{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Du Y"],"funding":["The Guangxi Promotion of Appropriate Health Technologies Project","The Natural Science Foundation of Guangxi Zhuang Autonomous Region","The Guangxi Medical\"139\" Project for Training High-level Backbone Talents","The Guangxi Medical”139” Project for Training High-level Backbone Talents"],"pagination":["89"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11020982"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(1)"],"pubmed_abstract":["Background
Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours.Methods
We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the "],"journal":["BMC medical imaging"],"pubmed_title":["Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study."],"pmcid":["PMC11020982"],"funding_grant_id":["G201903014","S2021055","2023GXNSFDA026010"],"pubmed_authors":["Wu J","Yao J","Guo W","Xiao Y","Du Y","Chen H"],"additional_accession":[]},"is_claimable":false,"name":"Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study.","description":"Background
Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours.Methods
We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the ","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Apr","modification":"2025-04-04T12:58:20.227Z","creation":"2025-04-04T12:58:20.227Z"},"accession":"S-EPMC11020982","cross_references":{"pubmed":["38622546"],"doi":["10.1186/s12880-024-01251-2"]}}