ML Decision Model for G-NEC Adjuvant Therapy

Machine Learning-Based Decision Model for Optimal Adjuvant Therapy in Primary Gastric Neuroendocrine Carcinoma: a National Real-World Evidence Study

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06663852

Acronym

G-NEC

Enrollment

1505

Registered

2024-10-29

Start date

2024-01-01

Completion date

2024-06-30

Last updated

2024-11-27

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

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

Keywords

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

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

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.

Design outcomes

Primary

Measure	Time frame	Description
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.

Countries

China

Outcome results