Diagnosis of hepatocellular carcinoma in cirrhotic patients: a proof-of-concept study using serum micro-Raman spectroscopy
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is the third most common cause of cancer death worldwide. The development of novel diagnostic methods is needed to detect tumours at an early stage when patients are eligible for curative treatments. The purpose of this proof-of-concept study was to determine if micro-Raman spectroscopy applied to the sera of cirrhotic patients may be an alternative method for rapidly discriminating patients with and without HCC. Serum samples were collected from 2 groups of patients: cirrhotic patients with HCC (n = 37) and without HCC (n = 34). Two different approaches were used, dried serum drops and freeze-dried serum, and micro-Raman spectra were acquired in the point-mode with a 785 nm laser excitation in the spectral range of 600-1800 cm(-1). Spectra were quality-tested and pre-processed (smoothing, baseline subtraction, vector normalization). Using principal component analysis, the 2 classes, corresponding to cirrhotic patients with and without HCC, could not be differentiated. In contrast, the support vector machine method using the leave-one-out cross validation procedure was able to correctly classify the two groups of patients with an overall rate of accuracy of 84.5% to 90.2% for dried serum drops and 86% to 91.5% for freeze-dried serum. These results are promising and support the concept that serum micro-Raman spectroscopy may become a useful diagnostic tool to detect biomarkers in the field of cancer, as described here for distinguishing between cirrhotic patients with and without HCC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".