Development and validation of a prognostic nomogram model for patients with the lower third and abdominal oesophageal adenocarcinoma_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

XU Zhengshui ¹ , LIU Dandan ² , JIANG Jiantao ¹ , KONG Ranran ¹ , LI Jianzhong ¹ , MA Yuefeng ¹ , MA Zhenchuan ¹ , CHEN Jia ³ , ZHU Minxia ^1,4 ,  LI Shaomin ¹

1. Department of Thoracic Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, P. R. China;
2. Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, P. R. China;
3. Department of Cardiovascular Surgery, Xijing Hospital, Airforce Medical University, Xi'an, 710032, P. R. China;
4. Department of Thoracic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, P. R. China;

Corresponding author：

LI Shaomin, Email: lism51487@xjtu.edu.cn

Keywords：

Esophageal adenocarcinoma; prognosis; nomogram model; overall survival

DOI：

10.7507/1007-4848.202310034

Video：

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Objective To establish an individualized nomogram model and evaluate its efficacy to provide a possible evaluation basis for the prognosis of lower third and abdominal part of oesophageal adenocarcinoma (EAC). Methods Lower third and abdominal part of EAC patients from 2010 to 2015 were chosen from the SEER Research Plus Database (17 Regs, 2022nov sub). The patients were randomly allocated to the training cohort and the internal validation cohort with a ratio of 7∶3 using bootstrap resampling. The Cox proportional hazards regression analysis was used to determine significant contributors to overall survival (OS) in EAC patients, which would be elected to construct the nomogram prediction model. C-index, calibration curve and receiver operating characteristic (ROC) curve were performed to evaluate its efficacy. Finally, the efficacy to evaluate the OS of EAC patients was compared between the nomogram prediction model and TNM staging system. Results In total, 3945 patients with lower third and abdominal part of EAC were enrolled, including 3475 males and 470 females with a median age of 65 (57-72) years. The 2761 patients were allocated to the training cohort and the remaining 1184 patients to the internal validation cohort. In the training and the internal validation cohorts, the C-index of the nomogram model was 0.705 and 0.713, respectively. Meanwhile, the calibration curve also suggested that the nomogram model had a strong capability of predicting 1-, 3-, and 5-year OS rates of EAC patients. The nomogram also had a higher efficacy than the TNM staging system in predicting 1-, 3-, and 5-year OS rates of EAC patients. Conclusion This nomogram prediction model has a high efficiency for predicting OS in the patients with lower third and abdominal part of EAC, which is higher than that of the current TNM staging system.

Citation： XU Zhengshui, LIU Dandan, JIANG Jiantao, KONG Ranran, LI Jianzhong, MA Yuefeng, MA Zhenchuan, CHEN Jia, ZHU Minxia, LI Shaomin. Development and validation of a prognostic nomogram model for patients with the lower third and abdominal oesophageal adenocarcinoma. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2025, 32(2): 201-207. doi: 10.7507/1007-4848.202310034 Copy

1.	Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2.	Chen W, Zheng R, Zhang S, et al. Cancer incidence and mortality in China, 2013. Cancer Lett, 2017, 401: 63-71.
3.	He S, Xu J, Liu X, et al. Advances and challenges in the treatment of esophageal cancer. Acta Pharm Sin B, 2021, 11(11): 3379-3392.
4.	赫捷, 陈万青, 李兆申, 等. 中国食管癌筛查与早诊早治指南(2022, 北京). 中国肿瘤, 2022, 31(6): 401-436.He J, Chen WQ, Li ZS, et al. China guideline for the screening, early detection and early treatment of esophageal cancer (2022, Beijing). China Cancer, 2022, 31(6): 401-436.
5.	Shah MA, Altorki N, Patel P, et al. Improving outcomes in patients with oesophageal cancer. Nat Rev Clin Oncol, 2023, 20(6): 390-407.
6.	李湘杰, 沈磊. 食管腺癌危险因素及治疗进展. 医学综述, 2020, 26(10): 1951-1955.Li XJ, Shen L. Risk factors and treatment progress of esophageal adenocarcinoma. Med Recapitul, 2020, 26(10): 1951-1955.
7.	Arnold M, Laversanne M, Brown LM, et al. Predicting the future burden of esophageal cancer by histological subtype: International trends in incidence up to 2030. Am J Gastroenterol, 2017, 112(8): 1247-1255.
8.	Morgan E, Soerjomataram I, Rumgay H, et al. The global landscape of esophageal squamous cell carcinoma and esophageal adenocarcinoma incidence and mortality in 2020 and projections to 2040: New estimates from GLOBOCAN 2020. Gastroenterology, 2022, 163(3): 649-658.
9.	Lee C, Light A, Alaa A, et al. Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database. Lancet Digit Health, 2021, 3(3): e158-e165.
10.	Wu J, Zhang H, Li L, et al. A nomogram for predicting overall survival in patients with low-grade endometrial stromal sarcoma: A population-based analysis. Cancer Commun (Lond), 2020, 40(7): 301-312.
11.	Yang R, Wu Y, Qi Y, et al. A nomogram for predicting breast cancer specific survival in elderly patients with breast cancer: A SEER population-based analysis. BMC Geriatr, 2023, 23(1): 594.
12.	Zhou Y, He W, Guo P, et al. Development and validation of a recurrence-free survival prediction model for locally advanced esophageal squamous cell carcinoma with neoadjuvant chemoradiotherapy. Ann Surg Oncol, 2024, 31(1): 178-191.
13.	Pan YX, Chen JC, Fang AP, et al. A nomogram predicting the recurrence of hepatocellular carcinoma in patients after laparoscopic hepatectomy. Cancer Commun (Lond), 2019, 39(1): 55.
14.	罗钞, 王高明, 胡力文, 等. 食管癌患者术后预测模型的构建和验证: 基于SEER数据库. 南方医科大学学报, 2022, 42(6): 794-804.Luo C, Wang GM, Hu LW, et al. Development and validation of a prognostic model based on SEER data for patients with esophageal carcinoma after esophagectomy. J South Med Univ, 2022, 42(6): 794-804.
15.	Weiss JA, Jain S. Neoadjuvant and adjuvant therapy in esophageal cancer. J Gastrointest Oncol, 2023, 14(4): 1927-1932.

1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2. Chen W, Zheng R, Zhang S, et al. Cancer incidence and mortality in China, 2013. Cancer Lett, 2017, 401: 63-71.
3. He S, Xu J, Liu X, et al. Advances and challenges in the treatment of esophageal cancer. Acta Pharm Sin B, 2021, 11(11): 3379-3392.
4. 赫捷, 陈万青, 李兆申, 等. 中国食管癌筛查与早诊早治指南(2022, 北京). 中国肿瘤, 2022, 31(6): 401-436.He J, Chen WQ, Li ZS, et al. China guideline for the screening, early detection and early treatment of esophageal cancer (2022, Beijing). China Cancer, 2022, 31(6): 401-436.
5. Shah MA, Altorki N, Patel P, et al. Improving outcomes in patients with oesophageal cancer. Nat Rev Clin Oncol, 2023, 20(6): 390-407.
6. 李湘杰, 沈磊. 食管腺癌危险因素及治疗进展. 医学综述, 2020, 26(10): 1951-1955.Li XJ, Shen L. Risk factors and treatment progress of esophageal adenocarcinoma. Med Recapitul, 2020, 26(10): 1951-1955.
7. Arnold M, Laversanne M, Brown LM, et al. Predicting the future burden of esophageal cancer by histological subtype: International trends in incidence up to 2030. Am J Gastroenterol, 2017, 112(8): 1247-1255.
8. Morgan E, Soerjomataram I, Rumgay H, et al. The global landscape of esophageal squamous cell carcinoma and esophageal adenocarcinoma incidence and mortality in 2020 and projections to 2040: New estimates from GLOBOCAN 2020. Gastroenterology, 2022, 163(3): 649-658.
9. Lee C, Light A, Alaa A, et al. Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database. Lancet Digit Health, 2021, 3(3): e158-e165.
10. Wu J, Zhang H, Li L, et al. A nomogram for predicting overall survival in patients with low-grade endometrial stromal sarcoma: A population-based analysis. Cancer Commun (Lond), 2020, 40(7): 301-312.
11. Yang R, Wu Y, Qi Y, et al. A nomogram for predicting breast cancer specific survival in elderly patients with breast cancer: A SEER population-based analysis. BMC Geriatr, 2023, 23(1): 594.
12. Zhou Y, He W, Guo P, et al. Development and validation of a recurrence-free survival prediction model for locally advanced esophageal squamous cell carcinoma with neoadjuvant chemoradiotherapy. Ann Surg Oncol, 2024, 31(1): 178-191.
13. Pan YX, Chen JC, Fang AP, et al. A nomogram predicting the recurrence of hepatocellular carcinoma in patients after laparoscopic hepatectomy. Cancer Commun (Lond), 2019, 39(1): 55.
14. 罗钞, 王高明, 胡力文, 等. 食管癌患者术后预测模型的构建和验证: 基于SEER数据库. 南方医科大学学报, 2022, 42(6): 794-804.Luo C, Wang GM, Hu LW, et al. Development and validation of a prognostic model based on SEER data for patients with esophageal carcinoma after esophagectomy. J South Med Univ, 2022, 42(6): 794-804.
15. Weiss JA, Jain S. Neoadjuvant and adjuvant therapy in esophageal cancer. J Gastrointest Oncol, 2023, 14(4): 1927-1932.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Development and validation of a prognostic nomogram model for patients with the lower third and abdominal oesophageal adenocarcinoma

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