Pan-Cancer Classification System with Explainable AI Interpretation: A Feasibility Study
Bibliographic record
Abstract
Cancer genomics identifies all genes playing critical roles in carcinogenesis. The state-of-the-art cancer genomics profiling characterized many clinically and biologically relevant patterns that are not resolvable by morphology nor distinguishable under the microscope for cancer diagnosis. With that genomic information, doctors can develop an individualized treatment plan for cancer patients and provide precision medicine. However, several technical challenges (such as low tumor purity, batch effects and formalin-fixed, paraffin-embedded (FFPE) tissue restoration) potentially led to ambiguous diagnoses that needed to be solved in the clinical setting. The purpose of this study is to develop a robust tumor classification framework to improve cancer diagnosis and provide Explainable Artificial Intelligence (XAI) based interpretable results with increased transparency model interpretability of the classification. We utilized a large set of over six thousand tumor samples (DNA methylation and gene expression) from The Cancer Genome Atlas (TCGA). We implemented realistic variable selection by separating the training and test datasets and removed artificial and technical sources of variabilities to overcome batch effect issues while identifying the biological variation and making the prediction meaningful and robust. The Random Forest classifier produced about 95 and 96% accuracy for mRNA and methylation-based models respectively with minimum features of 50 methylation probes and gene expression signatures. We further developed an XAI strategy and applied it to a large brain cancer patient group to make an explainable patient-specific decision while tailoring the provided recommendations based on each patient's characteristics. This strategy demonstrates more accurate and practical molecular subtype classification with explainable AI for model interpretation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".