OBJECTIVE: Fluorodeoxyglucose (FDG)-positron emission tomography (PET) is an accurate non-invasive imaging test for differentiating benign from malignant solitary pulmonary nodules (SPNs). We aimed to assess its diagnostic accuracy for differentiating benign from malignant SPNs in a tuberculosis (TB)-endemic area. METHODS: Thirty patients, 22 men and 8 women, mean age 60 years, underwent dual time point FDG-PET/computed tomography (CT) imaging, followed by histological examination of the SPN. Maximum standard uptake values (SUVmax) with the greatest uptake in the lesion were calculated for two time points (SUV1 and SUV2), and the percentage change over time per lesion was calculated (%DSUV). Routine histological findings served as the gold standard. RESULTS: Histological examination showed that 14 lesions were malignant and 16 benign, 12 of which were TB. SUVmax for benign and malignant lesions were 11.02 (standard deviation (SD) 6.6) v. 10.86 (SD 8.9); however, when tuberculomas were excluded from the analysis, a significant difference in mean SUV1max values between benign and malignant lesions was observed (p=0.0059). Using an SUVmax cut-off value of 2.5, a sensitivity of 85.7% and a specificity of 25% was obtained. Omitting the TB patients from analysis resulted in a sensitivity of 85.7% and a specificity of 100%. Mean %DSUV of benign lesions did not differ significantly from mean %DSUV of malignant lesions (17.1% (SD 16.3%) v. 19.4% (SD 23.7%)). Using a cut-off of %DSUV>10% as indicative of malignancy, a sensitivity of 85.7% and a specificity of 50% was obtained. Omitting the TB patients from the analysis yielded a sensitivity of 85.7% and a specificity of 75%. CONCLUSION: Our findings suggest that FDG-PET cannot distinguish malignancy from tuberculoma and therefore cannot reliably be used to reduce futile biopsy/thoracotomy.
OBJECTIVE:Fluorodeoxyglucose (FDG)-positron emission tomography (PET) is an accurate non-invasive imaging test for differentiating benign from malignant solitary pulmonary nodules (SPNs). We aimed to assess its diagnostic accuracy for differentiating benign from malignant SPNs in a tuberculosis (TB)-endemic area. METHODS: Thirty patients, 22 men and 8 women, mean age 60 years, underwent dual time point FDG-PET/computed tomography (CT) imaging, followed by histological examination of the SPN. Maximum standard uptake values (SUVmax) with the greatest uptake in the lesion were calculated for two time points (SUV1 and SUV2), and the percentage change over time per lesion was calculated (%DSUV). Routine histological findings served as the gold standard. RESULTS: Histological examination showed that 14 lesions were malignant and 16 benign, 12 of which were TB. SUVmax for benign and malignant lesions were 11.02 (standard deviation (SD) 6.6) v. 10.86 (SD 8.9); however, when tuberculomas were excluded from the analysis, a significant difference in mean SUV1max values between benign and malignant lesions was observed (p=0.0059). Using an SUVmax cut-off value of 2.5, a sensitivity of 85.7% and a specificity of 25% was obtained. Omitting the TB patients from analysis resulted in a sensitivity of 85.7% and a specificity of 100%. Mean %DSUV of benign lesions did not differ significantly from mean %DSUV of malignant lesions (17.1% (SD 16.3%) v. 19.4% (SD 23.7%)). Using a cut-off of %DSUV>10% as indicative of malignancy, a sensitivity of 85.7% and a specificity of 50% was obtained. Omitting the TB patients from the analysis yielded a sensitivity of 85.7% and a specificity of 75%. CONCLUSION: Our findings suggest that FDG-PET cannot distinguish malignancy from tuberculoma and therefore cannot reliably be used to reduce futile biopsy/thoracotomy.
Authors: Bruno Hochhegger; Matheus Zanon; Stephan Altmayer; Gabriel S Pacini; Fernanda Balbinot; Martina Z Francisco; Ruhana Dalla Costa; Guilherme Watte; Marcel Koenigkam Santos; Marcelo C Barros; Diana Penha; Klaus Irion; Edson Marchiori Journal: Lung Date: 2018-10-09 Impact factor: 2.584
Authors: Thomas Ebenhan; Isabel Schoeman; Daniel D Rossouw; Anne Grobler; Biljana Marjanovic-Painter; Judith Wagener; Hendrik G Kruger; Mike M Sathekge; Jan Rijn Zeevaart Journal: Mol Imaging Biol Date: 2017-06 Impact factor: 3.488
Authors: Stephen A Deppen; Jeffrey D Blume; Clark D Kensinger; Ashley M Morgan; Melinda C Aldrich; Pierre P Massion; Ronald C Walker; Melissa L McPheeters; Joe B Putnam; Eric L Grogan Journal: JAMA Date: 2014-09-24 Impact factor: 56.272