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Lim CY, Sohn B, Seong M, Kim EY, Kim ST, Won SY. Need for Transparency and Clinical Interpretability in Hemorrhagic Stroke Artificial Intelligence Research: Promoting Effective Clinical Application. Yonsei Med J 2024; 65:611-618. [PMID: 39313452 DOI: 10.3349/ymj.2024.0007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/08/2024] [Accepted: 05/20/2024] [Indexed: 09/25/2024] Open
Abstract
PURPOSE This study aimed to evaluate the quality of artificial intelligence (AI)/machine learning (ML) studies on hemorrhagic stroke using the Minimum Information for Medical AI Reporting (MINIMAR) and Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) frameworks to promote clinical application. MATERIALS AND METHODS PubMed, MEDLINE, and Embase were searched for AI/ML studies on hemorrhagic stroke. Out of the 531 articles found, 29 relevant original research articles were included. MINIMAR and MI-CLAIM scores were assigned by two experienced radiologists to assess the quality of the studies. RESULTS We analyzed 29 investigations that utilized AI/ML in the field of hemorrhagic stroke, involving a median of 224.5 patients. The majority of studies focused on diagnostic outcomes using computed tomography scans (89.7%) and were published in computer science journals (48.3%). The overall adherence rates to reporting guidelines, as assessed through the MINIMAR and MI-CLAIM frameworks, were 47.6% and 46.0%, respectively. In MINIMAR, none of the studies reported the socioeconomic status of the patients or how missing values had been addressed. In MI-CLAIM, only two studies applied model-examination techniques to improve model interpretability. Transparency and reproducibility were limited, as only 10.3% of the studies had publicly shared their code. Cohen's kappa between the two radiologists was 0.811 and 0.779 for MINIMAR and MI-CLAIM, respectively. CONCLUSION The overall reporting quality of published AI/ML studies on hemorrhagic stroke is suboptimal. It is necessary to incorporate model examination techniques for interpretability and promote code openness to enhance transparency and increase the clinical applicability of AI/ML studies.
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Affiliation(s)
- Chae Young Lim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Beomseok Sohn
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Minjung Seong
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Eung Yeop Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Sung Tae Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - So Yeon Won
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
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Wu R, Hong T, Li Y. Systematic Evaluation of Hematoma Expansion Models in Spontaneous Intracerebral Hemorrhage: A Meta-Analysis and Meta-Regression Approach. Cerebrovasc Dis 2024:1-11. [PMID: 39019017 DOI: 10.1159/000540223] [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: 04/10/2024] [Accepted: 07/02/2024] [Indexed: 07/19/2024] Open
Abstract
INTRODUCTION Accurate prediction of hematoma expansion (HE) in spontaneous intracerebral hemorrhage (sICH) is crucial for tailoring patient-specific treatments and improving outcomes. Recent advancements have yielded numerous HE risk factors and predictive models. This study aims to evaluate the characteristics and efficacy of existing HE prediction models, offering insights for performance enhancement. METHODS A comprehensive search was conducted in PubMed for observational studies and randomized controlled trials focusing on HE prediction, written in English. The prediction models were categorized based on their incorporated features and modeling methodology. Rigorous quality and bias assessments were performed. A meta-analysis of studies reporting C-statistics was executed to assess and compare the performance of current HE prediction models. Meta-regression was utilized to explore heterogeneity sources. RESULTS From 358 initial records, 22 studies were deemed eligible, encompassing traditional models, hematoma imaging feature models, and models based on artificial intelligence or radiomics. Meta-analysis of 11 studies, involving 12,087 sICH patients, revealed an aggregated C-statistic of 0.74 (95% CI: 0.69-0.78) across seven HE prediction models. Eight characteristics related to development cohorts were identified as key factors contributing to performance variability among these models. CONCLUSION The findings indicate that the current predictive capacity for HE risk remains suboptimal. Enhanced accuracy in HE prediction is vital for effectively targeting patient populations most likely to benefit from tailored treatment strategies.
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Affiliation(s)
- Ruoru Wu
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, China
| | - Tao Hong
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, China
| | - Ye Li
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, China
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Zhang H, Yang YF, Song XL, Hu HJ, Yang YY, Zhu X, Yang C. An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study. BMC Med Imaging 2024; 24:170. [PMID: 38982357 PMCID: PMC11234657 DOI: 10.1186/s12880-024-01352-y] [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: 04/28/2024] [Accepted: 07/01/2024] [Indexed: 07/11/2024] Open
Abstract
OBJECTIVES To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset. MATERIALS AND METHODS Retrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into a training cohort (n = 186, medical center 1) and an external testing cohort (n = 36, medical center 2). Following image preprocessing, the entire hematoma region was segmented by two radiologists as the volume of interest (VOI). Pyradiomics algorithm library was utilized to extract 1762 radiomics features, while a deep convolutional neural network (EfficientnetV2-L) was employed to extract 1000 deep learning features. Additionally, radiologists evaluated imaging features. Based on the three different modalities of features mentioned above, the Random Forest (RF) model was trained, resulting in three models (Radiomics Model, Radiomics-Clinical Model, and DL-Radiomics-Clinical Model). The performance and clinical utility of the models were assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curve, and Decision Curve Analysis (DCA), with AUC compared using the DeLong test. Furthermore, this study employs three methods, Shapley Additive Explanations (SHAP), Grad-CAM, and Guided Grad-CAM, to conduct a multidimensional interpretability analysis of model decisions. RESULTS The Radiomics-Clinical Model and DL-Radiomics-Clinical Model exhibited relatively good predictive performance, with an AUC of 0.86 [95% Confidence Intervals (CI): 0.71, 0.95; P < 0.01] and 0.89 (95% CI: 0.74, 0.97; P < 0.01), respectively, in the external testing cohort. CONCLUSION The multimodal explainable AI model proposed in this study can accurately predict the prognosis of ICH. Interpretability methods such as SHAP, Grad-CAM, and Guided Grad-Cam partially address the interpretability limitations of AI models. Integrating multimodal imaging features can effectively improve the performance of the model. CLINICAL RELEVANCE STATEMENT Predicting the prognosis of patients with ICH is a key objective in emergency care. Accurate and efficient prognostic tools can effectively prevent, manage, and monitor adverse events in ICH patients, maximizing treatment outcomes.
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Affiliation(s)
- Hao Zhang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning, China
| | - Yun-Feng Yang
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- Laboratory for Medical Imaging Informatics, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xue-Lin Song
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, 116027, Liaoning, China
| | - Hai-Jian Hu
- Department of Hemato-oncology, The First Hospital of Changsha, Changsha, 410005, Hunan, China
| | - Yuan-Yuan Yang
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
- Laboratory for Medical Imaging Informatics, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xia Zhu
- Department of Gynecology, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, 410028, Hunan, China
| | - Chao Yang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning, China.
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Ye H, Jiang Y, Wu Z, Ruan Y, Shen C, Xu J, Han W, Jiang R, Cai J, Liu Z. A Comparative Study of a Nomogram and Machine Learning Models in Predicting Early Hematoma Expansion in Hypertensive Intracerebral Hemorrhage. Acad Radiol 2024:S1076-6332(24)00338-6. [PMID: 38937153 DOI: 10.1016/j.acra.2024.05.035] [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: 03/23/2024] [Revised: 05/13/2024] [Accepted: 05/18/2024] [Indexed: 06/29/2024]
Abstract
RATIONALE AND OBJECTIVES Early identification for hematoma expansion can help improve patient outcomes. Presently, there are many methods to predict hematoma expansion. This study compared a variety of models to find a model suitable for clinical promotion. MATERIALS AND METHODS Non-contrast head CT images and clinical data were collected from 203 patients diagnosed with hypertensive intracerebral hemorrhage. Radiomics features were extracted from all CT images, and the dataset was randomly divided into training and validation sets (7:3 ratio) after applying the synthetic minority oversampling method. The radiomics score (Radscore) was calculated using least absolute shrinkage and selection operator (LASSO) regression, combined with selected clinical predictors, to develop a nomogram and four machine learning (ML) models: logistic regression, random forest, support vector machine, and extreme gradient boosting (XGBoost). Discrimination, calibration and clinical usefulness of the nomogram and ML models were assessed. RESULTS The nomogram and ML models were integrated with Radscore and clinical predictors. The nomogram demonstrated favorable discriminatory ability in the training set with an AUC of 0.80, which was confirmed in the validation set (AUC=0.76). Among the ML models, the XGBoost model achieved the highest AUC (training set=0.89 and validation set=0.85), surpassing that of the nomogram. The XGBoost model exhibited good clinical usefulness. CONCLUSION Both the nomogram and ML models constructed by non-contrast head CT image-based Radscore integrated with clinical predictors can predict early hematoma expansion of hypertensive intracerebral hemorrhage, and the XGBoost model had the highest prediction performance and best clinical usefulness.
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Affiliation(s)
- Haoyi Ye
- Department of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511300, China
| | - Yang Jiang
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518107, China
| | - Zhihua Wu
- Department of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511300, China
| | - Yaoqin Ruan
- Department of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511300, China
| | - Chen Shen
- Department of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511300, China
| | - Jiexiong Xu
- Department of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511300, China
| | - Wen Han
- Department of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511300, China
| | - Ruixin Jiang
- Department of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511300, China
| | - Jinhui Cai
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518107, China
| | - Zhifeng Liu
- Department of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511300, China.
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Liu Y, Zhao F, Niu E, Chen L. Machine learning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis. Neuroradiology 2024:10.1007/s00234-024-03399-8. [PMID: 38862772 DOI: 10.1007/s00234-024-03399-8] [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: 12/04/2023] [Accepted: 06/06/2024] [Indexed: 06/13/2024]
Abstract
PURPOSE Early identification of hematoma enlargement and persistent hematoma expansion (HE) in patients with cerebral hemorrhage is increasingly crucial for determining clinical treatments. However, due to the lack of clinically effective tools, radiomics has been gradually introduced into the early identification of hematoma enlargement. Though, radiomics has limited predictive accuracy due to variations in procedures. Therefore, we conducted a systematic review and meta-analysis to explore the value of radiomics in the early detection of HE in patients with cerebral hemorrhage. METHODS Eligible studies were systematically searched in PubMed, Embase, Cochrane and Web of Science from inception to April 8, 2024. English articles are considered eligible. The radiomics quality scoring (RQS) tool was used to evaluate included studies. RESULTS A total of 34 studies were identified with sample sizes ranging from 108 to 3016. Eleven types of models were involved, and the types of modeling contained mainly clinical, radiomic, and radiomic plus clinical features. The radiomics models seem to have better performance (0.77 and 0.73 C-index in the training cohort and validation cohort, respectively) than the clinical models (0.69 C-index in the training cohort and 0.70 C-index in the validation cohort) in discriminating HE. However, the C-index was the highest for the combined model in both the training (0.82) and validation (0.79) cohorts. CONCLUSIONS Machine learning based on radiomic plus clinical features has the best predictive performance for HE, followed by machine learning based on radiomic features, and can be used as a potential tool to assist clinicians in early judgment.
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Affiliation(s)
- Yihua Liu
- Department of General medical subjects, Ezhou Central Hospital, Ezhou Hubei, 436000, China
| | - Fengfeng Zhao
- School of Clinical Medicine, Weifang Medical University, Weifang, 261000, China
| | - Enjing Niu
- Department of Adult Internal Medicine, Qingdao Women's and Children's Hospital, No. 217 Liaoyang West Street, Shibei District, Qingdao, 266000, Shandong, China
| | - Liang Chen
- Department of Adult Internal Medicine, Qingdao Women's and Children's Hospital, No. 217 Liaoyang West Street, Shibei District, Qingdao, 266000, Shandong, China.
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Ai M, Zhang H, Feng J, Chen H, Liu D, Li C, Yu F, Li C. Research advances in predicting the expansion of hypertensive intracerebral hemorrhage based on CT images: an overview. PeerJ 2024; 12:e17556. [PMID: 38860211 PMCID: PMC11164062 DOI: 10.7717/peerj.17556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/21/2024] [Indexed: 06/12/2024] Open
Abstract
Hematoma expansion (HE) is an important risk factor for death or poor prognosis in patients with hypertensive intracerebral hemorrhage (HICH). Accurately predicting the risk of HE in patients with HICH is of great clinical significance for timely intervention and improving patient prognosis. Many imaging signs reported in literatures showed the important clinical value for predicting HE. In recent years, the development of radiomics and artificial intelligence has provided new methods for HE prediction with high accuracy. Therefore, this article reviews the latest research progress in CT imaging, radiomics, and artificial intelligence of HE, in order to help identify high-risk patients for HE in clinical practice.
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Affiliation(s)
- Min Ai
- Department of Anesthesiology, Nanan District People’s Hospital of Chongqing, Chongqing, China
| | - Hanghang Zhang
- Department of Breast and Thyroid Surgery, Chongqing Bishan District Maternal and Child Health Care Hospital, Chongqing, China
| | - Junbang Feng
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Hongying Chen
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Di Liu
- Department of Anesthesiology, Nanan District People’s Hospital of Chongqing, Chongqing, China
| | - Chang Li
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Fei Yu
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
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Chen Y, Gao R, Jing D, Shi L, Kuang F, Jing R. Classification and prediction of chemoradiotherapy response and survival from esophageal carcinoma histopathology images. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 312:124030. [PMID: 38368818 DOI: 10.1016/j.saa.2024.124030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 01/27/2024] [Accepted: 02/08/2024] [Indexed: 02/20/2024]
Abstract
Whole slide imaging (WSI) of Hematoxylin and Eosin-stained biopsy specimens has been used to predict chemoradiotherapy (CRT) response and overall survival (OS) of esophageal squamous cell carcinoma (ESCC) patients. This retrospective study collected 279 specimens in 89 non-surgical ESCC patients through endoscopic biopsy between January 2010 and January 2019. These patients were divided into a CRT response group (CR + PR group) and a CRT non-response group (SD + PD group). The WSIs have segmented approximately 1,206,000 non-overlapping patches. Two experienced pathologists manually delineated the eight types of tissues on 32 WSIs, including esophagus tumor cell (TUM), cancer-associated stroma (CAS), normal epithelium layer (NEL), smooth muscle (MUS), lymphocytes (LYM), Red cells (RED), debris (DEB), uneven areas (UNE). The chemoradiotherapy response prediction models were built using maximum relevance-minimum redundancy (MRMR) feature selection and least absolute shrinkage and selection operator (LASSO) regression. However, pathological features with p < 0.1 were selected and integrated to be further screened using a LASSO Cox regression model to build a multivariate Cox proportional hazards model for predicting the OS. The testing accuracy of the tissue classification model was 91.3 %. The pathological model created using two CAS in-depth features and eight TUM in-depth features performed best for the prediction of treatment response and achieved an AUC of 0.744. For the prediction of OS, the testing AUC of this model at one year and three years were 0.675 and 0.870, respectively. The TUM model showed the highest AUC at one year (0.712). With its high accuracy rate, the deep learning model has the potential to transform from bench to bedside in clinical practice, improve patient's quality of life, and prolong the OS rate.
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Affiliation(s)
- Yu Chen
- Department of Oncology, Xiangya Hospital National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Ruihuan Gao
- Department of Oncology, Xiangya Hospital National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Di Jing
- Department of Oncology, Xiangya Hospital National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Liting Shi
- Department of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian 271016, China
| | - Feng Kuang
- Department of Cardiovascular Surgery, The First Affiliated Hospital of Xiamen University, Teaching Hospital of Fujian Medical University, Xiamen, China
| | - Ran Jing
- Department of Cardiovascular Medicine, Xiangya Hospital National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, 410008 Changsha, China.
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Feng C, Ding Z, Lao Q, Zhen T, Ruan M, Han J, He L, Shen Q. Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography. Eur Radiol 2024; 34:2908-2920. [PMID: 37938384 DOI: 10.1007/s00330-023-10410-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/20/2023] [Accepted: 09/21/2023] [Indexed: 11/09/2023]
Abstract
OBJECTIVES Aimed to develop a nomogram model based on deep learning features and radiomics features for the prediction of early hematoma expansion. METHODS A total of 561 cases of spontaneous intracerebral hemorrhage (sICH) with baseline Noncontrast Computed Tomography (NCCT) were included. The metrics of hematoma detection were evaluated by Intersection over Union (IoU), Dice coefficient (Dice), and accuracy (ACC). The semantic features of sICH were judged by EfficientNet-B0 classification model. Radiomics analysis was performed based on the region of interest which was automatically segmented by deep learning. A combined model was constructed in order to predict the early expansion of hematoma using multivariate binary logistic regression, and a nomogram and calibration curve were drawn to verify its predictive efficacy by ROC analysis. RESULTS The accuracy of hematoma detection by segmentation model was 98.2% for IoU greater than 0.6 and 76.5% for IoU greater than 0.8 in the training cohort. In the validation cohort, the accuracy was 86.6% for IoU greater than 0.6 and 70.0% for IoU greater than 0.8. The AUCs of the deep learning model to judge semantic features were 0.95 to 0.99 in the training cohort, while in the validation cohort, the values were 0.71 to 0.83. The deep learning radiomics model showed a better performance with higher AUC in training cohort (0.87), internal validation cohort (0.83), and external validation cohort (0.82) than either semantic features or Radscore. CONCLUSION The combined model based on deep learning features and radiomics features has certain efficiency for judging the risk grade of hematoma. CLINICAL RELEVANCE STATEMENT Our study revealed that the deep learning model can significantly improve the work efficiency of segmentation and semantic feature classification of spontaneous intracerebral hemorrhage. The combined model has a good prediction efficiency for early hematoma expansion. KEY POINTS • We employ a deep learning algorithm to perform segmentation and semantic feature classification of spontaneous intracerebral hemorrhage and construct a prediction model for early hematoma expansion. • The deep learning radiomics model shows a favorable performance for the prediction of early hematoma expansion. • The combined model holds the potential to be used as a tool in judging the risk grade of hematoma.
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Affiliation(s)
- Changfeng Feng
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Hangzhou, Zhejiang, China
- Department of Radiology, Hangzhou Children's Hospital, Hangzhou, Zhejiang, China
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Hangzhou, Zhejiang, China
| | - Qun Lao
- Department of Radiology, Hangzhou Children's Hospital, Hangzhou, Zhejiang, China
| | - Tao Zhen
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Hangzhou, Zhejiang, China
| | - Mei Ruan
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Hangzhou, Zhejiang, China
| | - Jing Han
- Department of Radiology, Zhejiang Kangjing Hospital, Hangzhou, Zhejiang, China
| | - Linyang He
- Hangzhou Jianpei Technology Company Ltd, Xiaoshan District, Hangzhou, Zhejiang, China
| | - Qijun Shen
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Hangzhou, Zhejiang, China.
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Pei L, Fang T, Xu L, Ni C. A Radiomics Model Based on CT Images Combined with Multiple Machine Learning Models to Predict the Prognosis of Spontaneous Intracerebral Hemorrhage. World Neurosurg 2024; 181:e856-e866. [PMID: 37931880 DOI: 10.1016/j.wneu.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 11/01/2023] [Indexed: 11/08/2023]
Abstract
OBJECTIVE We aimed to construct 3 predictive models, including a clinical model, a radiomics model, and a combined model, to forecast the discharge prognosis of patients with intracerebral hemorrhage on admission. METHODS A retrospective study was conducted, involving a total of 161 patients with intracerebral hemorrhage (ICH). At a ratio of 7:3, 115 of these patients were assigned to the training cohort, and 46 of these patients were assigned to the validation cohort. To produce the radionics signature and pick the features to use in its construction, the least absolute shrinkage and selection operator (LASSO) regression was applied. Five machine models were applied, and the optimal model was chosen to construct the radionics model. A clinical model was constructed using univariate and stepwise analysis to identify independent risk variables for poor outcomes at discharge. A predictive combined model nomogram was generated by integrating the clinical model and radiomics model. The performance of the nomogram was assessed in the training cohort and validated in the validation cohort. Analyses of the receiver operating characteristic curve (ROC), the calibration curve, and the decision curve were performed to assess the performance of the combined model. RESULTS This study encompassed a cohort of 161 individuals diagnosed with intracerebral hemorrhage (ICH), consisting of 110 males and 51 females. Utilizing the modified Rankin Scale (mRS) at discharge, the analysis revealed that 89 patients (55.3%) had a good prognosis, while 72 patients (44.7%) had a poor prognosis. Only 8 out of 1130 radiomics features were selected and used in conjunction with the LR algorithm to develop the radiomics model. Sex, IVH, GCS score, and ICH volume were determined to be independent predictors of poor outcomes at the time of discharge. The AUC values of the combined model, radiomics model, and clinical model were 0.8583, 0.8364, and 0.7579 in the training cohort, and 0.9153, 0.8692, and 0.7114 in the validation cohort, respectively. The combined model nomogram exhibited good calibration and clinical benefit in both the training and validation cohorts. The decision curve analysis (DCA) displays that the combined model obtained the highest net benefit compared to the radiomics model and clinics model in the training cohort. CONCLUSIONS The combined model demonstrates reliability and efficacy in predicting the discharge prognosis of ICH, enabling physicians to perform individualized risk assessments, and make optimal choices about patients with ICH.
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Affiliation(s)
- Lei Pei
- Department of Radiology, Quzhou People's Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, China
| | - Tao Fang
- Department of Radiology, Quzhou People's Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, China
| | - Liang Xu
- Department of Radiology, Quzhou People's Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, China
| | - Chenfeng Ni
- Department of Radiology, Quzhou People's Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, China.
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Ducroux C, Nehme A, Rioux B, Panzini MA, Fahed R, Gioia LC, Létourneau-Guillon L. NCCT Markers of Intracerebral Hemorrhage Expansion Using Revised Criteria: An External Validation of Their Predictive Accuracy. AJNR Am J Neuroradiol 2023; 44:658-664. [PMID: 37169542 PMCID: PMC10249705 DOI: 10.3174/ajnr.a7871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 04/06/2023] [Indexed: 05/13/2023]
Abstract
BACKGROUND AND PURPOSE Several NCCT expansion markers have been proposed to improve the prediction of hematoma expansion. We retrospectively evaluated the predictive accuracy of 9 expansion markers. MATERIALS AND METHODS Patients admitted for intracerebral hemorrhage within 24 hours of last seen well were retrospectively included from April 2016 to April 2020. The primary outcome was revised hematoma expansion, defined as any of a ≥6-mL or ≥33% increase in intracerebral hemorrhage volume, a ≥ 1-mL increase in intraventricular hemorrhage volume, or de novo intraventricular hemorrhage. We assessed the predictive accuracy of expansion markers and determined their association with revised hematoma expansion. RESULTS We included 124 patients, of whom 51 (41%) developed revised hematoma expansion. The sensitivity of each marker for the prediction of revised hematoma expansion ranged from 4% to 78%; the specificity, 37%-97%; the positive likelihood ratio, 0.41-7.16; and the negative likelihood ratio, 0.49-1.06. By means of univariable logistic regressions, 5 markers were significantly associated with revised hematoma expansion: black hole (OR = 8.66; 95% CI, 2.15-58.14; P = .007), hypodensity (OR = 3.18; 95% CI, 1.49-6.93; P = .003), blend (OR = 2.90; 95% CI, 1.08-8.38; P = .04), satellite (OR = 2.84; 95% CI, 1.29-6.61; P = .01), and Barras shape (OR = 2.41, 95% CI; 1.17-5.10; P = .02). In multivariable models, only the black hole marker remained independently associated with revised hematoma expansion (adjusted OR = 5.62; 95% CI, 1.23-40.23; P = .03). CONCLUSIONS No single NCCT expansion marker had both high sensitivity and specificity for the prediction of revised hematoma expansion. Improved image-based analysis is needed to tackle limitations associated with current NCCT-based expansion markers.
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Affiliation(s)
- C Ducroux
- From the Département des Neurosciences (C.D., A.N., B.R., M.-A.P., L.C.G.), Faculté de Médecine
- Département de Médicine (Neurologie) (C.D., A.N., B.R., M.-A.P., L.C.G.)
- Neurovascular Health Program (C.D., L.C.G.)
- Department of Medicine (C.D., R.F.), Division of Neurology, The Ottawa Hospital Research Institute and University of Ottawa, Ottawa, Ontario, Canada
| | - A Nehme
- From the Département des Neurosciences (C.D., A.N., B.R., M.-A.P., L.C.G.), Faculté de Médecine
- Département de Médicine (Neurologie) (C.D., A.N., B.R., M.-A.P., L.C.G.)
| | - B Rioux
- From the Département des Neurosciences (C.D., A.N., B.R., M.-A.P., L.C.G.), Faculté de Médecine
- Département de Médicine (Neurologie) (C.D., A.N., B.R., M.-A.P., L.C.G.)
- Centre for Clinical Brain Sciences (B.R.), University of Edinburgh, Edinburgh, UK
| | - M-A Panzini
- From the Département des Neurosciences (C.D., A.N., B.R., M.-A.P., L.C.G.), Faculté de Médecine
- Département de Médicine (Neurologie) (C.D., A.N., B.R., M.-A.P., L.C.G.)
| | - R Fahed
- Department of Medicine (C.D., R.F.), Division of Neurology, The Ottawa Hospital Research Institute and University of Ottawa, Ottawa, Ontario, Canada
| | - L C Gioia
- From the Département des Neurosciences (C.D., A.N., B.R., M.-A.P., L.C.G.), Faculté de Médecine
- Département de Médicine (Neurologie) (C.D., A.N., B.R., M.-A.P., L.C.G.)
- Neurovascular Health Program (C.D., L.C.G.)
| | - L Létourneau-Guillon
- Département de Radiologie (L.L.-G.), Radio-oncologie et Médecine Nucléaire, Faculté de Médicine, Université de Montréal, Montréal, Quebec, Canada
- Département de Radiologie (L.L.-G.), Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada
- Imaging and Engineering Axis (L.L.-G.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada
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Sohn B, Won SY. Quality assessment of stroke radiomics studies: Promoting clinical application. Eur J Radiol 2023; 161:110752. [PMID: 36878154 DOI: 10.1016/j.ejrad.2023.110752] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 02/13/2023] [Accepted: 02/20/2023] [Indexed: 03/06/2023]
Abstract
PURPOSE To evaluate the quality of radiomics studies on stroke using a radiomics quality score (RQS), Minimum Information for Medial AI reporting (MINIMAR) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to promote clinical application. METHODS PubMed MEDLINE and Embase were searched to identify radiomics studies on stroke. Of 464 articles, 52 relevant original research articles were included. The RQS, MINIMAR and TRIPOD were scored to evaluate the quality of the studies by neuroradiologists. RESULTS Only four studies (7.7 %) performed external validation. The mean RQS was 3.2 of 36 (8.9 %), and the basic adherence rate was 24.9 %. The adherence rate was low for conducting phantom study (1.9 %), stating comparison to 'gold standard' (1.9 %), offering potential clinical utility (13.5 %) and performing cost-effectiveness analysis (1.9 %). None of the studies performed a test-retest, stated biologic correlation, conducted prospective studies, or opened codes and data to the public, resulting in low RQS. The total MINIMAR adherence rate was 47.4 %. The overall adherence rate for TRIPOD was 54.6 %, with low scores for reporting the title (2.0 %), key elements of the study setting (6.1 %), and explaining the sample size (2.0 %). CONCLUSIONS The overall radiomics reporting quality and reporting of published radiomics studies on stoke was suboptimal. More thorough validation and open data are needed to increase clinical applicability of radiomics studies.
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Affiliation(s)
- Beomseok Sohn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - So Yeon Won
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
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A Nomogram Based on CT Radiomics and Clinical Risk Factors for Prediction of Prognosis of Hypertensive Intracerebral Hemorrhage. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9751988. [DOI: 10.1155/2022/9751988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022]
Abstract
Purpose. To develop and validate a clinical-radiomics nomogram based on clinical risk factors and CT radiomics feature to predict hypertensive intracerebral hemorrhage (HICH) prognosis. Methods. A total of 195 patients with HICH treated in our hospital from January 2018 to January 2022 were retrospectively enrolled and randomly divided into two cohorts for training (n = 138) and validation (n = 57) according to the ratio of 7 : 3. All CT radiomics features were extracted from intrahematomal, perihematomal, and combined intra- and perihematomal regions by using free open-source software called 3D slicer. The least absolute shrinkage and selection operator method was used to select the optimal radiomics features, and the radiomics score (Rad-score) was calculated. The relationship between Rad-score, clinical risk factors, and the HICH prognosis was analyzed by univariate and multivariate logistic regression analyses, and the clinical-radiomics nomogram was built. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the clinical-radiomics nomogram in predicting the prognosis of HICH. Results. A total of 1702 radiomics features were extracted from the CT images of each patient for analysis. By univariate and stepwise multivariate logistic regression analyses, age, sex, RBC, serum glucose, D-dimer level, hematoma volume, and midline shift were clinical risk factors for the prognosis of HICH. Rad-score and clinical risk factors developed the clinical-radiomics nomogram. The nomogram showed the highest predictive efficiency in the training cohort (AUC = 0.95, 95% confidence interval (CI), 0.92 to 0.98) and the validation cohort (AUC = 0.90, 95% CI, 0.82 to 0.98). The calibration curve indicated that the clinical-radiomics nomogram had good calibration. DCA showed that the nomogram had high applicability in clinical practice. Conclusions. The clinical-radiomics nomogram incorporated with the radiomics features and clinical risk factors has good potential in predicting the prognosis of HICH.
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Early Prediction of Cerebral Computed Tomography under Intelligent Segmentation Algorithm Combined with Serological Indexes for Hematoma Enlargement after Intracerebral Hemorrhage. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:5863082. [PMID: 35747135 PMCID: PMC9213170 DOI: 10.1155/2022/5863082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 05/08/2022] [Accepted: 05/11/2022] [Indexed: 12/04/2022]
Abstract
The aim of this study was to explore the application value of brain computed tomography (CT) images under intelligent segmentation algorithm and serological indexes in the early prediction of hematoma enlargement in patients with intracerebral hemorrhage (ICH). Fuzzy C-means (FCM) intelligence segmentation algorithm was introduced, and 150 patients with early ICH were selected as the research objects. Patient cerebral CT images were intelligently segmented to assess the diagnostic value of this algorithm. According to different hematoma volumes during CT examination, patients were divided into observation group (hematoma enlargement occurred, n = 48) and control group (no hematoma enlargement occurred, n = 102). The predicative value of hematoma enlargement after ICH was investigated by assessing CT image quality and measuring intracerebral edema, hematoma volume, and serological indicators of the patients of the two groups. The results demonstrated that the sensitivity, specificity, and accuracy of CT images processed by intelligence segmentation algorithm amounted to 0.894, 0.898, and 0.930, respectively. Besides, early edema enlargement and hematoma of patients in the observation group were more significant than those of patients in the control group. Relative edema volume was 0.912, which was apparently lower than that in the control group (1.017) (P < 0.05). In terms of CT signs of ICH patients, the incidence of blend sign, low density sign, and stroke of the observation group was evidently higher than those of the control group (P < 0.05). Besides, absolute lymphocyte count (ALC) and hemoglobin (HGB) concentration of the patients in the observation group were 6.23 × 109/L and 6.29 × 109/L, respectively, both of which were higher than those of the control group (6.08 × 109/L and 4.25 × 109/L). Neutrophil to lymphocyte ratio (NLR) was 0.99 × 109/L, which was apparently lower than that in the control group (1.43 × 109/L) (P < 0.05). To sum up, cerebral CT images processed by FCM algorithm showed good diagnostic effect on ICH and high clinical values in the early prediction of hematoma among ICH patients.
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