A Confidence-Weighted Rule-Based Framework for Multimodal Brain Lesion Classification Using MRI and MRS
Bibliographic record
Abstract
Differentiating brain tumors from tumor-like lesions is a persistent clinical challenge due to their overlapping imaging features on conventional radiological scans.Tumor-like lesions such as demyelinating diseases, infections, or post-traumatic changes often mimic neoplastic growths in appearance, leading to potential misdiagnosis and inappropriate treatment decisions.To address this issue, we propose a novel machine-learning-based diagnostic framework that integrates Magnetic Resonance Spectroscopy (MRS) and structural Magnetic Resonance Imaging (MRI) through a confidence-weighted fusion strategy: Final Diagnosis = 0.7 MRS + 0.3 MRI.This weighting reflects the higher metabolic specificity of MRS, while retaining MRI's anatomical detail.Each modality is processed through a specialized pipeline.The MRS pipeline involves image-to-numeric transformation, noise filtering, metabolite concentration-based feature extraction, expertguided feature selection, and a rule-based classifier.The MRI pipeline includes skull stripping, a novel Dynamic Image Thresholding method, multidimensional feature extraction (statistical, volumetric, shape-based), and correlation-based feature selection with a rule-based classifier.Our integrated system achieved 90% classification accuracy on a clinically validated dataset, effectively distinguishing between tumors and tumor-like lesions.Despite the small dataset (n=50) from a single center, stratified cross-validation yielded consistent results (90% accuracy), demonstrating robustness.Future external validation is planned.By introducing a confidence-informed multimodal fusion strategy, the framework provides both high diagnostic accuracy and interpretability, supporting more reliable and informed clinical decision-making in neuro-oncology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".