[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-331548-105":59,"doc-detail-331548-en":131},{"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":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","latent-archetypes-ofthe-spatial-patterns-of-cancer-research-article","Latent Archetypes ofthe Spatial Patterns of Cancer - Research Article","","The cancer atlas edited by multiple countries serves as a primary resource for studying geographic variation in cancer risk. Linking observed spatial patterns to known or proposed risk factors is labor-intensive for epidemiologists, especially when each cancer is analyzed separately by sex and race. Recent approaches examine several cancers jointly, but typically cover only a small set of related cancers. This article introduces an exploratory method for latent spatial risk factors across many cancers using singular value decomposition and nonnegative matrix factorization, demonstrated through simulations and multiple-country atlas applications.",{"@graph":69,"@context":123},[70,84,106],{"@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/latent-archetypes-ofthe-spatial-patterns-of-cancer-research-article/331548/",{"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/latent-archetypes-ofthe-spatial-patterns-of-cancer-research-article/331548.png","ImageObject",300,407,{"name":92,"@type":93},"Seraphina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the paper address in cancer atlas analysis?","Question",{"text":113,"@type":114},"It addresses the high effort needed to correlate spatial cancer patterns with risk factors when cancers are analyzed one by one, often separately by groups such as sex and race.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the proposed method identify latent spatial risk factors?",{"text":118,"@type":114},"It uses singular value decomposition and nonnegative matrix factorization to search for latent spatial risk factors across a large number of cancers efficiently.",{"name":120,"@type":111,"acceptedAnswer":121},"What do the simulation and real atlas results show?",{"text":122,"@type":114},"With very few latent maps, the approach can reduce up to 90% of atlas maps while conserving most spatial variability, enabling higher-level epidemiological explanations across many cancers simultaneously.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},331548,1790451790,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},962075114101,"https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165","Statistics in Medicine  \nRESEARCH ARTICLE  OPEN ACCESS   \nLatent Archetypes ofthe Spatial Patterns of Cancer  \nThaís Pacheco Menezes1  | Marcos Oliveira Prates2  | Renato Assunção3, 4 | Mônica Silva Monteiro De Castro5  \n1 School of Mathematics and Statistics, UCD, Dublin, Ireland | 2 Departamento de Estatística, UFMG, Belo Horizonte, Minas Gerais, Brazil | 3 ESRI Inc., Redlands, California, USA | 4 Departamento de Ciência da Computação, UFMG, Belo Horizonte, Minas Gerais, Brazil | 5 René Rachou Institute, Oswaldo Cruz Foundation, FIOCRUZ, Belo Horizonte, Minas Gerais, Brazil  \nCorrespondence: Thaís Pacheco Menezes ([thaispacheco1207@gmail.com](thaispacheco1207@gmail.com))  \nReceived: 10 March 2023 | Revised: 2 August 2024 | Accepted: 13 September 2024  \nFunding: Marcos O. Prates would like to acknowledge (Conselho Nacional de Desenvolvimento Científico e Tecnológico) CNPq Grant 309186/2021-8 and FAPEMIG (Fundação de Amparo à Pesquisa do Estado de Minas Gerais) Grant APQ-01837-22 and CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) for financial support. Renato Assunção acknowledges the following grants to acquire computer equipment: CNPq 313120/2022-6 and FAPEMIGCEX-PPM-00598-17. This study was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (436948/2018-4) .  \nKeywords: cancer maps | low-rank approximation | nonnegative matrix factorization | singular value decomposition | spatial statistics  \nABSTRACT  \nThe cancer atlas edited by several countries is the main resource for the analysis of the geographic variation of cancer risk. Correlating the observed spatial patterns with known or hypothesized risk factors is time-consuming work for epidemiologists who need to deal with each cancer separately, breaking down the patterns according to sex and race. The recent literature has proposed to study more than one cancer simultaneously looking for common spatial risk factors. However, this previous work has two constraints: they consider only a very small (2–4) number of cancers previously known to share risk factors. In this article, we propose an exploratory method to search for latent spatial risk factors of a large number of supposedly unrelated cancers. The method is based on the singular value decomposition and nonnegative matrix factorization, it is computationally efficient, scaling easily with the number of regions and cancers. We carried out a simulation study to evaluate the method’s performance and apply it to cancer atlas from the USA, England, France, Australia, Spain, and Brazil. We conclude that with very few latent maps, which can represent a reduction of up to 90% of atlas maps, most of the spatial variability is conserved. By concentrating on the epidemiological analysis of these few latent maps a substantial amount of work is saved and, at the same time, high-level explanations affecting many cancers simultaneously can be reached.  \n1 | Introduction  \nCancer is a group of heterogeneous diseases whose main common behavior is the disordered growth of cells, which tend to be aggressive, uncontrollable, and invade different systems, tissues, and organs of the human body. It is one of the main causes of death in the world. On a global level, the International Agency for Research on Cancer (IARC) estimated that 19.3 million in new cancer cases occurred in 2020, together with 10.0 million in cancer deaths. Epidemiological studies are the main tools to  \nidentify risk factors that act by modifying the cell genetic code and triggering cancer. They have found that avoiding tobacco use, the maintenance of a healthy weight and diet, the practice of physical activities, and alcohol use limitation can help prevent cancer. As prevention is the main strategy for cancer control, the results of these studies are very important to reduce the risk of developing the disease.  \nAs early as 1975, the publication of a geographically detailed atlas of cancer mortality revealed the strong spat","cbCaivgxBFyqAKZT","https://ap.wps.com/l/cbCaivgxBFyqAKZT","pdf",3806441,23,"English","# Introduction\n# Methods and Simulation Study\n# Application to Multi-Country Cancer Atlases\n# Results and Conclusions","[{\"question\":\"What problem does the paper address in cancer atlas analysis?\",\"answer\":\"It addresses the high effort needed to correlate spatial cancer patterns with risk factors when cancers are analyzed one by one, often separately by groups such as sex and race.\"},{\"question\":\"How does the proposed method identify latent spatial risk factors?\",\"answer\":\"It uses singular value decomposition and nonnegative matrix factorization to search for latent spatial risk factors across a large number of cancers efficiently.\"},{\"question\":\"What do the simulation and real atlas results show?\",\"answer\":\"With very few latent maps, the approach can reduce up to 90% of atlas maps while conserving most spatial variability, enabling higher-level epidemiological explanations across many cancers simultaneously.\"}]","Latent Archetypes ofthe Spatial Patterns of Cancer - Research Article | PDF",1789993891,58]