Manuel Weber, Anja Maria Raab, Kai-Uwe Schmitt, Gilbert Büsching, Thimo Marcin, Marc Spielmanns, Milo Alan Puhan, Anja Frei
Efficacy of a digital lifestyle intervention on health-related QUAlity of life in non-small cell LUng CAncer survivors following inpatient rehabilitation: protocol of the QUALUCA Swiss multicentre randomised controlled trial
BMJ open
diagnosing subcentimeter solid pulmonary nodules sspns remains challenging clinical practice deep learning may perform better conventional methods differentiating benign malignant pulmonary nodules study aimed develop validate model differentiating malignant benign sspns using ct images retrospective study included consecutive patients sspns detected january 2015 october 2021 internal dataset malignancy confirmed pathologically benignity confirmed pathologically via followup evaluations sspns segmented manually selfsupervision pretrainingbased finegrained network developed predicting sspn malignancy pretrained model established using data national lung screening trial lung nodule analysis 2016 database 5478 pulmonary nodules previous study subsequent finetuning using internal dataset models efficacy investigated using external cohort another center accuracy sensitivity specificity area receiver operating characteristic curve auc determined overall 1276 patients mean age 56 10 years 497 males 1389 sspns mean diameter 75 20 mm 625 benign enrolled internal dataset specifically enriched malignancy models performance internal testing set 316 sspns auc 0964 95 confidence interval 95ci 09420986 accuracy 0934 sensitivity 0965 specificity 0908 models performance external test set 202 sspns auc 0945 95 ci 09100979 accuracy 0911 sensitivity 0977 specificity 0860 deep learning model robust exhibited good performance predicting malignancy sspns could help optimize patient management