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A yearly low-radiation CT scan for current and former heavy smokers that finds lung cancer early enough to cure it.
Proven by NLST (20% mortality reduction) and NELSON (24% in men). US eligibility: age 50-80, ≥20 pack-years, quit <15 years. Uptake remains under 20% in the US and lower elsewhere; risk models (PLCOm2012), AI nodule scoring (Sybil, Optellum), and blood biomarkers aim to widen and sharpen eligibility. Lung-RADS standardises reporting; volumetric management (NELSON) reduces false positives.
Non-contrast helical CT at ~1-1.5 mSv; nodules classified by size, volume, growth, and morphology; AI assists detection and malignancy scoring.
Nothing in the corpus depends on this yet.
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The first AI cleared to estimate how likely a lung nodule on a CT scan is to be cancer, helping doctors decide who needs a biopsy and who can wait.
Lung cancer in never-smokers is not smokers' lung cancer with the smoking removed; it is a different set of diseases with a different clock. The slow-growing piano subtype in particular is the argument that a screening test aimed at never-smokers would need to look for something other than what low-dose computed tomography was built to find.
The document that defines who is offered a scan in the United States, and therefore the document any argument about the screening eligibility gap has to engage with. Eligibility is still defined by pack-years and years since quitting rather than by an individual risk estimate.
Lung screening works when it uses volumetric nodule management, and it works against a no-screening control. The protocol underpins the UK Targeted Lung Health Check programme and European recommendations. Benefit in women remains less precisely estimated.
The clearest published demonstration that a screening eligibility rule can be accurate on average and systematically wrong for a group. It is the empirical core of the argument for replacing pack-year thresholds with individual risk models.
For people with a heavy smoking history, an annual low-dose CT scan is one of the few screening tests proven to reduce cancer deaths. Most abnormal scans are not cancer, so screening must be paired with careful nodule management. It does not apply to never-smokers or light smokers.
A negative screening trial that saved a generation from a useless test, and the reason low-dose computed tomography had to be proved separately rather than assumed to work because it saw more.
Query for this technology: (TITLE:"Low-dose CT lung screening" OR ABSTRACT:"Low-dose CT lung screening") AND (cancer OR tumor OR tumour OR oncology OR carcinoma OR lymphoma OR leukemia OR leukaemia OR myeloma OR sarcoma OR melanoma OR glioma). Results are unfiltered search hits about Low-dose CT lung screening, not a curated reading list.
Shares Radiomics, AI in radiology, CT (computed tomography), Non-small-cell lung cancer and the tag precision.
Shares CALGB 140503 (Alliance), Resectable stage I to III non-small-cell lung cancer, Lung cancer (all types), Non-small-cell lung cancer and the tag precision.
Shares MUSC Hollings Cancer Center, CT (computed tomography), Non-small-cell lung cancer and the tag precision.
Shares Endoscopic ultrasound and EBUS systems, Resectable stage I to III non-small-cell lung cancer, Small-cell lung cancer, Lung cancer (all types) and the tag precision.
Shares LUNGevity Foundation (and GO2 for Lung Cancer), Small-cell lung cancer, Lung cancer (all types), Non-small-cell lung cancer and the tag precision.
Shares Overdiagnosis and false alarms, The hardest cancers are found late and the tag precision.
Shares Resectable stage I to III non-small-cell lung cancer, Lung cancer (all types), Non-small-cell lung cancer and the tag precision.
Shares Treat lung cancer in never-smokers as its own disease, with its own detection programme, The hardest cancers are found late, Lung cancer (all types), Non-small-cell lung cancer and the tag precision.
Open-source projects that implement or serve this technology, from OnCo's own catalogue: licence and last activity as the repository reported them on the day of the fetch. Listing is not endorsement; check the licence before reuse and the validation before clinical use.
A Python interface to the LIDC-IDRI lung nodule dataset and its radiologist annotations.
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