Open AccessPost-imaging pulmonary nodule mathematical prediction models: are they clinically relevant?
Author(s) -
for the COPDGene Investigators,
Johanna Uthoff,
Nicholas Koehn,
Jared Larson,
Samantha K. N. Dilger,
Emily Hammond,
Ann G. Schwartz,
Brian F. Mullan,
Rolando Sanchez,
Richard M. Hoffman,
Jessica C. Sieren
Publication year - 2019
Publication title -
european radiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.606
H-Index - 149
eISSN - 1432-1084
pISSN - 0938-7994
DOI - 10.1007/s00330-019-06168-x
Subject(s) - neuroradiology , medicine , interventional radiology , radiology , solitary pulmonary nodule , ultrasound , nodule (geology) , medical physics , computed tomography , neurology , paleontology , biology , psychiatry
Post-imaging mathematical prediction models (MPMs) provide guidance for the management of solid pulmonary nodules by providing a lung cancer risk score from demographic and radiologists-indicated imaging characteristics. We hypothesized calibrating the MPM risk score threshold to a local study cohort would result in improved performance over the original recommended MPM thresholds. We compared the pre- and post-calibration performance of four MPM models and determined if improvement in MPM prediction occurs as nodules are imaged longitudinally.
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