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Hengrui Shi

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Proceedings Article | 18 December 2023 Paper

Diagnosing ATRX status in glioma utilizing terahertz spectroscopy and deep learning algorithms

Xianhao Wu, Rui Tao, Zhiyan Sun, Tianyao Zhang, Hengrui Shi, Zhaohui Zhang, Xiaoyan Zhao, Pei Yang

Proceedings Volume 12960, 129600P (2023) https://doi.org/10.1117/12.2692191

KEYWORDS: Deep learning, Terahertz spectroscopy, Machine learning, Diagnostics

Read Abstract +

Gliomas, the most prevalent primary brain tumors, exhibit complex genetic and epigenetic variations, including ATRX mutations. Existing ATRX status diagnostics, like immunohistochemistry, DNA, and RNA sequencing, face limitations. Terahertz spectroscopy, known for its interaction with biological materials, holds potential for ATRX diagnosis due to its non-invasive genetic and structural insights. This study proposes an innovative methodology integrating deep learning and terahertz spectroscopy for ATRX assessment. The approach begins by transforming one-dimensional terahertz data into two-dimensional images, enhancing data richness. A Deep Convolutional Generative Adversarial Network (DCGAN) augments the image dataset, addressing data scarcity. DCGAN generates realistic images by training a generator and discriminator in tandem. Subsequently, a Residual Network (ResNet) extracts features from augmented images, tackling the vanishing gradient issue. The ResNet model captures crucial complexities essential for accurate ATRX prediction. Extracted features feed into a classifier for final prediction. The study encompasses 22 patients with 440 terahertz spectral data. Dataset contained 220 ATRX-positive and 220 ATRX-negative spectral data. Employing terahertz data and deep learning, the model achieved up to 90.64% accuracy in diagnosing ATRX status. This research introduces a novel approach integrating terahertz spectroscopy and deep learning for enhanced precision in glioma ATRX diagnosis. The method's potential impact extends to personalized treatment and improved prognosis. Moreover, it underscores the broader utility of terahertz spectroscopy and deep learning in advancing genetic alteration diagnostics in diverse cancers.

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