance-weighted method was performed. RESULTS Twenty-seven studies were initially included in the systematic review and meta-analysis (SRMA). Of these, 17 studies had no overlapping data. They reported data from 2,149 patients, 1,369 (81.2%) were female. The mean age was 52.4 (SD 6.6) years. 45.2% of the patients had the diffuse subtype and 54.8% had the limited or sine scleroderma subtype. A total of 38.7% of the patients showed positive antitopoisomerase antibodies (ATA) and 14.2% positive anticentromere antibodies (ACA). The mean percentage of forced vital capacity (FVC) at baseline was 80.5% (SD 6.9) and of diffusing capacity of the lungs for carbon monoxide (DLco) was 59.1% (SD 9.6). Twelve studies presented SSc-ILD extension data adjusted for PFTs and were included in the meta-analysis. The 10 observational cohort studies were analyzed separately. The overall percentage of limited extension was estimated at 63.5% (95%CI 55.3-73; p < 0.001) using the random-effects model. Heterogeneity between studies (I2) was 9.8% (95%CI 0-68.2%) with the random-effects model. Extensive pulmonary involvement was estimated at 34.3% (95%CI 26-45.4; p < 0.001). Heterogeneity between studies (I2) was 0% (95%CI 0-61.6%) with the random-effects model. CONCLUSION The overall percentage of limited SSc-ILD at the time of diagnosis of SSc-ILD was estimated at 63.5% and extensive at 34.3%.
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Affiliation(s)
- Manuel Rubio-Rivas
- Department of Internal Medicine, Bellvitge University Hospital, Barcelona, Spain.
| | - Melani Pestaña-Fernández
- Department of Internal Medicine, Moisés Broggi Hospital, Esplugues de Llobregat, Barcelona, Spain.
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Xia T, Zhao B, Li B, Lei Y, Song Y, Wang Y, Tang T, Ju S. MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges. J Magn Reson Imaging 2024; 59:767-783. [PMID: 37647155 DOI: 10.1002/jmri.28982] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/15/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Tianyi Xia
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ben Zhao
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Binrong Li
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ying Lei
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China
| | - Yuancheng Wang
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Tianyu Tang
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Shenghong Ju
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
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Ferrigno I, Verzellesi L, Ottone M, Bonacini M, Rossi A, Besutti G, Bonelli E, Colla R, Facciolongo N, Teopompi E, Massari M, Mancuso P, Ferrari AM, Pattacini P, Trojani V, Bertolini M, Botti A, Zerbini A, Giorgi Rossi P, Iori M, Salvarani C, Croci S. CCL18, CHI3L1, ANG2, IL-6 systemic levels are associated with the extent of lung damage and radiomic features in SARS-CoV-2 infection. Inflamm Res 2024:10.1007/s00011-024-01852-1. [PMID: 38308760 DOI: 10.1007/s00011-024-01852-1] [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: 09/22/2023] [Revised: 01/17/2024] [Accepted: 01/21/2024] [Indexed: 02/05/2024] Open
Abstract
OBJECTIVE AND DESIGN We aimed to identify cytokines whose concentrations are related to lung damage, radiomic features, and clinical outcomes in COVID-19 patients. MATERIAL OR SUBJECTS Two hundred twenty-six patients with SARS-CoV-2 infection and chest computed tomography (CT) images were enrolled. METHODS CCL18, CHI3L1/YKL-40, GAL3, ANG2, IP-10, IL-10, TNFα, IL-6, soluble gp130, soluble IL-6R were quantified in plasma samples using Luminex assays. The Mann-Whitney U test, the Kruskal-Wallis test, correlation and regression analyses were performed. Mediation analyses were used to investigate the possible causal relationships between cytokines, lung damage, and outcomes. AVIEW lung cancer screening software, pyradiomics, and XGBoost classifier were used for radiomic feature analyses. RESULTS CCL18, CHI3L1, and ANG2 systemic levels mainly reflected the extent of lung injury. Increased levels of every cytokine, but particularly of IL-6, were associated with the three outcomes: hospitalization, mechanical ventilation, and death. Soluble IL-6R showed a slight protective effect on death. The effect of age on COVID-19 outcomes was partially mediated by cytokine levels, while CT scores considerably mediated the effect of cytokine levels on outcomes. Radiomic-feature-based models confirmed the association between lung imaging characteristics and CCL18 and CHI3L1. CONCLUSION Data suggest a causal link between cytokines (risk factor), lung damage (mediator), and COVID-19 outcomes.
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Affiliation(s)
- Ilaria Ferrigno
- Unit of Clinical Immunology, Allergy and Advanced Biotechnologies, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
- PhD Program in Clinical and Experimental Medicine, University of Modena and Reggio Emilia, Modena, Italy
| | - Laura Verzellesi
- Unit of Medical Physics, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Marta Ottone
- Unit of Epidemiology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Martina Bonacini
- Unit of Clinical Immunology, Allergy and Advanced Biotechnologies, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Alessandro Rossi
- Unit of Clinical Immunology, Allergy and Advanced Biotechnologies, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Giulia Besutti
- Unit of Radiology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
- Department of Surgery, Medicine, Dentistry and Morphological Sciences With Interest in Transplant, Oncology and Regenerative Medicine, University of Modena and Reggio Emilia, Modena, Italy
| | - Efrem Bonelli
- Unit of Radiology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
- Clinical Chemistry and Endocrinology Laboratory, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Rossana Colla
- Clinical Chemistry and Endocrinology Laboratory, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Nicola Facciolongo
- Unit of Respiratory Diseases, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Elisabetta Teopompi
- Multidisciplinary Internal Medicine Unit, Guastalla Hospital, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Marco Massari
- Unit of Infectious Diseases, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Pamela Mancuso
- Unit of Epidemiology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Anna Maria Ferrari
- Department of Emergency, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Pierpaolo Pattacini
- Unit of Radiology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Valeria Trojani
- Unit of Medical Physics, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Marco Bertolini
- Unit of Medical Physics, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Andrea Botti
- Unit of Medical Physics, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Alessandro Zerbini
- Unit of Clinical Immunology, Allergy and Advanced Biotechnologies, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Paolo Giorgi Rossi
- Unit of Epidemiology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Mauro Iori
- Unit of Medical Physics, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Carlo Salvarani
- Department of Surgery, Medicine, Dentistry and Morphological Sciences With Interest in Transplant, Oncology and Regenerative Medicine, University of Modena and Reggio Emilia, Modena, Italy
- Unit of Rheumatology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Stefania Croci
- Unit of Clinical Immunology, Allergy and Advanced Biotechnologies, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy.
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Huang W, Xiong W, Tang L, Chen C, Yuan Q, Zhang C, Zhou K, Sun Z, Zhang T, Han Z, Feng H, Liang X, Zhong Y, Deng H, Yu L, Xu Y, Wang W, Shen L, Li G, Jiang Y. Non-invasive CT imaging biomarker to predict immunotherapy response in gastric cancer: a multicenter study. J Immunother Cancer 2023; 11:e007807. [PMID: 38179695 PMCID: PMC10668251 DOI: 10.1136/jitc-2023-007807] [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] [Accepted: 10/24/2023] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Despite remarkable benefits have been provided by immune checkpoint inhibitors in gastric cancer (GC), predictions of treatment response and prognosis remain unsatisfactory, making identifying biomarkers desirable. The aim of this study was to develop and validate a CT imaging biomarker to predict the immunotherapy response in patients with GC and investigate the associated immune infiltration patterns. METHODS This retrospective study included 294 GC patients who received anti-PD-1/PD-L1 immunotherapy from three independent medical centers between January 2017 and April 2022. A radiomics score (RS) was developed from the intratumoral and peritumoral features on pretreatment CT images to predict immunotherapy-related progression-free survival (irPFS). The performance of the RS was evaluated by the area under the time-dependent receiver operating characteristic curve (AUC). Multivariable Cox regression analysis was performed to construct predictive nomogram of irPFS. The C-index was used to determine the performance of the nomogram. Bulk RNA sequencing of tumors from 42 patients in The Cancer Genome Atlas was used to investigate the RS-associated immune infiltration patterns. RESULTS Overall, 89 of 294 patients (median age, 57 years (IQR 48-66 years); 171 males) had an objective response to immunotherapy. The RS included 13 CT features that yielded AUCs of 12-month irPFS of 0.787, 0.810 and 0.785 in the training, internal validation, and external validation 1 cohorts, respectively, and an AUC of 24-month irPFS of 0.805 in the external validation 2 cohort. Patients with low RS had longer irPFS in each cohort (p<0.05). Multivariable Cox regression analyses showed RS is an independent prognostic factor of irPFS. The nomogram that integrated the RS and clinical characteristics showed improved performance in predicting irPFS, with C-index of 0.687-0.778 in the training and validation cohorts. The CT imaging biomarker was associated with M1 macrophage infiltration. CONCLUSION The findings of this prognostic study suggest that the non-invasive CT imaging biomarker can effectively predict immunotherapy outcomes in patients with GC and is associated with innate immune signaling, which can serve as a potential tool for individual treatment decisions.
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Affiliation(s)
- Weicai Huang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Wenjun Xiong
- Department of Gastrointestinal Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Lei Tang
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Chuanli Chen
- Department of Medical Imaging Center, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Qingyu Yuan
- Department of Medical Imaging Center, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Cheng Zhang
- Department of Gastrointestinal Oncology, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China
| | - Kangneng Zhou
- University of Science and Technology, Beijing, China
| | - Zepang Sun
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Taojun Zhang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Zhen Han
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Hao Feng
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Xiaokun Liang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen Colleges of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Yonghong Zhong
- Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Haijun Deng
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Lequan Yu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China
| | - Yikai Xu
- Department of Medical Imaging Center, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Wei Wang
- Department of Gastrointestinal Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Lin Shen
- Department of Gastrointestinal Oncology, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China
| | - Guoxin Li
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Yuming Jiang
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
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Suman G, Koo CW. Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases. J Thorac Imaging 2023; 38:S7-S18. [PMID: 37015833 DOI: 10.1097/rti.0000000000000705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
Interstitial lung disease (ILD) is a heterogeneous group of disorders with complex and varied imaging manifestations and prognosis. High-resolution computed tomography (HRCT) is the current standard-of-care imaging tool for ILD assessment. However, visual evaluation of HRCT is limited by interobserver variation and poor sensitivity for subtle changes. Such challenges have led to tremendous recent research interest in objective and reproducible methods to examine ILDs. Computer-aided CT analysis to include texture analysis and machine learning methods have recently been shown to be viable supplements to traditional visual assessment through improved characterization and quantification of ILDs. These quantitative tools have not only been shown to correlate well with pulmonary function tests and patient outcomes but are also useful in disease diagnosis, surveillance and management. In this review, we provide an overview of recent computer-aided tools in diagnosis, prognosis, and longitudinal evaluation of fibrotic ILDs, while outlining some of the pitfalls and challenges that have precluded further advancement of these tools as well as potential solutions and further endeavors.
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Affiliation(s)
- Garima Suman
- Division of Thoracic Imaging, Mayo Clinic, Rochester, MN
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15
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Guiot J, Walsh SLF. The ERS PROFILE.net Clinical Research Collaboration is dedicated to the set-up of an academic network to enhance imaging-based management of progressive pulmonary fibrosis. Eur Respir J 2023; 62:2300577. [PMID: 37690785 DOI: 10.1183/13993003.00577-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 07/05/2023] [Indexed: 09/12/2023]
Affiliation(s)
- Julien Guiot
- Respiratory Medicine Department, University Hospital of Liège, Liège, Belgium
| | - Simon L F Walsh
- National Heart and Lung Institute, Imperial College London, London, UK
- Royal Brompton Hospital, London, UK
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Qin S, Kang B, Liu H, Ji C, Li H, Zhang J, Wang X. A computed tomography-based radiomics nomogram for predicting overall survival in patients with connective tissue disease-associated interstitial lung disease. Eur J Radiol 2023; 165:110963. [PMID: 37437436 DOI: 10.1016/j.ejrad.2023.110963] [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: 04/19/2023] [Revised: 06/21/2023] [Accepted: 07/04/2023] [Indexed: 07/14/2023]
Abstract
OBJECTIVES Accurate prognostic prediction is beneficial for the management of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). The purpose of the present study was to develop and validate a nomogram using clinical features and computed tomography (CT) based radiomics features to predict overall survival (OS) in patients with CTD-ILD, and to assess the incremental prognostic value the radiomics might add to clinical risk factors. MATERIALS & METHODS Patients from two clinical centers with CTD-ILD were enrolled in the present retrospective study. A radiomics signature, a clinical model and a combined nomogram were developed and assessed in the cohorts. The incremental value of radiomics signature to the clinical independent risk factors in survival prediction was evaluated. The models were externally validated to evaluate the model generalization ability. RESULTS A total of 215 patients (mean age, 53 years ± 14 [standard deviation], 45 men) were evaluated. Patients with higher radiomics scores had higher mortality risk than those with lower radiomics scores (Hazard ratio, 12.396; 95% CI, 3.364-45.680; P < 0.001). The combined nomogram showed better predictive capability than the clinical model did with higher C-indices (0.800, 0.738, 0.742 vs. 0.747, 0.631, 0.587 in the training, internal- and external-validation cohort, respectively), time-AUCs and overall net-benefit. CONCLUSION The radiomics signature is a potential prognostic biomarker of CTD-ILD and add incremental value to the clinical independent risk factors. The combined nomogram can provide a more accurate estimation of OS than the clinical model for CTD-ILD patients. CLINICAL RELEVANCE STATEMENT The developed combined nomogram showed accurate prognostic prediction performance, which is beneficial for the management of CTD-ILD patients. It also proved radiomics could extract prognostic information from CT images.
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Affiliation(s)
- Songnan Qin
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan 250021, Shandong, China
| | - Bing Kang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324, Jing 5 Rd, Jinan 250021, Shandong, China
| | - Hongwu Liu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324, Jing 5 Rd, Jinan 250021, Shandong, China
| | - Congshan Ji
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324, Jing 5 Rd, Jinan 250021, Shandong, China
| | - Haiou Li
- Department of Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, China
| | - Juntao Zhang
- GE Healthcare, PDx GMS Advanced Analytics, Shanghai, China
| | - Ximing Wang
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jinan 250021, Shandong, China; Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324, Jing 5 Rd, Jinan 250021, Shandong, China.
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Liu S, Zhou Y, Wang C, Shen J, Zheng Y. Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images. BMC Med Imaging 2023; 23:101. [PMID: 37528338 PMCID: PMC10392004 DOI: 10.1186/s12880-023-01059-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 07/19/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Lymph node metastasis is an important factor affecting the treatment and prognosis of patients with cervical cancer. However, the comparison of different algorithms and features to predict lymph node metastasis is not well understood. This study aimed to construct a non-invasive model for predicting lymph node metastasis in patients with cervical cancer based on clinical features combined with the radiomic features of magnetic resonance imaging (MRI) images. METHODS A total of 180 cervical cancer patients were divided into the training set (n = 126) and testing set (n = 54). In this cross-sectional study, radiomic features of MRI images and clinical features of patients were collected. The least absolute shrinkage and selection operator (LASSO) regression was used to filter the features. Seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Logistic Regression, Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting Decision Tree (GBDT) are used to build the models. Receiver operating characteristics (ROC) curve and area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the performance of the models. RESULTS Of these 180 patients, 49 (27.22%) patients had lymph node metastases. Five of the 122 radiomic features and 3 clinical features were used to build predictive models. Compared with other models, the MNB model was the most robust, with its AUC, specificity, and accuracy on the testing set of 0.745 (95%CI: 0.740-0.750), 0.900 (95%CI: 0.807-0.993), and 0.778 (95%CI: 0.667-0.889), respectively. Furthermore, the AUCs of the MNB models with clinical features only, radiomic features only, and combined features were 0.698 (95%CI: 0.692-0.704), 0.632 (95%CI: 0.627-0.637), and 0.745 (95%CI: 0.740-0.750), respectively. CONCLUSION The MNB model, which combines the radiomic features of MRI images with the clinical features of the patient, can be used as a non-invasive tool for the preoperative assessment of lymph node metastasis.
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Affiliation(s)
- Shuyu Liu
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Bengbu Medical College, No.287 Changhuai Road, Longzihu District, Bengbu, Anhui, 233004, China
| | - Yu Zhou
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Bengbu Medical College, No.287 Changhuai Road, Longzihu District, Bengbu, Anhui, 233004, China
| | - Caizhi Wang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Bengbu Medical College, No.287 Changhuai Road, Longzihu District, Bengbu, Anhui, 233004, China
| | - Junjie Shen
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, 233004, China
| | - Yi Zheng
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Bengbu Medical College, No.287 Changhuai Road, Longzihu District, Bengbu, Anhui, 233004, China.
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18
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Monti S, Truppa ME, Albanese S, Mancini M. Radiomics and Radiogenomics in Preclinical Imaging on Murine Models: A Narrative Review. J Pers Med 2023; 13:1204. [PMID: 37623455 PMCID: PMC10455673 DOI: 10.3390/jpm13081204] [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: 06/09/2023] [Revised: 07/18/2023] [Accepted: 07/27/2023] [Indexed: 08/26/2023] Open
Abstract
Over the past decade, medical imaging technologies have become increasingly significant in both clinical and preclinical research, leading to a better understanding of disease processes and the development of new diagnostic and theranostic methods. Radiomic and radiogenomic approaches have furthered this progress by exploring the relationship between imaging characteristics, genomic information, and outcomes that qualitative interpretations may have overlooked, offering valuable insights for personalized medicine. Preclinical research allows for a controlled environment where various aspects of a pathology can be replicated in animal models, providing radiomic and radiogenomic approaches with the unique opportunity to investigate the causal connection between imaging and molecular factors. The aim of this review is to present the current state of the art in the application of radiomics and radiogenomics on murine models. This review will provide a brief description of relevant articles found in the literature with a discussion on the implications and potential translational relevance of these findings.
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Affiliation(s)
| | | | - Sandra Albanese
- National Research Council, Institute of Biostructures and Bioimaging, 80145 Naples, Italy; (S.M.); (M.E.T.); (M.M.)
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Rea G, Sverzellati N, Bocchino M, Lieto R, Milanese G, D'Alto M, Bocchini G, Maniscalco M, Valente T, Sica G. Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis "Expanding Horizons in Radiology". Diagnostics (Basel) 2023; 13:2333. [PMID: 37510077 PMCID: PMC10378251 DOI: 10.3390/diagnostics13142333] [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: 06/18/2023] [Revised: 07/07/2023] [Accepted: 07/08/2023] [Indexed: 07/30/2023] Open
Abstract
Diffuse lung disorders (DLDs) and interstitial lung diseases (ILDs) are pathological conditions affecting the lung parenchyma and interstitial network. There are approximately 200 different entities within this category. Radiologists play an increasingly important role in diagnosing and monitoring ILDs, as they can provide non-invasive, rapid, and repeatable assessments using high-resolution computed tomography (HRCT). HRCT offers a detailed view of the lung parenchyma, resembling a low-magnification anatomical preparation from a histological perspective. The intrinsic contrast provided by air in HRCT enables the identification of even the subtlest morphological changes in the lung tissue. By interpreting the findings observed on HRCT, radiologists can make a differential diagnosis and provide a pattern diagnosis in collaboration with the clinical and functional data. The use of quantitative software and artificial intelligence (AI) further enhances the analysis of ILDs, providing an objective and comprehensive evaluation. The integration of "meta-data" such as demographics, laboratory, genomic, metabolomic, and proteomic data through AI could lead to a more comprehensive clinical and instrumental profiling beyond the human eye's capabilities.
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Affiliation(s)
- Gaetano Rea
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Nicola Sverzellati
- Section of Radiology, Unit of Surgical Science, Department of Medicine and Surgery (DiMeC), University of Parma, 43121 Parma, Italy
| | - Marialuisa Bocchino
- Department of Clinical Medicine and Surgery, Section of Respiratory Diseases, University Federico II, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Roberta Lieto
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Gianluca Milanese
- Section of Radiology, Unit of Surgical Science, Department of Medicine and Surgery (DiMeC), University of Parma, 43121 Parma, Italy
| | - Michele D'Alto
- Department of Cardiology, University "L. Vanvitelli"-Monaldi Hospital, 80131 Naples, Italy
| | - Giorgio Bocchini
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Mauro Maniscalco
- Department of Pneumology Clinical and Scientific Institutes Maugeri IRCSS, 82037 Telese, Italy
| | - Tullio Valente
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Giacomo Sica
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
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Shan D, Wang S, Wang J, Lu J, Ren J, Chen J, Wang D, Qi P. Computed tomography angiography-based radiomics model for predicting carotid atherosclerotic plaque vulnerability. Front Neurol 2023; 14:1151326. [PMID: 37396779 PMCID: PMC10312009 DOI: 10.3389/fneur.2023.1151326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 05/30/2023] [Indexed: 07/04/2023] Open
Abstract
Vulnerable carotid atherosclerotic plaque (CAP) significantly contributes to ischemic stroke. Neovascularization within plaques is an emerging biomarker linked to plaque vulnerability that can be detected using contrast-enhanced ultrasound (CEUS). Computed tomography angiography (CTA) is a common method used in clinical cerebrovascular assessments that can be employed to evaluate the vulnerability of CAPs. Radiomics is a technique that automatically extracts radiomic features from images. This study aimed to identify radiomic features associated with the neovascularization of CAP and construct a prediction model for CAP vulnerability based on radiomic features. CTA data and clinical data of patients with CAPs who underwent CTA and CEUS between January 2018 and December 2021 in Beijing Hospital were retrospectively collected. The data were divided into a training cohort and a testing cohort using a 7:3 split. According to the examination of CEUS, CAPs were dichotomized into vulnerable and stable groups. 3D Slicer software was used to delineate the region of interest in CTA images, and the Pyradiomics package was used to extract radiomic features in Python. Machine learning algorithms containing logistic regression (LR), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and multi-layer perception (MLP) were used to construct the models. The confusion matrix, receiver operating characteristic (ROC) curve, accuracy, precision, recall, and f-1 score were used to evaluate the performance of the models. A total of 74 patients with 110 CAPs were included. In all, 1,316 radiomic features were extracted, and 10 radiomic features were selected for machine-learning model construction. After evaluating several models on the testing cohorts, it was discovered that model_RF outperformed the others, achieving an AUC value of 0.93 (95% CI: 0.88-0.99). The accuracy, precision, recall, and f-1 score of model_RF in the testing cohort were 0.85, 0.87, 0.85, and 0.85, respectively. Radiomic features associated with the neovascularization of CAP were obtained. Our study highlights the potential of radiomics-based models for improving the accuracy and efficiency of diagnosing vulnerable CAP. In particular, the model_RF, utilizing radiomic features extracted from CTA, provides a noninvasive and efficient method for accurately predicting the vulnerability status of CAP. This model shows great potential for offering clinical guidance for early detection and improving patient outcomes.
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Affiliation(s)
- Dezhi Shan
- Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Siyu Wang
- Department of Ultrasound, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Junjie Wang
- Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Jun Lu
- Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Junhong Ren
- Department of Ultrasound, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Juan Chen
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Daming Wang
- Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Peng Qi
- Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
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Makol A, Nagaraja V, Amadi C, Pugashetti JV, Caoili E, Khanna D. Recent innovations in the screening and diagnosis of systemic sclerosis-associated interstitial lung disease. Expert Rev Clin Immunol 2023; 19:613-626. [PMID: 36999788 PMCID: PMC10698514 DOI: 10.1080/1744666x.2023.2198212] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 03/29/2023] [Indexed: 04/01/2023]
Abstract
INTRODUCTION Interstitial lung disease (ILD) is the leading cause of mortality in patients with systemic sclerosis (SSc). Risk of developing progressive ILD is highest among patients with diffuse cutaneous disease, positive anti-topoisomerase I antibody, and elevated acute phase reactants. With the FDA approval of two medications and a pipeline of novel therapeutics in trials, early recognition and intervention is critical. High-resolution computed tomography of the chest is the current gold standard test for diagnosis of ILD. Yet, it is not offered as a screening tool to all patients due to which ILD can be missed in up to a third of patients. There is a need to develop and validate more innovative screening modalities. AREAS COVERED In this review, we provide an overview of screening and diagnosis of SSc-ILD, highlighting the recent innovations particularly the role of soluble serologic, radiomic (quantitative lung imaging, lung ultrasound), and breathomic (exhaled breath analysis) biomarkers in the early detection of SSc-ILD. EXPERT OPINION There is remarkable progress in the development of new radiomics and serum biomarkers in diagnosing SSc-ILD. There is an urgent need for conceptualizing and testing composite ILD screening strategies that incorporate these biomarkers.
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Affiliation(s)
- Ashima Makol
- Division of Rheumatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Vivek Nagaraja
- Division of Rheumatology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Chiemezie Amadi
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Janelle Vu Pugashetti
- Division of Pulmonary and Critical Care Medicine; University of Michigan, Ann Arbor, Michigan, USA
| | - Elaine Caoili
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Dinesh Khanna
- Michigan Scleroderma Program
- Division of Rheumatology; Department of Internal Medicine; University of Michigan, Ann Arbor, Michigan, USA
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22
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Wu A, Wu C, Zeng Q, Cao Y, Shu X, Luo L, Feng Z, Tu Y, Jie Z, Zhu Y, Zhou F, Huang Y, Li Z. Development and validation of a CT radiomics and clinical feature model to predict omental metastases for locally advanced gastric cancer. Sci Rep 2023; 13:8442. [PMID: 37231100 DOI: 10.1038/s41598-023-35155-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/13/2023] [Indexed: 05/27/2023] Open
Abstract
""We employed radiomics and clinical features to develop and validate a preoperative prediction model to estimate the omental metastases status of locally advanced gastric cancer (LAGC). A total of 460 patients (training cohort, n = 250; test cohort, n = 106; validation cohort, n = 104) with LAGC who were confirmed T3/T4 stage by postoperative pathology were continuously collected retrospectively, including clinical data and preoperative arterial phase computed tomography images (APCT). Dedicated radiomics prototype software was used to segment the lesions and extract features from the preoperative APCT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the extracted radiomics features, and a radiomics score model was constructed. Finally, a prediction model of omental metastases status and a nomogram were constructed combining the radiomics scores and selected clinical features. An area under the curve (AUC) of the receiver operating characteristic curve (ROC) was used to validate the capability of the prediction model and nomogram in the training cohort. Calibration curves and decision curve analysis (DCA) were used to evaluate the prediction model and nomogram. The prediction model was internally validated by the test cohort. In addition, 104 patients from another hospital's clinical and imaging data were gathered for external validation. In the training cohort, the combined prediction (CP) model (AUC 0.871, 95% CI 0.798-0.945) of the radiomics scores combined with the clinical features, compared with clinical features prediction (CFP) model (AUC 0.795, 95% CI 0.710-0.879) and radiomics scores prediction (RSP) model (AUC 0.805, 95% CI 0.730-0.879), had the better predictive ability. The Hosmer-Lemeshow test of the CP model showed that the prediction model did not deviate from the perfect fitting (p = 0.893). In the DCA, the clinical net benefit of the CP model was higher than that of the CFP model and RSP model. In the test and validation cohorts, the AUC values of the CP model were 0.836 (95% CI 0.726-0.945) and 0.779 (95% CI 0.634-0.923), respectively. The preoperative APCT-based clinical-radiomics nomogram showed good performance in predicting omental metastases status in LAGC, which may contribute to clinical decision-making.
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Affiliation(s)
- Ahao Wu
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
- Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Changlei Wu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital, Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Qingwen Zeng
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Yi Cao
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Xufeng Shu
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Lianghua Luo
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Zongfeng Feng
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Yi Tu
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Zhigang Jie
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, 330006, Jiangxi Province, China
| | - Ya Huang
- Department of Radiology, The Second Affiliated Hospital, Nanchang University, Nanchang, China
| | - Zhengrong Li
- Department of Digestive Surgery, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China.
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Pugashetti JV, Khanna D, Kazerooni EA, Oldham J. Clinically Relevant Biomarkers in Connective Tissue Disease-Associated Interstitial Lung Disease. Immunol Allergy Clin North Am 2023; 43:411-433. [PMID: 37055096 PMCID: PMC10584384 DOI: 10.1016/j.iac.2023.01.012] [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] [Indexed: 03/06/2023]
Abstract
Interstitial lung disease (ILD) complicates connective tissue disease (CTD) with variable incidence and is a leading cause of death in these patients. To improve CTD-ILD outcomes, early recognition and management of ILD is critical. Blood-based and radiologic biomarkers that assist in the diagnosis CTD-ILD have long been studied. Recent studies, including -omic investigations, have also begun to identify biomarkers that may help prognosticate such patients. This review provides an overview of clinically relevant biomarkers in patients with CTD-ILD, highlighting recent advances to assist in the diagnosis and prognostication of CTD-ILD.
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Affiliation(s)
- Janelle Vu Pugashetti
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan.
| | - Dinesh Khanna
- Scleroderma Program, Division of Rheumatology, Department of Internal Medicine, University of Michigan
| | - Ella A Kazerooni
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan; Division of Cardiothoracic Radiology, Department of Radiology, University of Michigan
| | - Justin Oldham
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan; Department of Epidemiology, University of Michigan
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Milam ME, Koo CW. The current status and future of FDA-approved artificial intelligence tools in chest radiology in the United States. Clin Radiol 2023; 78:115-122. [PMID: 36180271 DOI: 10.1016/j.crad.2022.08.135] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/19/2022] [Indexed: 01/18/2023]
Abstract
Artificial intelligence (AI) is becoming more widespread within radiology. Capabilities that AI algorithms currently provide include detection, segmentation, classification, and quantification of pathological findings. Artificial intelligence software have created challenges for the traditional United States Food and Drug Administration (FDA) approval process for medical devices given their abilities to evolve over time with incremental data input. Currently, there are 190 FDA-approved radiology AI-based software devices, 42 of which pertain specifically to thoracic radiology. The majority of these algorithms are approved for the detection and/or analysis of pulmonary nodules, for monitoring placement of endotracheal tubes and indwelling catheters, for detection of emergent findings, and for assessment of pulmonary parenchyma; however, as technology evolves, there are many other potential applications that can be explored. For example, evaluation of non-idiopathic pulmonary fibrosis interstitial lung diseases, synthesis of imaging, clinical and/or laboratory data to yield comprehensive diagnoses, and survival or prognosis prediction of certain pathologies. With increasing physician and developer engagement, transparency and frequent communication between developers and regulatory agencies, such as the FDA, AI medical devices will be able to provide a critical supplement to patient management and ultimately enhance physicians' ability to improve patient care.
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Affiliation(s)
- M E Milam
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - C W Koo
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
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Yan T, Yan Z, Liu L, Zhang X, Chen G, Xu F, Li Y, Zhang L, Peng M, Wang L, Li D, Zhao D. Survival prediction for patients with glioblastoma multiforme using a Cox proportional hazards denoising autoencoder network. Front Comput Neurosci 2023; 16:916511. [PMID: 36704230 PMCID: PMC9871481 DOI: 10.3389/fncom.2022.916511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 12/13/2022] [Indexed: 01/11/2023] Open
Abstract
Objectives This study aimed to establish and validate a prognostic model based on magnetic resonance imaging and clinical features to predict the survival time of patients with glioblastoma multiforme (GBM). Methods In this study, a convolutional denoising autoencoder (DAE) network combined with the loss function of the Cox proportional hazard regression model was used to extract features for survival prediction. In addition, the Kaplan-Meier curve, the Schoenfeld residual analysis, the time-dependent receiver operating characteristic curve, the nomogram, and the calibration curve were performed to assess the survival prediction ability. Results The concordance index (C-index) of the survival prediction model, which combines the DAE and the Cox proportional hazard regression model, reached 0.78 in the training set, 0.75 in the validation set, and 0.74 in the test set. Patients were divided into high- and low-risk groups based on the median prognostic index (PI). Kaplan-Meier curve was used for survival analysis (p = < 2e-16 in the training set, p = 3e-04 in the validation set, and p = 0.007 in the test set), which showed that the survival probability of different groups was significantly different, and the PI of the network played an influential role in the prediction of survival probability. In the residual verification of the PI, the fitting curve of the scatter plot was roughly parallel to the x-axis, and the p-value of the test was 0.11, proving that the PI and survival time were independent of each other and the survival prediction ability of the PI was less affected than survival time. The areas under the curve of the training set were 0.843, 0.871, 0.903, and 0.941; those of the validation set were 0.687, 0.895, 1.000, and 0.967; and those of the test set were 0.757, 0.852, 0.683, and 0.898. Conclusion The survival prediction model, which combines the DAE and the Cox proportional hazard regression model, can effectively predict the prognosis of patients with GBM.
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Affiliation(s)
- Ting Yan
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Zhenpeng Yan
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Lili Liu
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xiaoyu Zhang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Guohui Chen
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Feng Xu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ying Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Lijuan Zhang
- Shanxi Provincial People's Hospital, Taiyuan, China
| | - Meilan Peng
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Lu Wang
- Key Laboratory of Cellular Physiology of the Ministry of Education, Department of Pathology, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Dandan Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China,*Correspondence: Dandan Li ✉
| | - Dong Zhao
- Department of Stomatology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China,Dong Zhao ✉
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He W, Huang H, Chen X, Yu J, Liu J, Li X, Yin H, Zhang K, Peng L. Radiomic analysis of enhanced CMR cine images predicts left ventricular remodeling after TAVR in patients with symptomatic severe aortic stenosis. Front Cardiovasc Med 2022; 9:1096422. [PMID: 36620627 PMCID: PMC9815113 DOI: 10.3389/fcvm.2022.1096422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
Objective This study aimed to develop enhanced cine image-based radiomic models for non-invasive prediction of left ventricular adverse remodeling following transcatheter aortic valve replacement (TAVR) in symptomatic severe aortic stenosis. Methods A total of 69 patients (male:female = 37:32, median age: 66 years, range: 47-83 years) were retrospectively recruited, and severe aortic stenosis was confirmed via transthoracic echocardiography detection. The enhanced cine images and clinical variables were collected, and three types of regions of interest (ROIs) containing the left ventricular (LV) myocardium from the short-axis view at the basal, middle, and apical LV levels were manually labeled, respectively. The radiomic features were extracted and further selected by using the least absolute shrinkage and selection operator (LASSO) regression analysis. Clinical variables were also selected through univariate regression analysis. The predictive models using logistic regression classifier were developed and validated through leave-one-out cross-validation. The model performance was evaluated with respect to discrimination, calibration, and clinical usefulness. Results Five basal levels, seven middle levels, eight apical level radiomic features, and three clinical factors were finally selected for model development. The radiomic models using features from basal level (Rad I), middle level (Rad II), and apical level (Rad III) had achieved areas under the curve (AUCs) of 0.761, 0.909, and 0.913 in the training dataset and 0.718, 0.836, and 0.845 in the validation dataset, respectively. The performance of these radiomic models was improved after integrating clinical factors, with AUCs of the Combined I, Combined II, and Combined III models increasing to 0.906, 0.956, and 0.959 in the training dataset and 0.784, 0.873, and 0.891 in the validation dataset, respectively. All models showed good calibration, and the decision curve analysis indicated that the Combined III model had a higher net benefit than other models across the majority of threshold probabilities. Conclusion Radiomic models and combined models at the mid and apical slices showed outstanding and comparable predictive effectiveness of adverse remodeling for patients with symptomatic severe aortic stenosis after TAVR, and both models were significantly better than the models of basal slice. The cardiac magnetic resonance radiomic analysis might serve as an effective tool for accurately predicting left ventricular adverse remodeling following TAVR in patients with symptomatic severe aortic stenosis.
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Affiliation(s)
- Wenzhang He
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - He Huang
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoyi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jianqun Yu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Liu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Xue Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Hongkun Yin
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Kai Zhang
- Infervision Medical Technology Co., Ltd., Beijing, China
| | - Liqing Peng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China,*Correspondence: Liqing Peng,
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Gabryś HS, Gote-Schniering J, Brunner M, Bogowicz M, Blüthgen C, Frauenfelder T, Guckenberger M, Maurer B, Tanadini-Lang S. Transferability of radiomic signatures from experimental to human interstitial lung disease. Front Med (Lausanne) 2022; 9:988927. [DOI: 10.3389/fmed.2022.988927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 10/31/2022] [Indexed: 11/18/2022] Open
Abstract
BackgroundInterstitial lung disease (ILD) defines a group of parenchymal lung disorders, characterized by fibrosis as their common final pathophysiological stage. To improve diagnosis and treatment of ILD, there is a need for repetitive non-invasive characterization of lung tissue by quantitative parameters. In this study, we investigated whether CT image patterns found in mice with bleomycin induced lung fibrosis can be translated as prognostic factors to human patients diagnosed with ILD.MethodsBleomycin was used to induce lung fibrosis in mice (n_control = 36, n_experimental = 55). The patient cohort consisted of 98 systemic sclerosis (SSc) patients (n_ILD = 65). Radiomic features (n_histogram = 17, n_texture = 137) were extracted from microCT (mice) and HRCT (patients) images. Predictive performance of the models was evaluated with the area under the receiver-operating characteristic curve (AUC). First, predictive performance of individual features was examined and compared between murine and patient data sets. Second, multivariate models predicting ILD were trained on murine data and tested on patient data. Additionally, the models were reoptimized on patient data to reduce the influence of the domain shift on the performance scores.ResultsPredictive power of individual features in terms of AUC was highly correlated between mice and patients (r = 0.86). A model based only on mean image intensity in the lung scored AUC = 0.921 ± 0.048 in mice and AUC = 0.774 (CI95% 0.677-0.859) in patients. The best radiomic model based on three radiomic features scored AUC = 0.994 ± 0.013 in mice and validated with AUC = 0.832 (CI95% 0.745-0.907) in patients. However, reoptimization of the model weights in the patient cohort allowed to increase the model’s performance to AUC = 0.912 ± 0.058.ConclusionRadiomic signatures of experimental ILD derived from microCT scans translated to HRCT of humans with SSc-ILD. We showed that the experimental model of BLM-induced ILD is a promising system to test radiomic models for later application and validation in human cohorts.
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Yang R, Hui D, Li X, Wang K, Li C, Li Z. Prediction of single pulmonary nodule growth by CT radiomics and clinical features - a one-year follow-up study. Front Oncol 2022; 12:1034817. [PMID: 36387220 PMCID: PMC9650464 DOI: 10.3389/fonc.2022.1034817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 10/05/2022] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND With the development of imaging technology, an increasing number of pulmonary nodules have been found. Some pulmonary nodules may gradually grow and develop into lung cancer, while others may remain stable for many years. Accurately predicting the growth of pulmonary nodules in advance is of great clinical significance for early treatment. The purpose of this study was to establish a predictive model using radiomics and to study its value in predicting the growth of pulmonary nodules. MATERIALS AND METHODS According to the inclusion and exclusion criteria, 228 pulmonary nodules in 228 subjects were included in the study. During the one-year follow-up, 69 nodules grew larger, and 159 nodules remained stable. All the nodules were randomly divided into the training group and validation group in a proportion of 7:3. For the training data set, the t test, Chi-square test and Fisher exact test were used to analyze the sex, age and nodule location of the growth group and stable group. Two radiologists independently delineated the ROIs of the nodules to extract the radiomics characteristics using Pyradiomics. After dimension reduction by the LASSO algorithm, logistic regression analysis was performed on age and ten selected radiological features, and a prediction model was established and tested in the validation group. SVM, RF, MLP and AdaBoost models were also established, and the prediction effect was evaluated by ROC analysis. RESULTS There was a significant difference in age between the growth group and the stable group (P < 0.05), but there was no significant difference in sex or nodule location (P > 0.05). The interclass correlation coefficients between the two observers were > 0.75. After dimension reduction by the LASSO algorithm, ten radiomic features were selected, including two shape-based features, one gray-level-cooccurence-matrix (GLCM), one first-order feature, one gray-level-run-length-matrix (GLRLM), three gray-level-dependence-matrix (GLDM) and two gray-level-size-zone-matrix (GLSZM). The logistic regression model combining age and radiomics features achieved an AUC of 0.87 and an accuracy of 0.82 in the training group and an AUC of 0.82 and an accuracy of 0.84 in the verification group for the prediction of nodule growth. For nonlinear models, in the training group, the AUCs of the SVM, RF, MLP and boost models were 0.95, 1.0, 1.0 and 1.0, respectively. In the validation group, the AUCs of the SVM, RF, MLP and boost models were 0.81, 0.77, 0.81, and 0.71, respectively. CONCLUSIONS In this study, we established several machine learning models that can successfully predict the growth of pulmonary nodules within one year. The logistic regression model combining age and imaging parameters has the best accuracy and generalization. This model is very helpful for the early treatment of pulmonary nodules and has important clinical significance.
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Affiliation(s)
- Ran Yang
- Department of Radiology, Second People’s Hospital of JiuLongPo District, Chongqing, China
| | - Dongming Hui
- Department of Radiology, Second People’s Hospital of JiuLongPo District, Chongqing, China
| | - Xing Li
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Kun Wang
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Caiyong Li
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Zhichao Li
- Department of Radiology, Second People’s Hospital of JiuLongPo District, Chongqing, China
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Hoffmann-Vold AM, Distler O, Crestani B, Antoniou KM. Recent advances in the management of systemic sclerosis-associated interstitial lung disease. Curr Opin Pulm Med 2022; 28:441-447. [PMID: 35855572 DOI: 10.1097/mcp.0000000000000901] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Interstitial lung disease associated with systemic sclerosis (SSc-ILD) is a frequent organ manifestation leading to high morbidity and mortality. In 2020, the European management recommendations for SSc-ILD were published. Despite being comprehensive, several questions could not be answered or no consensus was reached. RECENT FINDINGS We highlight recent advances in the screening and early diagnosis, including surveys emphasizing that still 30-40% of all experts do not order baseline HRCTs in their SSc patients. We discuss recent advances in the assessment of disease progression, risk prediction and monitoring of SSc-ILD including novel insights in the disease course of SSc-ILD, clinical predictive factors for disease progression, the role of increasing extent of ILD on serial HRCT and radiomics, PET/CT and home spirometry as sensitive future tools to monitor SSc-ILD patients. We describe recent advances in the treatment of SSc-ILD, including novel data and trials as well as post hoc analyses of clinical trials on mycophenolate, cyclophosmphamide, tocilizumab, rituximab, riociguat and nintedanib. Lastly, we elucidate on peripheral blood cell gene expression profiling as a novel way to identify patients with a better treatment response to mycophenolate. SUMMARY In this review, we highlight recent advances in the management of SSc-ILD.
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Affiliation(s)
| | - Oliver Distler
- Department of Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Bruno Crestani
- APHP, Service de Pneumologie A, Centre de référence constitutif des Maladies Pulmonaires Rares, FHU APOLLO, Hôpital Bichat
- Université Paris Cité, Inserm, Unité 1152, laboratoire d'excellence INFLAMEX, Paris, France
| | - Katerina M Antoniou
- Laboratory of Molecular & Cellular Pneumonology, Department of Respiratory Medicine, School of Medicine, University of Crete, Andrea Kalokerinou 13, Heraklion, Greece
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The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review. J Pers Med 2022; 12:jpm12081198. [PMID: 35893293 PMCID: PMC9331823 DOI: 10.3390/jpm12081198] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/18/2022] [Accepted: 07/19/2022] [Indexed: 11/17/2022] Open
Abstract
Background: Systemic sclerosis (SSc) is a rare connective tissue disease that can affect different organs and has extremely heterogenous presentations. This complexity makes it difficult to perform an early diagnosis and a subsequent subclassification of the disease. This hinders a personalized approach in clinical practice. In this context, machine learning (ML), a branch of artificial intelligence (AI), is able to recognize relationships in data and predict outcomes. Methods: Here, we performed a narrative review concerning the application of ML in SSc to define the state of art and evaluate its role in a precision medicine context. Results: Currently, ML has been used to stratify SSc patients and identify those at high risk of severe complications. Additionally, ML may be useful in the early detection of organ involvement. Furthermore, ML might have a role in target therapy approach and in predicting drug response. Conclusion: Available evidence about the utility of ML in SSc is sparse but promising. Future improvements in this field could result in a big step toward precision medicine. Further research is needed to define ML application in clinical practice.
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Schniering J, Maciukiewicz M, Tanadini-Lang S, Maurer B. Reply to: The potential and challenges of radiomics in uncovering prognostic and molecular differences in interstitial lung disease associated with systemic sclerosis. Eur Respir J 2022; 59:13993003.00303-2022. [DOI: 10.1183/13993003.00303-2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 03/13/2022] [Indexed: 11/05/2022]
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Zhang L, Zheng J, Jin Z, Chen Q, Liu S, Zhang B. The potential and challenges of radiomics in uncovering prognostic and molecular differences in interstitial lung disease associated with systemic sclerosis. Eur Respir J 2022; 59:13993003.02792-2021. [PMID: 35604816 PMCID: PMC9203836 DOI: 10.1183/13993003.02792-2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 11/17/2021] [Indexed: 11/22/2022]
Abstract
We read with great interest the study by Schnieringet al. [1], recently published in the European Respiratory Journal. This study highlighted the potential of radiomics as a non-invasive tool for disease characterisation, prognosis stratification and lung pathophysiology evaluation in systemic sclerosis-associated interstitial lung disease (SSc-ILD). The results demonstrated that quantitative radiomic risk score (qRISSc) could accurately predict survival in SSc-ILD cohorts and was reverse translatable from human to animal ILD and correlated with fibrotic pathway activation. To the best of our knowledge, this is the first landmark study to validate the biological meaning of radiomic biomarkers through a cross-species approach, which may provide new insights into future radiomic works to break through the current bottleneck of traditional radiomics. The radiomic risk score is promising but challenging in the evaluation of prognostic and molecular differences in interstitial lung disease associated with systemic sclerosis.https://bit.ly/3oWg4UO
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