PROMISE: Prognostic Radiomic Outcome Measurement in Acute Subdural Hematoma Evacuation Post-Craniotomy

<b>Background/Objectives:</b> Traumatic acute subdural hematoma (aSDH) often requires surgical intervention, such as craniotomy, to relieve mass lesions and pressure. The extent of hematoma evacuation significantly impacts patient outcomes. This study utilizes 3D Slicer software to analyse post-craniotomy hematoma volume changes and evaluate their prognostic significance in aSDH patients. <b>Methods:</b> Among 178 adult patients diagnosed with aSDH from January 2015 to December 2022, 64 underwent hematoma evacuation via craniotomy. Initial scans were performed within 24 h of trauma, followed by routine postoperative scans to assess residual hematoma. We conducted radiomic analysis of preoperative and postoperative volumes, surface area, Feret diameter, sphericity, flatness, and elongation. Clinical parameters, including SOFA score, APACHE score, pupillary response, comorbidities, age, anticoagulation status, and preoperative haematocrit and haemoglobin levels, were also evaluated. <b>Results:</b> Changes in Δ surface area significantly correlated with 30-day outcomes (<i>p</i> = 0.03) and showed moderate predictive accuracy (AUC = 0.65). Patients with a Δ surface area > 30,090 mm<sup>2</sup> experienced poorer outcomes (OR = 6.66, <i>p</i> = 0.02). Significant features included preoperative surface area (<i>p</i> = 0.009), Feret diameter (<i>p</i> = 0.0012). In multivariate analysis, only the Feret diameter remained significant (<i>p</i> = 0.01). <b>Conclusions:</b> Postoperative Δ surface area is, among other variables, a strong predictor of 30-day outcomes, while in multivariate analysis, preoperative Feret diameter remains the only independent predictor. Radiomic analysis with 3D Slicer may enhance prognostic accuracy and inform tailored therapeutic strategies.

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Journal
Brain Sciences
Published
2025-01-10
DOI
https://doi.org/10.3390/brainsci15010058
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1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
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article
Field-Weighted Citation Impact
1.36
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article

PROMISE: Prognostic Radiomic Outcome Measurement in Acute Subdural Hematoma Evacuation Post-Craniotomy

Martin Vychopen, Erdem Güresir, Johannes Wach, Antonia Richter et al.
1 citations
Brain Sciences
Radiomics and Machine Learning in Medical Imaging
1.36
article

PROMISE: Prognostic Radiomic Outcome Measurement in Acute Subdural Hematoma Evacuation Post-Craniotomy

Martin Vychopen, Erdem Güresir, Johannes Wach, Antonia Richter, Alexandru Guranda
article en
1 citations

Abstract

<b>Background/Objectives:</b> Traumatic acute subdural hematoma (aSDH) often requires surgical intervention, such as craniotomy, to relieve mass lesions and pressure. The extent of hematoma evacuation significantly impacts patient outcomes. This study utilizes 3D Slicer software to analyse post-craniotomy hematoma volume changes and evaluate their prognostic significance in aSDH patients. <b>Methods:</b> Among 178 adult patients diagnosed with aSDH from January 2015 to December 2022, 64 underwent hematoma evacuation via craniotomy. Initial scans were performed within 24 h of trauma, followed by routine postoperative scans to assess residual hematoma. We conducted radiomic analysis of preoperative and postoperative volumes, surface area, Feret diameter, sphericity, flatness, and elongation. Clinical parameters, including SOFA score, APACHE score, pupillary response, comorbidities, age, anticoagulation status, and preoperative haematocrit and haemoglobin levels, were also evaluated. <b>Results:</b> Changes in Δ surface area significantly correlated with 30-day outcomes (<i>p</i> = 0.03) and showed moderate predictive accuracy (AUC = 0.65). Patients with a Δ surface area > 30,090 mm<sup>2</sup> experienced poorer outcomes (OR = 6.66, <i>p</i> = 0.02). Significant features included preoperative surface area (<i>p</i> = 0.009), Feret diameter (<i>p</i> = 0.0012). In multivariate analysis, only the Feret diameter remained significant (<i>p</i> = 0.01). <b>Conclusions:</b> Postoperative Δ surface area is, among other variables, a strong predictor of 30-day outcomes, while in multivariate analysis, preoperative Feret diameter remains the only independent predictor. Radiomic analysis with 3D Slicer may enhance prognostic accuracy and inform tailored therapeutic strategies.

Brain SciencesVol. 15(1)
University Hospital Leipzig (DE)
No poverty
Openalex Percentile: Top 25%
Radiomics and Machine Learning in Medical Imaging
1.36
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