Correction: Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features.
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ABSTRACT:
SUBMITTER: Grosser M
PROVIDER: S-EPMC7067434 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Correction: Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features.
Grosser Malte M Gellißen Susanne S Borchert Patrick P Sedlacik Jan J Nawabi Jawed J Fiehler Jens J Forkert Nils Daniel ND
PloS one 20200312 3
[This corrects the article DOI: 10.1371/journal.pone.0228113.]. ...[more]
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Project description:INTRODUCTION:In recent years, numerous methods have been proposed to predict tissue outcome in acute stroke patients using machine learning methods incorporating multiparametric imaging data. Most methods include diffusion and perfusion parameters as image-based parameters but do not include any spatial information although these parameters are spatially dependent, e.g. different perfusion properties in white and gray brain matter. This study aims to investigate if including spatial features improves the accuracy of multi-parametric tissue outcome prediction. MATERIALS AND METHODS:Acute and follow-up multi-center MRI datasets of 99 patients were available for this study. Logistic regression, random forest, and XGBoost machine learning models were trained and tested using acute MR diffusion and perfusion features and known follow-up lesions. Different combinations of atlas coordinates and lesion probability maps were included as spatial information. The stroke lesion predictions were compared to the true tissue outcomes using the area under the receiver operating characteristic curve (ROC AUC) and the Dice metric. RESULTS:The statistical analysis revealed that including spatial features significantly improves the tissue outcome prediction. Overall, the XGBoost and random forest models performed best in every setting and achieved state-of-the-art results regarding both metrics with similar improvements achieved including Montreal Neurological Institute (MNI) reference space coordinates or voxel-wise lesion probabilities. CONCLUSION:Spatial features should be integrated to improve lesion outcome prediction using machine learning models.
| S-EPMC6980585 | biostudies-literature
Project description:Decisions regarding acute stroke treatment rely heavily on imaging, but interpretation can be difficult for physicians. Machine learning methods can assist clinicians by providing tissue outcome predictions for different treatment approaches based on acute multi-parametric imaging. To produce such clinically viable machine learning models, factors such as classifier choice, data normalization, and data balancing must be considered. This study gives comprehensive consideration to these factors by comparing the agreement of voxel-based tissue outcome predictions using acute imaging and clinical parameters with manual lesion segmentations derived from follow-up imaging. This study considers random decision forest, generalized linear model, and k-nearest-neighbor machine learning classifiers in conjunction with three data normalization approaches (non-normalized, relative to contralateral hemisphere, and relative to contralateral VOI), and two data balancing strategies (full dataset and stratified subsampling). These classifier settings were evaluated based on 90 MRI datasets from acute ischemic stroke patients. Distinction was made between patients recanalized using intraarterial and intravenous methods, as well as those without successful recanalization. For primary quantitative comparison, the Dice metric was computed for each voxel-based tissue outcome prediction and its corresponding follow-up lesion segmentation. It was found that the random forest classifier outperformed the generalized linear model and the k-nearest-neighbor classifier, that normalization did not improve the Dice score of the lesion outcome predictions, and that the models generated lesion outcome predictions with higher Dice scores when trained with balanced datasets. No significant difference was found between the treatment groups (intraarterial vs intravenous) regarding the Dice score of the tissue outcome predictions.
| S-EPMC6744509 | biostudies-literature
Project description:BackgroundAccurate outcome prediction is of great clinical significance in customizing personalized treatment plans, reducing the situation of poor recovery, and objectively and accurately evaluating the treatment effect. This study intended to evaluate the performance of clinical text information (CTI), radiomics features, and survival features (SurvF) for predicting functional outcomes of patients with ischemic stroke.MethodsSurvF was constructed based on CTI and mRS radiomics features (mRSRF) to improve the prediction of the functional outcome in 3 months (90-day mRS). Ten machine learning models predicted functional outcomes in three situations (2-category, 4-category, and 7-category) using seven feature groups constructed by CTI, mRSRF, and SurvF.ResultsFor 2-category, ALL (CTI + mRSRF+ SurvF) performed best, with an mAUC of 0.884, mAcc of 0.864, mPre of 0.877, mF1 of 0.86, and mRecall of 0.864. For 4-category, ALL also achieved the best mAuc of 0.787, while CTI + SurvF achieved the best score with mAcc = 0.611, mPre = 0.622, mF1 = 0.595, and mRe-call = 0.611. For 7-category, CTI + SurvF performed best, with an mAuc of 0.788, mPre of 0.519, mAcc of 0.529, mF1 of 0.495, and mRecall of 0.47.ConclusionsThe above results indicate that mRSRF + CTI can accurately predict functional outcomes in ischemic stroke patients with proper machine learning models. Moreover, combining SurvF will improve the prediction effect compared with the original features. However, limited by the small sample size, further validation on larger and more varied datasets is necessary.
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Project description:Infarct volume (FIV) on follow-up diffusion-weighted imaging (FU-DWI) is only moderately associated with functional outcome in acute ischemic stroke patients. However, FU-DWI may contain other imaging biomarkers that could aid in improving outcome prediction models for acute ischemic stroke. We included FU-DWI data from the HERMES, ISLES, and MR CLEAN-NO IV databases. Lesions were segmented using a deep learning model trained on the HERMES and ISLES datasets. We assessed the performance of three classifiers in predicting functional independence for the MR CLEAN-NO IV trial cohort based on: (1) FIV alone, (2) the most important features obtained from a trained convolutional autoencoder (CAE), and (3) radiomics. Furthermore, we investigated feature importance in the radiomic-feature-based model. For outcome prediction, we included 206 patients: 144 scans were included in the training set, 21 in the validation set, and 41 in the test set. The classifiers that included the CAE and the radiomic features showed AUC values of 0.88 and 0.81, respectively, while the model based on FIV had an AUC of 0.79. This difference was not found to be statistically significant. Feature importance results showed that lesion intensity heterogeneity received more weight than lesion volume in outcome prediction. This study suggests that predictions of functional outcome should not be based on FIV alone and that FU-DWI images capture additional prognostic information.
| S-EPMC9331690 | biostudies-literature
Project description:Background and Purpose: The CT-DRAGON score was developed to predict long-term functional outcome after acute stroke in the anterior circulation treated by thrombolysis. Its implementation in clinical practice may be hampered by its plethora of variables. The current study was designed to develop and evaluate an alternative score, as a reduced set of features, derived from the original CT-DRAGON score. Methods: This single-center retrospective study included 564 patients treated for stroke, in the anterior and the posterior circulation. At 90 days, favorable [modified Rankin Scale score (mRS) of 0-2] and miserable outcome (mRS of 5-6) were predicted by the CT-DRAGON in 427 patients. Bootstrap forests selected the most relevant parameters of the CT-DRAGON, in order to develop a reduced set of features. Discrimination, calibration and misclassification of both models were tested. Results: The area under the receiver operating characteristic curve (AUROC) for the CT-DRAGON was 0.78 (95% CI 0.74-0.81) for favorable and 0.78 (95% CI 0.72-0.83) for miserable outcome. Misclassification was 29% for favorable and 13.5% for miserable outcome, with a 100% specificity for the latter. National Institutes of Health Stroke Scale (NIHSS), pre-stroke mRS and age were identified as the strongest contributors to favorable and miserable outcome and named the reduced features set. While CT-DRAGON was only available in 323 patients (57%), the reduced features set could be calculated in 515 patients (91%) (p < 0.001). Misclassification was 25.8% for favorable and 14.4% for miserable outcome, with a 97% specificity for miserable outcome. The reduced features set had better discriminative power than CT-DRAGON for both outcomes (both p < 0.005), with an AUROC of 0.82 (95% CI 0.79-0.86) and 0.83 (95% CI 0.77-0.87) for favorable and miserable outcome, respectively. Conclusions: The CT-DRAGON score revealed acceptable discrimination in our cohort of both anterior and posterior circulation strokes, receiving all treatment modalities. The reduced features set could be measured in a larger cohort and with better discrimination. However, the reduced features set needs further validation in a prospective, multicentre study. Clinical Trial Registration: http://www.clinicaltrials.gov. Identifiers: NCT03355690, NCT04092543.
| S-EPMC7412791 | biostudies-literature
Project description:Background and purposeNeurofilament light chain (NfL) is a marker of neuroaxonal damage. We aimed to study associations between serum NfL (sNfL) concentrations at different time points after ischemic stroke and outcomes.MethodsWe prospectively included ischemic stroke cases (n?=?595, mean age 59 years, 64% males) and assessed outcomes by both the modified Rankin Scale (mRS) and the NIH stroke scale (NIHSS) at 3 months and by mRS at 2 years. In a subsample, long-term (7-year) outcomes were also assessed by both mRS and NIHSS. We used the ultrasensitive single-molecule array assay to measure sNfL in the acute phase (range 1-14, median 4 days), after 3 months and 7 years in cases and once in controls (n?=?595).ResultsAcute-phase sNfL increased by the time to blood-draw and highest concentrations were observed at 3 months post-stroke. High sNfL associated to stroke severity and poor outcomes, and both associations were strongest for 3-month sNfL. After adjusting for age, previous stroke, stroke severity, and day of blood draw, 3-month sNfL was significantly associated to both outcomes at all time points (p?<?0.01 throughout). For all main etiological subtypes, both acute phase and 3-month sNfL were significantly higher than in controls, but the dynamics of sNfL differed by stroke subtype.ConclusionsThe results from this study inform on sNfL in ischemic stroke and subtypes over time, and show that sNfL predicts short- and long-term neurological and functional outcomes. Our findings suggest a potential utility of sNfL in ischemic stroke outcome prediction.
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Project description:Metastasis occurs frequently after resection of pancreatic cancer (PC). We hypothesized that multiparametric analysis of pre-metastatic liver biopsies will classify patients according to their metastatic risk, timing, and organ site. Liver biopsies obtained during pancreatectomy from 49 patients with localized PC and 18 control patients with non-cancerous pancreatic lesions were analyzed combining metabolomic, tissue and single cell transcriptomics, and multiplex imaging approaches. Patients were followed prospectively (median three years) and classified into four recurrence groups; early (<6 months post resection) or late (>6 months post resection) liver metastasis (LM), extrahepatic (EH) metastasis, and disease-free survivors (NED). Overall, PC livers exhibited signs of augmented inflammation, compared to controls. Enrichment of neutrophil extracellular traps (NETs), Ki-67 upregulation, and decreased liver creatine significantly distinguished those with future metastasis from NED. Patients with future LM were characterized by scant T cell lobular infiltration, less steatosis, and higher levels of citrullinated H3, compared to patients who developed EH metastasis, who had overexpression of interferon target genes (MX1, NR1D1) and an increase of CD11B+ NK cells. Upregulation of sortilin-1 and prominent NETs, together with the lack of T cells and a reduction in CD11B+ NK cells, differentiated patients with early-onset from those with late-onset LM. Liver profiles of NED closely resembled those of controls. Using the above parameters, a machine learning-based model was developed that successfully predicted the metastatic outcome at the time of surgery with 78% accuracy. Therefore, multiparametric profiling of liver biopsies at the time of PC diagnosis may determine metastatic risk and organotropism, and guide clinical stratification for optimal treatment selection.
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Project description:High variability in infarct size is common in experimental stroke models and affects statistical power and validity of neuroprotection trials. The aim of this study was to explore cerebral collateral flow as a stratification factor for the prediction of ischemic outcome. Transient intraluminal occlusion of the middle cerebral artery was induced for 90 min in 18 Wistar rats. Cerebral collateral flow was assessed intra-procedurally using multi-site laser Doppler flowmetry monitoring in both the lateral middle cerebral artery territory and the borderzone territory between middle cerebral artery and anterior cerebral artery. Multi-modal magnetic resonance imaging was used to assess acute ischemic lesion (diffusion-weighted imaging, DWI), acute perfusion deficit (time-to-peak, TTP), and final ischemic lesion at 24 h. Infarct volumes and typology at 24 h (large hemispheric versus basal ganglia infarcts) were predicted by both intra-ischemic collateral perfusion and acute DWI lesion volume. Collateral flow assessed by multi-site laser Doppler flowmetry correlated with the corresponding acute perfusion deficit using TTP maps. Multi-site laser Doppler flowmetry monitoring was able to predict ischemic outcome and perfusion deficit in good agreement with acute MRI. Our results support the additional value of cerebral collateral flow monitoring for outcome prediction in experimental ischemic stroke, especially when acute MRI facilities are not available.
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Project description:The purpose of the present study was to present a multi-delay multi-parametric pseudo-continuous arterial spin labeling (pCASL) protocol with background suppressed 3D GRASE (gradient and spin echo) readout for perfusion imaging in acute ischemic stroke. PCASL data at 4 post-labeling delay times (PLD = 1.5, 2, 2.5, 3 s) were acquired within 4.5 min in 24 patients (mean age 79.7 ± 11.4 years; 11 men) with acute middle cerebral artery (MCA) stroke who also underwent dynamic susceptibility contrast (DSC) enhanced perfusion imaging. Arterial transit times (ATT) were estimated through the calculation of weighted delays across the 4 PLDs, which were included in the calculation of cerebral blood flow (CBF) and arterial cerebral blood volume (CBV). Mean perfusion parameters derived using pCASL and DSC were measured within MCA territories and infarct regions identified on diffusion weighted MRI. The results showed highly significant correlations between pCASL and DSC CBF measurements (r > = 0.70, p < = 0.0001) and moderately significant correlations between pCASL and DSC CBV measurements (r > = 0.45, p < = 0.027) in both MCA territories and infarct regions. ASL ATT showed correlations with DSC time to the maximum of tissue residual function (Tmax)(r = 0.66, p = 0.0005) and mean transit time (MTT)(r = 0.59, p = 0.0023) in leptomeningeal MCA territories. The present study demonstrated the feasibility for noninvasive multi-parametric perfusion imaging using ASL for acute stroke imaging.
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