Cancer Risk in Subsolid Nodules in the National Lung Screening Trial

被引:70
作者
Hammer, Mark M. [1 ]
Palazzo, Lauren L. [2 ]
Kong, Chung Yin [2 ]
Hunsaker, Andetta R. [1 ]
机构
[1] Harvard Med Sch, Brigham & Womens Hosp, Dept Radiol, 75 Francis St, Boston, MA 02115 USA
[2] Massachusetts Gen Hosp, Inst Technol Assessment, Boston, MA 02114 USA
基金
美国国家卫生研究院;
关键词
PULMONARY NODULES; BASE-LINE; OVERDIAGNOSIS; PROBABILITY;
D O I
10.1148/radiol.2019190905
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Background: Subsolid pulmonary nodules, comprising pure ground-glass nodules (GGNs) and part-solid nodules (PSNs), have a high risk of indolent malignancy. Lung Imaging Reporting and Data System (Lung-RADS) nodule management guidelines are based on expert opinion and lack independent validation. Purpose: To evaluate Lung-RADS estimates of the malignancy rates of subsolid nodules, using nodules from the National Lung Screening Trial (NLST), and to compare Lung-RADS to the NELSON trial classification as well as the Brock University calculator. Materials and Methods: Subsets of GGNs and PSNs were selected from the NLST for this retrospective study. A thoracic radiologist reviewed the baseline and follow-up CT images, confirmed that they were true subsolid nodules, and measured the nodules. The primary outcome for each nodule was the development of malignancy within the follow-up period (median, 6.5 years). Nodules were stratified according to Lung-RADS, NELSON trial criteria, and the Brock model. For analyses, nodule subsets were weighted on the basis of frequency in the NLST data set. Nodule stratification models were tested by using receiver operating characteristic curves. Results: A total of 622 nodules were evaluated, of which 434 nodules were subsolid. At baseline, 304 nodules were classified as Lung-RADS category 2, with a malignancy rate of 3%, which is greater than the 1% in Lung-RADS (P=.004). The malignancy rate for GGNs smaller than 10 mm (two of 129, 1.3%) was smaller than that for GGNs measuring 10-19 mm (11 of 153, 6%) (P=.01). The malignancy rate for Lung-RADS category 3 was 14% (13 of 67), which is greater than the reported 2% in Lung-RADS (P<.001). The Brock model predicted malignancy better than Lung-RADS and the NELSON trial scheme (area under the receiver operating characteristic curve = 0.78, 0.70, and 0.67, respectively; P=.02 for Brock model vs NELSON trial scheme). Conclusion: Subsolid nodules classified as Lung Imaging Reporting and Data System (Lung-RADS) categories 2 and 3 have a higher risk of malignancy than reported. The Brock risk calculator performed better than measurement-based classification schemes such as Lung-RADS. (C)RSNA, 2019
引用
收藏
页码:441 / 448
页数:8
相关论文
共 21 条
[1]   Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening [J].
Aberle, Denise R. ;
Adams, Amanda M. ;
Berg, Christine D. ;
Black, William C. ;
Clapp, Jonathan D. ;
Fagerstrom, Richard M. ;
Gareen, Ilana F. ;
Gatsonis, Constantine ;
Marcus, Pamela M. ;
Sicks, JoRean D. .
NEW ENGLAND JOURNAL OF MEDICINE, 2011, 365 (05) :395-409
[2]  
American College of Radiology, 2019, LUNG RADS VERS 1 1
[3]   Lung Cancer Screening Overdiagnosis: Reports of Overdiagnosis in Screening for Lung Cancer Are Grossly Exaggerated [J].
Barbosa, Eduardo J. Mortani, Jr. .
ACADEMIC RADIOLOGY, 2015, 22 (08) :976-982
[4]   Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening Trial [J].
Cherezov, Dmitry ;
Hawkhis, Samuel H. ;
Goldga, Dmitry B. ;
Hall, Lawrence O. ;
Liu, Ying ;
Li, Qian ;
Balagurtmathan, Yoganand ;
Gillies, Robert J. ;
Schabath, Matthew B. .
CANCER MEDICINE, 2018, 7 (12) :6340-6356
[5]   Malignancy estimation of Lung-RADS criteria for subsolid nodules on CT: accuracy of low and high risk spectrum when using NLST nodules [J].
Chung, Kaman ;
Jacobs, Colin ;
Scholten, Ernst T. ;
Mets, Onno M. ;
Dekker, Irma ;
Prokop, Mathias ;
van Ginneken, Bram ;
Schaefer-Prokop, Cornelia M. .
EUROPEAN RADIOLOGY, 2017, 27 (11) :4672-4679
[6]   Lung-RADS Category 4X: Does It Improve Prediction of Malignancy in Subsolid Nodules? [J].
Chung, Kaman ;
Jacobs, Colin ;
Scholten, Ernst T. ;
Goo, Jin Mo ;
Prosch, Helmut ;
Sverzellati, Nicola ;
Ciompi, Francesco ;
Mets, Onno M. ;
Gerke, Paul K. ;
Prokop, Mathias ;
van Ginneken, Bram ;
Schaefer-Prokop, Cornelia M. .
RADIOLOGY, 2017, 284 (01) :264-271
[7]   A Decision Analysis of Follow-up and Treatment Algorithms for Nonsolid Pulmonary Nodules [J].
Hammer, Mark M. ;
Palazzo, Lauren L. ;
Eckel, Andrew L. ;
Barbosa, Eduardo M., Jr. ;
Kong, Chung Yin .
RADIOLOGY, 2019, 290 (02) :506-513
[8]   A METHOD OF COMPARING THE AREAS UNDER RECEIVER OPERATING CHARACTERISTIC CURVES DERIVED FROM THE SAME CASES [J].
HANLEY, JA ;
MCNEIL, BJ .
RADIOLOGY, 1983, 148 (03) :839-843
[9]   Research electronic data capture (REDCap)-A metadata-driven methodology and workflow process for providing translational research informatics support [J].
Harris, Paul A. ;
Taylor, Robert ;
Thielke, Robert ;
Payne, Jonathon ;
Gonzalez, Nathaniel ;
Conde, Jose G. .
JOURNAL OF BIOMEDICAL INFORMATICS, 2009, 42 (02) :377-381
[10]   CT Screening for Lung Cancer: Part-Solid Nodules in Baseline and Annual Repeat Rounds [J].
Henschke, Claudia I. ;
Yip, Rowena ;
Smith, James P. ;
Wolf, Andrea S. ;
Flores, Raja M. ;
Liang, Mingzhu ;
Salvatore, Mary M. ;
Liu, Ying ;
Xu, Dong Ming ;
Yankelevitz, David F. .
AMERICAN JOURNAL OF ROENTGENOLOGY, 2016, 207 (06) :1176-1184