An Explainable AI Exploration of the Machine Learning Classification of Neoplastic Intracerebral Hemorrhage from Non-Contrast CT
Simple Summary
Abstract
1. Introduction
2. Methods
2.1. Study Population
2.2. Image Analysis and Preprocessing
2.3. Classification Pipeline
2.4. Explanation Methods
2.5. Faithfulness Metric
2.6. Statistical Analysis
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AUC | Area under the Curve |
CNN | Convolutional Neural Network |
CT | Computed Tomography |
ICH | Intracerebral Hemorrhage |
HU | Hounsfield Units |
ML | Machine Learning |
MRI | Magnetic Resonance Imaging |
NifTI | Neuroimaging Informatics Technology Initiative |
PACS | Picture Archiving and Communication System |
PHE | Perihematomal Edema |
ResNet | Deep Residual Network |
XAI | Explainable Artificial Intelligence |
References
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All ICH (n = 349) | Neoplastic ICH (n = 144) | Non-Neoplastic ICH (n = 205) | p-Value | |
---|---|---|---|---|
Age (years), median (IQR) | 67 (53; 78) | 66.5 (53; 78) | 67 (53;78) | 0.998 |
Female, n (%) | 167 (47.85) | 69 (47.91) | 98 (47.80) | 0.983 |
Δ symptom onset to imaging (days), median (IQR) | 0.46 (0.13; 1.71) | 0.95 (0.2; 5.0) | 0.32 (0.09;1.0) | 0.013 |
Hypertension, n (%) | 157 (44.99) | 39 (27.08) | 118 (57.56) | <0.001 |
CAA, n (%) | 49 (14.04) | - | 49 (23.90) | - |
Oral anticoagulation, n (%) | 10 (2.87) | - | 10 (4.88) | - |
Vascular malformation, n (%) | 63 (18.05) | - | 63 (30.73) | - |
Metastasis, n (%) | 102 (29.23) | 102 (70.83) | - | - |
Tumor, n (%) | 42 (12.03) | 42 (29.17) | - | - |
Median ICH volume, mL (IQR) | 6.92 (2.12; 19.91) | 7.44 (2.50; 20.26) | 6.53 (1.76; 19.56) | 0.477 |
Median PHE volume, mL (IQR) | 16.45 (7.01; 39.71) | 23.94 (14.24; 64.30) | 10.47 (5.32; 24.37) | <0.001 |
Mean | Standard Deviation | |
---|---|---|
Saliency | 0.473 | 0.047 |
InputXGradient | −0.256 | 0.181 |
SmoothGrad | 0.233 | 0.077 |
Gradient Shap | −0.092 | 0.258 |
GradCam | 0.116 | 0.197 |
GradCam++ | 0.49 | 0.153 |
Guided GradCam | 0.031 | 0.051 |
Lesion Size | Region | Neoplastic | Non-Neoplastic | p-Value |
---|---|---|---|---|
All | ICH | 0.615 | 0.663 | <0.001 |
All | PHE | 0.430 | 0.439 | 0.54 |
All | BG | 0.017 | 0.011 | <0.001 |
Small | ICH | 0.684 | 0.717 | 0.002 |
Small | PHE | 0.471 | 0.521 | 0.023 |
Small | BG | 0.013 | 0.008 | <0.001 |
Large | ICH | 0.561 | 0.600 | 0.008 |
Large | PHE | 0.396 | 0.341 | 0.001 |
Large | BG | 0.019 | 0.014 | <0.001 |
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Schulze-Weddige, S.; Baumgärtner, G.L.; Orth, T.; Tietze, A.; Scheel, M.; Wasilewski, D.; Wattjes, M.P.; Hanning, U.; Kniep, H.; Penzkofer, T.; et al. An Explainable AI Exploration of the Machine Learning Classification of Neoplastic Intracerebral Hemorrhage from Non-Contrast CT. Cancers 2025, 17, 2502. https://doi.org/10.3390/cancers17152502
Schulze-Weddige S, Baumgärtner GL, Orth T, Tietze A, Scheel M, Wasilewski D, Wattjes MP, Hanning U, Kniep H, Penzkofer T, et al. An Explainable AI Exploration of the Machine Learning Classification of Neoplastic Intracerebral Hemorrhage from Non-Contrast CT. Cancers. 2025; 17(15):2502. https://doi.org/10.3390/cancers17152502
Chicago/Turabian StyleSchulze-Weddige, Sophia, Georg Lukas Baumgärtner, Tobias Orth, Anna Tietze, Michael Scheel, David Wasilewski, Mike P. Wattjes, Uta Hanning, Helge Kniep, Tobias Penzkofer, and et al. 2025. "An Explainable AI Exploration of the Machine Learning Classification of Neoplastic Intracerebral Hemorrhage from Non-Contrast CT" Cancers 17, no. 15: 2502. https://doi.org/10.3390/cancers17152502
APA StyleSchulze-Weddige, S., Baumgärtner, G. L., Orth, T., Tietze, A., Scheel, M., Wasilewski, D., Wattjes, M. P., Hanning, U., Kniep, H., Penzkofer, T., & Nawabi, J. (2025). An Explainable AI Exploration of the Machine Learning Classification of Neoplastic Intracerebral Hemorrhage from Non-Contrast CT. Cancers, 17(15), 2502. https://doi.org/10.3390/cancers17152502