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Study for the combination of medical image artificial intelligence technology and intraoperative frozen pathology can accurately predict the stage and subtypes of lung adenocarcinoma

Study fot the combination of medical image artificial intelligence technology and intraoperative frozen pathology can accurately predict the stage and subtypes of lung adenocarcinoma

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000032054
Enrollment
Unknown
Registered
2020-04-19
Start date
2021-01-01
Completion date
Unknown
Last updated
2020-05-11

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

Conditions

lung cancer

Interventions

Gold Standard:Pathological diagnosis of paraffin after operation
Index test:medical&#32
image&#32
artificial&#32
intelligence&#32
technology

Sponsors

Shanghai Pulmonary Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. Received surgical treatment in our hospital; 2. Pathologically confirmed lung adenocarcinoma; 3. Preoperative CT image was complete.

Exclusion criteria

Exclusion criteria: 1. There were no preoperative CT images or the time interval between preoperative images and the operation was more than 1 month; 2. The thickness of CT reconstruction is greater than 1-2mm; 3. CT image artifact is obvious; 4. A history of serious lung disease other than lung cancer; 5. Incomplete clinical and pathological information of the patient.

Design outcomes

Primary

MeasureTime frame
radiomics features based on CT images;Deep learning features based on CT images;Pathological diagnosis of intraoperative freezing;Pathological diagnosis of paraffin after operation;—

Countries

China

Contacts

Public ContactSun Xiwen

Shanghai Pulmonary Hospital

fk_sxw@163.com+86 13816593938

Outcome results

None listed

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