Clinical trial · Interventional
Machine Learning to Predict Lymph Node Metastasis in T1 Esophageal Squamous Cell Carcinoma
Machine Learning to Predict Lymph Node Metastasis in T1 Esophageal Squamous Cell Carcinoma: A Multicenter Study
NCT06256185CI-TRIAL-00073952completedN/AClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Lymph Node Metastasis | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| esophagectomy | Procedure | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- Arm used for predicting lymph node metastasis
- interventionNames
- Procedure: esophagectomy
Primary outcomes (3)
- measure
- Model performance: discrimination
- timeFrame
- 8 weeks
- description
- 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
- measure
- Variable importance
- timeFrame
- 6 weeks
- description
- Calculate the importance level of variables used in the model and sort them, and analyze the reasons for the most important variables
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
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
References
Publications (19)
- BACKGROUNDErratum: 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. PMID 32767693
- BACKGROUNDOhashi 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. PMID 26376349
- BACKGROUNDMerkow 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. PMID 25031273
- BACKGROUNDAlvarez Herrero L, Pouw RE, van Vilsteren FG, ten Kate FJ, Visser M, van Berge Henegouwen MI, Weusten BL, Bergman JJ. Risk of lymph node metastasis associated with deeper invasion by early adenocarcinoma of the esophagus and cardia: study based on endoscopic resection specimens. Endoscopy. 2010 Dec;42(12):1030-6. doi: 10.1055/s-0030-1255858. Epub 2010 Oct 19. PMID 20960392
- BACKGROUNDGamboa 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. PMID 27142247
- BACKGROUNDDubecz 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. PMID 25929888