Malignancy risk estimation of pulmonary nodules in screening CTs: Comparison between a computer model and human observers

Sarah J van Riel, Francesco Ciompi, Mathilde M Winkler Wille, Asger Dirksen, Stephen Lam, Ernst Th Scholten, Santiago E Rossi, Nicola Sverzellati, Matiullah Naqibullah, Rianne Wittenberg, Marieke C Hovinga- de Boer, Miranda Snoeren, Liesbeth Peters-Bax, Onno Mets, Monique Brink, Mathias Prokop, Cornelia Schaefer-Prokop and Bram van Ginneken

PLOS ONE, 2017, vol. 12, issue 11, 1-15

Abstract: Purpose: To compare human observers to a mathematically derived computer model for differentiation between malignant and benign pulmonary nodules detected on baseline screening computed tomography (CT) scans. Methods: A case-cohort study design was chosen. The study group consisted of 300 chest CT scans from the Danish Lung Cancer Screening Trial (DLCST). It included all scans with proven malignancies (n = 62) and two subsets of randomly selected baseline scans with benign nodules of all sizes (n = 120) and matched in size to the cancers, respectively (n = 118). Eleven observers and the computer model (PanCan) assigned a malignancy probability score to each nodule. Performances were expressed by area under the ROC curve (AUC). Performance differences were tested using the Dorfman, Berbaum and Metz method. Seven observers assessed morphological nodule characteristics using a predefined list. Differences in morphological features between malignant and size-matched benign nodules were analyzed using chi-square analysis with Bonferroni correction. A significant difference was defined at p

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0185032

DOI: 10.1371/journal.pone.0185032

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