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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Arm used for predicting lymph node metastasis | Experimental |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| esophagectomy | Procedure | Resection of esophageal tumor and lymph node dissection |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| 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 |
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Zhongshan Hospital Affiliated to Fudan University | Shanghai | Shanghai Municipality | 200032 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 32767693 | Background | 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. | |
| 26376349 | Background | 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. |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| SAP | No | Yes | No | Statistical Analysis Plan | Jul 15, 2021 | Feb 1, 2024 | SAP_000.pdf |
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| ID | Term |
|---|---|
| D008207 | Lymphatic Metastasis |
| ID | Term |
|---|---|
| D009362 | Neoplasm Metastasis |
| D009385 | Neoplastic Processes |
| D009369 | Neoplasms |
| D010335 | Pathologic Processes |
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| ID | Term |
|---|---|
| D016629 | Esophagectomy |
| ID | Term |
|---|---|
| D013505 | Digestive System Surgical Procedures |
| D013514 | Surgical Procedures, Operative |
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| 25031273 | Background | 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. |
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| 34244270 | Background | 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. |
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| D013568 |
| Pathological Conditions, Signs and Symptoms |