Deep learning algorithm could aid malignancy risk estimation of pulmonary nodules detected on low-dose screening CT<\/p>\n<\/h3>\n
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THURSDAY, May 20, 2021 (HealthDay News) — A deep learning (DL) algorithm has the potential to assist in the estimation of malignancy risk of pulmonary nodules detected at low-dose screening computed tomography (CT), according to a study published online May 18 in Radiology<\/em>.<\/p>\n Kiran Vaidhya Venkadesh, from the Radboud Institute for Health Sciences in Nijmegen, Netherlands, and colleagues developed and validated a DL algorithm for malignancy risk estimation of pulmonary nodules detected at screening CT in a retrospective study. The DL algorithm was developed with 16,077 nodules (1,249 malignant) collected from the National Lung Screening Trial between 2002 and 2004. External validation was performed in three cohorts: a full cohort with 883 nodules (65 malignant) and two cancer-enriched cohorts with size matching (175 nodules; 59 malignant) and without size matching (177 nodules; 59 malignant) of benign nodules. Algorithm performance was compared to that of the Pan-Canadian Early Detection of Lung Cancer (PanCan) model.<\/p>\n The researchers found that in the full cohort, the DL algorithm significantly outperformed the PanCan model (area under the receiver operating characteristic curve [AUC], 0.93 versus 0.90). Performance of the algorithm was comparable to that of thoracic radiologists in cancer-enriched cohorts with random benign nodules (AUC, 0.96 versus 0.90) and size-matched benign nodules (AUC, 0.86 versus 0.82).<\/p>\n “This deep learning algorithm may aid radiologists in optimizing follow-up recommendations for participants undergoing lung cancer screening and may lead to fewer unnecessary diagnostic interventions,” the authors write.<\/p>\n Several authors disclosed financial ties to medical device companies, including MeVis Medical Solutions, which funded the study.<\/p>\n Abstract\/Full Text<\/a><\/p>\n Editorial (subscription or payment may be required)<\/a><\/p>\n <\/i><\/p>\n