[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-375625-105":59,"doc-detail-375625-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","classification-of-cancer-cells-at-the-subcellular-level-by-phonon-microscopy-using-deep-learning","Classification of cancer cells at the sub‑cellular level by phonon microscopy using deep learning","","Strong links exist between the elasticity of cells and tissue and normal, dysplastic, and cancerous states, yet cell-mechanics advances have not translated well into clinical tools. This work uses phonon microscopy to quantify elastic properties of breast cells, applying deep learning to raw time-resolved phonon data to distinguish MDA‑MB‑231 from MCF10a with 93% accuracy from a single measurement. A physical-model–guided approach suggests unidentified mechanical markers, and a compact sensor prototype demonstrates feasibility for needle and endoscope use.",{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/classification-of-cancer-cells-at-the-subcellular-level-by-phonon-microscopy-using-deep-learning/375625/",{"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/classification-of-cancer-cells-at-the-subcellular-level-by-phonon-microscopy-using-deep-learning/375625.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the study differentiate cancerous from normal breast cells?","Question",{"text":112,"@type":113},"It measures elastic properties using phonon microscopy and applies deep learning to time-resolved phonon-derived data to classify MDA‑MB‑231 versus MCF10a cells.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What classification performance is reported?",{"text":117,"@type":113},"The model achieves 93% accuracy using a single phonon measurement within a volume of approximately 2.5 μm3.",{"name":119,"@type":110,"acceptedAnswer":120},"What evidence suggests additional mechanical markers beyond elasticity?",{"text":121,"@type":113},"The authors investigate classification methods based on a physical model that indicates the presence of unidentified mechanical markers.","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},375625,1790272223,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nClassification of cancer cells at the sub‑cellular level by phonon microscopy using deep learning  \nFernando Pérez‑Cota1*, Giovanna Martínez‑Arellano2, Salvatore La Cavera III1, William Hardiman1, Luke Thornton3, Rafael Fuentes‑Domínguez1, Richard J. Smith1, Alan McIntyre3 & Matt Clark1  \nThere is a consensus about the strong correlation between the elasticity of cells and tissue and  \ntheir normal, dysplastic, and cancerous states. However, developments in cell mechanics have not seen significant progress in clinical applications. In this work, we explore the possibility of using phonon acoustics for this purpose. We used phonon microscopy to obtain a measure of the elastic properties between cancerous and normal breast cells. Utilising the raw time‑resolved phonon‑derived data (300 k individual inputs), we employed a deep learning technique to differentiate between MDA‑MB‑231 and MCF10a cell lines. We achieved a 93% accuracy using a single phonon measurement in a volume of approximately 2.5 μm3. We also investigated means for classification based on a physical model that suggest the presence of unidentified mechanical markers. We have successfully created a compact sensor design as a proof of principle, demonstrating its compatibility for use with needles and endoscopes, opening up exciting possibilities for future applications.  \nCancer is one of the biggest causes of death in the developed world. Globally, cancer was the cause of death for nearly 10 million people in 20201. The predicted 10-year survival rate after detection is typically only 50%2, so there is a significant clinical need to gain a better understanding of cancer and improve diagnostics and therapy. One aspect of cancer discovery that has gained significant interest lately, is the role that stiffness plays in the process of tumorigenesis3–5. Palpation is one historical and simple diagnostic technique that makes use of themechanobiology of some types of cancer. Distinct differences in mechanobiology also have been detected both at the single cell and macroscopic tumour scales, among normal, primary, and metastatic conditions3,5,6. Therefore, the contrast obtained through the characterisation of elasticity could contribute to addressing some of the challenges of cancer: identifying cancerous tissue and quantifying the malignant potential of individual cancer cells in-vivo is key to early diagnosis. This is where optics alone often struggle to differentiate normal cells/tissue from their cancerous counterpart and, more importantly, correlate the observed differences with mechanismson cancer-associated phenotypes.  \nConsiderable advancements have been made in the assessment and comprehension of cancer’s elastic properties at the single-cell level7,8. Although atomic force microscopy has been predominantly utilised in most studies9–11, alternative techniques, including microfluidics12, micropipette aspiration13, optical tweezers14 and ultrasound15, 16 have also been employed. It has been widely agreed upon that cancer cells exhibit lower elasticity compared to normal cells17 enabling their discrimination18–20. However, challenges persist in fully understanding these disparities and effectively applying them for cancer diagnosis in a clinical setting. These obstacles primarily stem from limitations in throughput, with most studies ignoring sub-cellular detail, as well as the inherent difficulties associated with conducting in-vivo measurements.  \nPhonon microscopy21–23 is a highly promising technique for the examination of elastic properties at the single-cell level. Its contrast is given by the sound velocity, its lateral resolution is given by optics and exhibits super-optical axial resolution. All these features arise due to the ability of phonon microscopy to generate and detect the time of flight of coherent phonons via Brillouin scattering22. The collection of Brillouin scatterin","cbCaimmAszanqXFJ","https://ap.wps.com/l/cbCaimmAszanqXFJ","pdf",2403160,11,"English","# Introduction\n## Clinical need and mechanobiology\n## Existing single-cell elasticity assessment methods\n# Phonon microscopy and optical classification challenges\n## Technique principles and prior applications\n# Artificial intelligence for cancer cell classification\n# This study: dataset, deep learning approach, and sensor prototype","[{\"question\":\"How does the study differentiate cancerous from normal breast cells?\",\"answer\":\"It measures elastic properties using phonon microscopy and applies deep learning to time-resolved phonon-derived data to classify MDA‑MB‑231 versus MCF10a cells.\"},{\"question\":\"What classification performance is reported?\",\"answer\":\"The model achieves 93% accuracy using a single phonon measurement within a volume of approximately 2.5 μm3.\"},{\"question\":\"What evidence suggests additional mechanical markers beyond elasticity?\",\"answer\":\"The authors investigate classification methods based on a physical model that indicates the presence of unidentified mechanical markers.\"}]","Classification of cancer cells at the sub‑cellular level by phonon microscopy using deep learning | PDF",1790215565,28]