Identify morphological features that correspond to specific genetic alterations in lung cancer histopathology images

Characterize the precise morphological features in whole-slide hematoxylin and eosin histopathology images of lung adenocarcinoma that correspond to specific genetic alterations in driver genes TP53, EGFR, KRAS, and ALK, in order to improve the interpretability and clinical trust of deep learning-based mutation prediction models trained on the PathGene dataset.

Background

The paper introduces PathGene, a multicenter dataset linking lung cancer whole-slide histopathology images to next-generation sequencing reports, and benchmarks multiple instance learning models for predicting driver gene mutations, mutation subtypes, exons, and TMB status. While the models achieve strong predictive performance, the authors highlight interpretability challenges.

Specifically, the authors note that it is frequently unclear which morphological features in the images correspond to particular genetic alterations. This uncertainty limits clinical trust and hinders the adoption of AI models in precision oncology. Establishing a clear mapping from visually discernible histological patterns to specific mutations would advance explainability and facilitate clinical integration.

References

The interpretability of deep learning models remains a critical concern; it is frequently unclear which morphological features correspond to specific genetic alterations, thereby limiting clinical trust and applicability.

— PathGene: Benchmarking Driver Gene Mutations and Exon Prediction Using Multicenter Lung Cancer Histopathology Image Dataset  (2506.00096 - Pan et al., 30 May 2025) in Section 5 (Limitation)

For most signatures, however, no such correlate has been documented, and for the substantial fraction of catalogued signatures with unknown etiology the question is still open.

— Predicting Mutational Signature Exposures from H&E Whole Slide Images: A Pan-Cancer Feasibility Study  (2609.30985 - Sartori et al., 25 Sep 2026) in Introduction, paragraph beginning “A central premise of this work is that mutational processes leave traces detectable at the tissue level”

Prospective evaluation in independent cohorts, together with expert review of the highlighted regions and comparison with molecular measurements, will be required to determine whether these potential applications translate into clinical value.

— Predicting Mutational Signature Exposures from H&E Whole Slide Images: A Pan-Cancer Feasibility Study  (2609.30985 - Sartori et al., 25 Sep 2026) in Discussion, final paragraph