Clinical trial · Observational
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Gastric Neuroendocrine Carcinoma (G-NEC) | Gastric Neuroendocrine Carcinoma | ONTOLOGY_EXACT | 0.85 |
| Machine Learning | — | UNRESOLVED | — |
| Postoperative Adjuvant Therapy for G-NEC | — | UNRESOLVED | — |
| Survival Outcomes | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (1)
- label
- 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).
Primary outcomes (1)
- measure
- Disease-Free Survival (DFS)
- timeFrame
- From date of surgery up to 5 years
- description
- 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.
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available