Publication-only abstracts (abstract number preceded by an "e"), published in conjunction with the 2019 ASCO Annual Meeting but not presented at the Meeting, can be found online only.
A 18F-FDG PET/CT-based radiomics model predicts prognosis of synchronous oligometastatic non-small cell lung cancer.
Metastatic Non-Small Cell Lung Cancer
Lung Cancer—Non-Small Cell Metastatic
2019 ASCO Annual Meeting
J Clin Oncol 37, 2019 (suppl; abstr e20610)
Author(s): Xiaoxia Zhu, Yu Zhang, Zhihao Zheng, Jiaxiu Luo; Radiation Oncology Department, Nanfang Hospital, Southern Medical University, Guangzhou, China; School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Background: Oligometastatic non-small cell lung cancer (NSCLC) exists high heterogeneity with distinct outcome, and there is a lack of available biomarkers for patient stratification. In this study, we identified a positron emission tomography (PET)/computed tomography(CT)-based radiomics signature capable of predicting overall survival (OS) in patients with synchronous oligometastatic NSCLC. Methods: This study consisted of 46 patients with synchronous oligometastatic NSCLC (≤5 metastases) between 2012-2018. Clinicopathologic data was acquired from medical records and database. A total of 20648 radiomic features were extracted from pretreatment CT and PET images, which were generated from the same PET/CT scanner. A radiomics signature was built by using the least absolute shrinkage and selection operator (LASSO) regression model. Multivariate Cox regression analysis was performed to establish the predictive model. The performance was evaluated with Harrell' concordance index (C-index). Results: 7 radiomics features were selected to build the radiomics signature. Multivariate analysis indicated that the radiomics signature (P = 0.007) was an independent prognostic factor, with a C-index of 0.810. Smoking status (P = 0.01) was the only independent clinicopathologic risk factor for overall survival prediction. Incorporating the radiomics signature with clinicopathologic risk factors resulted in higher performance with a C-index of 0.899. Conclusions: This study developed a radiomics model for predicting OS in synchronous oligometastatic NSCLC, which may serve as a predictive tool to identify individualized treatment strategy. Further internal and external validation of the model are required. Support: 81572279, 2016J004, LC2016PY016, 2018CR033.
|Characteristic||Value or no. of patients (%) (n = 46)|
|Median age at diagnosis of primary NSCLC, years (range)||58(26-84)|
|Median follow up, months(range)||21(3.5-67.1)|
|Squamous cell carcinoma||7(15)|
|Driver gene mutations|
Note: Other histologic types included lung clear cell carcinoma, large cell carcinoma and adenosquamous carcinoma.