Classification of glioma grade and Ki-67 level prediction in MRI data: A SHAP-driven interpretation.
Authors
Affiliations (14)
Affiliations (14)
- Center for Magnetic Resonance Research, University of Illinois Chicago, Chicago, 60612, IL, USA. Electronic address: [email protected].
- Department of Pathology and Biomedical Science, University of Otago, 2 Riccarton Ave, Christchurch, 8140, New Zealand. Electronic address: [email protected].
- Department of Biochemistry, University of Regina, 3737 Wascana Pkwy, Regina, S4S 0A2, SK, Canada. Electronic address: [email protected].
- Spiced Academy, Ritterstraße 12-14, 10969 Berlin, Germany. Electronic address: [email protected].
- Center for Magnetic Resonance Research, University of Illinois Chicago, Chicago, 60612, IL, USA; Department of Radiology, University of Illinois Chicago, Chicago, 60612, IL, USA. Electronic address: [email protected].
- Department of Physics, University of Virginia, Charlottesville, 22904-4714, VA, USA. Electronic address: [email protected].
- Center for Magnetic Resonance Research, University of Illinois Chicago, Chicago, 60612, IL, USA. Electronic address: [email protected].
- Department of Electrical Engineering, Qatar University, Doha, 213, Qatar. Electronic address: [email protected].
- Department of Neurology, University of Illinois Chicago, Chicago, 60612, IL, USA. Electronic address: [email protected].
- Center for Magnetic Resonance Research, University of Illinois Chicago, Chicago, 60612, IL, USA; Department of Biomedical Engineering, University of Illinois Chicago, Chicago, 60607, IL, USA. Electronic address: [email protected].
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Charlottesville, Wuhan, China. Electronic address: [email protected].
- Department of Electrical Engineering, Qatar University, Doha, 213, Qatar. Electronic address: [email protected].
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Charlottesville, Wuhan, China. Electronic address: [email protected].
- Center for Magnetic Resonance Research, University of Illinois Chicago, Chicago, 60612, IL, USA; Department of Biomedical Engineering, University of Illinois Chicago, Chicago, 60607, IL, USA; Departments of Radiology and Neurosurgery, University of Illinois College of Medicine at Chicago, Chicago, 60612, IL, USA. Electronic address: [email protected].
Abstract
This study focuses on artificial intelligence-driven classification of glioma and Ki-67 leveling using T2w-FLAIR MRI, exploring the association of Ki-67 biomarkers with deep learning (DL) features through explainable artificial intelligence (XAI) and SHapley Additive exPlanations (SHAP). This IRB-approved study included 101 patients with glioma brain tumor acquired MR images with the T2W-FLAIR sequence. We extracted DL bottleneck features using ResNet50 from glioma MR images. Principal component analysis (PCA) was deployed for dimensionality reduction. XAI was used to identify potential features. The XGBosst classified the histologic grades of the glioma and the level of Ki-67. We integrated potential DL features with patient demographics (age and sex) and Ki-67 biomarkers, utilizing SHAP to determine the model's essential features and interactions. Glioma grade classification and Ki-67 level predictions achieved overall accuracies of 0.94 and 0.91, respectively. It achieved precision scores of 0.92, 0.94, and 0.96 for glioma grades 2, 3, and 4, and 0.88, 0.94, and 0.97 for Ki-67 levels (low: 5%≤Ki-67<10%, moderate: 10%≤Ki-67≤20, and high: Ki-67>20%). Corresponding F1-scores were 0.95, 0.88, and 0.96 for glioma grades and 0.92, 0.93, and 0.87 for Ki-67 levels. SHAP analysis further highlighted a strong association between bottleneck DL features and Ki-67 biomarkers, demonstrating their potential to differentiate glioma grades and Ki-67 levels while offering valuable insights into glioma aggressiveness. This study demonstrates the precise classification of glioma grades and the prediction of Ki-67 levels to underscore the potential of AI-driven MRI analysis to enhance clinical decision-making in glioma management.