Factors influencing the reliability of a CT angiography-based deep learning method for infarct volume estimation

TL;DR

This study examines why deep learning methods using CT angiography tend to overestimate infarct volumes in stroke patients.

Contribution

The study identifies CTA timing as a key factor affecting the reliability of machine learning infarct volume estimation.

Findings

Collateral circulation status had no significant effect on the reliability of CNN-based infarct volume estimation.

The strongest correlation between CNN estimates and final infarct volumes was observed in patients with futile thrombectomies.

CTA timing in the mid arterial phase likely contributes to overestimation of infarct volumes.

Abstract

CT angiography (CTA)-based machine learning methods for infarct volume estimation have shown a tendency to overestimate infarct core and final infarct volumes (FIV). Our aim was to assess factors influencing the reliability of these methods.

The effect of collateral circulation on the correlation between convolutional neural network (CNN) estimations and FIV was assessed based on the Miteff system and hypoperfusion intensity ratio (HIR) in 121 patients with anterior circulation acute ischaemic stroke using Pearson correlation coefficients and median volumes. Correlation was also assessed between successful and futile thrombectomies. The timing of individual CTAs in relation to CTP studies was analysed.

The strength of correlation between CNN estimated volumes and FIV did not change significantly depending on collateral status as assessed with the Miteff system or HIR, being poor to moderate (r = 0.09-0.50). The strongest correlation was found in patients with futile thrombectomies (r = 0.61). Median CNN estimates showed a trend for overestimation compared to FIVs. CTA was acquired in the mid arterial phase in virtually all patients (120/121).

This study showed no effect of collateral status on the reliability of the CNN and best correlation was found in patients with futile thrombectomies. CTA timing in the mid arterial phase in virtually all patients can explain infarct volume overestimation.

CTA timing seems to be the most important factor influencing the reliability of current CTA-based machine learning methods, emphasizing the need for CTA protocol optimization for infarct core estimation.

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