ML Decision Model for G-NEC Adjuvant Therapy

Completed

• Conditions: Gastric Neuroendocrine Carcinoma (G-NEC); Postoperative Adjuvant Therapy for G-NEC; Survival Outcomes; Machine Learning

• Registration Number: NCT06663852
• Lead Sponsor: Chang-Ming Huang, Prof.

Brief Summary

Gastric neuroendocrine carcinoma (G-NEC) is a rare and aggressive tumor originating from neuroendocrine cells in the stomach lining. It is characterized by a high propensity for recurrence and a generally poor prognosis. Due to its rarity, there is limited data and no established consensus on the optimal postoperative adjuvant therapy, making treatment decisions challenging for healthcare providers.

This study is a retrospective analysis focusing on evaluating survival rates, identifying prognostic factors, and formulating treatment recommendations for patients with G-NEC. By analyzing real-world clinical data, we aim to better understand the factors that influence patient outcomes and to develop evidence-based strategies for improving survival. Our goal is to provide clinicians with valuable insights and tools to make more informed treatment decisions, ultimately enhancing the quality of care and outcomes for patients with this challenging disease.

Detailed Description

Not available

Study Timeline

• Study Start: 2024-01-01
• Primary Completion: 2024-06-01
• Study Completion: 2024-06-30
• Status Verified: 2024-10
• Study First Submitted: 2024-10-26
• Study First Submitted QC: 2024-10-26
• Study First Posted: 2024-10-29
• Last Update Submitted: 2024-11-25
• Last Update Posted: 2024-11-27

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 1505

Inclusion Criteria

• (1) patients who underwent radical surgery without any neoadjuvant therapy;
• (2) pathology confirmed NEC or mixed adenoneuroendocrine carcinoma (MANEC).

Exclusion Criteria

• (1) history of other malignant neoplasms;
• (2) treatment with endoscopic submucosal dissection or endoscopic mucosal resection or thoracotomy;
• (3) incomplete clinical data (including pathological, adjuvant chemotherapy, and follow-up information);
• (4) receipt of alternative adjuvant treatment regimens;
• (5) death within 30 days postoperatively.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Disease-Free Survival (DFS)	From date of surgery up to 5 years	Disease-free survival is defined as the time from the date of surgery to disease recurrence, death from any cause, or last follow-up, whichever occurs first. The machine learning model's performance in predicting DFS and recommending optimal adjuvant therapy will be evaluated.

Trial Locations

Locations (1)

Fujian Medical University; 🇨🇳 Fuzhou, Fujian, China