Serum Diagnosis of Pancreatic Adenocarcinoma Using Surface-Enhanced Laser Desorption and Ionization Mass Spectrometry
Bibliographic record
Abstract
PURPOSE: Each year in the United States, approximately 30,000 people die from pancreatic cancer. Fewer than 5% of patients survive >5 years after diagnosis, because most patients present with advanced disease. Early diagnosis may improve the prognosis of patients with pancreatic cancer. EXPERIMENTAL DESIGN: In an attempt to improve on current approaches to the serological diagnosis of pancreatic cancer, we analyzed serum samples from patients with and without pancreatic cancer using surface-enhanced laser desorption and ionization (SELDI) protein chip mass spectrometry. Using a case-control study design, serum samples from 60 patients with resectable pancreatic adenocarcinoma were compared with samples from 60 age- and sex-matched patients with nonmalignant pancreatic diseases, as well as 60 age- and sex-matched healthy controls. To increase the number of proteins potentially identifiable, serum was fractionated using anion exchange and profiled on two ProteinChip surfaces (metal affinity capture and weak cation exchange). RESULTS: We determined a minimum set of protein peaks able to discriminate between patient groups and used the unified maximum separability algorithm to compare the performance of the individual marker panels alone or in conjunction with CA19-9. Among the peaks identified by SELDI profiling that had the ability to distinguish between patient groups, the 2 most discriminating protein peaks could differentiate patients with pancreatic cancer from healthy controls with a sensitivity of 78% and specificity of 97%. These 2 markers performed significantly better than the current standard serum marker, CA19-9 (P < 0.05). The diagnostic accuracy of the 2 markers was improved by using them in combination with CA 19-9. Similarly, a combination of 3 SELDI markers and CA19-9 was superior to CA19-9 alone in distinguishing individuals with pancreatic cancer from the combined pancreatic disease controls and healthy subject groups (P = 0.078). SELDI markers were also better than CA19-9 in distinguishing patients with pancreatic cancer from those with pancreatitis. CONCLUSION: SELDI profiling of serum can be used to accurately differentiate patients with pancreatic cancer from those with other pancreatic diseases and from healthy controls.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".