Automatic classification of pancreatic cancer from urinary biomarkers using equivariant quantum convolutional neural networks with hybrid optimization algorithm
Bibliographic record
Abstract
Early detection of Pancreatic Ductal Adenocarcinoma (PDAC) is crucial to improve survival rates. This study proposes an automated classification framework using equivariant quantum convolutional neural networks (EQCNNs) optimized with a Hybrid Adam-Dingo and Quantum Artificial Hummingbird Algorithm (HybADOQAHA). Urinary biomarkers – creatinine, lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1), regenerating islet-derived protein 1 beta (REG1B), and trefoil factor 1(TFF1) were analyzed from 590 samples comprising healthy, benign, and PDAC cases. Pre-processing with dual-feature filtering and feature extraction via lifted Euler characteristic transform enhanced data quality. The experimental results demonstrate better accuracy, precision, recall, specificity, and Area Under the Curve (AUC) compared with baseline models, establishing the proposed method as a promising non-invasive diagnostic tool for early PDAC detection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".