ML Decision Model for G-NEC Adjuvant Therapy

Chang-Ming Huang, Prof.

Status: Completed

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

Study type: Observational

Funder types: Other

Identifiers

NCT06663852

2024GNEC

Details and patient eligibility

About

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.

Enrollment: 1,505 patients

Sex: All

Volunteers: No Healthy Volunteers

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.

Trial design

1,505 participants in 1 patient group

Gastric Neuroendocrine Carcinoma (G-NEC) Patients

Description:

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).