ML Decision Model for G-NEC Adjuvant Therapy

NCT06663852 | Completed DMC

• 1 other identifier: 2024GNEC
• Study Type: observational
• Target: 1,505
• Locations: 1 country
• Sites: 1

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.

Conditions

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

Keywords

Gastric Neuroendocrine Carcinoma (G-NEC); Adjuvant Chemotherapy; Machine Learning; Decision Support Model; Random Survival Forest

Outcome Measures

Primary Outcomes (1)

• Disease-Free Survival (DFS)

  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.

  From date of surgery up to 5 years

Study Arms (1)

Gastric Neuroendocrine Carcinoma (G-NEC) Patients

This study focuses on patients diagnosed with gastric neuroendocrine carcinoma (G-NEC) who have undergone radical surgery. The cohort includes adult patients (≥18 years) treated at 38 tertiary hospitals in China between January 2006 and December 2020. Patients are divided into three groups based on their postoperative adjuvant treatment: no adjuvant chemotherapy, etoposide and platinum derivatives-based chemotherapy, and fluorouracil-based chemotherapy. The study aims to develop and validate a machine learning-based decision support model to optimize individualized adjuvant therapy strategies for G-NEC patients, with the primary outcome being disease-free survival (DFS).

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This study includes adult patients diagnosed with gastric neuroendocrine carcinoma (G-NEC) or mixed adenoneuroendocrine carcinoma (MANEC) who underwent radical surgery at 38 tertiary hospitals in China between January 2006 and December 2020. The study population consists of patients who received either no adjuvant chemotherapy, etoposide and platinum derivatives-based chemotherapy, or fluorouracil-based chemotherapy following surgery.

You may qualify if:

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

You may not qualify if:

• (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.

Sponsors & Collaborators

• Chang-Ming Huang, Prof.: lead

Study Sites (1)

Fujian Medical University

Fuzhou, Fujian, 350001, China

Location

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Prof.

Study Record Dates

• First Submitted: October 26, 2024
• First Posted: October 29, 2024
• Study Start: January 1, 2024
• Primary Completion: June 1, 2024
• Study Completion: June 30, 2024
• Last Updated: November 27, 2024

Record last verified: 2024-10

Locations

CN (1)