Oncologically Relevant Findings Reporting and Data System (ONCO-RADS): Guidelines for the Acquisition, Interpretation, and Reporting of Whole-Body MRI for Cancer Screening
Bibliographic record
Abstract
Acknowledging the increasing number of studies describing the use of whole-body MRI for cancer screening, and the increasing number of examinations being performed in patients with known cancers, an international multidisciplinary expert panel of radiologists and a geneticist with subject-specific expertise formulated technical acquisition standards, interpretation criteria, and limitations of whole-body MRI for cancer screening in individuals at higher risk, including those with cancer predisposition syndromes. The Oncologically Relevant Findings Reporting and Data System (ONCO-RADS) proposes a standard protocol for individuals at higher risk, including those with cancer predisposition syndromes. ONCO-RADS emphasizes structured reporting and five assessment categories for the classification of whole-body MRI findings. The ONCO-RADS guidelines are designed to promote standardization and limit variations in the acquisition, interpretation, and reporting of whole-body MRI scans for cancer screening. Published under a CC BY 4.0 license
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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.013 |
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".