Prediction Model of Tumor Mutation Burden for Lung Adenocarcinoma Based on Pathological Tissue Slice
Meng Xiangfu1*, Yang Ziyi1, Yang Xiaolin2, Hou Jiayue3
1(School of Electronics and Information Engineering, Liaoning Technical University, Huludao 125000, Liaoning, China) 2(Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing 100005,China) 3(School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)
Abstract：Lung cancer is one of the deadliest malignancies, particularly non-small cell lung cancer (NSCLC), poses a significant threat to public health. Recent medical research has found out the crucial role of tumor mutation burden (TMB) in predicting the efficacy of immunotherapy and chemotherapy for cancer treatment. However, traditional methods for calculating TMB through genetic sequencing suffer from drawbacks, such as high detection costs, lengthy processing periods, and sample dependency. To address above problems, this paper proposed a novel deep learning model named as FCA-Former, which combined convolutional neural networks and self-attention mechanisms to predict TMB. The model employed CoAtNet as a backbone network, integrating coordinate attention and depth wise separable convolutions to enhance computational efficiency and global feature extraction capabilities from pathological tissue biopsy images. Experimental data sourced from the TCGA database comprised a dataset of lung adenocarcinoma digital pathology images, including 271 samples with high TMB levels and 66 samples with low TMB levels. The experimental results demonstrated the effectiveness of the proposed approach, achieving a remarkable maximumarea under the curve (AUC) of 98.1%. This AUC outperformed the state-of-the-art RcaNet method by 9.8%. The results of this study have significant implications for guiding prognostic and therapeutic strategies for NSCLC patients.
孟祥福, 杨子毅, 杨啸林, 侯嘉玥. 基于病理组织切片的肺腺癌肿瘤突变负荷预测模型[J]. 中国生物医学工程学报, 2023, 42(6): 698-709.
Meng Xiangfu, Yang Ziyi, Yang Xiaolin, Hou Jiayue. Prediction Model of Tumor Mutation Burden for Lung Adenocarcinoma Based on Pathological Tissue Slice. Chinese Journal of Biomedical Engineering, 2023, 42(6): 698-709.
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