Differentiating Gastric Cancers from Acid Peptic Diseases through Integrative Targeted Proteomics and Machine Learning Approaches
Bibliographic record
Abstract
Gastric cancers (GCs) are often diagnosed in advanced stages owing to nonspecific early symptoms resembling Acid Peptic Diseases (APDs). Despite recent efforts, a simple, liquid biopsy-based multiprotein panel prediagnostic assay capable of differentiating GCs from APDs is lacking. Mass spectrometry (MS)-based targeted proteomics methods, including Multiple Reaction Monitoring (MRM), are utilized as the method of choice to develop Laboratory Developed Tests (LDTs) that revolutionize GC early diagnosis and screening. In this study, a 22-min MS-MRM LDT was developed and tested to quantify a serum protein panel in 135 serum samples from treatment-naive cases of GCs, APDs, and healthy individuals. Notably, a novel Deep Neural Network (DNN)-based pattern recognition scoring architecture, integrated with a model explainability tool (SHAP), was developed to score and categorize GCs. The MRM-MS assay produced minimal carryover and matrix effects, with adequate limits of detection/quantification. Quantities of SAA1 and IGFBP2, as determined through ELISA, demonstrated similar sensitivity compared to the LDT. Importantly, the DNN-based scoring architecture efficiently differentiated GCs from the rest of the samples (AUROC = 0.95), with average precision marking >0.90 and minimal bias in protein expression affecting model performance. This LDT can serve as a prediagnostic screening method to distinguish GCs from APDs, guiding clinicians and patients in proceeding with a confirmatory diagnosis.
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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) | 0.001 | 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) | 0.001 | 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".