Machine Learning for Early Lung Cancer Identification Using Routine Clinical and Laboratory Data
Bibliographic record
Abstract
Abstract Rationale Most lung cancers are diagnosed at an advanced stage. Presymptomatic identification of high-risk individuals can prompt earlier intervention and improve long-term outcomes. Objectives To develop a model to predict a future diagnosis of lung cancer on the basis of routine clinical and laboratory data by using machine learning. Methods We assembled data from 6,505 case patients with non–small cell lung cancer (NSCLC) and 189,597 contemporaneous control subjects and compared the accuracy of a novel machine learning model with a modified version of the well-validated 2012 Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial risk model (mPLCOm2012), by using the area under the receiver operating characteristic curve (AUC), sensitivity, and diagnostic odds ratio (OR) as measures of model performance. Measurements and Main Results Among ever-smokers in the test set, a machine learning model was more accurate than the mPLCOm2012 for identifying NSCLC 9–12 months before clinical diagnosis (P < 0.00001) and demonstrated an AUC of 0.86, a diagnostic OR of 12.3, and a sensitivity of 40.1% at a predefined specificity of 95%. In comparison, the mPLCOm2012 demonstrated an AUC of 0.79, an OR of 7.4, and a sensitivity of 27.9% at the same specificity. The machine learning model was more accurate than standard eligibility criteria for lung cancer screening and more accurate than the mPLCOm2012 when applied to a screening-eligible population. Influential model variables included known risk factors and novel predictors such as white blood cell and platelet counts. Conclusions A machine learning model was more accurate for early diagnosis of NSCLC than either standard eligibility criteria for screening or the mPLCOm2012, demonstrating the potential to help prevent lung cancer deaths through early detection.
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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 | 0.002 |
| 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 | 0.001 |
| 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".