{"title":"Deep Learning and Hyperspectral Imaging for Liver Cancer Staging and Cirrhosis Differentiation.","authors":"Tianyi Hang, Danfeng Fan, Tiefeng Sun, Zhengyuan Chen, Xiaoqing Yang, Xiaoqing Yue","doi":"10.1002/jbio.202400557","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202400557"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of biophotonics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/jbio.202400557","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.