Review of clinical and emerging biomarkers for early diagnosis and treatment management of pancreatic cancer: towards personalised medicine
Bibliographic record
Abstract
Abstract Background: Pancreatic cancer is the 12th most commonly diagnosed cancer and the 3rd leading cause of cancer mortality and accounts for approximately 2·7% of all newly diagnosed cancer cases and 6·4% of all cancer mortalities in Canada. It has a very poor survival rate mainly due to the difficulty of detecting the disease at an early stage. Consequently, in the advancement of disease management towards the concept of precision medicine that takes individual patient variabilities into account, several investigators have focused on the identification of effective clinical biomarkers with high specificity and sensitivity, capable of early diagnosis of symptomatic patients and early detection of the disease in asymptomatic individuals at high risk for developing pancreatic cancer. Materials and methods: We searched several databases from August to December 2020 for relevant studies published in English between 2000 and 2020 and reporting on biomarkers for the management of pancreatic cancer. In this narrative review paper, we describe 13 clinical and emerging biomarkers for pancreatic cancers used in screening for early detection and diagnosis, to identify patients’ risk for metastatic disease and subsequent relapse, to monitor patient response to specific treatment and to provide clinicians the possibility of prospectively identifying groups of patients who will benefit from a particular treatment. Conclusions: Current and emerging biomarkers for pancreatic cancer with high specificity and sensitivity has the potential to account for individual patient variabilities, for early detection of disease before the onset of metastasis to improve treatment outcome and patients’ survival, help screen high-risk populations, predict prognosis, provide accurate information of patient response to specific treatment and improve patients monitoring during treatment. Thus, the future holds promise for the use of effective clinical biomarkers or a panel of biomarkers for personalised patient-specific targeted medicine for pancreatic cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 teacher head, 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".