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Jiang J, Sheng K, Li M, Zhao H, Guan B, Dai L, Li Y. A dual-energy computed tomography-based radiomics nomogram for predicting time since stroke onset: a multicenter study. Eur Radiol 2024:10.1007/s00330-024-10802-8. [PMID: 38834786 DOI: 10.1007/s00330-024-10802-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 03/25/2024] [Accepted: 04/06/2024] [Indexed: 06/06/2024]
Abstract
OBJECTIVES We aimed to develop and validate a radiomics nomogram based on dual-energy computed tomography (DECT) images and clinical features to classify the time since stroke (TSS), which could facilitate stroke decision-making. MATERIALS AND METHODS This retrospective three-center study consecutively included 488 stroke patients who underwent DECT between August 2016 and August 2022. The eligible patients were divided into training, test, and validation cohorts according to the center. The patients were classified into two groups based on an estimated TSS threshold of ≤ 4.5 h. Virtual images optimized the visibility of early ischemic lesions with more CT attenuation. A total of 535 radiomics features were extracted from polyenergetic, iodine concentration, virtual monoenergetic, and non-contrast images reconstructed using DECT. Demographic factors were assessed to build a clinical model. A radiomics nomogram was a tool that the Rad score and clinical factors to classify the TSS using multivariate logistic regression analysis. Predictive performance was evaluated using receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) was used to compare the clinical utility and benefits of different models. RESULTS Twelve features were used to build the radiomics model. The nomogram incorporating both clinical and radiomics features showed favorable predictive value for TSS. In the validation cohort, the nomogram showed a higher AUC than the radiomics-only and clinical-only models (AUC: 0.936 vs 0.905 vs 0.824). DCA demonstrated the clinical utility of the radiomics nomogram model. CONCLUSIONS The DECT-based radiomics nomogram provides a promising approach to predicting the TSS of patients. CLINICAL RELEVANCE STATEMENT The findings support the potential clinical use of DECT-based radiomics nomograms for predicting the TSS. KEY POINTS Accurately determining the TSS onset is crucial in deciding a treatment approach. The radiomics-clinical nomogram showed the best performance for predicting the TSS. Using the developed model to identify patients at different times since stroke can facilitate individualized management.
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Affiliation(s)
- Jingxuan Jiang
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Kai Sheng
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Minda Li
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China
| | - Huilin Zhao
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Baohui Guan
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lisong Dai
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuehua Li
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Jingxuan J, Baohui G, Jingyi Z, Hongmei G, Minda L, Ye H, Yuehua L. Dual-energy computed tomography angiography-based quantification of lesion net water uptake to identify stroke onset time. Heliyon 2024; 10:e23540. [PMID: 38169834 PMCID: PMC10758880 DOI: 10.1016/j.heliyon.2023.e23540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 12/03/2023] [Accepted: 12/06/2023] [Indexed: 01/05/2024] Open
Abstract
Objectives To explore whether dual-energy computed tomography (DECT) angiography can provide reliable quantitative information on net water uptake (NWU) of ischemic brain to identify stroke patients within 4.5 h. Methods We retrospectively reviewed 142 patients with stroke occurrence and who underwent DECT angiography between August 2016 and May 2022. DECT angiography manual drawn the ischemic area by referring to the normal area of the contralateral hemisphere and follow-up images. The NWU in the ischemic area was determined using virtual non-contrast and monoenergetic (VNC &VM) images acquired from DECT angiography. The NWU values in the ischemic area were compared between stroke patients within and beyond 4.5 h. The diagnostic performance of the NWU values derived from the VNC and VM images was assessed through receiver operating characteristic curve analysis. Additionally, Furthermore, we examined the correlation between the NWU values and the stroke onset time. Results Seventy-eight (54.93 %) stroke patients underwent DECT angiography and within 4.5 h. These patients with lower median National Institute of Health stroke scale (NIHSS) scores on admission than those beyond 4.5 h (p < 0.05). Furthermore, the group within 4.5 h had lower NWU values than did the group beyond 4.5 h on all VNC and VM images (p < 0.001). The analysis revealed that the NWU values determined using the VM (60 keV) images had the highest predictive efficiency (AUC, 0.95; sensitivity, 100 %; and specificity, 89.06 %) and showed the strongest positive correlation with stroke onset time (r-value = 0.58, p < 0.001). Conclusions Our findings showed that DECT angiography-based quantification of NWU helps identify the stroke patients within 4.5 h with high predictive efficiency. Thus, NWU values determined using VM (60 keV) images could serve as a significant biomarker for stroke onset time.
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Affiliation(s)
- Jiang Jingxuan
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guan Baohui
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhou Jingyi
- Department of Radiology, Kunshan second People's Hospital, Kunshan, China
| | - Gu Hongmei
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China
| | - Li Minda
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China
| | - Hua Ye
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China
| | - Li Yuehua
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Sporns PB, Kemmling A, Meyer L, Krogias C, Puetz V, Thierfelder KM, Duering M, Lukas C, Kaiser D, Langner S, Brehm A, Rotkopf LT, Kunz WG, Beuker C, Heindel W, Fiehler J, Schramm P, Wiendl H, Minnerup H, Psychogios MN, Minnerup J. Computed tomography hypoperfusion-hypodensity mismatch vs. automated perfusion mismatch to identify stroke patients eligible for thrombolysis. Front Neurol 2023; 14:1320620. [PMID: 38225983 PMCID: PMC10788186 DOI: 10.3389/fneur.2023.1320620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 12/04/2023] [Indexed: 01/17/2024] Open
Abstract
Background and purpose Automated perfusion imaging can detect stroke patients with unknown time of symptom onset who are eligible for thrombolysis. However, the availability of this technique is limited. We, therefore, established the novel concept of computed tomography (CT) hypoperfusion-hypodensity mismatch, i.e., an ischemic core lesion visible on cerebral perfusion CT without visible hypodensity in the corresponding native cerebral CT. We compared both methods regarding their accuracy in identifying patients suitable for thrombolysis. Methods In a retrospective analysis of the MissPerfeCT observational cohort study, patients were classified as suitable or not for thrombolysis based on established time window and imaging criteria. We calculated predictive values for hypoperfusion-hypodensity mismatch and automated perfusion imaging to compare accuracy in the identification of patients suitable for thrombolysis. Results Of 247 patients, 219 (88.7%) were eligible for thrombolysis and 28 (11.3%) were not eligible for thrombolysis. Of 197 patients who were within 4.5 h of symptom onset, 190 (96.4%) were identified by hypoperfusion-hypodensity mismatch and 88 (44.7%) by automated perfusion mismatch (p < 0.001). Of 22 patients who were beyond 4.5 h of symptom onset but were eligible for thrombolysis, 5 patients (22.7%) were identified by hypoperfusion-hypodensity mismatch. Predictive values for the hypoperfusion-hypodensity mismatch vs. automated perfusion mismatch were as follows: sensitivity, 89.0% vs. 50.2%; specificity, 71.4% vs. 100.0%; positive predictive value, 96.1% vs. 100.0%; and negative predictive value, 45.5% vs. 20.4%. Conclusion The novel method of hypoperfusion-hypodensity mismatch can identify patients suitable for thrombolysis with higher sensitivity and lower specificity than established techniques. Using this simple method might therefore increase the proportion of patients treated with thrombolysis without the use of special automated software.The MissPerfeCT study is a retrospective observational multicenter cohort study and is registered with clinicaltrials.gov (NCT04277728).
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Affiliation(s)
- Peter B. Sporns
- Department of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, Basel, Switzerland
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Radiology, Westfaelische Wilhelms-University of Münster and University Hospital of Münster, Münster, Germany
| | - André Kemmling
- Department of Radiology, Westfaelische Wilhelms-University of Münster and University Hospital of Münster, Münster, Germany
- Department of Neuroradiology, Westpfalz-Klinikum, Kaiserslautern, Germany
- Department of Neuroradiology, University Medical Center Schleswig-Holstein, Lübeck, Germany
| | - Lennart Meyer
- Department of Neurology with Institute of Translational Neurology, University of Münster, Münster, Germany
| | - Christos Krogias
- Department of Neurology, St. Josef-Hospital, Ruhr University Bochum, Bochum, Germany
| | - Volker Puetz
- Department of Neurology, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Kolja M. Thierfelder
- Department of Radiology and Institute of Diagnostic and Interventional Radiology, University Medical Center Rostock, Rostock, Germany
| | - Marco Duering
- Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- Institute for Stroke and Dementia Research, University Hospital, LMU Munich, Munich, Germany
| | - Carsten Lukas
- Department of Neuroradiology, St. Josef-Hospital, Ruhr University Bochum, Bochum, Germany
| | - Daniel Kaiser
- Department of Neuroradiology, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Sönke Langner
- Department of Radiology and Institute of Diagnostic and Interventional Radiology, University Medical Center Rostock, Rostock, Germany
| | - Alex Brehm
- Department of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, Basel, Switzerland
| | - Lukas T. Rotkopf
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Wolfgang G. Kunz
- Department of Radiology, University Hospital, LMU Munich, Germany
| | - Carolin Beuker
- Department of Neurology with Institute of Translational Neurology, University of Münster, Münster, Germany
| | - Walter Heindel
- Department of Radiology, Westfaelische Wilhelms-University of Münster and University Hospital of Münster, Münster, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Peter Schramm
- Department of Neuroradiology, University Medical Center Schleswig-Holstein, Lübeck, Germany
| | - Heinz Wiendl
- Department of Neurology with Institute of Translational Neurology, University of Münster, Münster, Germany
| | - Heike Minnerup
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Marios Nikos Psychogios
- Department of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, Basel, Switzerland
| | - Jens Minnerup
- Department of Neurology with Institute of Translational Neurology, University of Münster, Münster, Germany
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Broocks G, Meyer L, Hanning U, Faizy TD, Bechstein M, Kniep H, Van Horn N, Schön G, Barow E, Thomalla G, Fiehler J, Kemmling A. Haemorrhage after thrombectomy with adjuvant thrombolysis in unknown onset stroke depends on high early lesion water uptake. Stroke Vasc Neurol 2023:svn-2022-002264. [PMID: 37699728 DOI: 10.1136/svn-2022-002264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 08/29/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND AND PURPOSE In wake-up stroke, CT-based quantitative net water uptake (NWU) might serve as an alternative tool to MRI to guide intravenous thrombolysis with alteplase (IVT). An important complication after IVT is symptomatic intracerebral haemorrhage (sICH). As NWU directly implies ischaemic lesion progression, reflecting blood-brain barrier injury, we hypothesised that NWU predicts sICH in patients who had a ischaemic stroke undergoing thrombectomy with unknown onset. METHODS Consecutive analysis of all patients who had unknown onset anterior circulation ischaemic stroke who underwent CT at baseline and endovascular treatment between December 2016 and October 2020. Quantitative NWU was assessed on baseline CT. The primary endpoint was sICH. The association of NWU and other baseline parameters to sICH was investigated using inverse-probability weighting (IPW) analysis. RESULTS A total of 88 patients were included, of which 46 patients (52.3%) received IVT. The median NWU was 10.7% (IQR: 5.1-17.7). The proportion of patients with any haemorrhage and sICH were 35.2% and 13.6%. NWU at baseline was significantly higher in patients with sICH (19.1% vs 9.6%, p<0.0001) and the median Alberta Stroke Program Early CT Score (ASPECTS) was lower (5 vs 8, p<0.0001). Following IPW, there was no association between IVT and sICH in unadjusted analysis. However, after adjusting for ASPECTS and NWU, there was a significant association between IVT administration and sICH (14.6%, 95% CI: 3.3% to 25.6%, p<0.01). CONCLUSION In patients with ischaemic stroke with unknown onset, the combination of high NWU with IVT is directly linked to higher rates of sICH. Besides ASPECTS for evaluating the extent of the early infarct lesion, quantitative NWU could be used as an imaging biomarker to assess the degree of blood-brain barrier damage in order to predict the risk of sICH in patients with wake up stroke.
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Affiliation(s)
- Gabriel Broocks
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Lukas Meyer
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Uta Hanning
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias Djamsched Faizy
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Matthias Bechstein
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Helge Kniep
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Noel Van Horn
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Gerhard Schön
- Department of Medical Biometry and Epidemiology, University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - Ewgenia Barow
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Andre Kemmling
- Department of Neuroradiology, University Marburg, Marburg, Germany
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Rusche T, Wasserthal J, Breit HC, Fischer U, Guzman R, Fiehler J, Psychogios MN, Sporns PB. Machine Learning for Onset Prediction of Patients with Intracerebral Hemorrhage. J Clin Med 2023; 12:jcm12072631. [PMID: 37048712 PMCID: PMC10094957 DOI: 10.3390/jcm12072631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/13/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023] Open
Abstract
Objective: Intracerebral hemorrhage (ICH) has a high mortality and long-term morbidity and thus has a significant overall health–economic impact. Outcomes are especially poor if the exact onset is unknown, but reliable imaging-based methods for onset estimation have not been established. We hypothesized that onset prediction of patients with ICH using artificial intelligence (AI) may be more accurate than human readers. Material and Methods: A total of 7421 computed tomography (CT) datasets between January 2007–July 2021 from the University Hospital Basel with confirmed ICH were extracted and an ICH-segmentation algorithm as well as two classifiers (one with radiomics, one with convolutional neural networks) for onset estimation were trained. The classifiers were trained based on the gold standard of 644 datasets with a known onset of >1 and <48 h. The results of the classifiers were compared to the ratings of two radiologists. Results: Both the AI-based classifiers and the radiologists had poor discrimination of the known onsets, with a mean absolute error (MAE) of 9.77 h (95% CI (confidence interval) = 8.52–11.03) for the convolutional neural network (CNN), 9.96 h (8.68–11.32) for the radiomics model, 13.38 h (11.21–15.74) for rater 1 and 11.21 h (9.61–12.90) for rater 2, respectively. The results of the CNN and radiomics model were both not significantly different to the mean of the known onsets (p = 0.705 and p = 0.423). Conclusions: In our study, the discriminatory power of AI-based classifiers and human readers for onset estimation of patients with ICH was poor. This indicates that accurate AI-based onset estimation of patients with ICH based only on CT-data may be unlikely to change clinical decision making in the near future. Perhaps multimodal AI-based approaches could improve ICH onset prediction and should be considered in future studies.
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Affiliation(s)
- Thilo Rusche
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
- Correspondence:
| | - Jakob Wasserthal
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
| | - Hanns-Christian Breit
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
| | - Urs Fischer
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland
| | - Raphael Guzman
- Department of Neurosurgery, University Hospital Basel, 4031 Basel, Switzerland
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, 55131 Hamburg, Germany
| | - Marios-Nikos Psychogios
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
| | - Peter B. Sporns
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, 55131 Hamburg, Germany
- Department of Radiology and Neuroradiology, Stadtspital Zürich, 8063 Zürich, Switzerland
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Sporns PB, Ospel JM, Psychogios MN. Editorial: Ischemic stroke management: From symptom onset to successful reperfusion and beyond. Front Neurol 2022; 13:1042342. [PMID: 36313515 PMCID: PMC9607946 DOI: 10.3389/fneur.2022.1042342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 09/29/2022] [Indexed: 11/25/2022] Open
Affiliation(s)
- Peter B. Sporns
- Department of Neuroradiology, Clinic for Radiology & Nuclear Medicine, University Hospital Basel, Basel, Switzerland
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- *Correspondence: Peter B. Sporns
| | - Johanna M. Ospel
- Department of Neuroradiology, Clinic for Radiology & Nuclear Medicine, University Hospital Basel, Basel, Switzerland
| | - Marios-Nikos Psychogios
- Department of Neuroradiology, Clinic for Radiology & Nuclear Medicine, University Hospital Basel, Basel, Switzerland
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