Brain Tumour Classification by Machine Learning Applications with Selected Biological Features: Towards A Newer Diagnostic Regime
Bibliographic record
Abstract
Histopathologically classified low-grade brain tumours show overlapping biological characteristics making them difficult to distinguish. In the present study low-grade brain tumour patient samples of three different histopathological types have been trained through machine learning technique using selected features for its classification. We used specifically the fundamental proliferation, invasion, macrophage infiltration triangle of cancer hallmark with propidium iodide (PI) marked cell-cycle, Ki67 marked proliferative indexing, invasion with MMP2 expression and presence of macrophage/microglia by silver-gold staining, CD11b+ and Iba1+ cell presence as biological parameters. These parameters when trained with proper machine learning protocol through extraction of underling features and represented in a 2D perceivable space are found capable of distinguishing the tumour types. Extracted features from such parameters in a six-dimensional featured space were trained through statistical learning theory while support vector machine (SVM) maximizes their predictive precision. The leave one out (LOO) cross validation process was applied to judge the accuracy of training followed by auto-encoder (AE) to reduce feature dimension at two which is visually perceptible. From the biological features quantified with standard methods it was found impossible to demarcate the three types of low grade brain tumours. However, after training through SVM and LOO cross validation when the six-dimensional featured space had been reduced into two-dimension using AE, the combined output of the features showed clear zonation in that 2D space. This indicates that the overlapping biological characteristics of these tumour types, when trained through proper support vector machine and reduced from multiple to two dimensional space provides a clear patho-clinical classification edge using a combination of common biological features. Hence, machine learning applications may potentially be used as a complementary diagnostic protocol with the conventional practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".