Assessing the Impact of Incidental Findings in a Lung Cancer Screening Study by Using Low-dose Computed Tomography
Bibliographic record
Abstract
PURPOSE: To assess the prevalence and nature of incidental findings (IF) seen in low-dose computed tomographies (LDCT) from a lung cancer screening study for at-risk individuals. MATERIALS AND METHODS: Radiology reports from LDCTs of 4073 participants of a lung cancer screening study were retrospectively reviewed for findings other than lung nodules, that is, IFs, which were regarded as actionable. The frequency, nature, and expected cost of these IFs, and their anticipated follow-up were estimated. RESULTS: There were 880 IFs described in 782 study participants (19%); the median age of the participants was 62 years (range, 46-80 years). More IFs were found in men (55%) than in women. The majority of these findings were noncardiovascular (76%), for which imaging was suggested for 74%. There were 7 severe IFs (0.8%) that merited immediate attention. Seven known cancers were diagnosed from follow-ups of the IFs. The majority of IFs (n = 486 [55%]) would require imaging follow-up if clinically indicated, with an estimated total a cost of CAN$45,500 to CAN$51,000 to provide initial diagnostic workup. CONCLUSION: IFs on lung cancer screening studies are not uncommon and frequently require imaging or other follow-up for definitive diagnoses and to assess their clinical relevance. The implication of IFs has to be considered when determining a cost-effective and ethical protocol for the utilisation of LDCT in a high-risk population.
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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".