Prediction of recurrent ischaemic stroke using radiomics data and machine learning methods in patients with acute ischaemic stroke: protocol for a multicentre, large sample, prospective observational cohort study in China

• Published in: BMJ open 2023-10, Vol.13 (10), p.e076406-e076406
• Main Authors: Li, Jingyi, Han, Mengqi, Chen, Yongsen, Wu, Bin, Wu, Yifan, Jia, Weijie, Liu, JianMo, Luo, Haowen, Yu, Pengfei, Tu, Jianglong, Kuang, Jie, Yi, Yingping
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Blood pressure; Cohort analysis; Coma; Data collection; Diabetes; Drugs; Genotype & phenotype; Ischemia; Machine learning; Medical imaging; Neuroimaging; Neurology; Neuroradiology; Observational studies; Patient safety; Physiology; RADIOLOGY & IMAGING; Radiomics; Risk factors; Stroke
• ISSN: 2044-6055; 2044-6055
• DOI: 10.1136/bmjopen-2023-076406

IntroductionStroke is a leading cause of mortality and disability worldwide. Recurrent strokes result in prolonged hospitalisation and worsened functional outcomes compared with the initial stroke. Thus, it is critical to identify patients who are at high risk of stroke recurrence. This study is positioned to develop and validate a prediction model using radiomics data and machine learning methods to identify the risk of stroke recurrence in patients with acute ischaemic stroke (AIS).Methods and analysisA total of 1957 patients with AIS will be needed. Enrolment at participating hospitals will continue until the required sample size is reached, and we will recruit as many participants as possible. Multiple indicators including basic clinical data, image data, laboratory data, CYP2C19 genotype and follow-up data will be assessed at various time points during the registry, including baseline, 24 hours, 7 days, 1 month, 3 months, 6 months, 9 months and 12 months. The primary outcome was stroke recurrence. The secondary outcomes were death events, prognosis scores and adverse events. Imaging images were processed using deep learning algorithms to construct a programme capable of automatically labelling the lesion area and extracting radiomics features. The machine learning algorithms will be applied to integrate cross-scale, multidimensional data for exploratory analysis. Then, an ischaemic stroke recurrence prediction model of the best performance for patients with AIS will be established. Calibration, receiver operating characteristic and decision curve analyses will be evaluated.Ethics and disseminationThis study has received ethical approval from the Medical Ethics Committee of the Second Affiliated Hospital of Nanchang University (medical research review No.34/2021), and informed consent will be obtained voluntarily. The research findings will be disseminated through publication in journals and presented at conferences.Trial registration numberChiCTR2200055209.