Liquid Biopsy in Lung Cancer: A Perspective From Members of the Pulmonary Pathology Society
Bibliographic record
Abstract
Liquid biopsy has received extensive media coverage and has been called the holy grail of cancer detection. Attempts at circulating tumor cell and genetic material capture have been progressing for several years, and recent financially and technically feasible improvements of cell capture devices, plasma isolation techniques, and highly sensitive polymerase chain reaction- and sequencing-based methods have advanced the possibility of liquid biopsy of solid tumors. Although practical use of circulating RNA-based testing has been hindered by the need to fractionate blood to enrich for RNAs, the detection of circulating tumor cells has profited from advances in cell capture technology. In fact, the US Food and Drug Administration has approved one circulating tumor cell selection platform, the CellSearch System. Although the use of liquid biopsy in a patient population with a genomically defined solid tumor may potentially be clinically useful, it currently does not supersede conventional pretreatment tissue diagnosis of lung cancer. Liquid biopsy has not been validated for lung cancer diagnosis, and its lower sensitivity could lead to significant diagnostic delay if liquid biopsy were to be used in lieu of tissue biopsy. Ultimately, notwithstanding the enthusiasm encompassing liquid biopsy, its clinical utility remains unproven.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".