[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120087-en":3,"doc-seo-120087-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120087,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","In Vivo Time-Resolved Fluorescence Detection of Liver Cancer Supported by Machine Learning - Key research findings","Time-resolved fluorescence is evaluated as an optical biopsy approach for studying metabolic shifts in liver disease, with emphasis on malignancy-associated changes in energy production and antioxidant defenses. The work combines in vivo measurements with machine learning to automatically classify liver parenchyma and tumors, including primary malignant lesions, metastases, and benign tumors. Using an ultrasound-guided fine-needle optical probe and fluorescence decays linked to histopathology, trained models achieve reliable cancer vs noncancer separation and preliminary tumor-type discrimination.","Lasers in Surgery and Medicine  \n BASIC SCIENCE ARTICLE  OPEN ACCESS   \nIn Vivo Time‐Resolved Fluorescence Detection of Liver Cancer Supported by Machine Learning  \nElena V. Potapova1  | Valery V. Shupletsov1  | Viktor V. Dremin1,2  | Evgenii A. Zherebtsov3  | Andrian V. Mamoshin1,4  | Andrey V. Dunaev1   \n1Research & Development Center of Biomedical Photonics, Orel State University, Orel, Russia | 2College of Engineering and Physical Sciences, Aston University, Birmingham, UK | 3Optoelectronics and Measurement Techniques Unit, University of Oulu, Oulu, Finland | 4Orel Regional Clinical Hospital, Orel, Russia  \nCorrespondence: Viktor V. Dremin ([v.dremin1@aston.ac.uk](v.dremin1@aston.ac.uk))  \nReceived: 22 May 2024 | Revised: 23 October 2024 | Accepted: 4 November 2024  \nFunding: This research was supported by Russian Science Foundation (Grant Number 21‐15‐00325) .  \nKeywords: liver cancer | machine learning | optical biopsy | percutaneous needle biopsy | time‐resolved fluorescence  \nABSTRACT  \nObjectives: One of the widely used optical biopsy methods for monitoring cellular and tissue metabolism is time‐resolved fluorescence. The use of this method in optical liver biopsy has a high potential for studying the shift in energy‐type production from oxidative phosphorylation to glycolysis and changes in the antioxidant defense of malignant cells. On the other hand, machine learning methods have proven to be an excellent solution to classification problems in medical practice, including biomedical optics. We aim to combine time‐resolved fluorescence measurements and machine learning to automate the division of liver parenchyma and tumors (primary malignant, metastases and benign tumors) into classes.  \nMaterials and Methods: An optical biopsy was performed using a developed setup with a fine‐needle optical probe in clinical conditions under ultrasound control. Fluorescence decays were recorded in a conditionally healthy liver and lesions during percutaneous needle biopsy. The labeled data set was created on the basis of the recorded fluorescence results and the histopathological classification of the biopsies obtained. Several machine learning methods were trained using different separation strategies of the training test set, and their respective accuracy was compared.  \nResults: Our results show that each of the tumor types had its own characteristic metabolic shifts recorded by the time‐resolved fluorescence spectroscopy. The application of machine learning demonstrates a reliable separation of the liver and all tumor types into cancer and noncancer classes with sensitivity, specificity and corresponding accuracy greater than 0.91, 0.79 and 0.90, using the random forest method. We also show that our method is capable of giving a preliminary diagnosis of the type of liver tumor (primary malignant, metastases and benign tumors) with a sensitivity, specificity and accuracy of at least 0.80, 0.95 and 0 .90.  \nConclusions: These promising results highlight its potential as a key tool in the future development of diagnostic and  \ntherapeutic strategies for liver cancers. Lasers Surg. Med. 00:00–00, 2024 . 2024 Wiley Periodicals LLC.  \n\n| Elena V. Potapova and Valery V. Shupletsov are co‐first authors with equal contribution. |\n| --- |\n| This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.\u003Cbr>© 2024 The Author(s) . Lasers in Surgery and Medicine published by Wiley Periodicals LLC. |\n\nLasers in Surgery and Medicine, 2024; 1–9 1 of 9  \n[https://doi.org/10.1002/lsm.23861](https://doi.org/10.1002/lsm.23861)  \n1 | Introduction  \nLiver cancer is one of the most fatal cancers, and the liver is a common site for metastasis of extrahepatic tumors [1] . Differentiation of primary liver cancer from metastases is important for the selection of treatment strategies such as hepatectomy, liver transplan","cbCaifC5IEWH8ZR1","https://ap.wps.com/l/cbCaifC5IEWH8ZR1","pdf",1942066,1,9,"English","en",105,"# Introduction\n# Objectives\n# Materials and Methods\n## Optical biopsy and data collection\n## Machine learning training and evaluation\n# Results\n# Conclusions","[{\"question\":\"What problem does the study address in liver cancer diagnosis?\",\"answer\":\"The study targets the need for improved diagnostic effectiveness in optical liver biopsy and automates classification of liver tissue versus tumor types using time-resolved fluorescence and machine learning.\"},{\"question\":\"How was the optical biopsy performed in the study?\",\"answer\":\"An ultrasound-controlled setup used a fine-needle optical probe in clinical conditions. Fluorescence decays were recorded in a conditionally healthy liver and in lesions during percutaneous needle biopsy.\"},{\"question\":\"What performance did the machine learning models achieve?\",\"answer\":\"Random forest provided reliable separation of liver and all tumor types into cancer and noncancer classes with sensitivity, specificity, and accuracy greater than 0.91, 0.79, and 0.90, and tumor-type diagnosis with sensitivity, specificity, and accuracy of at least 0.80, 0.95, and about 0.90.\"}]","In Vivo Time-Resolved Fluorescence Detection of Liver Cancer Supported by Machine Learning - Key research findings | PDF",1785728099,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"in-vivo-time-resolved-fluorescence-detection-of-liver-cancer-supported-by-machine-learning-key-research-findings","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/in-vivo-time-resolved-fluorescence-detection-of-liver-cancer-supported-by-machine-learning-key-research-findings/120087/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in liver cancer diagnosis?","Question",{"text":75,"@type":76},"The study targets the need for improved diagnostic effectiveness in optical liver biopsy and automates classification of liver tissue versus tumor types using time-resolved fluorescence and machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the optical biopsy performed in the study?",{"text":80,"@type":76},"An ultrasound-controlled setup used a fine-needle optical probe in clinical conditions. Fluorescence decays were recorded in a conditionally healthy liver and in lesions during percutaneous needle biopsy.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance did the machine learning models achieve?",{"text":84,"@type":76},"Random forest provided reliable separation of liver and all tumor types into cancer and noncancer classes with sensitivity, specificity, and accuracy greater than 0.91, 0.79, and 0.90, and tumor-type diagnosis with sensitivity, specificity, and accuracy of at least 0.80, 0.95, and about 0.90.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]