Format

Send to

Choose Destination
PLoS One. 2019 Feb 8;14(2):e0211969. doi: 10.1371/journal.pone.0211969. eCollection 2019.

Computerized texture analysis of pulmonary nodules in pediatric patients with osteosarcoma: Differentiation of pulmonary metastases from non-metastatic nodules.

Cho YJ1,2, Kim WS1,2,3, Choi YH1,2, Ha JY4, Lee S1,2, Park SJ1,5, Cheon JE1,2,3, Kang HJ5,6,7, Shin HY5,6,7, Kim IO1,2,3.

Author information

1
Department of Radiology, Seoul National University Hospital, Jongno-gu, Seoul, Republic of Korea.
2
Department of Radiology, Seoul National University College of Medicine, Jongno-gu, Seoul, Republic of Korea.
3
Institute of Radiation Medicine, Seoul National University Medical Research Center, Jongno-gu, Seoul, Republic of Korea.
4
Department of Radiology, Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea.
5
Cancer Research Institute, Seoul National University, Seoul, South Korea.
6
Department of Pediatrics, Seoul National University Hospital, Jongno-gu, Seoul, Republic of Korea.
7
Department of Pediatrics, Seoul National University College of Medicine, Jongno-gu, Seoul, Republic of Korea.

Abstract

OBJECTIVE:

To retrospectively evaluate the value of computerized 3D texture analysis for differentiating pulmonary metastases from non-metastatic lesions in pediatric patients with osteosarcoma.

MATERIALS AND METHODS:

This retrospective study was approved by the institutional review board. The study comprised 42 pathologically confirmed pulmonary nodules in 16 children with osteosarcoma who had undergone preoperative computed tomography between January 2009 and December 2014. Texture analysis was performed using an in-house program. Multivariate logistic regression analysis was performed to identify factors for differentiating metastatic nodules from non-metastases. A subgroup analysis was performed to identify differentiating parameters in small non-calcified pulmonary nodules. The receiver operator characteristic curve was created to evaluate the discriminating performance of the established model.

RESULTS:

There were 24 metastatic and 18 non-metastatic lesions. Multivariate analysis revealed that higher mean attenuation (adjusted odds ratio [OR], 1.014, P = 0.003) and larger effective diameter (OR, 1.745, P = 0.012) were significant differentiators. The analysis with small non-calcified pulmonary nodules (7 metastases and 18 non-metastases) revealed significant inter-group differences in various parameters. Logistic regression analysis revealed that higher mean attenuation (OR, 1.007, P = 0.008) was a significant predictor of non-calcified pulmonary metastases. The established logistic regression model of subgroups showed excellent discriminating performance in the ROC analysis (area under the curve, 0.865).

CONCLUSION:

Pulmonary metastases from osteosarcoma could be differentiated from non-metastases by using computerized texture analysis. Higher mean attenuation and larger diameter were significant predictors for pulmonary metastases, while higher mean attenuation was a significant predictor for small non-calcified pulmonary metastases.

PMID:
30735557
PMCID:
PMC6368316
DOI:
10.1371/journal.pone.0211969
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center