Ignite Creation Date:
2024-10-26 @ 3:37 PM
Last Modification Date:
2024-10-26 @ 3:37 PM
Study NCT ID:
NCT06548464
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-08-06
Brief Title:
Interpretable Machine Learning Models for Prognosis in Gastric Cancer Patients
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Development and Validation of Interpretable Machine Learning Models for Prognosis in Gastric Cancer Patients a Multicenter Retrospective Study
Status:
COMPLETED
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
This multicenter retrospective cohort study aimed to develop and validate an explainable prediction model for prognosis after gastrectomy in patients with gastric cancer
Detailed Description:
This multicenter retrospective cohort study aimed to develop and validate an explainable prediction model for prognosis after gastrectomy in patients with gastric cancer The study included patients who underwent radical gastrectomy for primary gastric or gastroesophageal junction cancer across multiple institutions in China
The primary objective was to create a machine learning-based model to predict postoperative outcomes following gastrectomy using readily available clinical and pathological parameters The main outcome of interest was early recurrence within 2 years after surgery which significantly impacts overall prognosis
The study employed various machine learning algorithms to develop prediction models which were then compared and validated Model performance was assessed through measures such as area under the receiver operating characteristic curve AUC calibration and Brier score The SHapley Additive exPlanations SHAP method was used to interpret the model and rank feature importance
This research aims to provide clinicians with a tool for identifying patients at higher risk of poor postoperative outcomes who may benefit from more intensive post-operative monitoring and early intervention strategies potentially improving prognosis for gastric cancer patients
The primary objective was to create a machine learning-based model to predict postoperative outcomes following gastrectomy using readily available clinical and pathological parameters The main outcome of interest was early recurrence within 2 years after surgery which significantly impacts overall prognosis
The study employed various machine learning algorithms to develop prediction models which were then compared and validated Model performance was assessed through measures such as area under the receiver operating characteristic curve AUC calibration and Brier score The SHapley Additive exPlanations SHAP method was used to interpret the model and rank feature importance
This research aims to provide clinicians with a tool for identifying patients at higher risk of poor postoperative outcomes who may benefit from more intensive post-operative monitoring and early intervention strategies potentially improving prognosis for gastric cancer patients
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None