Detection of Lung Cancer Cases in Chest CT Scans Utilizing Artificial Intelligence: A Retrospective Analysis of Data During the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Purpose To evaluate the potential of using artificial intelligence (AI) focused pulmonary nodule search on chest CT data obtained during the COVID-19 pandemic to identify lung cancer (LC) patients. Methods A multicenter, retrospective study in the Krasnoyarsk region, Russia analyzed CTs of COVID-19 patients using the automated algorithm, Chest-IRA by IRA Labs. Pulmonary nodules larger than 100 mm³ were identified by the AI and assessed by four radiologists, who categorized them into three groups: “high probability of LC”, “insufficiently convincing evidence of LC”, “without evidence of LC”. Patients with findings were analyzed by radiologists, checked with the state cancer registry and electronic medical records. Patients with confirmed findings that were not available in the cancer registry were invited for chest CT and verification was performed according to the decision of the medical consilium. The study also estimated the economic impact of the AI by considering labor costs and savings on treatment for patients in the early stages compared to late stages, taking into account the saved life years and their potential contribution to the gross regional product. Results An AI identified lung nodules in 484 out of 10,500 chest CTs. Of the 484, 355 could be evaluated, the remaining 129 had de-anonymization problems and were excluded. Of the 355, 252 cases having high and intermediate probabilities of LC, 103 were found to be false positives. From 252 was 100 histologically verified LC cases, 35 were in stages I-II and 65 were in stages III-IV. 2 lung cancers were diagnosed for the first time. Using AI instead of CT review by radiologists will save 2.43 million rubles (23,786 EUR/ 26690 USD/ 196,536 CNY) in direct salary, with expected savings to the regional budget of 8.22 million rubles (80,463 EUR/ 90,466 USD/ 666,162 CNY). The financial equivalent of the life years saved was 173.25 million rubles (1,695,892 EUR/ 1905750 USD/ 14,033,250 CNY). The total effect over five years is estimated at 183.9 million rubles (1,800,142 EUR/ 2,022,907 USD/ 14,895,949 CNY). Conclusion Using AI to evaluate large volumes of chest CTs done for reasons unrelated to lung cancer screening may facilitate early and cost-effective detection of incidental pulmonary nodules that might otherwise be missed.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".