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Yang F, Chen R, Yang Y, Yang Z, Su Y, Ji M, Pang Z, Wang D. Computed tomography-based radiomics model to predict adverse clinical outcomes in acute pulmonary embolism. J Thromb Thrombolysis 2024; 57:428-436. [PMID: 38280936 DOI: 10.1007/s11239-023-02929-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/18/2023] [Indexed: 01/29/2024]
Abstract
This preliminary study investigated the feasibility of a combined model constructed using radiomic features based on computed tomography (CT) and clinical features to predict adverse clinical outcomes in acute pulmonary embolism (APE). Currently, there is no widely recognized predictive model. Patients with confirmed APE who underwent CT pulmonary angiography were retrospectively categorized into good and poor prognosis groups. Seventy-four patients were randomized into a training (n = 51) or validation (n = 23) cohort. Feature extraction was performed using 3D-Slicer software. The least absolute shrinkage and selection operator regression was used to identify the optimal radiomics features and calculate the radiomics scores; subsequently, the radiomics model was developed. A combined predictive model was constructed based on radiomics scores and selected clinical features. The predictive efficacy of the three models (radiomics, clinical and combined) was assessed by plotting receiver operating characteristic curves. Furthermore, the calibration curves were graphed and the decision curve analysis was performed. Four radiomic features were screened to calculate the radiomic score. Right ventricular to left ventricular ratio (RV/LV) ≥ 1.0 and radiomics score were independent risk factors for adverse clinical outcomes. In the training and validation cohorts, the areas under the curve (AUCs) for the RV/LV ≥ 1.0 (clinical) and radiomics score prediction models were 0.778 and 0.833 and 0.907 and 0.817, respectively. The AUCs for the combined model of RV/LV ≥ 1.0 and radiomics score were 0.925 and 0.917, respectively. The combined and radiomics models had high clinical assessment efficacy for predicting adverse clinical outcomes in APE, demonstrating the clinical utility of both models. Calibration curves exhibited a strong level of consistency between the predictive and observed probabilities of poor and good prognoses in the combined model. The combined model of RV/LV ≥ 1.0 and radiomics score based on CT could accurately and non-invasively predict adverse clinical outcomes in patients with APE.
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Affiliation(s)
- Fei Yang
- Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Rong Chen
- Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Yue Yang
- Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Zhixiang Yang
- Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Yaying Su
- Department of Nuclear Medicine, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Mengmeng Ji
- Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Zhiying Pang
- Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Dawei Wang
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Hebei North University, 12 Changqing Road, Zhangjiakou, 075000, Hebei, China.
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2
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Beaudoin AM, Ho JK, Lam A, Thijs V. Radiomics Studies on Ischemic Stroke and Carotid Atherosclerotic Disease: A Reporting Quality Assessment. Can Assoc Radiol J 2024:8465371241234545. [PMID: 38420881 DOI: 10.1177/08465371241234545] [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: 03/02/2024] Open
Abstract
Objective: To assess the reporting quality of radiomics studies on ischemic stroke, intracranial and carotid atherosclerotic disease using the Image Biomarker Standardization Initiative (IBSI) reporting guidelines with the aim of finding avenues of improvement for future publications. Method: PubMed database was searched to identify relevant radiomics studies. Of 560 articles, 41 original research articles were included in this analysis. Based on IBSI radiomics reporting guidelines, checklists for CT-based and MRI-based studies were created to allow a structured and comprehensive evaluation of each study's adherence to these guidelines. Results: The main topics covered by the included radiomics studies were ischemic stroke, intracranial artery disease, and carotid atherosclerotic disease. The reporting checklist median score was 17/40 for the 20 CT-based radiomics studies and 22.5/50 for the 20 MRI-based studies. Basic items like imaging modality, region of interest, and image biomarker set utilized were included in all studies. However, details regarding image acquisition and reconstruction, post-acquisition image processing, and image biomarkers computation were inconsistently detailed across studies. Conclusion: The overall reporting quality of the included radiomics studies was suboptimal. These findings underscore a pressing need for improved reporting practices in radiomics research, to ensure validation and reproducibility of results. Our study provides insights into current reporting standards and highlights specific areas where adherence to IBSI guidelines could be significantly improved.
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Affiliation(s)
- Ann-Marie Beaudoin
- Université de Sherbrooke, Sherbrooke, QC, Canada
- The Florey, Heidelberg, VIC, Australia
| | - Jan Kee Ho
- The Florey, Heidelberg, VIC, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC, Australia
| | | | - Vincent Thijs
- The Florey, Heidelberg, VIC, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC, Australia
- Department of Medicine, University of Melbourne, Heidelberg, VIC, Australia
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3
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Wang Y, Luo Z, Zhou Z, Zhong Y, Zhang R, Shen X, Huang L, He W, Lin J, Fang J, Huang Q, Wang H, Zhang Z, Mao R, Feng ST, Li X, Huang B, Li Z, Zhang J, Chen Z. CT-based radiomics signature of visceral adipose tissue and bowel lesions for identifying patients with Crohn's disease resistant to infliximab. Insights Imaging 2024; 15:28. [PMID: 38289416 PMCID: PMC10828370 DOI: 10.1186/s13244-023-01581-9] [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: 09/01/2023] [Accepted: 11/25/2023] [Indexed: 02/02/2024] Open
Abstract
PURPOSE To develop a CT-based radiomics model combining with VAT and bowel features to improve the predictive efficacy of IFX therapy on the basis of bowel model. METHODS This retrospective study included 231 CD patients (training cohort, n = 112; internal validation cohort, n = 48; external validation cohort, n = 71) from two tertiary centers. Machine-learning VAT model and bowel model were developed separately to identify CD patients with primary nonresponse to IFX. A comprehensive model incorporating VAT and bowel radiomics features was further established to verify whether CT features extracted from VAT would improve the predictive efficacy of bowel model. Area under the curve (AUC) and decision curve analysis were used to compare the prediction performance. Clinical utility was assessed by integrated differentiation improvement (IDI). RESULTS VAT model and bowel model exhibited comparable performance for identifying patients with primary nonresponse in both internal (AUC: VAT model vs bowel model, 0.737 (95% CI, 0.590-0.854) vs. 0.832 (95% CI, 0.750-0.896)) and external validation cohort [AUC: VAT model vs. bowel model, 0.714 (95% CI, 0.595-0.815) vs. 0.799 (95% CI, 0.687-0.885)), exhibiting a relatively good net benefit. The comprehensive model incorporating VAT into bowel model yielded a satisfactory predictive efficacy in both internal (AUC, 0.840 (95% CI, 0.706-0.930)) and external validation cohort (AUC, 0.833 (95% CI, 0.726-0.911)), significantly better than bowel alone (IDI = 4.2% and 3.7% in internal and external validation cohorts, both p < 0.05). CONCLUSION VAT has an effect on IFX treatment response. It improves the performance for identification of CD patients at high risk of primary nonresponse to IFX therapy with selected features from RM. CRITICAL RELEVANCE STATEMENT Our radiomics model (RM) for VAT-bowel analysis captured the pathophysiological changes occurring in VAT and whole bowel lesion, which could help to identify CD patients who would not response to infliximab at the beginning of therapy. KEY POINTS • Radiomics signatures with VAT and bowel alone or in combination predicting infliximab efficacy. • VAT features contribute to the prediction of IFX treatment efficacy. • Comprehensive model improved the performance compared with the bowel model alone.
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Affiliation(s)
- Yangdi Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Zixin Luo
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, People's Republic of China
| | - Zhengran Zhou
- Zhongshan School of Medicine, Sun Yat-Sen University, 74 Zhongshan II Road, Guangzhou, Guangdong, People's Republic of China
| | - Yingkui Zhong
- Department of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Yuancun Er Heng Road, No. 26, Guangzhou, Guangdong, People's Republic of China
| | - Ruonan Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Xiaodi Shen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Lili Huang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Weitao He
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Jinjiang Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Jiayu Fang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Qiapeng Huang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, People's Republic of China
| | - Haipeng Wang
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, People's Republic of China
| | - Zhuya Zhang
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, People's Republic of China
| | - Ren Mao
- Department of Gastroenterology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, People's Republic of China
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Xuehua Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, 510080, People's Republic of China
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, People's Republic of China
| | - Zhoulei Li
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, 510080, People's Republic of China.
| | - Jian Zhang
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, Guangdong, People's Republic of China.
- Health Science Center, School of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong, People's Republic of China.
| | - Zhihui Chen
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan II Road, Guangzhou, Guangdong, People's Republic of China.
- Guangxi Hospital Division of The First Affiliated Hospital, Sun Yat-sen University, Nanning, Guangxi, People's Republic of China.
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Samaniego EA, Boltze J, Lyden PD, Hill MD, Campbell BCV, Silva GS, Sheth KN, Fisher M, Hillis AE, Nguyen TN, Carone D, Favilla CG, Deljkich E, Albers GW, Heit JJ, Lansberg MG. Priorities for Advancements in Neuroimaging in the Diagnostic Workup of Acute Stroke. Stroke 2023; 54:3190-3201. [PMID: 37942645 PMCID: PMC10841844 DOI: 10.1161/strokeaha.123.044985] [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: 09/03/2023] [Accepted: 10/03/2023] [Indexed: 11/10/2023]
Abstract
STAIR XII (12th Stroke Treatment Academy Industry Roundtable) included a workshop to discuss the priorities for advancements in neuroimaging in the diagnostic workup of acute ischemic stroke. The workshop brought together representatives from academia, industry, and government. The participants identified 10 critical areas of priority for the advancement of acute stroke imaging. These include enhancing imaging capabilities at primary and comprehensive stroke centers, refining the analysis and characterization of clots, establishing imaging criteria that can predict the response to reperfusion, optimizing the Thrombolysis in Cerebral Infarction scale, predicting first-pass reperfusion outcomes, improving imaging techniques post-reperfusion therapy, detecting early ischemia on noncontrast computed tomography, enhancing cone beam computed tomography, advancing mobile stroke units, and leveraging high-resolution vessel wall imaging to gain deeper insights into pathology. Imaging in acute ischemic stroke treatment has advanced significantly, but important challenges remain that need to be addressed. A combined effort from academic investigators, industry, and regulators is needed to improve imaging technologies and, ultimately, patient outcomes.
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Affiliation(s)
- Edgar A. Samaniego
- Department of Neurology, Radiology and Neurosurgery, University of Iowa Carver College of Medicine, Iowa City, Iowa, United States
| | - Johannes Boltze
- School of Life Sciences, The University of Warwick, Coventry, United Kingdom
| | - Patrick D. Lyden
- Zilkha Neurogenetic Institute of the Keck School of Medicine at USC, Los Angeles, California, United States
| | - Michael D. Hill
- Department of Clinical Neuroscience & Hotchkiss Brain Institute, University of Calgary & Foothills Medical Centre, Calgary, Canada
| | - Bruce CV Campbell
- Department of Medicine and Neurology, Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria, Australia
| | - Gisele Sampaio Silva
- Department of Neurology and Neurosurgery, Federal University of São Paulo, São Paulo, Brazil
| | - Kevin N Sheth
- Department of Neurology, Division of Neurocritical Care and Emergency Neurology, Yale School of Medicine, New Haven, United States
| | - Marc Fisher
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States
| | - Argye E. Hillis
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, United Stated
| | - Thanh N. Nguyen
- Department of Neurology, Boston Medical Center, Massachusetts, United States
| | - Davide Carone
- Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Christopher G. Favilla
- Department of Neurology, University of Pennsylvania Philadelphia, Pennsylvania, Unites States
| | | | - Gregory W. Albers
- Department of Neurology, Stanford University, Stanford, California, United States
| | - Jeremy J. Heit
- Department of Radiology and Neurosurgery, Stanford University, Stanford, California, United States
| | - Maarten G Lansberg
- Department of Neurology, Stanford University, Stanford, California, United States
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5
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van Voorst H, Bruggeman AAE, Yang W, Andriessen J, Welberg E, Dutra BG, Konduri PR, Arrarte Terreros N, Hoving JW, Tolhuisen ML, Kappelhof M, Brouwer J, Boodt N, van Kranendonk KR, Koopman MS, Hund HM, Krietemeijer M, van Zwam WH, van Beusekom HMM, van der Lugt A, Emmer BJ, Marquering HA, Roos YBWEM, Caan MWA, Majoie CBLM. Thrombus radiomics in patients with anterior circulation acute ischemic stroke undergoing endovascular treatment. J Neurointerv Surg 2023; 15:e79-e85. [PMID: 35882552 DOI: 10.1136/jnis-2022-019085] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 07/10/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND Thrombus radiomics (TR) describe complex shape and textural thrombus imaging features. We aimed to study the relationship of TR extracted from non-contrast CT with procedural and functional outcome in endovascular-treated patients with acute ischemic stroke. METHODS Thrombi were segmented on thin-slice non-contrast CT (≤1 mm) from 699 patients included in the MR CLEAN Registry. In a pilot study, we selected 51 TR with consistent values across two raters' segmentations (ICC >0.75). Random forest models using TR in addition or as a substitute to baseline clinical variables (CV) and manual thrombus measurements (MTM) were trained with 499 patients and evaluated on 200 patients for predicting successful reperfusion (extended Thrombolysis in Cerebral Ischemia (eTICI) ≥2B), first attempt reperfusion, reperfusion within three attempts, and functional independence (modified Rankin Scale (mRS) ≤2). Three texture and shape features were selected based on feature importance and related to eTICI ≥2B, number of attempts to eTICI ≥2B, and 90-day mRS with ordinal logistic regression. RESULTS Random forest models using TR, CV or MTM had comparable predictive performance. Thrombus texture (inverse difference moment normalized) was independently associated with reperfusion (adjusted common OR (acOR) 0.85, 95% CI 0.72 to 0.99). Thrombus volume and texture were also independently associated with the number of attempts to successful reperfusion (acOR 1.36, 95% CI 1.03 to 1.88 and acOR 1.24, 95% CI 1.04 to 1.49). CONCLUSIONS TR describing thrombus volume and texture were associated with more attempts to successful reperfusion. Compared with models using CV and MTM, TR had no added value for predicting procedural and functional outcome.
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Affiliation(s)
- Henk van Voorst
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Agnetha A E Bruggeman
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Wenjin Yang
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
- Department of Neurosurgery, Shanghai Pudong New Area People's Hospital, Shanghai, China
| | - Jurr Andriessen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Elise Welberg
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Bruna G Dutra
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Praneeta R Konduri
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Nerea Arrarte Terreros
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Jan W Hoving
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Manon L Tolhuisen
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Manon Kappelhof
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Josje Brouwer
- Department of Neurology, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Nikki Boodt
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Katinka R van Kranendonk
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Miou S Koopman
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Hajo M Hund
- Department of Radiology and Nuclear Medicine, Haaglanden Medical Center Bronovo, Den Haag, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Menno Krietemeijer
- Department of Radiology and Nuclear Medicine, Catharina Hospital, Eindhoven, The Netherlands
| | - Wim H van Zwam
- Department of Radiology, Maastricht University Medical Center, Maastricht, The Netherlands
| | | | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Bart J Emmer
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Henk A Marquering
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Yvo B W E M Roos
- Department of Neurology, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Matthan W A Caan
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Charles B L M Majoie
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
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Ye W, Chen X, Li P, Tao Y, Wang Z, Gao C, Cheng J, Li F, Yi D, Wei Z, Yi D, Wu Y. OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features. Front Neurol 2023; 14:1158555. [PMID: 37416306 PMCID: PMC10321134 DOI: 10.3389/fneur.2023.1158555] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 05/22/2023] [Indexed: 07/08/2023] Open
Abstract
Background Early stroke prognosis assessments are critical for decision-making regarding therapeutic intervention. We introduced the concepts of data combination, method integration, and algorithm parallelization, aiming to build an integrated deep learning model based on a combination of clinical and radiomics features and analyze its application value in prognosis prediction. Methods The research steps in this study include data source and feature extraction, data processing and feature fusion, model building and optimization, model training, and so on. Using data from 441 stroke patients, clinical and radiomics features were extracted, and feature selection was performed. Clinical, radiomics, and combined features were included to construct predictive models. We applied the concept of deep integration to the joint analysis of multiple deep learning methods, used a metaheuristic algorithm to improve the parameter search efficiency, and finally, developed an acute ischemic stroke (AIS) prognosis prediction method, namely, the optimized ensemble of deep learning (OEDL) method. Results Among the clinical features, 17 features passed the correlation check. Among the radiomics features, 19 features were selected. In the comparison of the prediction performance of each method, the OEDL method based on the concept of ensemble optimization had the best classification performance. In the comparison to the predictive performance of each feature, the inclusion of the combined features resulted in better classification performance than that of the clinical and radiomics features. In the comparison to the prediction performance of each balanced method, SMOTEENN, which is based on a hybrid sampling method, achieved the best classification performance than that of the unbalanced, oversampled, and undersampled methods. The OEDL method with combined features and mixed sampling achieved the best classification performance, with 97.89, 95.74, 94.75, 94.03, and 94.35% for Macro-AUC, ACC, Macro-R, Macro-P, and Macro-F1, respectively, and achieved advanced performance in comparison with that of methods in previous studies. Conclusion The OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.
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Affiliation(s)
- Wei Ye
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Xicheng Chen
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Pengpeng Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Yongjun Tao
- Department of Neurology, Taizhou Municipal Hospital, Taizhou, Zhejiang, China
| | - Zhenyan Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Chengcheng Gao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Jian Cheng
- Department of Radiology, Taizhou Municipal Hospital, Taizhou, Zhejiang, China
| | - Fang Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Dali Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
- Department of Health Education, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Zeliang Wei
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Dong Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Yazhou Wu
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
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7
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Zhang X, Miao J, Yang J, Liu C, Huang J, Song J, Xie D, Yue C, Kong W, Hu J, Luo W, Liu S, Li F, Zi W. DWI-Based Radiomics Predicts the Functional Outcome of Endovascular Treatment in Acute Basilar Artery Occlusion. AJNR Am J Neuroradiol 2023; 44:536-542. [PMID: 37080720 PMCID: PMC10171394 DOI: 10.3174/ajnr.a7851] [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: 12/27/2022] [Accepted: 03/15/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND AND PURPOSE Endovascular treatment is a reference treatment for acute basilar artery occlusion (ABAO). However, no established and specific methods are available for the preoperative screening of patients with ABAO suitable for endovascular treatment. This study explores the potential value of DWI-based radiomics in predicting the functional outcomes of endovascular treatment in ABAO. MATERIALS AND METHODS Patients with ABAO treated with endovascular treatment from the BASILAR registry (91 patients in the training cohort) and the hospitals in the Northwest of China (31 patients for the external testing cohort) were included in this study. The Mann-Whitney U test, random forests algorithm, and least absolute shrinkage and selection operator were used to reduce the feature dimension. A machine learning model was developed on the basis of the training cohort to predict the prognosis of endovascular treatment. The performance of the model was evaluated on the independent external testing cohort. RESULTS A subset of radiomics features (n = 6) was used to predict the functional outcomes in patients with ABAO. The areas under the receiver operating characteristic curve of the radiomics model were 0.870 and 0.781 in the training cohort and testing cohort, respectively. The accuracy of the radiomics model was 77.4%, with a sensitivity of 78.9%, specificity of 75%, positive predictive value of 83.3%, and negative predictive value of 69.2% in the testing cohort. CONCLUSIONS DWI-based radiomics can predict the prognosis of endovascular treatment in patients with ABAO, hence allowing a potentially better selection of patients who are most likely to benefit from this treatment.
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Affiliation(s)
- X Zhang
- From the Department of Neurology (X.Z., J.M., J.Y., C.L., J.H., J.S., D.X., C.Y., W.K., J.H., W.L., S.L., F.L., W.Z.), Xinqiao Hospital and The Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China
- Department of Neurology (X.Z.), The Affiliated Hospital of Northwest University Xi'an No.3 Hospital, Xian, China
| | - J Miao
- From the Department of Neurology (X.Z., J.M., J.Y., C.L., J.H., J.S., D.X., C.Y., W.K., J.H., W.L., S.L., F.L., W.Z.), Xinqiao Hospital and The Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China
- Department of Neurology (J.M.), Xianyang Hospital of Yan'an University, Xianyang, China
| | - J Yang
- From the Department of Neurology (X.Z., J.M., J.Y., C.L., J.H., J.S., D.X., C.Y., W.K., J.H., W.L., S.L., F.L., W.Z.), Xinqiao Hospital and The Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - C Liu
- From the Department of Neurology (X.Z., J.M., J.Y., C.L., J.H., J.S., D.X., C.Y., W.K., J.H., W.L., S.L., F.L., W.Z.), Xinqiao Hospital and The Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - J Huang
- From the Department of Neurology (X.Z., J.M., J.Y., C.L., J.H., J.S., D.X., C.Y., W.K., J.H., W.L., S.L., F.L., W.Z.), Xinqiao Hospital and The Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - J Song
- From the Department of Neurology (X.Z., J.M., J.Y., C.L., J.H., J.S., D.X., C.Y., W.K., J.H., W.L., S.L., F.L., W.Z.), Xinqiao Hospital and The Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - D Xie
- From the Department of Neurology (X.Z., J.M., J.Y., C.L., J.H., J.S., D.X., C.Y., W.K., J.H., W.L., S.L., F.L., W.Z.), Xinqiao Hospital and The Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - C Yue
- From the Department of Neurology (X.Z., J.M., J.Y., C.L., J.H., J.S., D.X., C.Y., W.K., J.H., W.L., S.L., F.L., W.Z.), Xinqiao Hospital and The Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - W Kong
- From the Department of Neurology (X.Z., J.M., J.Y., C.L., J.H., J.S., D.X., C.Y., W.K., J.H., W.L., S.L., F.L., W.Z.), Xinqiao Hospital and The Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - J Hu
- From the Department of Neurology (X.Z., J.M., J.Y., C.L., J.H., J.S., D.X., C.Y., W.K., J.H., W.L., S.L., F.L., W.Z.), Xinqiao Hospital and The Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - W Luo
- From the Department of Neurology (X.Z., J.M., J.Y., C.L., J.H., J.S., D.X., C.Y., W.K., J.H., W.L., S.L., F.L., W.Z.), Xinqiao Hospital and The Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - S Liu
- From the Department of Neurology (X.Z., J.M., J.Y., C.L., J.H., J.S., D.X., C.Y., W.K., J.H., W.L., S.L., F.L., W.Z.), Xinqiao Hospital and The Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - F Li
- From the Department of Neurology (X.Z., J.M., J.Y., C.L., J.H., J.S., D.X., C.Y., W.K., J.H., W.L., S.L., F.L., W.Z.), Xinqiao Hospital and The Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - W Zi
- From the Department of Neurology (X.Z., J.M., J.Y., C.L., J.H., J.S., D.X., C.Y., W.K., J.H., W.L., S.L., F.L., W.Z.), Xinqiao Hospital and The Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China
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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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Patel TR, Santo BA, Baig AA, Waqas M, Monterio A, Levy EI, Siddiqui AH, Tutino VM. Histologically interpretable clot radiomic features predict treatment outcomes of mechanical thrombectomy for ischemic stroke. Neuroradiology 2023; 65:737-749. [PMID: 36600077 DOI: 10.1007/s00234-022-03109-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 12/21/2022] [Indexed: 01/06/2023]
Abstract
PURPOSE Radiomics features (RFs) extracted from CT images may provide valuable information on the biological structure of ischemic stroke blood clots and mechanical thrombectomy outcome. Here, we aimed to identify RFs predictive of thrombectomy outcomes and use clot histomics to explore the biology and structure related to these RFs. METHODS We extracted 293 RFs from co-registered non-contrast CT and CTA. RFs predictive of revascularization outcomes defined by first-pass effect (FPE, near to complete clot removal in one thrombectomy pass), were selected. We then trained and cross-validated a balanced logistic regression model fivefold, to assess the RFs in outcome prediction. On a subset of cases, we performed digital histopathology on the clots and computed 227 histomic features from their whole slide images as a means to interpret the biology behind significant RF. RESULTS We identified 6 significantly-associated RFs. RFs reflective of continuity in lower intensities, scattered higher intensities, and intensities with abrupt changes in texture were associated with successful revascularization outcome. For FPE prediction, the multi-variate model had high performance, with AUC = 0.832 ± 0.031 and accuracy = 0.760 ± 0.059 in training, and AUC = 0.787 ± 0.115 and accuracy = 0.787 ± 0.127 in cross-validation testing. Each of the 6 RFs was related to clot component organization in terms of red blood cell and fibrin/platelet distribution. Clots with more diversity of components, with varying sizes of red blood cells and fibrin/platelet regions in the section, were associated with RFs predictive of FPE. CONCLUSION Upon future validation in larger datasets, clot RFs on CT imaging are potential candidate markers for FPE prediction.
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Affiliation(s)
- Tatsat R Patel
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Briana A Santo
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Ammad A Baig
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Muhammad Waqas
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Andre Monterio
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Elad I Levy
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Adnan H Siddiqui
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Vincent M Tutino
- Canon Stroke and Vascular Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14203, USA.
- Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, USA.
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, USA.
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY, USA.
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA.
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McCague C, Ramlee S, Reinius M, Selby I, Hulse D, Piyatissa P, Bura V, Crispin-Ortuzar M, Sala E, Woitek R. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78:83-98. [PMID: 36639175 DOI: 10.1016/j.crad.2022.08.149] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/31/2022] [Indexed: 01/12/2023]
Abstract
Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.
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Affiliation(s)
- C McCague
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S Ramlee
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - M Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - I Selby
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - D Hulse
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - P Piyatissa
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - V Bura
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - M Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Oncology, University of Cambridge, Cambridge, UK
| | - E Sala
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - R Woitek
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
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A nomogram for predicting thrombus composition in stroke patients with large vessel occlusion: combination of thrombus density and perviousness with clinical features. Neuroradiology 2023; 65:371-380. [PMID: 36064806 DOI: 10.1007/s00234-022-03046-0] [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: 04/26/2022] [Accepted: 08/24/2022] [Indexed: 01/25/2023]
Abstract
PURPOSE To establish a nomogram incorporating pretreatment imaging parameters and clinical characteristics for predicting the thrombus composition of acute ischemic stroke (AIS) with large vessel occlusion (LVO). METHODS We retrospectively enrolled patients with occlusion of the Middle Cerebral Artery (MCA) who underwent Mechanical Thrombectomy (MT). Retrieved thrombi were stained with Hematoxylin and Eosin (H&E) and Martius Scarlet Blue (MSB). Thrombi are assigned to the Fibrin-rich or RBC-rich group based on the relative fractions of Red Blood Cells (RBC), fibrin, and platelet. The independent risk factors for Fibrin-rich clots were determined via univariate and multivariate logistic regression analysis and were then integrated to establish a nomogram. RESULTS In total, 98 patients were included in this study. Patients with fibrin-rich clots had worse functional outcome [modified Rankin scale (mRS) 0-2, 34.7% vs 63.2%, p = 0.005], longer procedure time (76.8 min vs 50.8 min, p = 0.001), and increased maneuvers of MT (1.84 vs 1.46, p = 0.703) than those with RBC-rich clots. The independent risk factors for Fibrin-rich clots were lower perviousness measured by Non-Contrast Computer Tomography (NCCT) and CT Angiography (CTA), lower thrombus relative attenuation on NCCT, elevated Platelet-WBC ratio (PWR) of admission peripheral blood, and previous antithrombotic medication. The nomogram showed good discrimination with an area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.852 (95% CI: 0.778-0.926). The calibration curve and decision curve analysis also displayed satisfactory accuracy and clinical utility. CONCLUSION This study has developed and internally validated an easy-to-use nomogram which can help predict clot composition and optimize therapeutic strategies for thrombectomy.
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Dumitriu LaGrange D, Reymond P, Brina O, Zboray R, Neels A, Wanke I, Lövblad KO. Spatial heterogeneity of occlusive thrombus in acute ischemic stroke: A systematic review. J Neuroradiol 2023; 50:352-360. [PMID: 36649796 DOI: 10.1016/j.neurad.2023.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 01/12/2023] [Accepted: 01/12/2023] [Indexed: 01/15/2023]
Abstract
Following the advent of mechanical thrombectomy, occlusive clots in ischemic stroke have been amply characterized using conventional histopathology. Many studies have investigated the compositional variability of thrombi and the consequences of thrombus composition on treatment response. More recent evidence has emerged about the spatial heterogeneity of the clot or the preferential distribution of its components and compact nature. Here we review this emerging body of evidence, discuss its potential clinical implications, and propose the development of adequate characterization techniques.
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Affiliation(s)
- Daniela Dumitriu LaGrange
- Neurodiagnostic and Neurointerventional Division, Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
| | - Philippe Reymond
- Neurodiagnostic and Neurointerventional Division, Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Olivier Brina
- Division of Diagnostic and Interventional Neuroradiology, HUG Geneva University Hospitals, Geneva, Switzerland
| | - Robert Zboray
- Center for X-Ray Analytics, Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf 8600, Switzerland
| | - Antonia Neels
- Center for X-Ray Analytics, Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf 8600, Switzerland
| | - Isabel Wanke
- Division of Neuroradiology, Klinik Hirslanden, Zurich, Switzerland; Swiss Neuroradiology Institute, Zurich, Switzerland; Division of Neuroradiology, University of Essen, Essen, Germany
| | - Karl-Olof Lövblad
- Division of Diagnostic and Interventional Neuroradiology, HUG Geneva University Hospitals, Geneva, Switzerland; Neurodiagnostic and Neurointerventional Division, Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
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13
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Li L, Li M, Chen Z, Lu F, Zhao M, Zhang H, Tong D. Prognostic value of radiomics-based hyperdense middle cerebral artery sign for patients with acute ischemic stroke after thrombectomy strategy. Front Neurol 2023; 13:1037204. [PMID: 36712442 PMCID: PMC9880054 DOI: 10.3389/fneur.2022.1037204] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/23/2022] [Indexed: 01/15/2023] Open
Abstract
Background and purpose The purpose of this study was to evaluate the prognostic value of radiomics-based hyperdense middle cerebral artery sign (HMCAS) for patients with acute ischemic stroke (AIS) after mechanical thrombectomy (MT) and to establish prediction models to identify patients who may benefit more from MT. Methods In this retrospective study, a total of 102 consecutive patients who presented with HMCAS on non-contrast computed tomography (NCCT) at admission and underwent MT in our hospital between January 2019 and December 2020 were recruited. Among them, 46 experienced favorable outcomes (modified Rankin Scale [mRS] ≤ 2) at 3 months of follow-up. All patients were categorized into two sets, namely, the training set (n = 81) and the test set (n = 21). Radiomics features (RFs) and clinical features (CFs) in the training set were selected by statistical analysis to create models. The models' discriminative ability was quantified using the area under the curve (AUC) and confirmed by decision curve analyses. Results The prediction model established using CFs before MT includes baseline National Institutes of Health Stroke Scale (NIHSS) and neutrophil-to-lymphocyte ratio (NLR) [AUC [95% confidence interval (CI)] = 0.596 (0.312-0.881)]. A total of 1,389 RFs were extracted from each hyperdense territory and 8 RFs were left to build the radiomics model [RM; AUC (95%CI) = 0.798 (0.598-0.998)]. The model using preoperative CFs and RFs showed good performance [AUC (95%CI) = 0.817 (0.625-1.000)]. The models using post-operative CFs alone [AUC (95%CI) = 0.856 (0.685-1.000)] or post-operative CFs with RFs [AUC (95%CI) = 0.894 (0.757-1.000)] also showed good discrimination. Conclusion The radiomics-based HMCAS might be a promising tool to predict the prognoses of patients with AIS after MT.
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Affiliation(s)
- Linna Li
- Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Mingyang Li
- Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Zhongping Chen
- Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Fei Lu
- Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Min Zhao
- Pharmaceutical Diagnostics, GE Healthcare, Beijing, China
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Dan Tong
- Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin, China,*Correspondence: Dan Tong ✉
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