[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-128846-105":59,"doc-detail-128846-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","detecting-collagen-by-machine-learning-improved-photoacoustic-spectral-analysis-for-breast-cancer-diagnostics-feasibility-studies-with-murine-models","Detecting Collagen by Machine Learning Improved Photoacoustic Spectral Analysis for Breast Cancer Diagnostics - Feasibility Studies with Murine Models","","Collagen is a key extracellular matrix component that undergoes significant remodeling during carcinogenesis, yet effective in vivo detection methods for breast cancer diagnostics remain limited. This research evaluates a non-invasive diagnostic strategy that combines photoacoustic spectral analysis with machine learning to target collagen as a biomarker. Murine model results show stronger collagen associations with cancer components than normal tissues, and genetic-algorithm feature selection identifies collagen-dominant absorption wavebands. Using the optimal spectra, the diagnostic algorithm achieves 72% accuracy, 66% sensitivity, and 78% specificity, improving over full-range spectra and supporting early-stage cancer detection potential.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/detecting-collagen-by-machine-learning-improved-photoacoustic-spectral-analysis-for-breast-cancer-diagnostics-feasibility-studies-with-murine-models/128846/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/detecting-collagen-by-machine-learning-improved-photoacoustic-spectral-analysis-for-breast-cancer-diagnostics-feasibility-studies-with-murine-models/128846.png","ImageObject",300,407,{"name":92,"@type":93},"Aria","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-17","2026-08-06",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is collagen important for breast cancer diagnostics?","Question",{"text":112,"@type":113},"Collagen remodeling in the extracellular matrix accompanies tumor initiation and progression, making collagen levels a useful biomarker for diagnostic and outcome-related insights.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What method does the study propose for detecting collagen?",{"text":117,"@type":113},"It proposes machine learning–improved photoacoustic spectral analysis focused on collagen as a salient biomarker, using murine model data and genetic-algorithm wavelength feature selection.",{"name":119,"@type":110,"acceptedAnswer":120},"How does feature wavelength selection affect diagnostic performance?",{"text":121,"@type":113},"An optimal wavelength set identified by a genetic algorithm boosts performance: using optimal spectra yields 72% accuracy, 66% sensitivity, and 78% specificity, outperforming full-range spectra.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},128846,1786003842,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":41,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143},2336474459895,"https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916","Article type: Research Article  \nDetecting collagen by machine learning improved photoacoustic spectral analysis for breast cancer diagnostics: feasibility studies with murine models  \nJiayan Li1, Lu Bai2, Yingna Chen1, Junmei Cao1, Jingtao Zhu3, Wenxiang Zhi2,*, Qian Cheng 1,4,5,*  \n1 Institute of Acoustics, School of Physics Science and Engineering, Tongji University, Shanghai, P. R. China  \n2 Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, P. R. China  \n3 School of Physics Science and Engineering, Tongji University, Shanghai, P. R. China  \n4 National Key Laboratory of Autonomous Intelligent Unmanned Systems, P. R. China  \n5 Frontiers Science Center for Intelligent Autonomous Systems, Ministry of Education, P. R.  \nChina  \n*Correspondence  \nWenxiang Zhi, Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, P. R. China  \nEmail: [zwenx1123@163.com](zwenx1123@163.com)  \nQian Cheng, Institute of Acoustics, School of Physics and Engineering, Tongji University, Shanghai, P. R. China  \nEmail: [q.cheng@tongji.edu.cn](q.cheng@tongji.edu.cn)  \nAbstract  \nCollagen, a key structural component of the extracellular matrix, undergoes significant remodeling during carcinogenesis. However, the important role of collagen levels in breast cancer diagnostics still lacks effective in vivo detection techniques to provide a deeper understanding. This study presents photoacoustic spectral analysis improved by machine learning as a promising non-invasive diagnostic method, focusing on exploring collagen as a salient biomarker. Murine model experiments revealed more profound associations of collagen with other cancer components than in normal tissues. Moreover, an optimal set of feature wavelengths was identified by a genetic algorithm for enhanced diagnostic performance, among which 75% were from collagen-dominated absorption wavebands. Using optimal spectra, the diagnostic algorithm achieved 72% accuracy, 66% sensitivity, and 78% specificity, surpassing full-range spectra by 6%, 4%, and 8%, respectively. The proposed photoacoustic methods examine the feasibility of offering valuable biochemical insights into existing techniques, showing great potential for early-stage cancer detection.  \nKeywords: breast cancer diagnostics; collagen; machine learning; murine models; photoacoustic spectral analysis  \nAbbreviations: PASA, photoacoustic spectral analysis; PA, photoacoustic; ECM, extracellular matrix; GA, genetic algorithm; ML, machine learning; PAI, photoacoustic imaging; APSD, area of power spectrum density; SVMDA, support vector machine discriminant analysis; KNN, K-nearest neighbor; PLSDA, partial least-squares discriminant analysis; H&E, hematoxylineosin; MRI, magnetic resonance imaging.  \n1 INTRODUCTION  \nBreast cancer is the most common malignancy affecting women, and its incidence has continued to rise by 0.6% per year since 2004, according to Cancer Statistics 2024 [1] . Earlystage breast cancer is curable in approximately 75% of patients [2] . Therefore, early diagnosis is vital for improving survival and prognosis. Mammography is currently the primary method for clinical breast cancer screening [3] . However, it has a risk of radiation exposure and is susceptible to high breast tissue density, which leads to low sensitivity (24–47%) [4–6] .  \nUltrasonography is a vastly used adjunct but suffers from operator dependency and insufficient specificity (65-89%) [7,8] . Further, while magnetic resonance imaging (MRI) has been used to evaluate the clinical outcomes of patients, it is restricted by the high costs [9] . Thus, the advent of novel practical tools for providing complementary information and expanding the scope of cancer research is in demand.  \nThe impact of extracellular matrix (ECM) on the behavior of malignant cells has become a developing research area in oncology. Collagen is the mos","cbCaikvxHX5IrDbZ","https://ap.wps.com/l/cbCaikvxHX5IrDbZ","pdf",2185609,"English","# Introduction\n## Breast cancer screening challenges\n## Extracellular matrix and collagen as biomarkers\n## Limitations of existing collagen characterization methods\n## Photoacoustic spectral analysis for non-invasive diagnostics\n## Photoacoustic applications and prior work","[{\"question\":\"Why is collagen important for breast cancer diagnostics?\",\"answer\":\"Collagen remodeling in the extracellular matrix accompanies tumor initiation and progression, making collagen levels a useful biomarker for diagnostic and outcome-related insights.\"},{\"question\":\"What method does the study propose for detecting collagen?\",\"answer\":\"It proposes machine learning–improved photoacoustic spectral analysis focused on collagen as a salient biomarker, using murine model data and genetic-algorithm wavelength feature selection.\"},{\"question\":\"How does feature wavelength selection affect diagnostic performance?\",\"answer\":\"An optimal wavelength set identified by a genetic algorithm boosts performance: using optimal spectra yields 72% accuracy, 66% sensitivity, and 78% specificity, outperforming full-range spectra.\"}]","Detecting Collagen by Machine Learning Improved Photoacoustic Spectral Analysis for Breast Cancer Diagnostics - Feasibility Studies with Murine Models | PDF",76]