{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["11"],"submitter":["Zhang H"],"pubmed_abstract":["To improve the accuracy of preoperative diagnoses and avoid over- or undertreatment, we aimed to develop and compare computed tomography-based radiomics machine learning models for the prediction of histological invasiveness using sub-centimeter subsolid pulmonary nodules. Three predictive models based on radiomics were built using three machine learning classifiers to discriminate the invasiveness of the sub-centimeter subsolid pulmonary nodules. A total of 203 sub-centimeter nodules from 177 patients were collected and assigned randomly to the training set (<i>n</i> = 143) or test set (<i>n</i> = 60). The areas under the curve of the predictive models were 0.743 (95% confidence interval CI [0.661-0.824]) for the logistic regression, 0.828 (95% CI [0.76-0.896]) for the support vector machine, and 0.917 (95% CI [0.869-0.965]) for the XGBoost classifier models in the training set, and 0.803 (95% CI [0.694-0.913]), 0.726 (95% CI [0.598-0.854]), and 0.874 (95% CI [0.776-0.972]) in the test set, respectively. In addition, the decision curve showed that the XGBoost model added more net benefit within the range of 0.06 to 0.93."],"journal":["PeerJ"],"pagination":["e14559"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9838201"],"repository":["biostudies-literature"],"pubmed_title":["Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study."],"pmcid":["PMC9838201"],"pubmed_authors":["Li Y","Yang Y","Wang S","Zhang H","Huang H","Deng Z"],"additional_accession":[]},"is_claimable":false,"name":"Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study.","description":"To improve the accuracy of preoperative diagnoses and avoid over- or undertreatment, we aimed to develop and compare computed tomography-based radiomics machine learning models for the prediction of histological invasiveness using sub-centimeter subsolid pulmonary nodules. Three predictive models based on radiomics were built using three machine learning classifiers to discriminate the invasiveness of the sub-centimeter subsolid pulmonary nodules. A total of 203 sub-centimeter nodules from 177 patients were collected and assigned randomly to the training set (<i>n</i> = 143) or test set (<i>n</i> = 60). The areas under the curve of the predictive models were 0.743 (95% confidence interval CI [0.661-0.824]) for the logistic regression, 0.828 (95% CI [0.76-0.896]) for the support vector machine, and 0.917 (95% CI [0.869-0.965]) for the XGBoost classifier models in the training set, and 0.803 (95% CI [0.694-0.913]), 0.726 (95% CI [0.598-0.854]), and 0.874 (95% CI [0.776-0.972]) in the test set, respectively. In addition, the decision curve showed that the XGBoost model added more net benefit within the range of 0.06 to 0.93.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023","modification":"2024-12-03T15:48:48.736Z","creation":"2024-12-03T15:48:48.736Z"},"accession":"S-EPMC9838201","cross_references":{"pubmed":["36643621"],"doi":["10.7717/peerj.14559"]}}