Lung cancer screening with low-dose CT, by the numbers

Written and reviewed by a practicing emergency physician · Updated August 2026

Low-dose CT screening for lung cancer holds two records at once. It is one of the only cancer screens ever to prove, in a large randomized trial, that it saves lives — and it generates false alarms at a rate that would sink almost any other test's reputation. In the National Lung Screening Trial, 96.4% of positive low-dose CT results turned out not to be cancer. The trial still found a 20% drop in lung-cancer deaths. Holding both of those in mind at the same time is the whole skill this page practices.

Three numbers, and where they come from

• Sensitivity ≈ 93.1% — of high-risk people who have lung cancer, about 93 in 100 have a positive scan. This and the specificity below come from Pinsky and colleagues' analysis of the National Lung Screening Trial's own data.
• Specificity ≈ 76.5% — only about 77 of every 100 people without lung cancer are correctly cleared. Nearly a quarter are flagged anyway, usually for small indeterminate nodules that later prove benign. This is strikingly low for a screening test, and it is the number that drives everything unusual below.
• Prevalence ≈ 1.1% — among the trial's high-risk participants (heavy current or former smokers), about 11 in 1,000 had a screen-detectable lung cancer. Note that this is already an enriched population: eligibility rules exist precisely to prop this number up, because — as you're about to see — the arithmetic gets ugly even at 1.1%.

The 2×2, worked by hand

Screen 1,000 high-risk people. Split by the truth first: 1,000 × 0.011 = 11 with lung cancer, 989 without.

Apply the test. Of the 11 with cancer, 11 × 0.931 ≈ 10 are caught (true positives) and about 1 is missed (false negative). Of the 989 without cancer, 989 × 0.235 ≈ 232 are flagged anyway (false positives) and about 757 are correctly cleared (true negatives).

About 242 of 1,000 people — nearly one in four — get a positive result, and only about 10 of them have cancer:

PPV = (0.931 × 0.011) ÷ [(0.931 × 0.011) + (0.235 × 0.989)] = 0.01024 ÷ 0.24266 ≈ 0.042 — about 1 in 24.

The trial's observed numbers land almost exactly where this idealized model puts them: 24.2% of low-dose CT screens in NLST were positive, and 96.4% of those positives were false — our model says 24.2% and 95.8%. When a real trial and a four-input model agree that closely, the lesson is that nothing exotic is going on: a 23.5% false-positive rate applied to a 98.9% cancer-free population simply buries the true positives. It is the base-rate fallacy with a proven life-saving test attached.

A negative scan, by contrast, is excellent news

Follow the negative column: 758 people cleared, 757 correctly. Residual risk after a negative scan is about 0.1% — down from 1.1% before it. In ratio form, LR− = (1 − 0.931) ÷ 0.765 ≈ 0.09: a negative divides the odds of cancer by about eleven. The same asymmetry shows up on the positive side as LR+ = 0.931 ÷ 0.235 ≈ 4 — a positive multiplies the odds by only four. This scan rules out far better than it rules in, which is the signature of any test whose specificity, not sensitivity, is the weak link. (The likelihood-ratio guide works these same two numbers through the Fagan nomogram.)

That profile fits the clinical reality of what a "positive" means here. A flagged scan rarely leads straight to a biopsy; it leads to surveillance imaging — a repeat CT in a few months to see whether the nodule grows. Most false positives cost anxiety and radiation rather than surgery. But not all: in NLST, working up the positives produced roughly one invasive procedure for every 56 people with a positive screen, procedures happening overwhelmingly in people who never had cancer.

The ledger that made it worth it anyway

Here is the part that makes low-dose CT the strongest test of your intuitions on this site. Everything above sounds damning — 24 false alarms per cancer found, one in four screens positive, invasive work-ups in the healthy. And yet the National Lung Screening Trial, randomizing 53,454 high-risk people, found that three annual rounds of low-dose CT cut lung-cancer mortality by 20.0% relative to chest X-ray (95% CI 6.8 to 26.7), and all-cause mortality by 6.7%. In absolute terms that works out to roughly one lung-cancer death prevented for every 320 people screened — a number-needed-to-screen that compares favorably with almost every other cancer screen in use.

Both columns of the ledger are real. Screening 320 people to save one life also means, by the arithmetic above, flagging about 77 of them at least once and sending a handful to procedures they didn't need. Reasonable people weigh those differently — the screening-harms guide is about how to weigh them honestly, and the NNT/NNH/NNS guide is about what those per-person-count numbers actually promise. What the numbers themselves settle is narrower but important: the benefit is proven, the false-alarm burden is enormous, and neither cancels the other.

The eligibility rules are the system's acknowledgment of all this. The U.S. Preventive Services Task Force recommends annual low-dose CT only for adults 50 to 80 with a heavy smoking history — a deliberate act of prevalence engineering. Screen a lower-risk population and the 1.1% base rate falls, the PPV falls with it, and the same scan buys more false alarms per life saved. The eligibility criteria are not gatekeeping for its own sake; they are the dial that keeps the arithmetic tolerable.

What generalizes

Low-dose CT is the proof that "most positives are false" and "screening saves lives" can both be true, and that neither slogan settles anything alone. The way through is always the same: work the 2×2 at the real prevalence, price the follow-up cascade, and put the benefit and the harm on the same page in whole people. That is what the calculator's outcome view does with the sliders this page just set.