Deep-learning-based white matter lesion volume in CT is associated with outcome after acute ischemic stroke

Abstract Background It remains unclear if deep-learning-based white matter lesion (DL-WML) volume can predict outcome after ischemic stroke. Purpose We aimed to develop, validate, and evaluate DL-WML volume in NCCT as a risk factor and IVT effect modifier compared to the Fazekas scale (WML-Faz) in patients also receiving EVT in an EVT-capable center. Methods A deep-learning model for WML segmentation in NCCT was developed and validated internally and externally. The volumetric correspondence of DL-WML volume per mL was reported relative to expert annotation with the intraclass correlation coefficient (ICC) and a Bland-Altman analysis reporting bias and limits of agreement (LoA). In a post-hoc analysis of the MR CLEAN No-IV trial, univariable and multivariable regression models were used to report (un)adjusted common odds ratios ([a]cOR) to associate DL-WML volume and WML-Faz with the occurrence of symptomatic-intracerebral hemorrhage (sICH) and 90-day functional outcome with the modified Rankin Scale (mRS). Results DL-WML volumes were comparable with those of the ground truth for both the internal test set (10/20(50%) male, age median:72[IQR:67-85], ICC mean:0.91 95%CI:[0.87;0.94];bias:-3mL LoA:[-12mL;7mL]) and the external test set (36/101(36%) male, age median:59[IQR:42-73], ICC mean:0.87 95%CI:[0.71;0.95];bias:-2mL LoA:[-11mL;7mL]). 516 patients from the MR CLEAN No-IV trial (291/516(56%) male, age median:71 IQR:[62-79],) were analyzed. Both DL-WML volume and WML-Faz were associated with sICH (DL-WML volume acOR:1.31 95%CI[1.08;1.60], WML-Faz acOR:1.53 95%CI[1.02;2.31]) and mRS (DL-WML volume acOR:0.84 95%CI[0.76;0.94], WML-Faz acOR:0.73 95%CI[0.60;0.88]). Only for the unadjusted analysis, WML-Faz was an IVT effect modifier (p=0.046), DL-WML was not (p=0.274). Conclusion DL-WML volume and WML-Faz had a similar relationship with functional outcome and sICH. Summary statement Deep-learning-based white matter lesion volume in non-contrast CT can substitute human ratings to prognosticate symptomatic intracerebral hemorrhage and functional outcome at 90-days in acute ischemic stroke patients. Key points This was the first study to show that white matter lesion volume in non-contrast CT based on deep-learning segmentations (DL-WML volume) was associated with symptomatic intracerebral hemorrhages and a worse functional outcome. Compared to the Fazekas scale, regression models using DL-WML volume had a similar fit to the data. White matter lesion load might be associated with more symptomatic intracerebral hemorrhages if IVT was given before EVT.