{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Peeters D"],"funding":["KWF Kankerbestrijding"],"pagination":["6639-6651"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11399205"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["34(10)"],"pubmed_abstract":["<h4>Objective</h4>To investigate the effect of uncertainty estimation on the performance of a Deep Learning (DL) algorithm for estimating malignancy risk of pulmonary nodules.<h4>Methods and materials</h4>In this retrospective study, we integrated an uncertainty estimation method into a previously developed DL algorithm for nodule malignancy risk estimation. Uncertainty thresholds were developed using CT data from the Danish Lung Cancer Screening Trial (DLCST), containing 883 nodules (65 malignant) collected between 2004 and 2010. We used thresholds on the 90th and 95th percentiles of the uncertainty score distribution to categorize nodules into certain and uncertain groups. External validation was performed on clinical CT data from a tertiary academic center containing 374 nodules (207 ma"],"journal":["European radiology"],"pubmed_title":["Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation."],"pmcid":["PMC11399205"],"funding_grant_id":["14113"],"pubmed_authors":["Venkadesh KV","Peeters D","Dinnessen R","Scholten ET","Schaefer-Prokop C","Vliegenthart R","Alves N","Prokop M","Jacobs C","Saghir Z"],"additional_accession":[]},"is_claimable":false,"name":"Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation.","description":"<h4>Objective</h4>To investigate the effect of uncertainty estimation on the performance of a Deep Learning (DL) algorithm for estimating malignancy risk of pulmonary nodules.<h4>Methods and materials</h4>In this retrospective study, we integrated an uncertainty estimation method into a previously developed DL algorithm for nodule malignancy risk estimation. Uncertainty thresholds were developed using CT data from the Danish Lung Cancer Screening Trial (DLCST), containing 883 nodules (65 malignant) collected between 2004 and 2010. We used thresholds on the 90th and 95th percentiles of the uncertainty score distribution to categorize nodules into certain and uncertain groups. External validation was performed on clinical CT data from a tertiary academic center containing 374 nodules (207 ma","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Oct","modification":"2026-06-02T09:22:17.018Z","creation":"2025-04-04T01:03:17.254Z"},"accession":"S-EPMC11399205","cross_references":{"pubmed":["38536463"],"doi":["10.1007/s00330-024-10714-7"]}}