{"title":"Hyperspectral Imaging for Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Differentiation","authors":"Yunze Li, Haiyan Chen, Wei Li, Meng Yu, Jinlin Deng, Qize Lv, Yifei Liu, Shuai Gao","doi":"10.1002/jbio.202500227","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>This study proposed an intelligent intraoperative diagnostic framework that combines hyperspectral imaging (HSI) with deep reinforcement learning to accurately differentiate hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), the two main subtypes of primary liver cancer. To address the limitations of conventional imaging techniques and serum biomarkers, the authors constructed the first clinical HSI dataset of liver tumors (<i>n</i> = 131, spectral range 400–1000 nm). The proposed method integrates a 3D residual neural network (3D-ResNet) with a Proximal Policy Optimization (PPO)-based reinforcement learning algorithm, framing spectral band selection as a Markov decision process. An intraclass constrained cross-entropy loss further enhances class separability and compactness. Experimental results demonstrate a classification accuracy of 95%, outperforming traditional band selection approaches. This framework enables rapid, real-time tumor subtyping during surgery, addressing the critical clinical need for timely and accurate liver cancer diagnosis, and offers a promising tool for advancing precision oncology and improving intraoperative decision making.</p>\n </div>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 10","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biophotonics","FirstCategoryId":"101","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jbio.202500227","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
This study proposed an intelligent intraoperative diagnostic framework that combines hyperspectral imaging (HSI) with deep reinforcement learning to accurately differentiate hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), the two main subtypes of primary liver cancer. To address the limitations of conventional imaging techniques and serum biomarkers, the authors constructed the first clinical HSI dataset of liver tumors (n = 131, spectral range 400–1000 nm). The proposed method integrates a 3D residual neural network (3D-ResNet) with a Proximal Policy Optimization (PPO)-based reinforcement learning algorithm, framing spectral band selection as a Markov decision process. An intraclass constrained cross-entropy loss further enhances class separability and compactness. Experimental results demonstrate a classification accuracy of 95%, outperforming traditional band selection approaches. This framework enables rapid, real-time tumor subtyping during surgery, addressing the critical clinical need for timely and accurate liver cancer diagnosis, and offers a promising tool for advancing precision oncology and improving intraoperative decision making.
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.