Lymph Node Metastasis
Conditions
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.
Interventions
PROCEDUREesophagectomy
Resection of esophageal tumor and lymph node dissection
Sponsors
Shanghai Zhongshan Hospital
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Model performance: discrimination | 8 weeks | 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 |
| Variable importance | 6 weeks | Calculate the importance level of variables used in the model and sort them, and analyze the reasons for the most important variables |
| Sub-analysis (ML Model vs. Logistic Model vs. NCCN Guideline) | 8 weeks | 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 |
Countries
China
Outcome results
None listed