Related Experiment Video
Updated: Aug 14, 2025

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
Published on: September 8, 2023
The value of prognostic and predictive parameters in early-stage lung adenocarcinomas: A comparison between biopsies
J L Wolf1, T E Trandafir1, F Akram1
1Department of Pathology and Clinical Bioinformatics, Erasmus Medical Center, Rotterdam, the Netherlands.
Introduction:
Since lung adenocarcinoma (LUAD) biopsies are usually small, it is questionable if their prognostic and predictive information is comparable to what is offered by large resection specimens. This study compares LUAD biopsies and resection specimens for their ability to provide prognostic and predictive parameters.
Methods:
We selected 187 biopsy specimens with stage I and II LUAD. In 123 cases, subsequent resection specimens were also available. All specimens were evaluated for growth pattern, nuclear grade, fibrosis, inflammation, and genomic alterations. Findings were compared using non-parametric testing for categorical variables. Model performance was assessed using the area under the curve for both biopsies and resection specimens, and overall (OS) and disease-free survival (DFS) was calculated.
Results:
The overall growth pattern concordance between biopsies and resections was 73.9%. The dominant growth pattern correlated with OS and DFS in resected adenocarcinomas and for high-grade growth pattern in biopsies. Multivariate analysis of biopsy specimens revealed that T2-tumors, N1-status, KRAS mutations and a lack of other driver mutations were associated with poorer survival. Model performance using clinical, histological and genetic data from biopsy specimens for predicting OS and DSF demonstrated an AUC of 0.72 and 0.69, respectively.
Conclusions:
Our data demonstrated the prognostic relevance of a high-grade growth pattern in biopsy specimens of LUAD. Combining clinical, histological and genetic information in one model demonstrated a suboptimal performance for DFS prediction and good performance for OS prediction. However, for daily practice, more robust (bio)markers are required to predict prognosis and stratify patients for therapy and follow-up.
More Related Videos
13:19Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025