Histopathological and Molecular Predictors of the First Site of Dissemination in Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Background: Non-small-cell lung cancer (NSCLC) is often diagnosed at stage IV, when prognosis depends on metastatic spread. The impact of histopathology on the first metastatic site remains underexplored. Methods: We retrospectively analyzed 364 patients with stage IV NSCLC diagnosed at OncoHelp Medical Center, Timișoara, Romania (2020–2024). All underwent baseline CT chest–abdomen–pelvis, whole-body FDG PET-CT, and brain MRI within seven days of histological confirmation. Patients were stratified into adenocarcinoma (n = 164), squamous cell carcinoma (n = 112), and large-cell carcinoma (n = 88). The first metastatic site was defined as the earliest confirmed location. Associations were evaluated using Fisher’s exact test and multinomial logistic regression. Results: Histology was associated with the first metastatic site (global p = 0.013). Adenocarcinoma was more likely than squamous carcinoma to present with brain metastases (RRR 3.74, 95% CI 1.48–9.45; p = 0.005; pFDR = 0.053) and showed directional signals toward bone and adrenal involvement. Squamous carcinoma more frequently spread to the pleura as the first site (adjusted p = 0.008). Large-cell carcinoma showed no consistent differences compared to squamous carcinoma. In adenocarcinoma subgroups, EGFR-mutant tumors most often metastasized to the brain (55.6%), KRAS-mutant tumors to the liver (44.4%), and ALK-rearranged tumors to bone (100%). Conclusions: The first metastatic site in NSCLC follows histology-specific patterns, with adenocarcinoma favoring hematogenous spread and squamous carcinoma showing locoregional involvement. Molecular status further refines these patterns in adenocarcinoma. Incorporating histology into baseline staging may improve diagnostic efficiency and prognostic accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".