Abstract

Background and objective

Mediastinal lymph node (LN) staging in individuals with non-small-cell lung cancer plays an important role in staging and treatment planning. This study aimed to assess the accuracy of computed tomography (CT) texture analysis (CTTA) in differentiating benign and malignant mediastinal LNs.

Methods

Pathologically confirmed malignant and benign mediastinal LN samples, obtained using endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), were retrospectively reviewed, in addition to chest CT and 18-fluorodeoxyglucose (FDG) uptake positron emission tomography (PET) data. For each LN, CTTA was performed using “AVIEW” software (Coreline Soft, Republic of Korea) by drawing a region of interest.

Results

A total of 132 LNs from 80 patients were included and classified into two groups according to pathology results: malignant (n = 61) and benign (n = 71). In EBUS, size > 1 cm, round shape, heterogeneous echogenicity, and presence of coagulation necrosis sign were more prevalent in malignant than in benign LNs; length was the only feature that distinguished the two groups. Among CTTA features, compactness and normalized standard deviation (SD) showed differences between the two groups. The ability to distinguish malignant LNs was higher using high standard uptake value (SUV) on FDG PET/CT (SUVmax ≥ 5) and normalized SD on CTTA (area under the receiver operating characteristic curve 0.739 versus 0.742, respectively); however, normalized SD demonstrated very low sensitivity despite high specificity.

Conclusions

CTTA may be helpful in distinguishing between benign and malignant LNs; however, the diagnostic value was not high. Therefore, integrated evaluation with other imaging modalities is needed.

Details

Title
Quantitative computed tomography texture analysis: can it improve diagnostic accuracy to differentiate malignant lymph nodes?
Author
So Youn Shin; Hong, Il Ki; Yong Suk Jo
Publication year
2019
Publication date
2019
Publisher
BioMed Central
ISSN
17405025
e-ISSN
14707330
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2243574967
Copyright
© 2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.