{"doi":"10.1016/j.modpat.2023.100233","title":"Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer","abstract":null,"journal":"Modern Pathology","year":2023,"id":604223,"datarank":0.5101796072493234,"base_score":3.4011973816621555,"endowment":3.4011973816621555,"self_citation_contribution":0.5101796072493234,"citation_network_contribution":0.0,"self_endowment_contribution":0.5101796072493234,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":29,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":245481,"name":"Francesco Ciompi","orcid":"0000-0001-8327-9606","position":1,"is_corresponding":false},{"id":1550207,"name":"Sonay Kus Öztürk","orcid":null,"position":2,"is_corresponding":false},{"id":1550208,"name":"Ayse Selcen Oguz Erdogan","orcid":null,"position":3,"is_corresponding":false},{"id":422069,"name":"Michael Vieth","orcid":"0000-0002-4336-7721","position":4,"is_corresponding":false},{"id":1058144,"name":"Heather Dawson","orcid":"0000-0001-7527-336X","position":5,"is_corresponding":false},{"id":871374,"name":"Richard Kirsch","orcid":"0000-0002-2013-6541","position":6,"is_corresponding":false},{"id":1550209,"name":"Femke Simmer","orcid":null,"position":7,"is_corresponding":false},{"id":1550211,"name":"Kieran Sheahan","orcid":null,"position":8,"is_corresponding":false},{"id":1058143,"name":"Alessandro Lugli","orcid":"0000-0002-9264-5185","position":9,"is_corresponding":false},{"id":107283,"name":"Inti Zlobec","orcid":"0000-0001-6741-3000","position":10,"is_corresponding":false},{"id":97856,"name":"Jeroen van der Laak","orcid":"0000-0001-7982-0754","position":11,"is_corresponding":false},{"id":1472480,"name":"Iris D. Nagtegaal","orcid":null,"position":12,"is_corresponding":false},{"id":1550206,"name":"John-Melle Bokhorst","orcid":"0000-0001-7231-8833","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer","abstract":"Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current international guidelines, hotspots at the invasive front should be counted in hematoxylin and eosin (H&E)-stained slides. This is time-consuming and prone to interobserver variability; therefore, there is a need for computer-aided diagnosis solutions. In this study, we report an artificial intelligence-based method for detecting TB in H&E-stained whole slide images. We propose a fully automated pipeline to identify the tumor border, detect tumor buds, characterize them based on the number of tumor cells, and produce a TB density map to identify the TB hotspot. The method outputs the TB count in the hotspot as a computational biomarker. We show that the proposed automated TB detection workflow performs on par with a panel of 5 pathologists at detecting tumor buds and that the hotspot-based TB count is an independent prognosticator in both the univariate and the multivariate analysis, validated on a cohort of n = 981 patients with CRC. Computer-aided detection of tumor buds based on deep learning can perform on par with expert pathologists for the detection and quantification of tumor buds in H&E-stained CRC histopathology slides, strongly facilitating the introduction of budding as an independent prognosticator in clinical routine and clinical trials.","is_dataset_classified":null,"base_score":3.367295829986474,"endowment":3.367295829986474,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"37257824","pmcid":null,"openalex_id":"https://openalex.org/W4378952948","authors":[],"funders":[{"funder_name":"European Commission","grant_id":"825292","title":"EXtreme-scale Analytics via Multimodal Ontology Discovery & Enhancement"},{"funder_name":"KWF Kankerbestrijding","grant_id":"","title":null},{"funder_name":"Swiss Cancer Foundation","grant_id":"","title":null}],"total_grants":3,"fwci":3.6715,"citation_percentile":0.94383944,"influential_citations":0,"citation_trend":[{"year":2023,"count":1},{"year":2024,"count":9},{"year":2025,"count":10},{"year":2026,"count":8}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"http://www.modernpathology.org/article/S0893395223001382/pdf","host_type":"journal"},{"url":"http://www.modernpathology.org/article/S0893395223001382/pdf","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S0893395223001382?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S0893395223001382?httpAccept=text/plain","host_type":"publisher"},{"url":"https://doi.org/10.1016/j.modpat.2023.100233","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/37257824","host_type":"repository"},{"url":"http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-196786","host_type":"repository"},{"url":"https://repository.ubn.ru.nl/handle/2066/295283","host_type":"repository"},{"url":"https://boris.unibe.ch/183105/","host_type":"repository"},{"url":"https://liu.diva-portal.org/smash/get/diva2:1790691/FULLTEXT01","host_type":"repository"},{"url":"https://repository.ubn.ru.nl//bitstream/handle/2066/295283/295283.pdf","host_type":"repository"},{"url":"https://dx.doi.org/10.48350/183105","host_type":""},{"url":"https://hdl.handle.net/https://repository.ubn.ru.nl/handle/2066/295283","host_type":""},{"url":"https://hdl.handle.net/2066/295283","host_type":""},{"url":"http://dx.doi.org/10.1016/j.modpat.2023.100233","host_type":""}],"fields_of_study":["AI in cancer detection","Colorectal Cancer Screening and Detection","Radiomics and Machine Learning in Medical Imaging","03 medical and health sciences","0302 clinical medicine","Humans","Hematoxylin","Eosine Yellowish-(YS)","Artificial Intelligence","Colorectal Neoplasms","Diagnosis, Computer-Assisted"],"mesh_terms":["Artificial Intelligence","Diagnosis, Computer-Assisted","Eosine Yellowish-(YS)","Hematoxylin","Humans","Colorectal Neoplasms"],"keywords":["Tumor budding","H&E stain","Digital pathology","Medicine","Colorectal cancer","Pathology","Histopathology","Immunohistochemistry","Metastasis","Cancer","Internal medicine","Artificial intelligence","Prognosis","Automated Assessment","Computational Pathology","Cancer och onkologi","Radboud University Medical Center","Radboudumc 17: Women's cancers Pathology","Radboudumc 9: Rare cancers Pathology","automated assessment; colorectal cancer; computational pathology; prognosis; tumor budding","Cancer and Oncology","Humans","Eosine Yellowish-(YS)","Diagnosis, Computer-Assisted","Radboudumc 14: Tumours of the digestive tract Pathology","Hematoxylin","Colorectal Neoplasms"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. Good health"},{"sdg_number":0,"sdg_label":"Partnerships for the goals"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-29T23:36:16.667532Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}