Identifying malignant nodules on chest X-rays
Bibliographic record
Abstract
Background: Three and half million anonymous X-rays were gathered from 45 locations worldwide (in-hospital and outpatient settings). qXR was initially trained on this massive dataset. We used an independent dataset of 13,426 chest X-rays from radiologists’ reports. The test data set included 213,459 X-rays chosen at random from a pool of 3.5 million X-rays. The dataset (development) was developed using the remaining X-rays received from the remaining patients. Methods: qXR is a deep learning algorithm-enabled software that is used to study nodules and malignant nodules on X-rays. We observed moderate to a substantial agreement even when observations were made with normal X-rays. Results: qXR presented a high area under the curve (AUC) of 0.99 with a 95% confidence interval calculated with the Clopper–Pearson method. The specificity obtained with qXR was 0.90, and the sensitivity was 1 at the operating threshold. The sensitivity value of qXR in detecting nodules was 0.99, and the specificity ranged from 0.87 to 0.92, with AUC ranging between 0.98 and 0.99. The malignant nodules were detected with a sensitivity ranging from 0.95 to 1.00, specificity between 0.96 and 0.99, and AUC from 0.99 to 1. The sensitivity of radiologists 1 and 2 was between 0.74 and 0.76, with a specificity ranging from 0.98 to 0.99. In detecting the malignant nodules, specificity ranged between 0.98 and 0.99, and sensitivity fell between 0.88 and 0.94. Conclusion: Machine learning model can be used as a passive tool to find incidental cases of lung cancer or as a triaging tool, which accelerate the patient journey through standard care pipeline for lung cancer.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".