Dissecting AI-based mutation prediction in lung adenocarcinoma: a comprehensive real-world study
Introduction
Molecular profiling of lung cancer is essential to identify genetic alterations that predict response to targeted therapy. While deep learning shows promise for predicting oncogenic mutations from whole tissue images, existing studies often face challenges such as limited sample sizes, a focus on earlier stage patients, and insufficient analysis of robustness and generalizability.
Methods
This retrospective study evaluates factors influencing mutation prediction accuracy using the extensive Heidelberg Lung Adenocarcinoma Cohort (HLCC) dataset of 2356 late-stage FFPE samples. Validation is performed using the publicly available TCGA-LUAD cohort.
Results
Models trained on the larger HLCC cohort generalize well to the TCGA dataset for mutations in EGFR (AUC 0.76), STK11 (AUC 0.71) and TP53 (AUC 0.75), supporting the hypothesis that larger cohort sizes improve model robustness. Variation in performance due to pre-processing and modeling choices, such as mutation variant calling, can affect EGFR prediction accuracy by up to 7%.
Discussion
Model explanations suggest that acinar and papillary growth patterns are critical for the detection of EGFR mutations, whereas solid growth patterns and large nuclei are indicative of TP53 mutations. These findings highlight the importance of morphological features in mutation detection and the potential of deep learning models to improve mutation prediction accuracy.
Conclusion
Although deep learning models trained on larger cohorts show improved robustness and generalizability in predicting oncogenic mutations, they cannot replace comprehensive molecular profiling. However, they can significantly aid clinical trials and the study of genotype-phenotype relationships.
