CT perfusion-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease
PurposeTo build CT perfusion (CTP)-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease (MMD).MethodsFifty-three MMD patients who underwent CTP and digital subtraction angiography (DSA) examination were retrospectively enrolle...
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.974096/full |
| _version_ | 1811309956884856832 |
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| author | Jizhen Li Jizhen Li Yan Zhang Di Yin Hui Shang Kejian Li Tianyu Jiao Caiyun Fang Yi Cui Ming Liu Jun Pan Qingshi Zeng Qingshi Zeng |
| author_facet | Jizhen Li Jizhen Li Yan Zhang Di Yin Hui Shang Kejian Li Tianyu Jiao Caiyun Fang Yi Cui Ming Liu Jun Pan Qingshi Zeng Qingshi Zeng |
| author_sort | Jizhen Li |
| collection | DOAJ |
| description | PurposeTo build CT perfusion (CTP)-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease (MMD).MethodsFifty-three MMD patients who underwent CTP and digital subtraction angiography (DSA) examination were retrospectively enrolled. Patients were divided into good and poor groups based on postoperative DSA. CTP parameters, such as mean transit time (MTT), time to drain (TTD), time to maximal plasma concentration (Tmax), and flow extraction product (FE), were obtained. CTP efficacy in evaluating surgical treatment were compared between the good and poor groups. The changes in the relative CTP parameters (ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE) were calculated to evaluate the differences between pre- and postoperative CTP values. CTP parameters were selected to build delta-radiomics models for identifying collateral vessel formation. The identification performance of machine learning classifiers was assessed using area under the receiver operating characteristic curve (AUC).ResultsOf the 53 patients, 36 (67.9%) and 17 (32.1%) were divided into the good and poor groups, respectively. The postoperative changes of ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE in the good group were significantly better than the poor group (p < 0.05). Among all CTP parameters in the perfusion improvement evaluation, the ΔrTTD had the largest AUC (0.873). Eleven features were selected from the TTD parameter to build the delta-radiomics model. The classifiers of the support vector machine and k-nearest neighbors showed good diagnostic performance with AUC values of 0.933 and 0.867, respectively.ConclusionThe TTD-based delta-radiomics model has the potential to identify collateral vessel formation after the operation. |
| first_indexed | 2024-04-13T09:50:11Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-13T09:50:11Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-d6acc1382b2843fc8d3fa082704cb0a52022-12-22T02:51:37ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-08-011610.3389/fnins.2022.974096974096CT perfusion-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya diseaseJizhen Li0Jizhen Li1Yan Zhang2Di Yin3Hui Shang4Kejian Li5Tianyu Jiao6Caiyun Fang7Yi Cui8Ming Liu9Jun Pan10Qingshi Zeng11Qingshi Zeng12Department of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, ChinaDepartment of Radiology, Shandong Mental Health Center Affiliated to Shandong University, Jinan, ChinaDepartment of Radiology, Shandong Mental Health Center Affiliated to Shandong University, Jinan, ChinaDepartment of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, ChinaDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, ChinaDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, ChinaDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, ChinaDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, ChinaDepartment of Radiology, Qilu Hospital of Shandong University, Jinan, ChinaDepartment of Neurosurgery, Qilu Hospital of Shandong University, Jinan, ChinaDepartment of Radiology, Shandong Mental Health Center Affiliated to Shandong University, Jinan, ChinaDepartment of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, ChinaDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, ChinaPurposeTo build CT perfusion (CTP)-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease (MMD).MethodsFifty-three MMD patients who underwent CTP and digital subtraction angiography (DSA) examination were retrospectively enrolled. Patients were divided into good and poor groups based on postoperative DSA. CTP parameters, such as mean transit time (MTT), time to drain (TTD), time to maximal plasma concentration (Tmax), and flow extraction product (FE), were obtained. CTP efficacy in evaluating surgical treatment were compared between the good and poor groups. The changes in the relative CTP parameters (ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE) were calculated to evaluate the differences between pre- and postoperative CTP values. CTP parameters were selected to build delta-radiomics models for identifying collateral vessel formation. The identification performance of machine learning classifiers was assessed using area under the receiver operating characteristic curve (AUC).ResultsOf the 53 patients, 36 (67.9%) and 17 (32.1%) were divided into the good and poor groups, respectively. The postoperative changes of ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE in the good group were significantly better than the poor group (p < 0.05). Among all CTP parameters in the perfusion improvement evaluation, the ΔrTTD had the largest AUC (0.873). Eleven features were selected from the TTD parameter to build the delta-radiomics model. The classifiers of the support vector machine and k-nearest neighbors showed good diagnostic performance with AUC values of 0.933 and 0.867, respectively.ConclusionThe TTD-based delta-radiomics model has the potential to identify collateral vessel formation after the operation.https://www.frontiersin.org/articles/10.3389/fnins.2022.974096/fullperfusion imagingmoyamoya diseasecerebral revascularizationdelta-radiomicsmachine learning |
| spellingShingle | Jizhen Li Jizhen Li Yan Zhang Di Yin Hui Shang Kejian Li Tianyu Jiao Caiyun Fang Yi Cui Ming Liu Jun Pan Qingshi Zeng Qingshi Zeng CT perfusion-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease Frontiers in Neuroscience perfusion imaging moyamoya disease cerebral revascularization delta-radiomics machine learning |
| title | CT perfusion-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease |
| title_full | CT perfusion-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease |
| title_fullStr | CT perfusion-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease |
| title_full_unstemmed | CT perfusion-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease |
| title_short | CT perfusion-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease |
| title_sort | ct perfusion based delta radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease |
| topic | perfusion imaging moyamoya disease cerebral revascularization delta-radiomics machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.974096/full |
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