ML Decision Model for G-NEC Adjuvant Therapy

NCT ID: NCT06663852

Last Updated: 2024-11-27

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

1505 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-01-01

Study Completion Date

2024-06-30

Brief Summary

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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

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Conditions

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

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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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).

No interventions assigned to this group

Eligibility Criteria

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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.
Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Chang-Ming Huang, Prof.

OTHER

Sponsor Role lead

Responsible Party

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Chang-Ming Huang, Prof.

Prof.

Responsibility Role SPONSOR_INVESTIGATOR

Locations

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Fujian Medical University

Fuzhou, Fujian, China

Site Status

Countries

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China

Other Identifiers

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2024GNEC

Identifier Type: -

Identifier Source: org_study_id