Yuan Zeng1,2, Nicholas Mayne3, Chi-Fu Jeffrey Yang3, Thomas A D'Amico3, Calvin S H Ng4, Chia-Chuan Liu5, René Horsleben Petersen6, Gaetano Rocco7, Alessandro Brunelli8, Jun Liu1,2, Yang Liu1,2, Weizhe Huang1,2, Jiaxi He1,2, Wei Wang1,2, Long Jiang1,2, Fei Cui1,2, Wenjun Wang1,2, Wenhua Liang9,10, Jianxing He11,12. 1. Department of Thoracic Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, People's Republic of China. 2. Guangzhou Institute of Respiratory Disease and China State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou, Guangdong Province, People's Republic of China. 3. Section of General Thoracic Surgery, Duke University Medical Center, Durham, NC, USA. 4. Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Sha Tin, Hong Kong. 5. Division of Thoracic Surgery, Sun Yat-Sen Cancer Center, Taipei, Taiwan. 6. Department of Cardiothoracic Surgery, University Hospital of Copenhagen, Rigshospitalet, Copenhagen, Denmark. 7. Division of Thoracic Surgical Oncology, Department of Thoracic Surgery and Oncology, Istituto Nazionale Tumori, IRCCS, Fondazione Pascale, Naples, Italy. 8. Department of Thoracic Surgery, St James's University Hospital, Leeds, UK. 9. Department of Thoracic Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, People's Republic of China. liangwh1987@163.com. 10. Guangzhou Institute of Respiratory Disease and China State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou, Guangdong Province, People's Republic of China. liangwh1987@163.com. 11. Department of Thoracic Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, People's Republic of China. drhe_jianxing@163.com. 12. Guangzhou Institute of Respiratory Disease and China State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou, Guangdong Province, People's Republic of China. drhe_jianxing@163.com.
Abstract
BACKGROUND: Models for predicting the survival outcomes of stage I non-small-cell lung cancer (NSCLC) defined by the newly released 8th edition TNM staging system are scarce. This study aimed to develop a nomogram for predicting the cancer-specific survival (CSS) of these patients and identifying individuals with a higher risk for CSS. METHODS: A total of 30,475 NSCLC cases were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We identified and integrated the risk factors to build a nomogram. The model was subjected to bootstrap internal validation with the SEER database, and external validation with a multicenter cohort of 1133 patients from China. The difference in the impact of adjuvant chemotherapy on model-defined high- and low-risk patients was examined using the National Cancer Database (NCDB). RESULTS: Eight independent prognostic factors were identified and integrated into the model. The calibration curves showed good agreement. The concordance index (C-index) of the nomogram was higher than that of the staging system (IA1, IA2, IA3, and IB) (internal validation set 0.63 vs. 0.56; external validation set 0.66 vs. 0.55; both p < 0.01). Specifically, 21.7% of stage IB patients (7.5% of all stage I) were categorized into the high-risk group (score > 30). There was a significant interaction effect between the adjuvant chemotherapy and risk groups in the NCDB cohort (p = 0.003). CONCLUSIONS: We established a practical nomogram to predict CSS for 8th edition stage I NSCLC. A prospective study is warranted to determine its role in identifying adjuvant chemotherapy candidates.
BACKGROUND: Models for predicting the survival outcomes of stage I non-small-cell lung cancer (NSCLC) defined by the newly released 8th edition TNM staging system are scarce. This study aimed to develop a nomogram for predicting the cancer-specific survival (CSS) of these patients and identifying individuals with a higher risk for CSS. METHODS: A total of 30,475 NSCLC cases were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We identified and integrated the risk factors to build a nomogram. The model was subjected to bootstrap internal validation with the SEER database, and external validation with a multicenter cohort of 1133 patients from China. The difference in the impact of adjuvant chemotherapy on model-defined high- and low-risk patients was examined using the National Cancer Database (NCDB). RESULTS: Eight independent prognostic factors were identified and integrated into the model. The calibration curves showed good agreement. The concordance index (C-index) of the nomogram was higher than that of the staging system (IA1, IA2, IA3, and IB) (internal validation set 0.63 vs. 0.56; external validation set 0.66 vs. 0.55; both p < 0.01). Specifically, 21.7% of stage IB patients (7.5% of all stage I) were categorized into the high-risk group (score > 30). There was a significant interaction effect between the adjuvant chemotherapy and risk groups in the NCDB cohort (p = 0.003). CONCLUSIONS: We established a practical nomogram to predict CSS for 8th edition stage I NSCLC. A prospective study is warranted to determine its role in identifying adjuvant chemotherapy candidates.
Authors: Yuan Zeng; Nicholas Mayne; Chi-Fu Jeffrey Yang; Jun Liu; Fei Cui; Jingpei Li; Wenhua Liang; Jianxing He Journal: Transl Lung Cancer Res Date: 2021-04