{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li Y"],"funding":["State Key Laboratory of Pathogenesis,Prevention,Treatment of Central Asian High Incidence Diseases Fund."],"pagination":["3393-3401"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9525946"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["121(12)"],"pubmed_abstract":["The purpose of this study was to establish a clinical prediction model for the differential diagnosis of pulmonary cystic echinococcosis (CE) and pulmonary abscess according to computed tomography (CT)-based radiomics signatures and clinical indicators. This is a retrospective single-centre study. A total of 117 patients, including 53 with pulmonary CE and 64 with pulmonary abscess, were included in our study and were randomly divided into a training set (n = 95) and validation set (n = 22). Radiomics features were extracted from CT images, a radiomics signature was constructed, and clinical indicators were evaluated to establish a clinical prediction model. Finally, a model combining imaging radiomics features and clinical indicators was constructed. The performance of the nomogram, radio"],"journal":["Parasitology research"],"pubmed_title":["A CT-based radiomics nomogram for the differentiation of pulmonary cystic echinococcosis from pulmonary abscess."],"pmcid":["PMC9525946"],"funding_grant_id":["SKL-HIDCA-2020-17"],"pubmed_authors":["Li Y","Li S","Qi H","Xing Y","Liu Q","Xin J","Yu Y"],"additional_accession":[]},"is_claimable":false,"name":"A CT-based radiomics nomogram for the differentiation of pulmonary cystic echinococcosis from pulmonary abscess.","description":"The purpose of this study was to establish a clinical prediction model for the differential diagnosis of pulmonary cystic echinococcosis (CE) and pulmonary abscess according to computed tomography (CT)-based radiomics signatures and clinical indicators. This is a retrospective single-centre study. A total of 117 patients, including 53 with pulmonary CE and 64 with pulmonary abscess, were included in our study and were randomly divided into a training set (n = 95) and validation set (n = 22). Radiomics features were extracted from CT images, a radiomics signature was constructed, and clinical indicators were evaluated to establish a clinical prediction model. Finally, a model combining imaging radiomics features and clinical indicators was constructed. The performance of the nomogram, radio","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2025-04-03T21:30:24.717Z","creation":"2025-04-03T21:30:24.717Z"},"accession":"S-EPMC9525946","cross_references":{"pubmed":["36181541"],"doi":["10.1007/s00436-022-07663-9"]}}