Tingting Wang1, Jiajun Deng2, Yunlang She2, Lei Zhang2, Bin Wang1, Yijiu Ren2, Junqi Wu2, Dong Xie2, Xiwen Sun1, Chang Chen3. 1. Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. 2. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. 3. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. Electronic address: chenthoracic@163.com.
Abstract
BACKGROUND: We aimed to explore the predictive value of radiomics signature for the recurrence-free survival (RFS) in patients with resected stage I non-small cell lung cancer. METHODS: From January 2009 to December 2011, patients with resected stage I non-small cell lung cancer were divided into sub-solid and pure-solid groups according to presence of ground glass opacity in computed tomography. A total of 107 extracted radiomics features were reduced to 8 features by using LASSO Cox analysis to develop a radiomics signature for RFS prediction. Univariate and multivariate survival analyses were applied to identify independent prognostic variables, the Harrell concordance index (C-index) was measured to assess their prediction performance. RESULTS: Our study included 378 patients with a median follow-up time of 63.2 months. The radiomics signature could stratify all patients into high-risk (180 of 378) and low-risk group (198 of 378) with different RFS (P < .001). In the sub-solid group (n = 115), 3 patients who occurred relapse were categorized into the high-risk group by the radiomics signature. In the pure-solid group, patients with low risk (141 of 263) had a better outcome than those with high risk (122 of 263) (P < .001). Multivariate analyses revealed that the histology (P < .001) and the developed radiomics signature (P < .001) remained independent prognostic factors for RFS. CONCLUSIONS: Radiomics signature may be an independent imaging biomarker for predicting the survival, which may guide for personalizing treatment option in patients with stage I non-small cell lung cancer.
BACKGROUND: We aimed to explore the predictive value of radiomics signature for the recurrence-free survival (RFS) in patients with resected stage I non-small cell lung cancer. METHODS: From January 2009 to December 2011, patients with resected stage I non-small cell lung cancer were divided into sub-solid and pure-solid groups according to presence of ground glass opacity in computed tomography. A total of 107 extracted radiomics features were reduced to 8 features by using LASSO Cox analysis to develop a radiomics signature for RFS prediction. Univariate and multivariate survival analyses were applied to identify independent prognostic variables, the Harrell concordance index (C-index) was measured to assess their prediction performance. RESULTS: Our study included 378 patients with a median follow-up time of 63.2 months. The radiomics signature could stratify all patients into high-risk (180 of 378) and low-risk group (198 of 378) with different RFS (P < .001). In the sub-solid group (n = 115), 3 patients who occurred relapse were categorized into the high-risk group by the radiomics signature. In the pure-solid group, patients with low risk (141 of 263) had a better outcome than those with high risk (122 of 263) (P < .001). Multivariate analyses revealed that the histology (P < .001) and the developed radiomics signature (P < .001) remained independent prognostic factors for RFS. CONCLUSIONS: Radiomics signature may be an independent imaging biomarker for predicting the survival, which may guide for personalizing treatment option in patients with stage I non-small cell lung cancer.
Authors: Jose Arimateia Batista Araujo-Filho; Maria Mayoral; Junting Zheng; Kay See Tan; Peter Gibbs; Annemarie Fernandes Shepherd; Andreas Rimner; Charles B Simone; Gregory Riely; James Huang; Michelle S Ginsberg Journal: Ann Thorac Surg Date: 2021-04-09 Impact factor: 5.102
Authors: Yoshiharu Ohno; Joon Beom Seo; Grace Parraga; Kyung Soo Lee; Warren B Gefter; Sean B Fain; Mark L Schiebler; Hiroto Hatabu Journal: Radiology Date: 2021-04-06 Impact factor: 29.146
Authors: José Marcio Luna; Andrew R Barsky; Russell T Shinohara; Leonid Roshkovan; Michelle Hershman; Alexandra D Dreyfuss; Hannah Horng; Carolyn Lou; Peter B Noël; Keith A Cengel; Sharyn Katz; Eric S Diffenderfer; Despina Kontos Journal: Cancers (Basel) Date: 2022-01-29 Impact factor: 6.639
Authors: Jie Lian; Yonghao Long; Fan Huang; Kei Shing Ng; Faith M Y Lee; David C L Lam; Benjamin X L Fang; Qi Dou; Varut Vardhanabhuti Journal: Front Oncol Date: 2022-07-13 Impact factor: 5.738