Li-Na He1, Tao Chen2, Sha Fu3, Chen Chen4, Yongluo Jiang2, Xuanye Zhang1, Wei Du1, Haifeng Li1, Yixing Wang1, Wael Abdullah Sultan Ali1, Yixin Zhou5, Zuan Lin6, Yunpeng Yang1, Yan Huang1, Hongyun Zhao6, Wenfeng Fang1, Li Zhang7, Shaodong Hong8. 1. State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China. 2. State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China. 3. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Pathology Department, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China. 4. State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China. 5. State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of VIP Region, Sun Yat-sen University Cancer Center, Guangzhou, China. 6. State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China. 7. State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China. Electronic address: zhangli6@mail.sysu.edu.cn. 8. State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China. Electronic address: hongshd@sysucc.org.cn.
Abstract
OBJECTIVES: The Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 provides conventional and standardized response assessment for multiple solid tumors. We investigated the smallest number of target lesions that can be measured without compromising response categorization and survival prediction in patients with advanced non-small-cell lung cancer (aNSCLC) undergoing anti-PD-1/PD-L1 monotherapy. MATERIAL AND METHODS: 125 aNSCLC patients with at least two measurable lesions undergoing PD-1/PD-L1 inhibitor treatment were retrospectively studied. Tumor measurements allowing up to two lesions per organ and five lesions in total were reviewed. Inter-individual agreement and κ values for inter-method concordance on response status were evaluated based on up to five target lesions versus the largest one through four lesions. C-index was calculated to evaluate the prognostic accuracy of response categorization based on the selected number of target lesions for predicting overall survival (OS). Cox regression analysis was conducted for survival analysis. RESULTS: The highly consistent response assignment (99.2%) could be obtained when measuring the largest two lesions versus up to five lesions. Using the largest two through four lesions produced κ values of 0.986, 1.000 and 1.000 for response assessment, values significantly higher than those obtained when measuring the largest single lesion (κ = 0.850). C-index for overall survival (OS) was similar when assessing the largest one through five lesions, ranging from 0.646 to 0.654. Cox regression analyses showed that radiological response significantly predicted OS, irrespective of the number of target lesions selected. CONCLUSIONS: Reducing the number of target lesions does not affect OS prediction in aNSCLC patients treated with anti-PD-1/PD-L1 therapy. Considering the high intra-individual and inter-method concordance, using the largest two lesions in total is proposed to assess response.
OBJECTIVES: The Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 provides conventional and standardized response assessment for multiple solid tumors. We investigated the smallest number of target lesions that can be measured without compromising response categorization and survival prediction in patients with advanced non-small-cell lung cancer (aNSCLC) undergoing anti-PD-1/PD-L1 monotherapy. MATERIAL AND METHODS: 125 aNSCLC patients with at least two measurable lesions undergoing PD-1/PD-L1 inhibitor treatment were retrospectively studied. Tumor measurements allowing up to two lesions per organ and five lesions in total were reviewed. Inter-individual agreement and κ values for inter-method concordance on response status were evaluated based on up to five target lesions versus the largest one through four lesions. C-index was calculated to evaluate the prognostic accuracy of response categorization based on the selected number of target lesions for predicting overall survival (OS). Cox regression analysis was conducted for survival analysis. RESULTS: The highly consistent response assignment (99.2%) could be obtained when measuring the largest two lesions versus up to five lesions. Using the largest two through four lesions produced κ values of 0.986, 1.000 and 1.000 for response assessment, values significantly higher than those obtained when measuring the largest single lesion (κ = 0.850). C-index for overall survival (OS) was similar when assessing the largest one through five lesions, ranging from 0.646 to 0.654. Cox regression analyses showed that radiological response significantly predicted OS, irrespective of the number of target lesions selected. CONCLUSIONS: Reducing the number of target lesions does not affect OS prediction in aNSCLC patients treated with anti-PD-1/PD-L1 therapy. Considering the high intra-individual and inter-method concordance, using the largest two lesions in total is proposed to assess response.