Clinical trial NCT06256185: Machine Learning to Predict Lymph Node Metastasis in T1 Esophageal Squamous Cell Carcinoma
Official title: Machine Learning to Predict Lymph Node Metastasis in T1 Esophageal Squamous Cell Carcinoma: A Multicenter Study
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Lymph Node Metastasis
Lead sponsor: Shanghai Zhongshan Hospital
Sponsor class: OTHER
First posted: 2024-02-13
Enrollment: 1267
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

Brief 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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Model performance: discrimination
timeFrame: 8 weeks
measure: Variable importance
timeFrame: 6 weeks
measure: Sub-analysis (ML Model vs. Logistic Model vs. NCCN Guideline)
timeFrame: 8 weeks
Secondary outcomes:
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Zhongshan Hospital Affiliated to Fudan University
state: Shanghai
Study officials:
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