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Can machine learning of post-procedural cone-beam CT images in acute ischemic stroke improve the detection of 24-h hemorrhagic transformation? A preliminary study

  • Interventional Neuroradiology
  • Published:
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Abstract

Purpose

Hemorrhagic transformation (HT) is an independent predictor of unfavorable outcome in acute ischemic stroke (AIS) patients undergoing endovascular thrombectomy (EVT). Its early identification could help tailor AIS management. We hypothesize that machine learning (ML) applied to cone-beam computed tomography (CB-CT), immediately after EVT, improves performance in 24-h HT prediction.

Methods

We prospectively enrolled AIS patients undergoing EVT, post-procedural CB-CT, and 24-h non-contrast CT (NCCT). Three raters independently analyzed imaging at four anatomic levels qualitatively and quantitatively selecting a region of interest (ROI) < 5 mm2. Each ROI was labeled as “hemorrhagic” or “non-hemorrhagic” depending on 24-h NCCT. For each level of CB-CT, Mean Hounsfield Unit (HU), minimum HU, maximum HU, and signal- and contrast-to-noise ratios were calculated, and the differential HU-ROI value was compared between both hemispheres. The number of anatomic levels affected was computed for lesion volume estimation. ML with the best validation performance for 24-h HT prediction was selected.

Results

One hundred seventy-two ROIs from affected hemispheres of 43 patients were extracted. Ninety-two ROIs were classified as unremarkable, whereas 5 as parenchymal contrast staining, 29 as ischemia, 7 as subarachnoid hemorrhages, and 39 as HT. The Bernoulli Naïve Bayes was the best ML classifier with a good performance for 24-h HT prediction (sensitivity = 1.00; specificity = 0.75; accuracy = 0.82), though precision was 0.60.

Conclusion

ML demonstrates high-sensitivity but low-accuracy 24-h HT prediction in AIS. The automated CB-CT imaging evaluation resizes sensitivity, specificity, and accuracy rates of visual interpretation reported in the literature so far. A standardized quantitative interpretation of CB-CT may be warranted to overcome the inter-operator variability.

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Adapted from Ma et al. [24]

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Data Availability

Data is available upon reasonable request from the corresponding author.

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Author notes

  1. Valerio Da Ros and Andrea Duggento contributed equally to this work.

Authors and Affiliations

  1. Department of Biomedicine and Prevention, University Hospital of Rome “Tor Vergata”, Viale Oxford 81, Rome, Italy

    Valerio Da Ros, Andrea Duggento, Armando Ugo Cavallo, Luigi Bellini, Francesca Pitocchi, Nicola Toschi, Francesca Di Giuliano, Roberto Floris & Francesco Garaci

  2. Stroke Center, Department of Systems Medicine, University Hospital of Rome “Tor Vergata”, Viale Oxford 81, 00133, Rome, Italy

    Alfredo Paolo Mascolo, Fabrizio Sallustio, Marina Diomedi & Ilaria Maestrini

  3. Clinic of Radiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03659, Martin, Slovakia

    Kamil Zeleňák

Authors
  1. Valerio Da Ros
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  14. Ilaria Maestrini
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Contributions

Dr. Da Ros, Dr. Duggento, and Dr. Maestrini contributed substantially to the study’s conception and design. Material preparation and data collection were performed by Dr. Da Ros, Dr. Bellini, Dr. Pitocchi, Dr. Mascolo, and Dr. Maestrini. Neuroimaging analysis was performed by Dr. Da Ros, Dr. Pitocchi, and Dr. Maestrini. Statistical analyses were performed by Dr. Duggento, Dr. Cavallo, and Dr. Maestrini. All authors made a substantial contribution to data interpretation. The first draft of the manuscript was written by Dr. Da Ros, Dr. Duggento, Dr. Cavallo, and Dr. Maestrini. All authors revised critically previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ilaria Maestrini.

Ethics declarations

Ethics approval

All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments. The local ethics committee approved prospectively data collection on patients undergoing EVT (Registro Sperimentazioni, R.S. 25/18). All the procedures performed were part of the routine care.

Informed consent

Informed consent was obtained from all individual participants included in the study. The authors affirm that human research participants provided informed consent for the publication of the images in Fig. 3A-F.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

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Da Ros, V., Duggento, A., Cavallo, A.U. et al. Can machine learning of post-procedural cone-beam CT images in acute ischemic stroke improve the detection of 24-h hemorrhagic transformation? A preliminary study. Neuroradiology 65, 599–608 (2023). https://doi.org/10.1007/s00234-022-03070-0

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