DBFormer: a deep learning model to predict tumor mutation burden of lung adenocarcinoma

Xueyong Yang; Xin Guan; Xiangfu Meng; Xiaolin Yang

doi:10.1117/12.3034845

11 July 2024 DBFormer: a deep learning model to predict tumor mutation burden of lung adenocarcinoma

Xueyong Yang, Xin Guan, Xiangfu Meng, Xiaolin Yang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132102U (2024) https://doi.org/10.1117/12.3034845
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Tumor Mutation Burden (TMB) is a critical biomarker for predicting the response to cancer im- munotherapy. Typically, TMB is usually evaluated using Whole-Exome Sequencing (WES) in clinical practice. However, this approach requires use of finite tissue specimens and time-consuming laboratory processes. To address these issues, we have developed a deep learning model called DBFormer, which can predict TMB using readily available histopathological images of lung adenocarcinoma. DBFormer is a four-tier classification pyramid structure that incorporates color convolution and Bi- Routing At- tention and consists of two primary components. The first part is the color deconvolution layer. Each patch passes through the color deconvolution layer to produces an image that combines RGB and HED image information. The second component has four stages, each with a maximum pooling layer and a DBFormer block. The maximum pooling layer reduces the image size while increasing the feature matrix’s dimension. The DBFormer block extracts and classifies the characteristics of the image. We selected 337 and 200 whole slide images (WSIs) of lung cancer from the TCGALUAD dataset as bi- nary and triple classification datasets, respectively. The experiments conducted on the aforementioned dataset demonstrate the efficacy of our model in comparison to classical deep learning models, and es- tablish its superiority over state-of-the-art methods. DBFormer achieves an area under curve (AUC)of 0.997 in the binary classification dataset and an average accuracy of 0.973 in the ternary classification dataset. Source code is available at https://github.com/yyyyxxxyyyy/DBFormer

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xueyong Yang, Xin Guan, Xiangfu Meng, and Xiaolin Yang "DBFormer: a deep learning model to predict tumor mutation burden of lung adenocarcinoma", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132102U (11 July 2024); https://doi.org/10.1117/12.3034845

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KEYWORDS

Matrices

RGB color model

Deep learning

Tumors

Deconvolution

Image processing

Data modeling

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