Computed tomography-guided cutting needle biopsy for lung nodules: when the biopsy-based benign results are real benign
Tóm tắt
Computed tomography (CT)-guided cutting needle biopsy (CNB) is an effective diagnostic method for lung nodules (LNs). The false-negative rate of CT-guided lung biopsy is reported to be up to 16%. This study aimed to determine the predictors of true-negative results in LNs with CNB-based benign results. From January 2011 to December 2015, 96 patients with CNB-based nonspecific benign results were included in this study as the training group to detect predictors of true-negative results. From January 2016 to December 2018, an additional 57 patients were included as a validation group to test the reliability of the predictors. In the training group, a total of 96 patients underwent CT-guided CNB for 96 LNs. The CNB-based results were true negatives for 82 LNs and false negatives for 14 LNs. The negative predictive value of the CNB-based benign results was 85.4% (82/96). Univariate and multivariate logistic regression analyses revealed that CNB-based granulomatous inflammation (P = 0.013, hazard ratio = 0.110, 95% confidential interval = 0.019–0.625) was the independent predictor of true-negative results. The area under the receiver operator characteristic (ROC) curve was 0.697 (P = 0.019). In the validation group, biopsy results for 47 patients were true negative, and 10 were false negative. When the predictor was used on the validation group, the area under the ROC curve was 0.759 (P = 0.011). Most of the CNB-based benign results were true negatives, and CNB-based granulomatous inflammation could be considered a predictor of true-negative results.
Tài liệu tham khảo
Yang W, Sun W, Li Q, et al. Diagnostic accuracy of CT-guided transthoracic needle biopsy for solitary pulmonary nodules. PLoS One. 2015;10:e0131373.
Choo JY, Park CM, Lee NK, et al. Percutaneous transthoracic needle biopsy of small (≤ 1 cm) lung nodules under C-arm cone-beam CT virtual navigation guidance. Eur Radiol. 2013;23:712–9.
Li Y, Du Y, Yang HF, et al. CT-guided percutaneous core needle biopsy for small (≤ 20 mm) pulmonary lesions. Clin Radiol. 2013;68:e43–8.
Li GC, Fu YF, Cao W, et al. Computed tomography-guided percutaneous cutting needle biopsy for small (≤ 20 mm) lung nodules. Medicine (Baltimore). 2017;96:e8703.
Lee KH, Lim KY, Suh YJ, et al. Diagnostic accuracy of percutaneous transthoracic needle lung biopsies: a multicenter study. Korean J Radiol. 2019;20:1300–10.
De Filippo M, Onniboni M, Rusca M, et al. Advantages of multidetector-row CT with multiplanar reformation in guiding percutaneous lung biopsies. Radiol Med. 2008;113:945–53.
Choi SH, Chae EJ, Kim JE, et al. Percutaneous CT-guided aspiration and core biopsy of pulmonary nodules smaller than 1 cm: analysis of outcomes of 305 procedures from a tertiary referral center. AJR Am J Roentgenol. 2013;201:964–70.
Yeow KM, Tsay PK, Cheung YC, et al. Factors affecting diagnostic accuracy of CT-guided coaxial cutting needle lung biopsy: retrospective analysis of 631 procedures. J Vasc Interv Radiol. 2003;14:581–8.
Rui Y, Han M, Zhou W, et al. Non-malignant pathological results on transthoracic CT guided core-needle biopsy: when is benign really benign? Clin Radiol. 2018;73:757.e1–7.
Kim JI, Park CM, Kim H, et al. Non-specific benign pathological results on transthoracic core-needle biopsy: how to differentiate false-negatives? Eur Radiol. 2017;27:3888–95.
Winer-Muram HT. The solitary pulmonary nodule. Radiology. 2006;239:34–49.
Liu XL, Li W, Yang WX, et al. Computed tomography guided biopsy of small lung nodules diagnostic accuracy and analysis for true negatives. J Int Med Res. 2020;48:300060519879006.
Lee KH, Lim KY, Suh YJ, et al. Nondiagnostic percutaneous transthoracic needle biopsy of lung lesions: a multicenter study of malignancy risk. Radiology. 2019;290:814–23.
Bonham CA, Strek ME, Patterson KC. From granuloma to fibrosis: sarcoidosis associated pulmonary fibrosis. Curr Opin Pulm Med. 2016;2:484–91.
Rosenbaum JT, Choi D, Wilson DJ, et al. Fibrosis, gene expression and orbital inflammatory disease. Br J Ophthalmol. 2015;99:1424–9.
Mukhopadhyay S, Farver CF, Vaszar LT, et al. Causes of pulmonary granulomas: a retrospective study of 500 cases from seven countries. J Clin Pathol. 2012;65:51–7.
Auger M, Moriarty AT, Laucirica R, et al. Granulomatous inflammation-an underestimated cause of false-positive diagnoses in lung fine-needle aspirates: observations from the college of American pathologists nongynecologic cytopathology interlaboratory comparison program. Arch Pathol Lab Med. 2010;134:1793–6.
van’t Westeinde SC, de Koning HJ, Xu DM, et al. How to deal with incidentally detected pulmonary nodules less than 10mm in size on CT in a healthy person. Lung Cancer. 2008;60:151–9.
MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology. 2017;284:228–43.
Li X, Zhang Q, Jin X, et al. Combining serum miRNAs, CEA, and CYFRA21-1 with imaging and clinical features to distinguish benign and malignant pulmonary nodules: a pilot study : Xianfeng Li et al.: Combining biomarker, imaging, and clinical features to distinguish pulmonary nodules. World J Surg Oncol. 2017;15:107.
Zhao M, Chang B, Wei Z, et al. The role of 18F-FDG uptake features in the differential diagnosis of solitary pulmonary lesions with PET/CT. World J Surg Oncol. 2015;13:271.