Low-dose CT for lung cancer screening in a high-risk population (SUMMIT): a prospective, longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: Low-dose CT screening reduces lung cancer mortality. In advance of planned national lung cancer screening programmes, research is needed to inform policies regarding implementation. We aimed to assess the implementation of low-dose CT for lung cancer screening in a high-risk population and to validate a multicancer early detection blood test. METHODS: In this prospective, longitudinal cohort study, individuals aged 55-77 years recorded as current smokers in their primary care records at any point within the past 20 years were identified from 329 primary care practices in London (UK) and invited for a lung health check via postal letter. Individuals meeting the 2013 United States Preventive Services Taskforce criteria (current or former smokers within the past 15 years with at least 30 pack-year smoking histories) or having a Prostate, Lung, Colorectal and Ovarian 2012 model 6-year risk of 1·3% or greater, and not currently receiving treatment for an active cancer (except adjuvant hormonal therapy), were eligible for the study. These individuals underwent lung cancer screening via non-contrast, thin collimation low-dose CT. In this analysis, we report the results of the baseline round of low-dose CT screening. Key primary endpoints were those associated with examining the performance of a lung cancer screening service. Outcome measures were analysed on a per-participant level using descriptive frequencies. The study was registered with ClinicalTrials.gov, NCT03934866. FINDINGS: Between April 8, 2019, and May 14, 2021, 12 773 participants were recruited and analysed. 7353 (57·6%) of 12 773 participants were male and 5420 (42·4%) were female, and 10 665 (83·5%) participants were White. 261 (2·0%) of 12 773 participants were diagnosed with lung cancer (including 163 [1·3%] participants with screen-detected lung cancer and 98 [0·8%] with delayed screen-detected lung cancer [ie, after a 3-month or 6-month nodule follow-up CT]) and 276 (2·2%) participants were diagnosed with any intrathoracic malignancy after a positive baseline screen. 207 (79·3%) of 261 individuals with prevalent screen-detected lung cancer were diagnosed at stage I or II and surgical resection was the primary treatment modality in 201 (77·0%) of 261 individuals. Including cases where multiple resections were done in the same participant (eg, for synchronous primaries), 28 (11·6%) of 241 surgical resections were benign, and there was one (0·4%) death within 90 days of surgery. At 12 months, the episode sensitivity of our low-dose CT screening protocol for detecting lung cancer was 97·0% (95% CI 95·0-99·1; 261 of 269 participants). The specificity was 95·2% (94·8-95·6; 11 905 of 12 504 participants), with a false-positive rate of 4·8% (4·4-5·2). INTERPRETATION: Large-scale lung cancer screening is effective and can be delivered efficiently to an ethnically and socioeconomically diverse population. FUNDING: GRAIL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".