Risk stratification in the investigation of pulmonary nodules in a high-risk cohort: positron emission tomography/computed tomography outperforms clinical risk prediction algorithms

Gibson, Glenna, Kumar, Aravind Ravi, Steinke, Karin, Bashirzadeh, Farzad, Roach, Rebecca, Windsor, Morgan, Ware, Robert and Fielding, David (2017) Risk stratification in the investigation of pulmonary nodules in a high-risk cohort: positron emission tomography/computed tomography outperforms clinical risk prediction algorithms. Internal Medicine Journal, 47 12: 1385-1392. doi:10.1111/imj.13576


Author Gibson, Glenna
Kumar, Aravind Ravi
Steinke, Karin
Bashirzadeh, Farzad
Roach, Rebecca
Windsor, Morgan
Ware, Robert
Fielding, David
Title Risk stratification in the investigation of pulmonary nodules in a high-risk cohort: positron emission tomography/computed tomography outperforms clinical risk prediction algorithms
Journal name Internal Medicine Journal   Check publisher's open access policy
ISSN 1445-5994
1444-0903
Publication date 2017-12-10
Year available 2017
Sub-type Article (original research)
DOI 10.1111/imj.13576
Open Access Status Not yet assessed
Volume 47
Issue 12
Start page 1385
End page 1392
Total pages 8
Place of publication Richmond, VIC Australia
Publisher Wiley-Blackwell Publishing Asia
Language eng
Subject 2724 Internal Medicine
Abstract Background: Clinical prediction models and 18-fluorine-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) are used for the assessment of solitary pulmonary nodules (SPN); however, a biopsy is still required before treatment, which carries risk. Aim: To determine the combined predictive benefit of one such model combined with modern PET/CT data to improve decision-making about biopsy prior to treatment and possibly reduce costs. Methods: Patients with a SPN undergoing 18F-FDG-PET/CT from January 2011 to December 2012 were retrospectively identified; 143 patients met inclusion criteria. PET/CT studies were rated (5-point visual scale), and CT characteristics were determined. Tissue was obtained by endobronchial ultrasonography with guide sheath (EBUS-GS), CT-guided biopsy and/or surgery. EBUS-transbronchial needle aspiration (TBNA) was used instead of nodule biopsy if there were PET-positive sub-centimetre lymph nodes. Results: The prediction model yielded an area under the receiver operating characteristic curve (AUC-ROC) of 64% (95% confidence interval (CI) 0.55–0.75). PET/CT increased this to 75% (95% CI 0.65–0.84). The 11% improvement is statistically significant. PET/CT score was the best single predictor for malignancy. A PET score of 1–2 had a specificity of 100% (CI 0.73–1.0), whereas a score of 4–5 had a sensitivity of only 76% (CI 0.68–0.84). No significant difference in clinical prediction scores between groups was noted. PET/CT showed the greatest benefit in true negatives and in detecting small mediastinal lymph nodes to allow EBUS-TBNA with a higher diagnostic rate. Cost analysis did not support a policy of resection-without-tissue diagnosis. Conclusion: PET/CT improves the clinical prediction of SPN, but its greatest use is in proving benignity. High PET scores had high false positive rates and did not add to clinical prediction. PET should be incorporated early in decision-making to allow for more effective biopsy strategies.
Keyword clinical prediction model
FDG-PET/CT
Solitary pulmonary nodule
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
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Created: Sun, 24 Dec 2017, 01:01:29 EST