[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-351063-105":59,"doc-detail-351063-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","thyroid-cancer-detection-and-classification-using-spectral-imaging-and-artificial-intelligence","Thyroid cancer detection and classification using spectral imaging and artificial intelligence","","Thyroid cancer is a common endocrine malignancy whose diagnosis depends on microscopic tissue assessment and is prone to misclassification with major prognostic impact. An accurate diagnostic approach is proposed using a newly developed spectral imaging system to rapidly measure visible spectra on routine hematoxylin-eosin sections. Spectral images are analyzed with machine learning to classify each nucleus with expert interpretability. The method supports pathologists by robustly identifying normal and tumor cells using standard specimens and targeted pathological features.",{"@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/thyroid-cancer-detection-and-classification-using-spectral-imaging-and-artificial-intelligence/351063/",{"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/thyroid-cancer-detection-and-classification-using-spectral-imaging-and-artificial-intelligence/351063.png","ImageObject",300,407,{"name":92,"@type":93},"Asher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-26","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the proposed method address in thyroid cancer diagnosis?","Question",{"text":112,"@type":113},"It addresses misclassification risks in traditional microscopic tissue examination, which can have critical prognostic consequences, especially for difficult thyroid cancer subtypes.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the spectral imaging system work in the method described?",{"text":117,"@type":113},"It rapidly measures the visible spectrum at each point on routinely prepared hematoxylin-and-eosin-stained tissue sections to generate spectral images.",{"name":119,"@type":110,"acceptedAnswer":120},"What is the role of machine learning in this approach?",{"text":121,"@type":113},"Machine learning algorithms analyze the spectral images to classify each nucleus while preserving interpretability for expert pathologists.","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},351063,1790201322,{"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":81,"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},687197207639,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nThyroid cancer detection and classification using spectral imaging and artificial intelligence  \nMaya Almagor1,4, Yotam Shapira1,4􀀍, Adam Soker1, Gabor Fischer2, John Gartner2, Sabine Mai3 & Yuval Garini1􀀍  \nThyroid cancer is the most prevalent endocrine cancer, with a steadily rising incidence. Its diagnosis involves microscopic examination of tissue specimens, a process that can lead to misclassification with critical prognostic consequences. Numerous artificial intelligence techniques have been proposed for thyroid cancer detection, however, none have proven clinically relevant. We present an accurate diagnostic approach based on a newly developed spectral imaging system that rapidly measures the visible spectrum at each point on routinely prepared hematoxylin and eosin-stained tissue sections. These spectral images are analyzed with machine learning algorithms, classifying each nucleus while preserving interpretability for experts. The integration of spectral imaging and artificial intelligence enables precise, robust identification of normal and tumor cells, offering a straightforward, powerful approach for thyroid cancer assessment. By utilizing routinely stained tissue specimens and targeting specific pathological features, our method provides a tool to support pathologists, facilitating accurate and timely evaluations of thyroid cancer.  \nThyroid cancer is the most prevalent endocrine cancer and is estimated to have led to 44,000 new diagnosed cases and ~ 2200 deaths in the US in 20241. Among the various subtypes, our study focuses on the most common: classic papillary thyroid carcinoma (PTC), which accounts for 80–85% of all thyroid cancers2, follicular variant PTC (FVPTC), comprising 9–22% of PTC cases3, and non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), which represents 4–9% of PTC cases4,5. Thyroid cancer is diagnosed by histopathological evaluation of tumor resection specimens. It is a difficult cancer to diagnose at times, especially its sub-types, which are critical for prognostic purposes. The diagnosis of NIFTP has proved to be particularly problematic since these lesions share overlapping histopathological features with FVPTC on one hand, and can also exhibit benign characteristics on the other hand4,6,7. Therefore, the distinction between benign and malignant neoplasms is important for prognosis and treatment decisions. For the diagnosis, pathologists evaluate many properties including the histopathologic features, the presence of molecular markers by immunohistochemistry, the level of homogeneity or heterogeneity of the cells, invasion of tumor cells into normal adjacent tissues and more. This procedure is time-consuming, requires experience, and is somewhat subjective. Notably, many pathologists specialize in one specific type of cancer, while difficult cases, such as thyroid cancer subtypes, are often reviewed by a few specialized pathologists in order to establish consensus. This could be facilitated by harnessing advanced methods to assist pathologists in their clinical observation and decision making.  \nIn recent years, digital pathology (DP) has emerged as a helpful adjunct in histopathology. It commonly employs whole slide imaging (WSI) systems that can rapidly scan whole tissue sections, thereby allowing pathologists to diagnose the cases on a computer screen, rather than through the microscope. The availability of digital images simplifies the sharing of knowledge for consultation between pathologists and allows the extraction of vital information for diagnosis and treatment8–13. These scans also facilitate the application of computational analysis methods on the images of the tissue samples, such as AI that has emerged as a pivotal tool in all scientific domains and has great potential in pathological diagnostics and prognostics. Machine learning (ML) and deep learning (DL) based","cbCaiqsfWKYXPqkj","https://ap.wps.com/l/cbCaiqsfWKYXPqkj","pdf",4373200,13,"English","# Introduction\n## Diagnostic challenges in thyroid cancer\n## Digital pathology and deep learning\n## Rationale for interpretable, robust AI approaches","[{\"question\":\"What problem does the proposed method address in thyroid cancer diagnosis?\",\"answer\":\"It addresses misclassification risks in traditional microscopic tissue examination, which can have critical prognostic consequences, especially for difficult thyroid cancer subtypes.\"},{\"question\":\"How does the spectral imaging system work in the method described?\",\"answer\":\"It rapidly measures the visible spectrum at each point on routinely prepared hematoxylin-and-eosin-stained tissue sections to generate spectral images.\"},{\"question\":\"What is the role of machine learning in this approach?\",\"answer\":\"Machine learning algorithms analyze the spectral images to classify each nucleus while preserving interpretability for expert pathologists.\"}]","Thyroid cancer detection and classification using spectral imaging and artificial intelligence | PDF",1790092450,33]