Analysis of Serum Proteomic Patterns for Early Cancer Diagnosis: Drawing Attention to Potential Problems
Bibliographic record
Abstract
In a recent update (1) of already impressive data (2), it was reported that the use of proteomic patterns in serum to diagnose ovarian and prostate cancers can achieve perfect diagnostic sensitivity and specificity. A diagnostic sensitivity and specificity of 100% is unprecedented for any tumor marker known to date and, if reproducible, this finding could have a major impact on the way we diagnose cancer in the future. Over the last 2 years, results reported by several groups (2–6) have suggested that such proteomic patterns, particularly those generated by surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry, may facilitate the early diagnosis of various cancers, including those of the ovary, prostate, breast, and bladder. SELDI-TOF proteomic profiling technology has been reviewed (7,8). The impressive results reported with this new technology were welcomed by scientists, the popular press, the public, and even by politicians (9). Although there has been little published criticism of this methodology (10–12), serious skepticism about its utility has been expressed publicly at various scientific meetings. Many investigators and clinicians have adopted a wait-and-see approach pending the outcomes of prospective clinical studies using this technology which are starting now but will require years to complete.
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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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".