Selection Instability and External Performance of Transcriptomic Models Beyond Age, Sex and Stage in Resected Lung Adenocarcinoma
Year: 2026
Type: article
Abstract: BACKGROUND/OBJECTIVES: We evaluated molecular selection stability and external prediction beyond age, sex and broad pathological stage in resected stage I-II lung adenocarcinoma. METHODS: Models were developed in 228 patients from two sources and frozen before evaluation in four historical cohorts containing 778 patients. The primary comparison added pathway scores using elastic-net Cox regression. Secondary analyses examined a gene model, 200 development bootstrap resamples, alternative modeling specifications and locally updated baselines. Prediction error was measured by the integrated Brier score over 1-3 years (IBS); negative molecular-minus-clinical differences (ΔIBS) favored molecular models. Evaluation intervals used 2000 paired bootstrap resamples. RESULTS: The pathway model retained no molecular terms. The final gene model retained 16 genes, whereas both source-held-out fits and 159/200 development resamples retained none. Penalties of 0.5, 0.15 and 0.05 were selected in 160, 38 and 2 resamples. Alternative specifications changed gene retention from 0 to 61 and pathway retention from 0 to 5. Frozen gene-model ΔIBS ranged from -0.0034 to 0.0055 across cohorts. Its equal cohort mean was 0.0016 (95% CI, -0.0012 to 0.0042), compared with -0.0008 (-0.0024 to 0.0007) after cross-fitted local baseline updating. The clinical model predicted a 3-year mortality of 19.6% in Japan and 22.6% in Moffitt, versus observed values of 9.1% and 28.6%. CONCLUSIONS: Molecular selection was unstable, and incremental performance depended on specification and population. The results characterize these learning procedures and their limited development sample; broader transcriptomic prognostic value and contemporary clinical utility remain unestablished.
Source: Cancers
Authors: Zhihao Lei, Chenxi Zou, Wei Zhao
Institutions: Cornell University, Chinese PLA General Hospital
Language: English
Topic: Lung Cancer Research Studies
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,499
OpenAlex ID: W7222978727