Identifying actionable driver mutations in lung cancer using an efficient Asymmetric Transformer Decoder

TL;DR

This paper introduces an efficient Asymmetric Transformer Decoder for detecting key driver mutations in lung cancer, improving accuracy and addressing biological relevance, thus advancing ML-based genetic testing.

Contribution

The study presents a novel Asymmetric Transformer Decoder model that enhances mutation detection and incorporates tissue type information, outperforming existing MIL models.

Findings

Outperforms top MIL models by 3% on average

Achieves over 4% improvement for rare mutations

Moves ML-based testing closer to clinical adoption

Abstract

Identifying actionable driver mutations in non-small cell lung cancer (NSCLC) can impact treatment decisions and significantly improve patient outcomes. Despite guideline recommendations, broader adoption of genetic testing remains challenging due to limited availability and lengthy turnaround times. Machine Learning (ML) methods for Computational Pathology (CPath) offer a potential solution; however, research often focuses on only one or two common mutations, limiting the clinical value of these tools and the pool of patients who can benefit from them. This study evaluates various Multiple Instance Learning (MIL) techniques to detect six key actionable NSCLC driver mutations: ALK, BRAF, EGFR, ERBB2, KRAS, and MET ex14. Additionally, we introduce an Asymmetric Transformer Decoder model that employs queries and key-values of varying dimensions to maintain a low query dimensionality. This approach efficiently extracts information from patch embeddings and minimizes overfitting risks, proving highly adaptable to the MIL setting. Moreover, we present a method to directly utilize tissue type in the model, addressing a typical MIL limitation where either all regions or only some specific regions are analyzed, neglecting biological relevance. Our method outperforms top MIL models by an average of 3%, and over 4% when predicting rare mutations such as ERBB2 and BRAF, moving ML-based tests closer to being practical alternatives to standard genetic testing.