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15.09.2016 | Oncology

Multicenter external validation of two malignancy risk prediction models in patients undergoing 18F-FDG-PET for solitary pulmonary nodule evaluation

verfasst von: 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

Erschienen in: European Radiology | Ausgabe 5/2017

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Abstract

Objectives

To achieve multicentre external validation of the Herder and Bayesian Inference Malignancy Calculator (BIMC) models.

Methods

Two hundred and fifty-nine solitary pulmonary nodules (SPNs) collected from four major hospitals which underwent 18-FDG-PET characterization were included in this multicentre retrospective study. The Herder model was tested on all available lesions (group A). A subgroup of 180 SPNs (group B) was used to provide unbiased comparison between the Herder and BIMC models. Receiver operating characteristic (ROC) area under the curve (AUC) analysis was performed to assess diagnostic accuracy. Decision analysis was performed by adopting the risk threshold stated in British Thoracic Society (BTS) guidelines.

Results

Unbiased comparison performed In Group B showed a ROC AUC for the Herder model of 0.807 (95 % CI 0.742–0.862) and for the BIMC model of 0.822 (95 % CI 0.758–0.875).

Conclusions

Both the Herder and the BIMC models were proven to accurately predict the risk of malignancy when tested on a large multicentre external case series. The BIMC model seems advantageous on the basis of a more favourable decision analysis.

Key Points

• The Herder model showed a ROC AUC of 0.807 on 180 SPNs.

• The BIMC model showed a ROC AUC of 0.822 on 180 SPNs.

• Decision analysis is more favourable to the BIMC model.

Trotman-Dickenson B, Baumert B (2003) Multidetector-row CT of the solitary pulmonary nodule. In: Seminars in roentgenology, vol. 38, No. 2. WB Saunders, pp 158–167

Hansell DM, Bankier AA, MacMahon H, McLoud TC, Muller NL, Remy J (2008) Fleischner society: glossary of terms for thoracic imaging 1. Radiology 246:697–722CrossRefPubMed

Swensen SJ, Silverstein MD, Edell ES, Trastek VF, Aughenbaugh GL, Ilstrup DM, Schleck CD (1999) Solitary pulmonary nodules: clinical prediction model versus physicians. In: Mayo Clinic Proceedings, vol. 74, No. 4. Elsevier, pp 319–329

Nakamura K, Yoshida H, Engelmann R et al (2000) Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks 1. Radiology 214:823–830CrossRefPubMed

Gurney JW (1993) Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian analysis. Part I. Theory. Radiology 186:405–413CrossRefPubMed

Detterbeck FC, Mazzone PJ, Naidich DP, Bach PB (2013) Screening for lung cancer: diagnosis and management of lung cancer: American College of Chest Physicians evidence-based clinical practice guidelines. CHEST J 143:e78S–e92SCrossRef

Callister MEJ, Baldwin DR, Akram AR et al (2015) British Thoracic Society guidelines for the investigation and management of pulmonary nodules. Thorax 70:ii1–ii54CrossRefPubMed

McWilliams A, Tammemagi MC, Mayo JR, Roberts H, Liu G, Soghrati K, … Lam S (2013) Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med 369(10):910–919

Herder GJ, van Tinteren H, Golding RP, Kostense PJ, Comans EF, Smit EF et al (2005) Clinical prediction model to characterize pulmonary nodules: validation and added value of 18F-fluorodeoxyglucose positron emission tomography. CHEST J 128:2490–2496CrossRef

10.

Perandini S, Soardi GA, Motton M, Dallaserra C, Montemezzi S (2014) Limited value of logistic regression analysis in solid solitary pulmonary nodules characterization: a single‐center experience on 288 consecutive cases. J Surg Oncol 110:883–887CrossRefPubMed

11.

Perandini S, Soardi GA, Motton M, Montemezzi S (2015) Critique of Al-Ameri et al.(2015)-Risk of malignancy in pulmonary nodules: a validation study of four prediction models. Lung cancer (Amsterdam, Neth) 90:118CrossRef

12.

Isbell JM, Deppen S, Putnam JB Jr et al (2011) Existing general population models inaccurately predict lung cancer risk in patients referred for surgical evaluation. Ann Thorac Surg 91:227–33CrossRefPubMedPubMedCentral

13.

Soardi GA, Perandini S, Motton M, Montemezzi S (2015) Assessing probability of malignancy in solid solitary pulmonary nodules with a new Bayesian calculator: improving diagnostic accuracy by means of expanded and updated features. Eur Radiol 25:155–162CrossRefPubMed

14.

Al-Ameri A, Malhotra P, Thygesen H, Plant PK, Vaidyanathan S, Karthik S, Callister ME (2015) Risk of malignancy in pulmonary nodules: a validation study of four prediction models. Lung Cancer

Titel: Multicenter external validation of two malignancy risk prediction models in patients undergoing 18F-FDG-PET for solitary pulmonary nodule evaluation
verfasst von: 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
Publikationsdatum: 15.09.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 5/2017
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-016-4580-3

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Multicenter external validation of two malignancy risk prediction models in patients undergoing 18F-FDG-PET for solitary pulmonary nodule evaluation