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The application of artificial intelligence diagnosis system in the pathological greades differentiation in lung adenocarcinoma

Deep learning in the pathological grades differentiation and prognostic evaluation of lung adenocarcinoma

Status
Active, not recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000035539
Enrollment
Unknown
Registered
2020-08-13
Start date
2020-11-01
Completion date
Unknown
Last updated
2020-08-17

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

Conditions

lung adenocarcinoma

Interventions

Gold Standard:the diagnosis of pathological grades for each patients
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intermediate,&#32
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AI&#32
model

Sponsors

Shanghai Pulmonary Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
20 Years to 75 Years

Inclusion criteria

Inclusion criteria: 1. pathologically diagnosed lung adenocarcinoma; 2. have corresponding slices saved 3. patients without a history of malignancy in 5 years; 4. obtained writtent informed consent.

Exclusion criteria

Exclusion criteria: 1. Pathological diagnosis is not lung adenocarcinoma; 2. Have a history of malignant tumors; 3. No pathological slices saved.

Design outcomes

Primary

MeasureTime frame
Accuracy;Sensitivity;Specificity;AUC;—

Countries

China

Contacts

Public ContactHou Likun

Shanghai Pulmonary Hospital

zhaosurgery@163.com+86 15900892079

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026