Clinical trial · Observational
A Pan-cancer Screening and Diagnosis Model Based on Abdominal CT Was Established
NCT06614179CI-TRIAL-00081074recruitingClinicalTrials.gov clinicaltrialsProvenance
- 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)
Abdominal noncontrast scan and contrast-enhanced CT were used to establish a screening and diagnostic model for abdominal tumors
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Abdominal Neoplasm | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
No intervention recorded.
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Whether or not there is a diagnosis of abdominal tumor questionnaire
- timeFrame
- first visited at baseline
- description
- Whether or not there is a diagnosis of abdominal tumor
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 75 Years
Show eligibility criteria text
Inclusion Criteria: * Patients with abdominal tumors: * all patients were pathologically diagnosed with abdominal tumors; * The clinical case data of all patients were complete, and complete follow-up was obtained, with clear information on medical visits, operation time and survival status within 2 years. If the cause of death is unknown, it will be recorded as censored data; * All patients had no history of active abdominal bleeding, no serious infection or other abdominal diseases that affected the observation of CT imaging within 3 months before surgery. * Non-tumor population: * all patients have complete clinical case data, complete abdominal CT, no history of malignant tumors, no serious infections or other abdominal diseases that affect the diagnosis and observation of CT imaging. Exclusion Criteria: * Cases in which contrast-enhanced or noncontrast CT images show unclear lesions, with significant noise and artifacts;
References
Publications (1)
- DERIVEDHu C, Xia Y, Zheng Z, Cao M, Zheng G, Chen S, Sun J, Chen W, Zheng Q, Pan S, Zhang Y, Chen J, Yu P, Xu J, Xu J, Qiu Z, Lin T, Yun B, Yao J, Guo W, Gao C, Kong X, Chen K, Wen Z, Zhu G, Qiao J, Pan Y, Li H, Gong X, Ye Z, Ao W, Zhang L, Yan X, Tong Y, Yang X, Zheng X, Fan S, Cao J, Yan C, Xie K, Zhang S, Wang Y, Zheng L, Wu Y, Ge Z, Tian X, Zhang X, Wang Y, Zhang R, Wei Y, Zhu W, Zhang J, Qiu H, Su M, Shi L, Xu Z, Zhang L, Cheng X. AI-based large-scale screening of gastric cancer from noncontrast CT imaging. Nat Med. 2025 Sep;31(9):3011-3019. doi: 10.1038/s41591-025-03785-6. Epub 2025 Jun 24. PMID 40555751