Liquid Biopsy: A New, Non-Invasive Early Diagnostic and Prognostic Tool in Oncology
Bibliographic record
Abstract
Cancer is essentially a genetic disease. Neoplastic progression consists of a subsequent series of genetic alterations that cumulate. In the bloodstream of an affected subject, circulating tumor cells (CTC) and/or small deoxy-ribonucleic acid (DNA) fragments, known as circulating tumor DNA (ctDNA), can be found as a consequence of cancer cells death. Cell-free circulating DNA (cfDNA) consists of small fragments of DNA that are found free in plasma or serum, but also in other body fluids. The term liquid biopsy (LB) describes a highly sensitive method (based on a simple sampling of peripheral blood) for the isolation and analysis of cfDNA, which can also contain ctDNA and CTC. Its purpose is to look for cancer cells or portions of their DNA that are circulating in the blood. LB can be used to help find cancer in an early stage. It also has the additional advantage of being largely non-invasive and, therefore, being done more frequently, allowing better tumor and genetic mutations tracking. It can also be used to validate the efficacy of a drug for cancer treatment by taking multiple samples of LB within a few weeks. This technology can also be beneficial for patients after treatment to control relapse. The aim of this work is to give an overview of this technique, from its history, state-of-the-art, and methodology of execution, to its applications in oncology and with a hint to the gynecological field.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".