Prognostic value of multi-PLD ASL radiomics in acute ischemic stroke
IntroductionEarly prognosis prediction of acute ischemic stroke (AIS) can support clinicians in choosing personalized treatment plans. The aim of this study is to develop a machine learning (ML) model that uses multiple post-labeling delay times (multi-PLD) arterial spin labeling (ASL) radiomics fea...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2024.1544578/full |
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| author | Zhenyu Wang Yuan Shen Xianxian Zhang Qingqing Li Congsong Dong Shu Wang Haihua Sun Mingzhu Chen Xiaolu Xu Pinglei Pan Pinglei Pan Zhenyu Dai Fei Chen Fei Chen |
| author_facet | Zhenyu Wang Yuan Shen Xianxian Zhang Qingqing Li Congsong Dong Shu Wang Haihua Sun Mingzhu Chen Xiaolu Xu Pinglei Pan Pinglei Pan Zhenyu Dai Fei Chen Fei Chen |
| author_sort | Zhenyu Wang |
| collection | DOAJ |
| description | IntroductionEarly prognosis prediction of acute ischemic stroke (AIS) can support clinicians in choosing personalized treatment plans. The aim of this study is to develop a machine learning (ML) model that uses multiple post-labeling delay times (multi-PLD) arterial spin labeling (ASL) radiomics features to achieve early and precise prediction of AIS prognosis.MethodsThis study enrolled 102 AIS patients admitted between December 2020 and September 2024. Clinical data, such as age and baseline National Institutes of Health Stroke Scale (NIHSS) score, were collected. Radiomics features were extracted from cerebral blood flow (CBF) images acquired through multi-PLD ASL. Features were selected using least absolute shrinkage and selection operator regression, and three models were developed: a clinical model, a CBF radiomics model, and a combined model, employing eight ML algorithms. Model performance was assessed using receiver operating characteristic curves and decision curve analysis (DCA). Shapley Additive exPlanations was applied to interpret feature contributions.ResultsThe combined model of extreme gradient boosting demonstrated superior predictive performance, achieving an area under the curve (AUC) of 0.876. Statistical analysis using the DeLong test revealed its significant outperformance compared to both the clinical model (AUC = 0.658, p < 0.001) and the CBF radiomics model (AUC = 0.755, p = 0.002). The robustness of all models was confirmed through permutation testing. Furthermore, DCA underscored the clinical utility of the combined model. The prognostic prediction of AIS was notably influenced by the baseline NIHSS score, age, as well as texture and shape features of CBF.ConclusionThe integration of clinical data and multi-PLD ASL radiomics features in a model offers a secure and dependable approach for predicting the prognosis of AIS, particularly beneficial for patients with contraindications to contrast agents. This model aids clinicians in devising individualized treatment plans, ultimately enhancing patient prognosis. |
| format | Article |
| id | doaj-art-56a5645a67ff4ba1950286e63fc25969 |
| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-56a5645a67ff4ba1950286e63fc259692025-01-13T05:10:47ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-01-011510.3389/fneur.2024.15445781544578Prognostic value of multi-PLD ASL radiomics in acute ischemic strokeZhenyu Wang0Yuan Shen1Xianxian Zhang2Qingqing Li3Congsong Dong4Shu Wang5Haihua Sun6Mingzhu Chen7Xiaolu Xu8Pinglei Pan9Pinglei Pan10Zhenyu Dai11Fei Chen12Fei Chen13Department of Radiology, Affiliated Hospital 6 of Nantong University, Medical School of Nantong University, Nantong, Jiangsu, ChinaDepartment of Neurology, Affiliated Hospital 6 of Nantong University, Yancheng Third People’s Hospital, Yancheng, Jiangsu, ChinaDepartment of Neurology, Affiliated Hospital 6 of Nantong University, Yancheng Third People’s Hospital, Yancheng, Jiangsu, ChinaDepartment of Radiology, Suzhou Wuzhong People’s Hospital, Suzhou, Jiangsu, ChinaDepartment of Radiology, Affiliated Hospital 6 of Nantong University, Yancheng Third People’s Hospital, Yancheng, Jiangsu, ChinaDepartment of Radiology, Affiliated Hospital 6 of Nantong University, Yancheng Third People’s Hospital, Yancheng, Jiangsu, ChinaDepartment of Neurology, Affiliated Hospital 6 of Nantong University, Yancheng Third People’s Hospital, Yancheng, Jiangsu, ChinaDepartment of Neurology, Affiliated Hospital 6 of Nantong University, Yancheng Third People’s Hospital, Yancheng, Jiangsu, ChinaDepartment of Neurology, Affiliated Hospital 6 of Nantong University, Yancheng Third People’s Hospital, Yancheng, Jiangsu, ChinaDepartment of Neurology, Affiliated Hospital 6 of Nantong University, Yancheng Third People’s Hospital, Yancheng, Jiangsu, ChinaDepartment of Central Laboratory, Affiliated Hospital 6 of Nantong University, Yancheng Third People’s Hospital, Yancheng, Jiangsu, ChinaDepartment of Radiology, Affiliated Hospital 6 of Nantong University, Medical School of Nantong University, Nantong, Jiangsu, ChinaDepartment of Radiology, Affiliated Hospital 6 of Nantong University, Yancheng Third People’s Hospital, Yancheng, Jiangsu, ChinaMedical Imaging Institute of Jiangsu Medical College, Yancheng, Jiangsu, ChinaIntroductionEarly prognosis prediction of acute ischemic stroke (AIS) can support clinicians in choosing personalized treatment plans. The aim of this study is to develop a machine learning (ML) model that uses multiple post-labeling delay times (multi-PLD) arterial spin labeling (ASL) radiomics features to achieve early and precise prediction of AIS prognosis.MethodsThis study enrolled 102 AIS patients admitted between December 2020 and September 2024. Clinical data, such as age and baseline National Institutes of Health Stroke Scale (NIHSS) score, were collected. Radiomics features were extracted from cerebral blood flow (CBF) images acquired through multi-PLD ASL. Features were selected using least absolute shrinkage and selection operator regression, and three models were developed: a clinical model, a CBF radiomics model, and a combined model, employing eight ML algorithms. Model performance was assessed using receiver operating characteristic curves and decision curve analysis (DCA). Shapley Additive exPlanations was applied to interpret feature contributions.ResultsThe combined model of extreme gradient boosting demonstrated superior predictive performance, achieving an area under the curve (AUC) of 0.876. Statistical analysis using the DeLong test revealed its significant outperformance compared to both the clinical model (AUC = 0.658, p < 0.001) and the CBF radiomics model (AUC = 0.755, p = 0.002). The robustness of all models was confirmed through permutation testing. Furthermore, DCA underscored the clinical utility of the combined model. The prognostic prediction of AIS was notably influenced by the baseline NIHSS score, age, as well as texture and shape features of CBF.ConclusionThe integration of clinical data and multi-PLD ASL radiomics features in a model offers a secure and dependable approach for predicting the prognosis of AIS, particularly beneficial for patients with contraindications to contrast agents. This model aids clinicians in devising individualized treatment plans, ultimately enhancing patient prognosis.https://www.frontiersin.org/articles/10.3389/fneur.2024.1544578/fullacute ischemic strokeradiomicsarterial spin labelingcerebral blood flowmachine learning |
| spellingShingle | Zhenyu Wang Yuan Shen Xianxian Zhang Qingqing Li Congsong Dong Shu Wang Haihua Sun Mingzhu Chen Xiaolu Xu Pinglei Pan Pinglei Pan Zhenyu Dai Fei Chen Fei Chen Prognostic value of multi-PLD ASL radiomics in acute ischemic stroke Frontiers in Neurology acute ischemic stroke radiomics arterial spin labeling cerebral blood flow machine learning |
| title | Prognostic value of multi-PLD ASL radiomics in acute ischemic stroke |
| title_full | Prognostic value of multi-PLD ASL radiomics in acute ischemic stroke |
| title_fullStr | Prognostic value of multi-PLD ASL radiomics in acute ischemic stroke |
| title_full_unstemmed | Prognostic value of multi-PLD ASL radiomics in acute ischemic stroke |
| title_short | Prognostic value of multi-PLD ASL radiomics in acute ischemic stroke |
| title_sort | prognostic value of multi pld asl radiomics in acute ischemic stroke |
| topic | acute ischemic stroke radiomics arterial spin labeling cerebral blood flow machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2024.1544578/full |
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