Oncotarget

Clinical Research Papers:

Tumor heterogeneity assessed by texture analysis on contrast-enhanced CT in lung adenocarcinoma: association with pathologic grade

Ying Liu _, Shichang Liu, Fangyuan Qu, Qian Li, Runfen Cheng and Zhaoxiang Ye

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Oncotarget. 2017; 8:53664-53674. https://doi.org/10.18632/oncotarget.15399

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Abstract

Ying Liu1, Shichang Liu1, Fangyuan Qu1, Qian Li1, Runfen Cheng2 and Zhaoxiang Ye1

1 Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China

2 Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China

Correspondence to:

Ying Liu, email:

Keywords: lung; adenocarcinoma; international association for the study of lung cancer/American thoracic society/European respiratory society; pathologic grade; heterogeneity

Received: July 18, 2016 Accepted: January 31, 2017 Published: February 16, 2017

Abstract

Objectives To investigate whether texture features on contrast-enhanced computed tomography (CECT) images of lung adenocarcinoma have association with pathologic grade.

Methods A cohort of 148 patients with surgically operated adenocarcinoma was retrospectively reviewed. Fifty-four CT features of the primary lung tumor were extracted from CECT images using open-source 3D Slicer software; meanwhile, enhancement homogeneity was evaluated by two radiologists using visual assessment. Multivariate logistic regression analysis was performed to determine significant image indicator of pathologic grade.

Results Tumors of intermediate grade were more likely to be never smokers (P=0.020). Enhancement heterogeneity by visual assessment showed no statistical difference between intermediate grade and high grade (P=0.671). Among those 54 features, 29 of them were significantly associated with pathologic grade. Multivariate logistic regression analyses identified F33 (Homogeneity 1) (P=0.005) and F38 (Inverse Variance) (P=0.032) as unique independent image indicators of pathologic grade, and the AUC calculated from this model (AUC=0.834) was higher than clinical model (AUC=0.615) (P=0.0001).

Conclusions Our study revealed that texture analysis on CECT images could be helpful in predicting pathologic grade of lung adenocarcinoma.


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