{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["10(19)"],"submitter":["Xie N"],"pubmed_abstract":["<h4>Background</h4>Ki-67 is a key indicator of the proliferation activity of tumors. However, no standardized criterion has been established for Ki-67 index calculation. Scale-invariant feature transform (SIFT) algorithm can identify the robust invariant features to rotation, translation, scaling and linear intensity changes for matching and registration in computer vision. Thus, this study aimed to develop a SIFT-based computer-aided system for Ki-67 calculation in breast cancer.<h4>Methods</h4>Hematoxylin and eosin (HE)-stained and Ki-67-stained slides were scanned and whole slide images (WSIs) were obtained. The regions of breast cancer (BC) tissues and non-BC tissues were labeled by experienced pathologists. All the labeled WSIs were randomly divided into the training set, verification"],"journal":["Annals of translational medicine"],"pagination":["1067"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9622502"],"repository":["biostudies-literature"],"pubmed_title":["Artificial intelligence scale-invariant feature transform algorithm-based system to improve the calculation accuracy of Ki-67 index in invasive breast cancer: a multicenter retrospective study."],"pmcid":["PMC9622502"],"pubmed_authors":["Jiang Y","Yu L","Huang S","Ouyang Q","Tian C","Xie N","Li K","Zhou H","Hu ZY"],"additional_accession":[]},"is_claimable":false,"name":"Artificial intelligence scale-invariant feature transform algorithm-based system to improve the calculation accuracy of Ki-67 index in invasive breast cancer: a multicenter retrospective study.","description":"<h4>Background</h4>Ki-67 is a key indicator of the proliferation activity of tumors. However, no standardized criterion has been established for Ki-67 index calculation. Scale-invariant feature transform (SIFT) algorithm can identify the robust invariant features to rotation, translation, scaling and linear intensity changes for matching and registration in computer vision. Thus, this study aimed to develop a SIFT-based computer-aided system for Ki-67 calculation in breast cancer.<h4>Methods</h4>Hematoxylin and eosin (HE)-stained and Ki-67-stained slides were scanned and whole slide images (WSIs) were obtained. The regions of breast cancer (BC) tissues and non-BC tissues were labeled by experienced pathologists. All the labeled WSIs were randomly divided into the training set, verification","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Oct","modification":"2025-04-19T02:39:07.634Z","creation":"2025-04-07T13:02:53.96Z"},"accession":"S-EPMC9622502","cross_references":{"pubmed":["36330383"],"doi":["10.21037/atm-22-4254"]}}