Radiomics machine learning based on asymmetrically prominent cortical and deep medullary veins combined with clinical features to predict prognosis in acute ischemic stroke: a retrospective study
Background Acute ischemic stroke (AIS) has a poor prognosis and a high recurrence rate. Predicting the outcomes of AIS patients in the early stages of the disease is therefore important. The establishment of intracerebral collateral circulation significantly improves the survival of brain cells and...
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PeerJ Inc.
2025-06-01
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| author | Hongyi Li Cancan Chang Bo Zhou Yu Lan Peizhuo Zang Shannan Chen Shouliang Qi Ronghui Ju Yang Duan |
| author_facet | Hongyi Li Cancan Chang Bo Zhou Yu Lan Peizhuo Zang Shannan Chen Shouliang Qi Ronghui Ju Yang Duan |
| author_sort | Hongyi Li |
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| description | Background Acute ischemic stroke (AIS) has a poor prognosis and a high recurrence rate. Predicting the outcomes of AIS patients in the early stages of the disease is therefore important. The establishment of intracerebral collateral circulation significantly improves the survival of brain cells and the outcomes of AIS patients. However, no machine learning method has been applied to investigate the correlation between the dynamic evolution of intracerebral venous collateral circulation and AIS prognosis. Therefore, we employed a support vector machine (SVM) algorithm to analyze asymmetrically prominent cortical veins (APCVs) and deep medullary veins (DMVs) to establish a radiomic model for predicting the prognosis of AIS by combining clinical indicators. Methods The magnetic resonance imaging (MRI) data and clinical indicators of 150 AIS patients were retrospectively analyzed. Regions of interest corresponding to the DMVs and APCVs were delineated, and least absolute shrinkage and selection operator (LASSO) regression was used to select features extracted from these regions. An APCV-DMV radiomic model was created via the SVM algorithm, and independent clinical risk factors associated with AIS were combined with the radiomic model to generate a joint model. The SVM algorithm was selected because of its proven efficacy in handling high-dimensional radiomic data compared with alternative classifiers (e.g., random forest) in pilot experiments. Results Nine radiomic features associated with AIS patient outcomes were ultimately selected. In the internal training test set, the AUCs of the clinical, DMV–APCV radiomic and joint models were 0.816, 0.976 and 0.996, respectively. The DeLong test revealed that the predictive performance of the joint model was better than that of the individual models, with a test set AUC of 0.996, sensitivity of 0.905, and specificity of 1.000 (P < 0.05). Conclusions Using radiomic methods, we propose a novel joint predictive model that combines the imaging histologic features of the APCV and DMV with clinical indicators. This model quantitatively characterizes the morphological and functional attributes of venous collateral circulation, elucidating its important role in accurately evaluating the prognosis of patients with AIS and providing a noninvasive and highly accurate imaging tool for early prognostic prediction. |
| format | Article |
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| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-6ed54a5fc9a64cf29c547d714ba6be212025-08-20T02:05:40ZengPeerJ Inc.PeerJ2167-83592025-06-0113e1946910.7717/peerj.19469Radiomics machine learning based on asymmetrically prominent cortical and deep medullary veins combined with clinical features to predict prognosis in acute ischemic stroke: a retrospective studyHongyi Li0Cancan Chang1Bo Zhou2Yu Lan3Peizhuo Zang4Shannan Chen5Shouliang Qi6Ronghui Ju7Yang Duan8Dalian Medical University, Dalian, Liaoning, ChinaDepartment of Medical Imaging, Bozhou Hospital of Traditional Chinese Medicine, Bozhou, Anhui, ChinaDepartment of Radiology, The People’s Hospital of China Medical University, The People’s Hospital of Liaoning Province, Shenyang, Liaoning, ChinaDepartment of Medical Imaging, Liaoning Cancer Hospital, Shenyang, Liaoning, ChinaDepartment of Cerebrovascular Disease Treatment Center, The People’s Hospital of China Medical University, The People’s Hospital of Liaoning Province, Shenyang, Liaoning, ChinaCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, ChinaCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, ChinaDepartment of Radiology, The People’s Hospital of China Medical University, The People’s Hospital of Liaoning Province, Shenyang, Liaoning, ChinaDalian Medical University, Dalian, Liaoning, ChinaBackground Acute ischemic stroke (AIS) has a poor prognosis and a high recurrence rate. Predicting the outcomes of AIS patients in the early stages of the disease is therefore important. The establishment of intracerebral collateral circulation significantly improves the survival of brain cells and the outcomes of AIS patients. However, no machine learning method has been applied to investigate the correlation between the dynamic evolution of intracerebral venous collateral circulation and AIS prognosis. Therefore, we employed a support vector machine (SVM) algorithm to analyze asymmetrically prominent cortical veins (APCVs) and deep medullary veins (DMVs) to establish a radiomic model for predicting the prognosis of AIS by combining clinical indicators. Methods The magnetic resonance imaging (MRI) data and clinical indicators of 150 AIS patients were retrospectively analyzed. Regions of interest corresponding to the DMVs and APCVs were delineated, and least absolute shrinkage and selection operator (LASSO) regression was used to select features extracted from these regions. An APCV-DMV radiomic model was created via the SVM algorithm, and independent clinical risk factors associated with AIS were combined with the radiomic model to generate a joint model. The SVM algorithm was selected because of its proven efficacy in handling high-dimensional radiomic data compared with alternative classifiers (e.g., random forest) in pilot experiments. Results Nine radiomic features associated with AIS patient outcomes were ultimately selected. In the internal training test set, the AUCs of the clinical, DMV–APCV radiomic and joint models were 0.816, 0.976 and 0.996, respectively. The DeLong test revealed that the predictive performance of the joint model was better than that of the individual models, with a test set AUC of 0.996, sensitivity of 0.905, and specificity of 1.000 (P < 0.05). Conclusions Using radiomic methods, we propose a novel joint predictive model that combines the imaging histologic features of the APCV and DMV with clinical indicators. This model quantitatively characterizes the morphological and functional attributes of venous collateral circulation, elucidating its important role in accurately evaluating the prognosis of patients with AIS and providing a noninvasive and highly accurate imaging tool for early prognostic prediction.https://peerj.com/articles/19469.pdfAcute ischemic strokeSusceptibility weighted imagingRadiomicsDeep medullary veinAsymmetrically prominent cortical veins |
| spellingShingle | Hongyi Li Cancan Chang Bo Zhou Yu Lan Peizhuo Zang Shannan Chen Shouliang Qi Ronghui Ju Yang Duan Radiomics machine learning based on asymmetrically prominent cortical and deep medullary veins combined with clinical features to predict prognosis in acute ischemic stroke: a retrospective study PeerJ Acute ischemic stroke Susceptibility weighted imaging Radiomics Deep medullary vein Asymmetrically prominent cortical veins |
| title | Radiomics machine learning based on asymmetrically prominent cortical and deep medullary veins combined with clinical features to predict prognosis in acute ischemic stroke: a retrospective study |
| title_full | Radiomics machine learning based on asymmetrically prominent cortical and deep medullary veins combined with clinical features to predict prognosis in acute ischemic stroke: a retrospective study |
| title_fullStr | Radiomics machine learning based on asymmetrically prominent cortical and deep medullary veins combined with clinical features to predict prognosis in acute ischemic stroke: a retrospective study |
| title_full_unstemmed | Radiomics machine learning based on asymmetrically prominent cortical and deep medullary veins combined with clinical features to predict prognosis in acute ischemic stroke: a retrospective study |
| title_short | Radiomics machine learning based on asymmetrically prominent cortical and deep medullary veins combined with clinical features to predict prognosis in acute ischemic stroke: a retrospective study |
| title_sort | radiomics machine learning based on asymmetrically prominent cortical and deep medullary veins combined with clinical features to predict prognosis in acute ischemic stroke a retrospective study |
| topic | Acute ischemic stroke Susceptibility weighted imaging Radiomics Deep medullary vein Asymmetrically prominent cortical veins |
| url | https://peerj.com/articles/19469.pdf |
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