Machine Learning to Predict Lymph Node Metastasis in T1 Esophageal Squamous Cell Carcinoma

Lymph Node Metastasis Clinical Trial

Official title:

Machine Learning to Predict Lymph Node Metastasis in T1 Esophageal Squamous Cell Carcinoma: A Multicenter Study

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06256185
• Other study ID #: 81902396
• Status: Completed
• Phase: N/A
• Start date: January 15, 2010
• Est. completion date: July 15, 2023

Study information

• Verified date: February 2024
• Source: Shanghai Zhongshan Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

Existing models do poorly when it comes to quantifying the risk of Lymph node metastases (LNM). This study generated elastic net regression (ELR), random forest (RF), extreme gradient boosting (XGB), and a combined (ensemble) model of these for LNM in patients with T1 esophageal squamous cell carcinoma.

Description:

Lymph node metastases (LNM) is a relatively uncommon but possible complication of T1 esophageal squamous cell carcinoma (ESCC). Existing models do poorly when it comes to quantifying this risk. This study aimed to develop a machine learning model for LNM in patients with T1 esophageal squamous cell carcinoma. Patients with T1 squamous cell carcinoma treated with surgery between January 2010 and September 2021 from 3 institutions were included in this study. Machine-learning models were developed using data on patients' age and sex, depth of tumor invasion, tumor size, tumor location, macroscopic tumor type, lymphatic and vascular invasion, and histologic grade. Elastic net regression (ELR), random forest (RF), extreme gradient boosting (XGB), and a combined (ensemble) model of these was generated. Use Area Under Curve (AUC) to evaluate the predictive ability of the model. The contribution to the model of each factor was calculated. In order to better meet clinical needs, the investigators have designed the model as a user-friendly website.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 1267
• Est. completion date: July 15, 2023
• Est. primary completion date: December 15, 2019
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• (I) thoracic ESCC
• (II) no history of concomitant or prior malignancy
• (III) tumor with pT1 staging
• (IV) 15 or more lymph nodes examined

Exclusion Criteria:

• underwent neoadjuvant treatment or endoscopic submucosal dissection before surgery

Related Conditions & MeSH terms

• Carcinoma, Squamous Cell
• Esophageal Squamous Cell Carcinoma
• Lymph Node Metastasis
• Lymphatic Metastasis
• Neoplasm Metastasis

Intervention

Procedure:
• esophagectomy
Resection of esophageal tumor and lymph node dissection

Locations

Country	Name	City	State
China	Zhongshan Hospital Affiliated to Fudan University	Shanghai	Shanghai

Sponsors (1)

Lead Sponsor	Collaborator
Shanghai Zhongshan Hospital

Country where clinical trial is conducted

China, 

References & Publications (19)

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Duan X, Shang X, Yue J, Ma Z, Chen C, Tang P, Jiang H, Yu Z. A nomogram to predict lymph node metastasis risk for early esophageal squamous cell carcinoma. BMC Cancer. 2021 Apr 20;21(1):431. doi: 10.1186/s12885-021-08077-z. — View Citation

Dubecz A, Kern M, Solymosi N, Schweigert M, Stein HJ. Predictors of Lymph Node Metastasis in Surgically Resected T1 Esophageal Cancer. Ann Thorac Surg. 2015 Jun;99(6):1879-85; discussion 1886. doi: 10.1016/j.athoracsur.2015.02.112. Epub 2015 Apr 28. — View Citation

Emi M, Hihara J, Hamai Y, Furukawa T, Ibuki Y, Okada M. Clinicopathologic Features of Submucosal Esophageal Squamous Cell Carcinoma. Ann Thorac Surg. 2017 Dec;104(6):1858-1864. doi: 10.1016/j.athoracsur.2017.06.037. Epub 2017 Oct 21. — View Citation

Erratum: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2020 Jul;70(4):313. doi: 10.3322/caac.21609. Epub 2020 Apr 6. No abstract available. — View Citation

Gamboa AM, Kim S, Force SD, Staley CA, Woods KE, Kooby DA, Maithel SK, Luke JA, Shaffer KM, Dacha S, Saba NF, Keilin SA, Cai Q, El-Rayes BF, Chen Z, Willingham FF. Treatment allocation in patients with early-stage esophageal adenocarcinoma: Prevalence and predictors of lymph node involvement. Cancer. 2016 Jul 15;122(14):2150-7. doi: 10.1002/cncr.30040. Epub 2016 May 3. — View Citation

Jiang KY, Huang H, Chen WY, Yan HJ, Wei ZT, Wang XW, Li HX, Zheng XY, Tian D. Risk factors for lymph node metastasis in T1 esophageal squamous cell carcinoma: A systematic review and meta-analysis. World J Gastroenterol. 2021 Feb 28;27(8):737-750. doi: 10.3748/wjg.v27.i8.737. — View Citation

Li B, Chen H, Xiang J, Zhang Y, Kong Y, Garfield DH, Li H. Prevalence of lymph node metastases in superficial esophageal squamous cell carcinoma. J Thorac Cardiovasc Surg. 2013 Nov;146(5):1198-203. doi: 10.1016/j.jtcvs.2013.07.006. Epub 2013 Aug 26. — View Citation

Merkow RP, Bilimoria KY, Keswani RN, Chung J, Sherman KL, Knab LM, Posner MC, Bentrem DJ. Treatment trends, risk of lymph node metastasis, and outcomes for localized esophageal cancer. J Natl Cancer Inst. 2014 Jul 16;106(7):dju133. doi: 10.1093/jnci/dju133. Print 2014 Jul. — View Citation

Ohashi S, Miyamoto S, Kikuchi O, Goto T, Amanuma Y, Muto M. Recent Advances From Basic and Clinical Studies of Esophageal Squamous Cell Carcinoma. Gastroenterology. 2015 Dec;149(7):1700-15. doi: 10.1053/j.gastro.2015.08.054. Epub 2015 Sep 12. — View Citation

Ou J, Wu L, Li R, Wu CQ, Liu J, Chen TW, Zhang XM, Tang S, Wu YP, Yang LQ, Tan BG, Lu FL. CT radiomics features to predict lymph node metastasis in advanced esophageal squamous cell carcinoma and to discriminate between regional and non-regional lymph node metastasis: a case control study. Quant Imaging Med Surg. 2021 Feb;11(2):628-640. doi: 10.21037/qims-20-241. — View Citation

Pavlou M, Ambler G, Seaman SR, Guttmann O, Elliott P, King M, Omar RZ. How to develop a more accurate risk prediction model when there are few events. BMJ. 2015 Aug 11;351:h3868. doi: 10.1136/bmj.h3868. Erratum In: BMJ. 2016 Jun 08;353:i3235. — View Citation

Shen W, Shen Y, Tan L, Jin C, Xi Y. A nomogram for predicting lymph node metastasis in surgically resected T1 esophageal squamous cell carcinoma. J Thorac Dis. 2018 Jul;10(7):4178-4185. doi: 10.21037/jtd.2018.06.51. — View Citation

van Vliet EP, Heijenbrok-Kal MH, Hunink MG, Kuipers EJ, Siersema PD. Staging investigations for oesophageal cancer: a meta-analysis. Br J Cancer. 2008 Feb 12;98(3):547-57. doi: 10.1038/sj.bjc.6604200. Epub 2008 Jan 22. — View Citation

Wang S, Chen X, Fan J, Lu L. Prognostic Significance of Lymphovascular Invasion for Thoracic Esophageal Squamous Cell Carcinoma. Ann Surg Oncol. 2016 Nov;23(12):4101-4109. doi: 10.1245/s10434-016-5416-8. Epub 2016 Jul 19. — View Citation

Zheng H, Tang H, Wang H, Fang Y, Shen Y, Feng M, Xu S, Fan H, Ge D, Wang Q, Tan L. Nomogram to predict lymph node metastasis in patients with early oesophageal squamous cell carcinoma. Br J Surg. 2018 Oct;105(11):1464-1470. doi: 10.1002/bjs.10882. Epub 2018 Jun 4. — View Citation

* Note: There are 19 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Model performance: discrimination	Draw the ROC curve of the model and obtain their AUC values, and select the best prediction model based on the results of the validation set	8 weeks
Primary	Variable importance	Calculate the importance level of variables used in the model and sort them, and analyze the reasons for the most important variables	6 weeks
Primary	Sub-analysis (ML Model vs. Logistic Model vs. NCCN Guideline)	Apply NCCN guidelines and logistic models for prediction, and compare their performance with the model obtained in this study to determine the actual application benefits of the model	8 weeks