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Proceedings Paper

Development and validation of a radiomics nomogram for progression-free survival prediction in stage IV EGFR-mutant non-small cell lung cancer
Author(s): Jiangdian Song; Yali Zang; Weimin Li; Wenzhao Zhong; Jingyun Shi; Di Dong; Mengjie Fang; Zaiyi Liu; Jie Tian
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Paper Abstract

Accurately predict the risk of disease progression and benefit of tyrosine kinase inhibitors (TKIs) therapy for stage IV non-small cell lung cancer (NSCLC) patients with activing epidermal growth factor receptor (EGFR) mutations by current staging methods are challenge. We postulated that integrating a classifier consisted of multiple computed tomography (CT) phenotypic features, and other clinicopathological risk factors into a single model could improve risk stratification and prediction of progression-free survival (PFS) of EGFR TKIs for these patients. Patients confirmed as stage IV EGFR-mutant NSCLC received EGFR TKIs with no resection; pretreatment contrast enhanced CT performed at approximately 2 weeks before the treatment was enrolled. A six-CT-phenotypic-feature-based classifier constructed by the LASSO Cox regression model, and three clinicopathological factors: pathologic N category, performance status (PS) score, and intrapulmonary metastasis status were used to construct a nomogram in a training set of 115 patients. The prognostic and predictive accuracy of this nomogram was then subjected to an external independent validation of 107 patients. PFS between the training and independent validation set is no statistical difference by Mann-Whitney U test (P = 0.2670). PFS of the patients could be predicted with good consistency compared with the actual survival. C-index of the proposed individualized nomogram in the training set (0·707, 95%CI: 0·643, 0·771) and the independent validation set (0·715, 95%CI: 0·650, 0·780) showed the potential of clinical prognosis to predict PFS of stage IV EGFR-mutant NSCLC from EGFR TKIs. The individualized nomogram might facilitate patient counselling and individualise management of patients with this disease.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343R (3 March 2017); doi: 10.1117/12.2253998
Show Author Affiliations
Jiangdian Song, Northeastern Univ. (China)
Institute of Automation (China)
Yali Zang, Institute of Automation (China)
Weimin Li, West China Hospital (China)
Wenzhao Zhong, Guangdong Lung Cancer Institute, Guangdong Academy of Medical Sciences (China)
Jingyun Shi, Shanghai Pulmonary Hospital (China)
Di Dong, Institute of Automation (China)
Mengjie Fang, Institute of Automation (China)
Zaiyi Liu, Guangdong General Hospital (China)
Jie Tian, Institute of Automation (China)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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