OBJECTIVE: To determine whether a clinical prediction model developed to identify malignant lung nodules based on clinical data and radiologic lung nodule characteristics could predict a malignant lung nodule diagnosis with higher accuracy than physicians. MATERIAL AND METHODS: One hundred cases were obtained by using a stratified random sample from a retrospective cohort of 629 patients with newly discovered 4- to 30-mm radiologically indeterminate solitary pulmonary nodules (SPNs) on chest radiography. A chest radiologist, pulmonologist, thoracic surgeon, and general internist made predictions of a malignant lesion and recommendations for management (thoracotomy, transthoracic needle aspiration biopsy, or observation) on the basis of radiologic and clinical data used to develop the clinical prediction rule. The predictions of a malignant lung nodule were compared with the probability of malignant involvement from a previously validated clinical prediction model to identify malignant nodules on the basis of three clinical characteristics (age, smoking status, and history of cancer greater than or equal to 5 years previously) and three radiologic characteristics (nodule diameter, spiculation, and upper lobe location). RESULTS: Receiver operating characteristic analysis showed no significant difference between the logistic model and the physicians' predictions. Calibration curves revealed that physicians overestimated the probability of a malignant lesion in patients with low risk of malignant disease by the prediction rule; this finding suggests a potential for the decision rule to improve the management of patients with SPNs that are likely to be benign. CONCLUSION: The prediction model was not better than physicians' predictions of malignant SPNs. The prediction rule may have potential to improve the management of patients with SPNs that are likely to be benign.
OBJECTIVE: To determine whether a clinical prediction model developed to identify malignant lung nodules based on clinical data and radiologic lung nodule characteristics could predict a malignant lung nodule diagnosis with higher accuracy than physicians. MATERIAL AND METHODS: One hundred cases were obtained by using a stratified random sample from a retrospective cohort of 629 patients with newly discovered 4- to 30-mm radiologically indeterminate solitary pulmonary nodules (SPNs) on chest radiography. A chest radiologist, pulmonologist, thoracic surgeon, and general internist made predictions of a malignant lesion and recommendations for management (thoracotomy, transthoracic needle aspiration biopsy, or observation) on the basis of radiologic and clinical data used to develop the clinical prediction rule. The predictions of a malignant lung nodule were compared with the probability of malignant involvement from a previously validated clinical prediction model to identify malignant nodules on the basis of three clinical characteristics (age, smoking status, and history of cancer greater than or equal to 5 years previously) and three radiologic characteristics (nodule diameter, spiculation, and upper lobe location). RESULTS: Receiver operating characteristic analysis showed no significant difference between the logistic model and the physicians' predictions. Calibration curves revealed that physicians overestimated the probability of a malignant lesion in patients with low risk of malignant disease by the prediction rule; this finding suggests a potential for the decision rule to improve the management of patients with SPNs that are likely to be benign. CONCLUSION: The prediction model was not better than physicians' predictions of malignant SPNs. The prediction rule may have potential to improve the management of patients with SPNs that are likely to be benign.
Authors: William G Hocking; Martin C Tammemagi; John Commins; Martin M Oken; Paul A Kvale; Ping Hu; Lawrence R Ragard; Tom L Riley; Paul Pinsky; Thomas M Beck; Philip C Prorok Journal: Lung Cancer Date: 2013-08-07 Impact factor: 5.705
Authors: Johanna Uthoff; Nicholas Koehn; Jared Larson; Samantha K N Dilger; Emily Hammond; Ann Schwartz; Brian Mullan; Rolando Sanchez; Richard M Hoffman; Jessica C Sieren Journal: Eur Radiol Date: 2019-04-01 Impact factor: 5.315
Authors: Simone Perandini; G A Soardi; A R Larici; A Del Ciello; G Rizzardi; A Solazzo; L Mancino; F Zeraj; M Bernhart; M Signorini; M Motton; S Montemezzi Journal: Eur Radiol Date: 2016-09-15 Impact factor: 5.315
Authors: Oisin J O'Connell; Francisco A Almeida; Michael J Simoff; Lonny Yarmus; Ray Lazarus; Benjamin Young; Yu Chen; Roy Semaan; Timothy M Saettele; Joseph Cicenia; Harmeet Bedi; Corrine Kliment; Liang Li; Sonali Sethi; Javier Diaz-Mendoza; David Feller-Kopman; Juhee Song; Thomas Gildea; Hans Lee; Horiana B Grosu; Michael Machuzak; Macarena Rodriguez-Vial; George A Eapen; Carlos A Jimenez; Roberto F Casal; David E Ost Journal: Am J Respir Crit Care Med Date: 2017-06-15 Impact factor: 21.405