Impact of the reperfusion status for predicting the final stroke infarct using deep learning

 Damn it all, prediction crapola like this DOES NOTHING FOR SURVIVORS.  Will you please solve stroke instead of beating around the bushes? Here is a list you can work on.

13 problems with no cure.

The latest crapola here:

Impact of the reperfusion status for predicting the final stroke infarct using deep learning

Noëlie Debs,^a Tae-Hee Cho,^a,b David Rousseau,^c Yves Berthezène,^a,d Marielle Buisson,^e Omer Eker,^a,d Laura Mechtouff,^b,e Norbert Nighoghossian,^a,b Michel Ovize,^d and Carole Frindel^a,⁎

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Graphical abstract

Keywords: Stroke, Prediction, Convolutional neural network, Magnetic resonance imaging, Reperfusion status

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Abstract

Background

Predictive maps of the final infarct may help therapeutic decisions in acute ischemic stroke patients. Our objectives were to assess whether integrating the reperfusion status into deep learning models would improve their performance, and to compare them to current clinical prediction methods.

Methods

We trained and tested convolutional neural networks (CNNs) to predict the final infarct in acute ischemic stroke patients treated by thrombectomy in our center. When training the CNNs, non-reperfused patients from a non-thrombectomized cohort were added to the training set to increase the size of this group. Baseline diffusion and perfusion-weighted magnetic resonance imaging (MRI) were used as inputs, and the lesion segmented on day-6 MRI served as the ground truth for the final infarct. The cohort was dichotomized into two subsets, reperfused and non-reperfused patients, from which reperfusion status specific CNNs were developed and compared to one another, and to the clinically-used perfusion-diffusion mismatch model. Evaluation metrics included the Dice similarity coefficient (DSC), precision, recall, volumetric similarity, Hausdorff distance and area-under-the-curve (AUC).

Results

We analyzed 109 patients, including 35 without reperfusion. The highest DSC were achieved in both reperfused and non-reperfused patients (DSC = 0.44 ± 0.25 and 0.47 ± 0.17, respectively) when using the corresponding reperfusion status-specific CNN. CNN-based models achieved higher DSC and AUC values compared to those of perfusion-diffusion mismatch models (reperfused patients: AUC = 0.87 ± 0.13 vs 0.79 ± 0.17, P < 0.001; non-reperfused patients: AUC = 0.81 ± 0.13 vs 0.73 ± 0.14, P < 0.01, in CNN vs perfusion-diffusion mismatch models, respectively).

Conclusion

The performance of deep learning models improved when the reperfusion status was incorporated in their training. CNN-based models outperformed the clinically-used perfusion-diffusion mismatch model. Comparing the predicted infarct in case of successful vs failed reperfusion may help in estimating the treatment effect and guiding therapeutic decisions in selected patients.

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1. Introduction

Early reperfusion, by means of intravenous thrombolysis or thrombectomy, is the main therapeutic goal in acute ischemic stroke (Powers et al., 2019). Acute treatment decisions have increasingly incorporated advanced neuroimaging to estimate patients’ prognosis and likelihood of benefiting from revascularization procedures (Nogueira et al., 2018, Albers et al., 2018). Currently, both computed-tomography (CT) and Magnetic Resonance Imaging (MRI) entail threshold-based methods to delineate the still salvageable brain (i.e. ischemic penumbra) from the already lost tissue (infarct core). Specifically in MRI, criteria for the infarct core is based on Apparent Diffusion Coefficient (ADC) extracted from Diffusion-Weighted Imaging (DWI), and criteria for the ischemic penumbra is based on Time to maximum of the residue function (T_max) extracted from perfusion-weighted imaging. Precisely, infarct core is defined as ADC voxel values < 600~620x10⁻⁶ mm²/s, and ischemic penumbra is defined as T_max voxel values >6 s (Kidwell et al., 2013, Olivot et al., 2009). Patients with a large penumbra and limited ischemic core (so-called ‘target mismatch’ profile) have a high probability of benefiting from reperfusion, even in late time windows (Nogueira et al., 2018, Albers et al., 2018). However, these fixed-threshold methods may fail to capture the significant interindividual heterogeneity observed in stroke progression (Rekik et al., 2012). While the clinical and imaging characteristics of some patients may clearly indicate urgent reperfusion therapies, the benefit/risk balance in others can appear more uncertain. Thus, personalized probability maps of the final infarct would be of high clinical value to guide acute revascularization decisions and possibly help evaluate novel neuroprotective strategies.

Convolutional neural networks (CNNs), a subtype of machine learning, are flexible, data-driven methods capable of automatic non-linear feature extraction, with promising results in stroke lesion segmentation (Qiu et al., 2020). A well-acknowledged limitation of CNNs is the large quantity of data required for their training and validation. Only a limited number of studies, with heterogeneous treatment paradigms and evaluations metrics, have evaluated CNNs for the prediction of the final stroke lesion from baseline MRI (Winzeck et al., 2018, Pinto et al., 2018, Nielsen et al., 2018, Yu et al., 2020) or CT (Robben et al., 2020). Sample size and performance were modest (~50 to ~200 patients, Dice similarity coefficient ~0.50 or lower), illustrating both the inherent difficulty of prediction tasks and scarcity of high-quality data, compared to simpler image segmentation tasks.

In the present work, we evaluated the impact of integrating the reperfusion status on the performance of CNNs for predicting the final infarct in patients with proximal intracranial occlusions treated by thrombectomy. Reperfusion is the single most important clinical metadata known to influence the progression of ischemic lesions from the baseline imaging (used as inputs to CNN) to the final infarct (Tsai and Albers, 2015). Previous studies have investigated direct integration of the reperfusion status during the learning process of CNN-based methods (Pinto et al., 2018, Robben et al., 2020). Another dichotomized the training set according to the reperfusion status with random forest-based methods (McKinley et al., 2017), but has not been evaluated with CNNs. We hypothesized that training CNNs from reperfusion status-specific subcohorts could improve their performance. Our objectives were: (1) to assess the impact of the reperfusion status on CNN-based predictive models; (2) to compare the predictive value of these CNNs against the threshold-based perfusion-diffusion mismatch models. An ancillary objective was to assess the relative predictive importance of the MRI inputs with an ablation study.

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