A nomogram to predict development of brain metastasis in non-small cell lung cancer patients: a retrospective analysis using routinely available medical records
Bibliographic record
Abstract
Background: Brain metastases (BrM) are a frequent complication among patients with non-small cell lung cancer (NSCLC). While guidelines exist for baseline CNS screening in advanced NSCLC, surveillance strategies for early-stage disease remain limited. This study aimed to develop a time-dependent BrM risk prediction nomogram using readily available clinical information. Methods: We analyzed a retrospective cohort of NSCLC patients at Penn State Health. Our objectives were to (1) systematically evaluate the performance of existing BrM risk prediction algorithms and (2) construct novel nomograms for BrM risk prediction in NSCLC. Using Cox-proportional hazard models with L1-regularization, we predicted BrM risk at 6-month, 1-year, and 2-year follow-up intervals. Findings: The patient cohort included 1904 patients (median age 68 years, range 38-94 years, BrM incidence 22.8%). The cohort included 1059 males (55.6%) and 845 females (44.4%). Of the cohort, 92.8% of patients identified as White (n = 1766), 1.0% as Asian (n = 19), 4.0% as Black (n = 77), and 2.2% as another race (n = 42). The Zhang 2021 model demonstrated the highest performance in predicting BrM incidence in our cohort, achieving an AUROC of 0.91 (95% CI: 0.87, 0.95). Two novel models were developed: a baseline model incorporating clinical and imaging data at diagnosis (cTNM stage, age at diagnosis), and an extended model including additional clinical and treatment data (number of extracranial metastatic sites, prior radiotherapy, chemotherapy, surgery, and histology) (https://nmikolajewicz.shinyapps.io/nomogram_wilding2024/). While both models showed similar short-term performance, the extended model demonstrated superior predictive capacity (AUROC 0.91 at 3-years) for longer-term outcomes. Our nomograms rely exclusively on clinical features routinely documented in patient records, thereby requiring no additional investigations. Interpretation: These clinically accessible nomograms for BrM prediction will facilitate prognostic modeling, risk stratification, refinement of CNS screening guidelines, and patient counseling. Funding: None.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".