A Pan-cancer Screening and Diagnosis Model Based on Abdominal CT Was Established

NCT06614179 | Recruiting

• 1 other identifier: IIT-2024-279
• Study Type: observational
• Target: 100,000
• Locations: 1 country
• Sites: 2

Brief Summary

Abdominal noncontrast scan and contrast-enhanced CT were used to establish a screening and diagnostic model for abdominal tumors

Conditions

Abdominal Neoplasm

Outcome Measures

Primary Outcomes (1)

• Whether or not there is a diagnosis of abdominal tumor questionnaire

  Whether or not there is a diagnosis of abdominal tumor

  first visited at baseline

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with abdominal tumors or Non-tumor population

You may qualify if:

• 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.

You may not qualify if:

• Cases in which contrast-enhanced or noncontrast CT images show unclear lesions, with significant noise and artifacts;

Sponsors & Collaborators

• Xiangdong Cheng: lead

Study Sites (2)

Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)

Hangzhou, Zhejiang, 310000, China

RECRUITING

Zhejiang Cancer Hospital；Cancer hospital of the university of chinese academy of sciences

Hangzhou, Zhejiang, 310022, China

RECRUITING

Related Publications (1)

• Hu 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 DERIVED

MeSH Terms

Conditions

Abdominal Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms

Study Officials

• Xiangdong Cheng

  Zhejiang Cancer Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Xiangdong Cheng

CONTACT

0086-571-881280; Chengxd516@126.com

Can Hu

CONTACT

13968032995; chengxd@zjcc.org.cn

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: September 1, 2024
• First Posted: September 26, 2024
• Study Start: July 1, 2024
• Primary Completion (Estimated): December 31, 2029
• Study Completion (Estimated): December 31, 2029
• Last Updated: September 26, 2024

Record last verified: 2024-09

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL

Locations

CN (2)