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Cytology-based AI model for multi-cancer diagnosis and pathological subtyping using expert-independent single-cell annotation

Cytology-based AI model for multi-cancer diagnosis and pathological subtyping using expert-independent single-cell annotation

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600127087
Enrollment
Unknown
Registered
2026-06-24
Start date
2026-07-01
Completion date
Unknown
Last updated
2026-06-29

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

Conditions

Malignant neoplasms, excluding lymphatic, hematopoietic, central nervous system, or related tissues

Interventions

Gold Standard:Single-cell sequencing
Index test:Morphological diagnostic criteria for pathologists

Sponsors

Zhejiang Cancer Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.Clinical or imaging findings suggest the presence of pleural effusion or ascites, and diagnostic puncture or drainage is planned. 2.Sample volume >=50 ml, sufficient for cytological diagnosis and single-cell sequencing analysis. 3.Age >=18 years;

Exclusion criteria

Exclusion criteria: 1.The source of the fluid accumulation is unknown. 2.The sample is severely contaminated or poorly preserved, rendering it unsuitable for effective analysis. 3.Patients with severe infections, bleeding tendencies, or systemic conditions that preclude puncture; 4.Women who are pregnant or breastfeeding; 5.Other circumstances deemed unsuitable for participation in this study;

Design outcomes

Primary

MeasureTime frame
Accuracy of AI model in identifying ETC of pleural effusion and ascites;

Countries

China

Contacts

Public ContactHaimiao Xu

Zhejiang Cancer Hospital

xuhaimiao@126.com+86 571 88128269

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Jul 3, 2026