Development of a Pan-Cancer Screening Model Based on Blood Biomarkers

NCT07554157 | Active Not Recruiting

• 1 other identifier: IRB-2025-1319[IIT]
• Study Type: observational
• Target: 10,000,000
• Locations: 1 country
• Sites: 1

Brief Summary

This study aims to develop a pan-cancer screening model using routine blood biomarkers (including complete blood count, biochemical tests, coagulation panel, and tumor markers). The study is retrospective, collecting data from approximately 10,000,000 cancer patients diagnosed at multiple centers in China between January 2006 and September 2025. All patients have confirmed pathological diagnosis and complete blood test records. A Mixture of Experts (MoE) machine learning model will be built to predict the presence of various cancers (e.g., gastric, colorectal, liver, lung, ovarian cancer). The goal is to establish a low-cost, non-invasive screening tool suitable for large-scale population screening.

Conditions

Neoplasms

Keywords

pan-cancer; blood biomarkers; machine learning; early detection

Outcome Measures

Primary Outcomes (3)

• Area under the ROC curve (AUC)

  AUC of the MoE model for discriminating cancer from non-cancer controls.

  At study completion, approximately December 2030

• Sensitivity of the model

  True positive rate of the pan-cancer screening model.

  At study completion, approximately December 2030

• Specificity of the model

  True negative rate of the pan-cancer screening model.

  At study completion, approximately December 2030

Study Arms (1)

All Participants

All enrolled individuals (cancer patients and non-cancer controls) with retrospective blood biomarker data.

Other: No intervention

Interventions

No intervention OTHER

This is an observational, retrospective study with no assigned interventions. Data are collected from existing medical records, including routine blood biomarkers (complete blood count, biochemistry, coagulation panel, tumor markers). No experimental drugs, devices, or procedures are administered. Only de-identified historical data are used for model development.

All Participants

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population consists of two groups: (1) patients with pathologically confirmed cancer (including gastric, colorectal, liver, lung, ovarian, and other solid tumors) and (2) non-cancer controls (individuals undergoing routine health checkups without cancer or other major organic diseases). All participants are aged 18-80 years. Data are retrospectively collected from medical records across multiple centers in China.

You may qualify if:

• Pathologically confirmed cancer patients (for case group) OR individuals without cancer (for control group)
• Age between 18 and 80 years
• Complete clinical data and blood test results (complete blood count, biochemistry, coagulation panel, tumor markers) available
• No history of other organic diseases (excluding cancer)

You may not qualify if:

• Presence of organic diseases other than cancer (e.g., severe heart, liver, kidney disease)
• Hematologic disorders or immunodeficiency diseases (e.g., AIDS)
• Incomplete data or missing timeline records

Sponsors & Collaborators

• Zhejiang Cancer Hospital: lead

Study Sites (1)

Zhejiang Cancer Hospital

Hangzhou, Zhejiang, 310022, China

Location

MeSH Terms

Conditions

Neoplasms

Study Officials

• Xiangdong Cheng, MD, PhD

  Zhejiang Cancer Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor and Principal Investigator

Study Record Dates

• First Submitted: April 14, 2026
• First Posted: April 28, 2026
• Study Start: October 22, 2025
• Primary Completion (Estimated): July 31, 2027
• Study Completion (Estimated): December 31, 2030
• Last Updated: April 28, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)