Pulmonary nodular ground-glass opacities in patients with extrapulmonary cancers: what is their clinical significance and how can we determine whether they are malignant or benign lesions?
BACKGROUND
The clinical significance of pulmonary nodular ground-glass opacities (NGGOs) in patients with extrapulmonary cancers is not known, although there is an urgent need for study on this topic. The purpose of this study, therefore, was to investigate the clinical significance of pulmonary NGGOs in these patients, and to develop a computerized scheme to distinguish malignant from benign NGGOs.


METHODS
Fifty-nine pathologically proven pulmonary NGGOs in 34 patients with a history of extrapulmonary cancer were studied. We reviewed the CT scan characteristics of NGGOs and the clinical features of these patients. Artificial neural networks (ANNs) were constructed and tested as a classifier distinguishing malignant from benign NGGOs. The performance of ANNs was evaluated with receiver operating characteristic analysis.


RESULTS
Twenty-eight patients (82.4%) were determined to have malignancies. Forty NGGOs (67.8%) were diagnosed as malignancies (adenocarcinomas, 24; bronchioloalveolar carcinomas, 16). Among the rest of the NGGOs, 14 were atypical adenomatous hyperplasias, 4 were focal fibrosis, and 1 was an inflammatory nodule. There were no cases of metastasis appearing as NGGOs. Between malignant and benign NGGOs, there were significant differences in lesion size; the presence of internal solid portion; the size and proportion of the internal solid portion; the lesion margin; and the presence of bubble lucency, air bronchogram, or pleural retraction (p < 0.05). Using these characteristics, ANNs showed excellent accuracy (z value, 0.973) in discriminating malignant from benign NGGOs.


CONCLUSIONS
Pulmonary NGGOs in patients with extrapulmonary cancers tend to have high malignancy rates and are very often primary lung cancers. ANNs might be a useful tool in distinguishing malignant from benign NGGOs.
Park
Chang Min
CM
Departments of Radiology, Seoul National University College of Medicine, Seoul, South Korea.
Goo
Jin Mo
JM
Departments of Radiology, Seoul National University College of Medicine, Seoul, South Korea. Electronic address: jmgoo@plaza.snu.ac.kr.
Kim
Tae Jung
TJ
Departments of Radiology, Seoul National University College of Medicine, Seoul, South Korea.
Lee
Hyun Ju
HJ
Departments of Radiology, Seoul National University College of Medicine, Seoul, South Korea.
Lee
Kyung Won
KW
Departments of Radiology, Seoul National University College of Medicine, Seoul, South Korea.
Lee
Chang Hyun
CH
Departments of Radiology, Seoul National University College of Medicine, Seoul, South Korea.
Kim
Young Tae
YT
Thoracic and Cardiovascular Surgery, Seoul National University College of Medicine, Seoul, South Korea.
Kim
Kwang Gi
KG
Department of Biomedical Engineering, Division of Basic & Applied Sciences, National Cancer Center, Gyeonggi-Do, South Korea.
Lee
Ho Yun
HY
Departments of Radiology, Seoul National University College of Medicine, Seoul, South Korea.
Park
Eun-Ah
EA
Departments of Radiology, Seoul National University College of Medicine, Seoul, South Korea.
Im
Jung-Gi
JG
Departments of Radiology, Seoul National University College of Medicine, Seoul, South Korea.
D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000145:classification / Q000000981:diagnostic imaging / Q000473:pathology*; D008297:Male; D008875:Middle Aged; D016609:Neoplasms, Second Primary / Q000145:classification / Q000473:pathology*; D016571:Neural Networks, Computer; D012372:ROC Curve; D011859:Radiography; D012189:Retrospective Studies; D003074:Solitary Pulmonary Nodule / Q000145:classification / Q000000981:diagnostic imaging* / Q000473:pathology