Deep learning and machine learning for differentiation between contrast extravasation and hemorrhagic transformation in post-thrombectomy stroke CT

ML models applied to NCCT can accurately differentiate CE from HT post-MT. While U-Net offers advantages in learning from raw imaging data, traditional models performed comparably. Larger studies and hybrid approaches integrating radiomics and deep learning are warranted.

Authors

Ona Wu
Daphne Fonseca (ORCID: https://orcid.org/0000-0002-6209-4765)
Millene Rodrigues Camilo (ORCID: https://orcid.org/0000-0002-0310-6033)
OCTÁVIO MARQUES PONTES-NETO (ORCID: https://orcid.org/0000-0003-0317-843X)
Ryosuke Miyaji
Júlio César Nather Júnior
Thiago Oscar Goulart

Institutions

Universidade de Ribeirão Preto (BR)
Harvard University (US)
Universidade de São Paulo (BR)
Clinics Hospital of Ribeirão Preto (BR)
Sunnybrook Hospital (CA)

Publication Details

Journal: Journal of Neuroradiology
Published: 2026-02-12
DOI: https://doi.org/10.1016/j.neurad.2026.101530
Primary Topic: Intracerebral and Subarachnoid Hemorrhage Research
Type: article
Field-Weighted Citation Impact: 0.00

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article

Deep learning and machine learning for differentiation between contrast extravasation and hemorrhagic transformation in post-thrombectomy stroke CT

Ona Wu, Daphne Fonseca, Millene Rodrigues Camilo, OCTÁVIO MARQUES PONTES-NETO et al.

Feb 12, 2026

Journal of Neuroradiology

Intracerebral and Subarachnoid Hemorrhage Research

article

Deep learning and machine learning for differentiation between contrast extravasation and hemorrhagic transformation in post-thrombectomy stroke CT

Ona Wu, Daphne Fonseca, Millene Rodrigues Camilo, OCTÁVIO MARQUES PONTES-NETO, Ryosuke Miyaji, Júlio César Nather Júnior, Thiago Oscar Goulart

February 12, 2026article en

Abstract

ML models applied to NCCT can accurately differentiate CE from HT post-MT. While U-Net offers advantages in learning from raw imaging data, traditional models performed comparably. Larger studies and hybrid approaches integrating radiomics and deep learning are warranted.

Journal of NeuroradiologyVol. 53(2)

Universidade de Ribeirão Preto (BR), Harvard University (US), Universidade de São Paulo (BR), Clinics Hospital of Ribeirão Preto (BR), Sunnybrook Hospital (CA)

Openalex Percentile: Top 60%

Intracerebral and Subarachnoid Hemorrhage Research

DOI Open Access PDF OpenAlex

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Deep learning and machine learning for differentiation between contrast extravasation and hemorrhagic transformation in post-thrombectomy stroke CT — Ona Wu, Daphne Fonseca, et al. · Journal of Neuroradiology (2026) | TGRS Research Map | TGRS