Deep Learning and Hyperspectral Imaging for Liver Cancer Staging and Cirrhosis Differentiation

IF 2 3区 物理与天体物理 Q3 BIOCHEMICAL RESEARCH METHODS
Tianyi Hang, Danfeng Fan, Tiefeng Sun, Zhengyuan Chen, Xiaoqing Yang, Xiaoqing Yue
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引用次数: 0

Abstract

Liver malignancies, particularly hepatocellular carcinoma (HCC), pose a formidable global health challenge. Conventional diagnostic techniques frequently fall short in precision, especially at advanced HCC stages. In response, we have developed a novel diagnostic strategy that integrates hyperspectral imaging with deep learning. This innovative approach captures detailed spectral data from tissue samples, pinpointing subtle cellular differences that elude traditional methods. A sophisticated deep convolutional neural network processes this data, effectively distinguishing high-grade liver cancer from cirrhosis with an accuracy of 89.45%, a sensitivity of 90.29%, and a specificity of 88.64%. For HCC differentiation specifically, it achieves an impressive accuracy of 93.73%, sensitivity of 92.53%, and specificity of 90.07%. Our results underscore the potential of this technique as a precise, rapid, and non-invasive diagnostic tool that surpasses existing clinical methods in staging liver cancer and differentiating cirrhosis.

Abstract Image

深度学习和高光谱成像在肝癌分期和肝硬化鉴别中的应用。
肝脏恶性肿瘤,特别是肝细胞癌(HCC),构成了一个巨大的全球健康挑战。传统的诊断技术往往不够精确,尤其是在肝癌晚期。为此,我们开发了一种新的诊断策略,将高光谱成像与深度学习相结合。这种创新的方法从组织样本中捕获详细的光谱数据,精确定位传统方法无法识别的细微细胞差异。一个复杂的深度卷积神经网络处理这些数据,有效区分高级别肝癌和肝硬化,准确率为89.45%,灵敏度为90.29%,特异性为88.64%。对于HCC特异性鉴别,准确率为93.73%,灵敏度为92.53%,特异性为90.07%。我们的研究结果强调了该技术作为一种精确、快速、无创的诊断工具的潜力,它在肝癌分期和肝硬化鉴别方面超越了现有的临床方法。
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来源期刊
Journal of Biophotonics
Journal of Biophotonics 生物-生化研究方法
CiteScore
5.70
自引率
7.10%
发文量
248
审稿时长
1 months
期刊介绍: The first international journal dedicated to publishing reviews and original articles from this exciting field, the Journal of Biophotonics covers the broad range of research on interactions between light and biological material. The journal offers a platform where the physicist communicates with the biologist and where the clinical practitioner learns about the latest tools for the diagnosis of diseases. As such, the journal is highly interdisciplinary, publishing cutting edge research in the fields of life sciences, medicine, physics, chemistry, and engineering. The coverage extends from fundamental research to specific developments, while also including the latest applications.
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