Establishing Lung Cancer Risk and Optimizing Incidental Pulmonary Nodule Management
Bibliographic record
Abstract
Abstract Rationale The majority of pulmonary nodules are found incidentally on imaging studies unrelated to screening. The incidence of lung cancer in patients with incidental pulmonary nodules (IPNs) is higher than that in a screening program, and over half are not eligible for lung cancer screening, representing an opportunity for early lung cancer detection. Most patients with IPNs have no follow-up and are poorly managed. It is important to determine efficient identification methods and establish an IPN management pathway. In this study we report the preliminary results of two different nodule management protocols, in patients with IPNs identified using a Named Entity Recognition and Classification Model (NERC)Methods The Vancouver site of the multi-centre, Canadian IPN trial – The Identifying Early Lung Cancer in a Diverse Population (IDEAL) study identified IPNs in a large health authority in British Columbia (BC), using NERC. Patients with IPNs were randomised to receive physician lead management in accordance with either PanCan nodule risk calculator or Fleischner Society Guideline. Results In the first 12-months, NERC identified 3,988 patients with nodules > 6 mm, 504 (13%) were determined by a pulmonologist to be true IPNs, 133 consenting patients were randomised to either PanCan or Fleischner guideline management. The mean age in the cohort was 66.5±7.8, with 49% female and 58% were lifelong non-tobacco users. Single IPNs were detected in 62/133 (47%) patients and 38/133 (24%) were detected in the upper lobes. IPNs were characterised as solid in 91/133 (68.4%) patients. Only 10% of patients were eligible for screening with a PLCOm2012 6-year lung cancer risk above 1.5%, the remaining were not eligible because of a light smoking history or have never smoked. Eighty-nine patients (77.4%) had a PanCan lung cancer risk score of < 5%: 44/89 (49 %) had a lung cancer risk of less than 1.5% requiring 24-month follow-up (Figure 1). Early recall was recommended for 26% and 6.8% required diagnostic work-up. Using Fleischner Society guidelines, 53% of patients would require early recall within 3-6 months and 9.7% would require diagnostic work-up. The current incidence of histologically confirmed lung malignancy within this IPN cohort is 5/133 (3.8%). Conclusion Our study demonstrated that patients with IPN are at high risk for lung cancer. Most of them would not meet current guidelines for lung cancer screening. The PanCan model appears to be a more efficient, by reducing the number of IPN patients requiring early recall and diagnostic work-up whilst still identifying high-risk patients.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".