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Lymph Node MetastasisNCT06256185

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

A study testing esophagectomy in people with Lymph Node Metastasis, run by Shanghai Zhongshan Hospital at 1 site in China.

Completed

Finished. The study has ended and is not enrolling. Results may be posted on the official record.

Official record
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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. Read more about this study

Who is it for?
Any age
Conditions the study is for
Lymph Node Metastasis
What is being tested?
esophagectomy
Type of treatment
Surgery or procedure
Could I receive a placebo?
No, everyone receives an active treatment
How many people
1,267 participants
Where
1 siteChina
Study dates
15 Jan 2010to 15 Jul 2023
Phase
Interventional
Are there results?
None posted in the registry

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages Any age
  • All sexes
  • No healthy volunteers
  • Child, Adult, Older adult

Who may be able to join

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

Who cannot join

  • underwent neoadjuvant treatment or endoscopic submucosal dissection before surgery

Trial locations and contacts

1 site in 1 country.

China1 site

  • Zhongshan Hospital Affiliated to Fudan University

    Shanghai, Shanghai Municipality, 200032

Other trials in China

About this study

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

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.

Read the detailed 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.

Being tested
esophagectomy
Medical subject headings
Lymphatic Metastasis

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • esophagectomyProcedure or surgery

    Resection of esophageal tumor and lymph node dissection

Study groups in NCT06256185
Study groupRoleWhat they receive
Arm used for predicting lymph node metastasisExperimentalNot described
Receives: Procedure: esophagectomy

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. 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

    Measured: 8 weeks

  2. Variable importance

    Calculate the importance level of variables used in the model and sort them, and analyze the reasons for the most important variables

    Measured: 6 weeks

  3. 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

    Measured: 8 weeks

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted13 Feb 2024
  2. Study start15 Jan 2010
  3. Primary completion15 Dec 2019
  4. Study completion15 Jul 2023

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Interventional
Phase
Phase N/A
Primary purpose
Diagnosis
Allocation
Not applicable
Intervention model
Single group
Masking
None (open label)
Enrolment
1,267 participants (actual)
IDs, dates and publications
ClinicalTrials.gov ID
NCT06256185
Sponsor's protocol ID
81902396
Status last verified
Feb 2024
First submitted
23 Jan 2024
Last updated
13 Feb 2024
Results posted
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
No

Publications

  • 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.

    PubMed 32767693

  • 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.

    PubMed 26376349

  • 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.

    PubMed 25031273

  • Alvarez 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.

    PubMed 20960392

  • 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.

    PubMed 27142247

  • 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.

    PubMed 25929888

  • 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.

    PubMed 29863776

  • 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.

    PubMed 33879102

  • Collins GS, Dhiman P, Andaur Navarro CL, Ma J, Hooft L, Reitsma JB, Logullo P, Beam AL, Peng L, Van Calster B, van Smeden M, Riley RD, Moons KG. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021 Jul 9;11(7):e048008. doi: 10.1136/bmjopen-2020-048008.

    PubMed 34244270

  • 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.

    PubMed 33716451

  • 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.

    PubMed 26264962

  • 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.

    PubMed 18212745

  • Choi J, Kim SG, Kim JS, Jung HC, Song IS. Comparison of endoscopic ultrasonography (EUS), positron emission tomography (PET), and computed tomography (CT) in the preoperative locoregional staging of resectable esophageal cancer. Surg Endosc. 2010 Jun;24(6):1380-6. doi: 10.1007/s00464-009-0783-x. Epub 2009 Dec 24.

    PubMed 20033712

  • 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.

    PubMed 33532263

  • 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.

    PubMed 27436201

Questions about this trial

What is this clinical trial studying?

A study testing esophagectomy in people with Lymph Node Metastasis, run by Shanghai Zhongshan Hospital at 1 site in China.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: finished, as of the sponsor's last update on 13 Feb 2024. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.

How can a doctor refer a patient or contact the research team?

The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Model performance: discrimination (time frame: 8 weeks). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT06256185, which also lists 19 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Feb 2024.

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