TG-Mamba: Leveraging text guidance for predicting tumor mutation burden in lung cancer

Abstract

Tumor mutation burden (TMB) is a crucial biomarker for predicting the response of lung cancer patients to immunotherapy. Traditionally, TMB is quantified through whole-exome sequencing (WES), but the high costs and time requirements of WES limit its widespread clinical use. To address this, we propose a deep learning model named TG-Mamba, capable of rapidly predicting TMB levels based on patients’ histopathological images and clinical information, and further estimating specific TMB values. Specifically, we employ a parallel feature extraction strategy. The upper layer consists of a series of text-guided attention modules designed to extract diagnostic textual features. Meanwhile, the lower layer leverages the VMamba backbone network for image feature extraction. To enhance performance, we design a novel hybrid module, Conv-SSM, which combines convolutional layers for local feature extraction with a state-space model (SSM) to capture global dependencies. During the feature extraction process, textual features progressively guide the extraction of image features, ensuring their effective integration. In a cohort of non-training lung cancer patients, TG-Mamba achieved an area under the receiver operating characteristic curve (AUC) of 0.994 in classification tasks and a mean absolute percentage error (MAPE) of 0.25 in regression tasks. These experimental results demonstrate TG-Mamba’s exceptional performance in TMB prediction, highlighting its potential to extend the benefits of immunotherapy to a broader population of lung cancer patients. The code for our model and the experimental data can be obtained at https://github.com/ukeLin/TG-Mamba.