Clinical trial NCT06548464: Interpretable Machine Learning Models for Prognosis in Gastric Cancer Patients
Official title: Development and Validation of Interpretable Machine Learning Models for Prognosis in Gastric Cancer Patients: a Multicenter Retrospective Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Stomach Neoplasms
Gastrectomy
Machine Learning
Keywords: Machine Learning
Prognosis
Stomach Neoplasms
Gastrectomy
Lead sponsor: Fujian Medical University
Sponsor class: OTHER
First posted: 2024-08-12
Enrollment: 18000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: This study includes adult patients of all genders who were diagnosed with primary gastric or gastroesophageal junction adenocarcinoma and underwent radical gastrectomy at participating institutions in China. The study population represents a diverse group of gastric cancer patients, varying in age, tumor stage, and treatment approaches, including those who received neoadjuvant therapy. This cohort aims to provide a comprehensive representation of gastric cancer patients treated with curative intent, allowing for the development and validation of prognostic models applicable to a wide range of clinical scenarios.

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.

Eligibility criteria: Inclusion Criteria:

* Patients diagnosed with primary gastric or gastroesophageal junction cancer
* Underwent radical gastrectomy
* Complete clinical and pathological data available

Exclusion Criteria:

* Presence of distant metastases before surgery
* Non-adenocarcinoma histology
* Incomplete follow-up data

Primary outcomes:
measure: Survival
timeFrame: Up to 5 years after surgery
Secondary outcomes:
measure: Early Recurrence
timeFrame: Within 2 years after surgery
measure: Late Recurrence
timeFrame: From 2 years up to 5 years after surgery
measure: Postoperative Complications
timeFrame: Within 30 days after surgery
measure: Neoadjuvant Treatment Efficacy
timeFrame: From initiation of neoadjuvant therapy to surgery (typically 2-3 months)
measure: 5-Year Survival Rate
timeFrame: 5 years after surgery
Other outcomes:
Locations:
city: Fuzhou
country: China
facility: Chang-ming Huang
state: Fujian
Study officials:
affiliation: Fujian Medical University Union Hospital
name: Chang-Ming Huang, MD
role: PRINCIPAL_INVESTIGATOR
References: