Can a Broad Molecular Screen Based on Circulating Tumor DNA Aid in Early Cancer Detection?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Early detection of cancer has been a major research focus for almost a century. Current methods for early cancer detection suffer from suboptimal sensitivity and specificity, especially when used for population screening. For most major cancers, including breast, prostate, lung, ovarian, and pancreatic cancer, population screening is still controversial or is not recommended by expert bodies. Circulating tumor DNA (ctDNA) is an exciting new cancer biomarker with potential applicability to all cancer types. Recent investigations have shown that genetic alterations or epigenetic modifications in ctDNA could be used for cancer detection with a liquid biopsy (i.e., a tube of blood). Tests based on ctDNA have attracted considerable attention for various applications, such as patient management, prognosis, early diagnosis, and population screening. Recently, new biotechnology companies were founded, with the goal of revolutionizing early cancer detection by using ctDNA. We previously examined this technology, as published by various academic laboratories and of one leading company, Grail, and drew attention to potential obstacles. After 3 years of intense development, this technology seems to have made some progress. Here, we will analyze the latest clinical data presented by Grail in October 2019, during the inaugural American Society of Clinical Oncology (ASCO) 2019 Breakthrough Conference. Despite considerable technical improvements, it seems that the sensitivity and specificity of the Grail test as a pan-cancer screening tool are still too low for clinical use. The prospects that this test could be further improved are also discussed.
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.
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 | 0.000 |
| 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 it