Abstract A21: Lung cancer surveillance with CT scan and autofluorescence bronchoscopy
Bibliographic record
Abstract
Abstract Background: More than 75% of lung cancer patients are diagnosed at an advanced stage, when the survival rate is less than 15%. Sputum cytology, x-ray and CT scan have been evaluated as screening tools for early lung cancers, without much success. Auto-fluorescence bronchoscopy (AFB) has been recently shown to be effective in diagnosing central bronchial cancers. Combined surveillance with both spiral CT scan and AFB might help to increase the detection rate of the both central and peripheral lung cancers. Methods: The study included 205 patients who were enrolled in the High Risk Lung Cancer Surveillance Cohort at Roswell Park Cancer Institute (RPCI) with at least 2 of the following risk factors: (1) radiographically documented pulmonary asbestosis or; (2) a history of previously treated aero-digestive cancer or; (3) > 20 pack years smoking history or; (4) COPD with an FEV1 < 70% of predicted. Patients underwent spirometry testing, chest X-ray, sputum cytology, non-enhanced low dose spiral CT scan of the chest, and conventional white light/AF bronchoscopy with biopsy. Results: A total of 20 invasive cancers/CIS were diagnosed in the 205 patients. Seven were diagnosed at baseline, 4 within 1 year of enrollment and 9 on follow up of more than 1 year. Between them, AFB and CT scan diagnosed all baseline cancers. Only 3/7 cancers were detected on x-ray screening and only 1/7 patients demonstrated atypia on sputum cytology. Overall, 17 invasive cancers and 3 CIS were diagnosed during the surveillance study. All the 3 CIS were identified only on AFB. Of the 17 invasive cancers, CT scan detected 15 cancers (88%) and AFB detected 5 of these cancers (30%). CT scan showed a 67% relative increase in sensitivity for detecting prevalent cancers and 3 times greater sensitivity for incident and prevalent cancers compared to x-ray screening. CT scan and AFB detected 19 of the 20 CIS/cancers (95%), whereas x-ray and sputum cytology together detected only 5/20 CIS/cancers (25%). The sensitivity of CT scan and AFB in diagnosing pre-malignant lesions and cancers improved by almost two and half times relative to x-ray and sputum. Conclusion: The addition of AFB exam to yearly spiral CT scan of the chest could be a more efficient surveillance tool to identify early stage lung cancers, both in the central and peripheral lung. A greater efficiency and cost effectiveness can be achieved by limiting the use of the combination of AFB and CT scan in very high risk patients, selected based on their exposures and risk factors. Citation Information: Cancer Prev Res 2010;3(1 Suppl):A21.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".