J Stroke.  2022 Jan;24(1):148-151. 10.5853/jos.2021.02817.

Diffusion-Weighted Imaging-Alone Endovascular Thrombectomy Triage in Acute Stroke: Simulating Diffusion-Perfusion Mismatch Using Machine Learning

• Kim YC
• Seo WK
• Baek IY
• Lee JE
• Song HN
• Chung JW
• Kim CK
• Oh K
• Suh Si
• Bang OY
• Kim GM
• Saver JL
• Liebeskind DS

• KMID: 2525340
• DOI: http://doi.org/10.5853/jos.2021.02817

Figure

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  Figure 1. (A) A block diagram of the proposed endovascular thrombectomy (EVT) triage method. Feature extraction is performed on apparent diffusion coefficient (ADC) images. Gain of thrombectomy (GoT) is calculated as a ratio of probability of good outcome in case of EVT to probability of good outcome in case of no EVT. (B, C) Two methods of calculating P(G|EVT). (B) A simple “unconditional” method. All EVT patient data are used to build a model. (C) A “conditional” method where successful recanalization (SR) and unsuccessful recanalization (UR) patient data are separately used to build machine learning (ML) models. In addition, an extra ML model for predicting the probability of EVT success is built. The output probability scores are combined to calculate P(G|EVT). G, good clinical outcome.

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  Figure 2. The receiver operating characteristic area under the curve (AUC) values for the mismatch ratio and machine learning methods in the external validation cohort with endovascular thrombectomy (EVT) for the prediction of good clinical outcome (i.e., modified Rankin Scale [mRS] score at 90 days, ≤2). LR, logistic regression; G, good clinical outcome; RF, random forest; SVM, support vector machine.

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  Figure 3. Distributions of the modified Rankin Scale (mRS) scores at 90 days for the (A) endovascular thrombectomy (EVT) and (B) no EVT groups, when the conditional random forest models were used to estimate gain of thrombectomy (GoT). In the EVT group, the high GoT group shows a larger proportion of low mRS scores at 90 days than the low GoT group. In the no EVT group, the low GoT group shows a larger proportion of low mRS scores at 90 days than the high GoT group.

Reference

References

1. Straka M, Albers GW, Bammer R. Real-time diffusion-perfusion mismatch analysis in acute stroke. J Magn Reson Imaging. 2010; 32:1024–1037.

2. Albers GW, Marks MP, Kemp S, Christensen S, Tsai JP, Ortega-Gutierrez S, et al. Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N Engl J Med. 2018; 378:708–718.

3. Calamante F. Arterial input function in perfusion MRI: a comprehensive review. Prog Nucl Magn Reson Spectrosc. 2013; 74:1–32.

4. Deutschmann H, Hinteregger N, Wießpeiner U, Kneihsl M, Fandler-Höfler S, Michenthaler M, et al. Automated MRI perfusion-diffusion mismatch estimation may be significantly different in individual patients when using different software packages. Eur Radiol. 2021; 31:658–665.

5. Kim YC, Lee JE, Yu I, Song HN, Baek IY, Seong JK, et al. Evaluation of diffusion lesion volume measurements in acute ischemic stroke using encoder-decoder convolutional network. Stroke. 2019; 50:1444–1451.

6. Oppenheim C, Grandin C, Samson Y, Smith A, Duprez T, Marsault C, et al. Is there an apparent diffusion coefficient threshold in predicting tissue viability in hyperacute stroke? Stroke. 2001; 32:2486–2491.

7. Rosso C, Hevia-Montiel N, Deltour S, Bardinet E, Dormont D, Crozier S, et al. Prediction of infarct growth based on apparent diffusion coefficients: penumbral assessment without intravenous contrast material. Radiology. 2009; 250:184–192.

8. Marler JR, Tilley BC, Lu M, Brott TG, Lyden PC, Grotta JC, et al. Early stroke treatment associated with better outcome: the NINDS rt-PA stroke study. Neurology. 2000; 55:1649–1655.

9. Kim BJ, Kim SM, Kang DW, Kwon SU, Suh DC, Kim JS. Vascular tortuosity may be related to intracranial artery atherosclerosis. Int J Stroke. 2015; 10:1081–1086.