{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:35:02Z","timestamp":1787027702119,"version":"build-2736575974"},"reference-count":35,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T00:00:00Z","timestamp":1656460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["82072021"],"award-info":[{"award-number":["82072021"]}]},{"name":"National Natural Science Foundation of China","award":["60972102"],"award-info":[{"award-number":["60972102"]}]},{"name":"National Natural Science Foundation of China","award":["yg2021-004"],"award-info":[{"award-number":["yg2021-004"]}]},{"name":"National Natural Science Foundation of China","award":["82072021"],"award-info":[{"award-number":["82072021"]}]},{"name":"National Natural Science Foundation of China","award":["60972102"],"award-info":[{"award-number":["60972102"]}]},{"name":"National Natural Science Foundation of China","award":["yg2021-004"],"award-info":[{"award-number":["yg2021-004"]}]},{"name":"Medical-Industrial Integration Project of Fudan University","award":["82072021"],"award-info":[{"award-number":["82072021"]}]},{"name":"Medical-Industrial Integration Project of Fudan University","award":["60972102"],"award-info":[{"award-number":["60972102"]}]},{"name":"Medical-Industrial Integration Project of Fudan University","award":["yg2021-004"],"award-info":[{"award-number":["yg2021-004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sciences"],"abstract":"<jats:p>Intravenous thrombolysis is the most commonly used drug therapy for patients with acute ischemic stroke, which is often accompanied by complications of intracerebral hemorrhage transformation (HT). This study proposed to build a reliable model for pretreatment prediction of HT. Specifically, 5400 radiomics features were extracted from 20 regions of interest (ROIs) of multiparametric MRI images of 71 patients. Furthermore, a minimal set of all-relevant features were selected by LASSO from all ROIs and used to build a radiomics model through the random forest (RF). To explore the significance of normal ROIs, we built a model only based on abnormal ROIs. In addition, a model combining clinical factors and radiomics features was further built. Finally, the models were tested on an independent validation cohort. The radiomics model with 14 All-ROIs features achieved pretreatment prediction of HT (AUC = 0.871, accuracy = 0.848), which significantly outperformed the model with only 14 Abnormal-ROIs features (AUC = 0.831, accuracy = 0.818). Besides, combining clinical factors with radiomics features further benefited the prediction performance (AUC = 0.911, accuracy = 0.894). So, we think that the combined model can greatly assist doctors in diagnosis. Furthermore, we find that even if there were no lesions in the normal ROIs, they also provide characteristic information for the prediction of HT.<\/jats:p>","DOI":"10.3390\/brainsci12070858","type":"journal-article","created":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T20:47:56Z","timestamp":1656535676000},"page":"858","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Prediction Model of Hemorrhage Transformation in Patient with Acute Ischemic Stroke Based on Multiparametric MRI Radiomics and Machine Learning"],"prefix":"10.3390","volume":"12","author":[{"given":"Yucong","family":"Meng","sequence":"first","affiliation":[{"name":"Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Wang","sequence":"additional","affiliation":[{"name":"Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanfu","family":"Wu","sequence":"additional","affiliation":[{"name":"Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linhao","family":"Qu","sequence":"additional","affiliation":[{"name":"Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2268-2087","authenticated-orcid":false,"given":"Yonghong","family":"Shi","sequence":"additional","affiliation":[{"name":"Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1016\/S1474-4422(10)70165-4","article-title":"Implementation and Outcome of Thrombolysis with Alteplase 3\u20134\u00b75 h after an Acute Stroke: An Updated Analysis from SITS-ISTR","volume":"9","author":"Ahmed","year":"2010","journal-title":"Lancet Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1634","DOI":"10.1016\/j.jvs.2008.10.047","article-title":"Thrombolysis with Alteplase 3 to 4.5 Hours After Acute Ischemic Stroke","volume":"48","author":"Hacke","year":"2008","journal-title":"J. Vasc. Surg."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1161\/CIRCRESAHA.121.319949","article-title":"Cerebral Hemorrhage: Pathophysiology, Treatment, and Future Directions","volume":"130","author":"Girard","year":"2022","journal-title":"Circ. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1212\/01.wnl.0000330297.58334.dd","article-title":"The HAT Score: A Simple Grading Scale for Predicting Hemorrhage after Thrombolysis","volume":"71","author":"Lou","year":"2008","journal-title":"Neurology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1002\/ana.23546","article-title":"Symptomatic Intracranial Hemorrhage after Stroke Thrombolysis: The SEDAN Score","volume":"71","author":"Strbian","year":"2012","journal-title":"Ann. Neurol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.1161\/STROKEAHA.111.644815","article-title":"Predicting the Risk of Symptomatic Intracerebral Hemorrhage in Ischemic Stroke Treated With Intravenous Alteplase: Safe Implementation of Treatments in Stroke (SITS) Symptomatic Intracerebral Hemorrhage Risk Score","volume":"43","author":"Mazya","year":"2012","journal-title":"Stroke"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1161\/01.STR.25.2.298","article-title":"Evaluation of Risk of Hemorrhagic Transformation in Local Intra-Arterial Thrombolysis in Acute Ischemic Stroke by Initial SPECT","volume":"25","author":"Ueda","year":"1994","journal-title":"Stroke"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1161\/01.STR.0000158913.91058.93","article-title":"Markedly Reduced Apparent Blood Volume on Bolus Contrast Magnetic Resonance Imaging as a Predictor of Hemorrhage After Thrombolytic Therapy for Acute Ischemic Stroke","volume":"36","author":"Alsop","year":"2005","journal-title":"Stroke"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1016\/j.mri.2013.03.013","article-title":"Multi-Center Prediction of Hemorrhagic Transformation in Acute Ischemic Stroke Using Permeability Imaging Features","volume":"31","author":"Scalzo","year":"2013","journal-title":"Magn. Reson. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1161\/CIRCRESAHA.120.317596","article-title":"TPA Mobilizes Immune Cells That Exacerbate Hemorrhagic Transformation in Stroke","volume":"128","author":"Shi","year":"2021","journal-title":"Circ. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"eaba6156","DOI":"10.1126\/sciadv.aba6156","article-title":"Detection of Response to Tumor Microenvironment\u2013Targeted Cellular Immunotherapy Using Nano-Radiomics","volume":"6","author":"Devkota","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1158\/0008-5472.CAN-20-0999","article-title":"Radiomic Detection of EGFR Mutations in NSCLC","volume":"81","author":"Rossi","year":"2021","journal-title":"Cancer Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1007\/s00259-020-05111-3","article-title":"Uncovering the Invisible\u2014Prevalence, Characteristics, and Radiomics Feature\u2013Based Detection of Visually Undetectable Intraprostatic Tumor Lesions in 68GaPSMA-11 PET Images of Patients with Primary Prostate Cancer","volume":"48","author":"Zamboglou","year":"2021","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_14","first-page":"6438861","article-title":"MR-Based Radiomics for Differential Diagnosis between Cystic Pituitary Adenoma and Rathke Cleft Cyst","volume":"2021","author":"Wang","year":"2021","journal-title":"Comput. Math. Methods Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1002\/jmri.27098","article-title":"Radiomics Based on Multimodal MRI for the Differential Diagnosis of Benign and Malignant Breast Lesions","volume":"52","author":"Zhang","year":"2020","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1097\/RCT.0000000000001114","article-title":"Comparison of Radiomics Analyses Based on Different Magnetic Resonance Imaging Sequences in Grading and Molecular Genomic Typing of Glioma","volume":"45","author":"Huang","year":"2021","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e25307","DOI":"10.1097\/MD.0000000000025307","article-title":"The Clinical Classification of Patients with COVID-19 Pneumonia Was Predicted by Radiomics Using Chest CT","volume":"100","author":"Xiong","year":"2021","journal-title":"Medicine"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6749","DOI":"10.1007\/s00330-020-06893-8","article-title":"CT-Based Radiomics to Predict the Pathological Grade of Bladder Cancer","volume":"30","author":"Zhang","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1093\/bfgp\/elab002","article-title":"Machine Learning Approaches for Predicting Biomolecule\u2013Disease Associations","volume":"20","author":"Ding","year":"2021","journal-title":"Brief. Funct. Genom."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1515\/hsz-2021-0109","article-title":"Machine Learning Based Disease Prediction from Genotype Data","volume":"402","author":"Katsaouni","year":"2021","journal-title":"Biol. Chem."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.nicl.2014.02.003","article-title":"Prediction of Stroke Thrombolysis Outcome Using CT Brain Machine Learning","volume":"4","author":"Bentley","year":"2014","journal-title":"Neuroimage Clin."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1109\/TBME.2017.2783241","article-title":"Prediction of Hemorrhagic Transformation Severity in Acute Stroke From Source Perfusion MRI","volume":"65","author":"Yu","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jiang, L., Zhou, L., Yong, W., Cui, J., Geng, W., Chen, H., Zou, J., Chen, Y., Yin, X., and Chen, Y. (2021). A Deep Learning-based Model for Prediction of Hemorrhagic Transformation after Stroke. Brain Pathol., e13023. online ahead of print.","DOI":"10.1111\/bpa.13023"},{"key":"ref_24","first-page":"177","article-title":"LSTM network for prediction of hemorrhagic transformation in Acute stroke","volume":"Volume 11767","author":"Shen","year":"2019","journal-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2019"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression Shrinkage and Selection Via the Lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","article-title":"FSL","volume":"62","author":"Jenkinson","year":"2012","journal-title":"NeuroImage"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","article-title":"FreeSurfer","volume":"62","author":"Fischl","year":"2012","journal-title":"NeuroImage"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational Radiomics System to Decode the Radiographic Phenotype","volume":"77","author":"Fedorov","year":"2017","journal-title":"Cancer Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-Sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s13195-019-0479-y","article-title":"Concepts for Brain Aging: Resistance, Resilience, Reserve, and Compensation","volume":"11","author":"Montine","year":"2019","journal-title":"Alz Res. Ther."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s11910-019-1004-1","article-title":"Stroke in the Young: A Global Update","volume":"19","author":"Hathidara","year":"2019","journal-title":"Curr. Neurol. Neurosci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.ebiom.2018.07.028","article-title":"Development and Validation of a Penumbra-Based Predictive Model for Thrombolysis Outcome in Acute Ischemic Stroke Patients","volume":"35","author":"Tang","year":"2018","journal-title":"EBioMedicine"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rudilosso, S., Rodr\u00edguez-V\u00e1zquez, A., Urra, X., and Arboix, A. (2022). The Potential Impact of Neuroimaging and Translational Research on the Clinical Management of Lacunar Stroke. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23031497"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.1212\/WNL.0b013e3182749f02","article-title":"On behalf of the SMART Study Group Cerebral Small-Vessel Disease and Progression of Brain Atrophy: The SMART-MR Study","volume":"79","author":"Kloppenborg","year":"2012","journal-title":"Neurology"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1159\/000316059","article-title":"Progressive Gray Matter Atrophy in Lacunar Patients with Vascular Mild Cognitive Impairment","volume":"30","author":"Arboix","year":"2010","journal-title":"Cerebrovasc Dis."}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/12\/7\/858\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:40:32Z","timestamp":1760139632000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/12\/7\/858"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,29]]},"references-count":35,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["brainsci12070858"],"URL":"https:\/\/doi.org\/10.3390\/brainsci12070858","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,29]]}}}