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Prediction for pathological subtype of pulmonary nodules based on surgical resection images through computer visual identification technique

Prediction for pathological subtype of pulmonary nodules based on surgical resection images through computer visual identification technique

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300075999
Enrollment
Unknown
Registered
2023-09-21
Start date
2020-12-31
Completion date
Unknown
Last updated
2023-09-26

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

Conditions

Non-small cell lung cancer

Interventions

Gold Standard:Pathological diagnosis in FFPE surgical specimen
Index test:Diagnostic model from artificial intelligence

Sponsors

Guangdong Provincial People's Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 85 Years

Inclusion criteria

Inclusion criteria: 1. Male or female18 years and older. 2. No anti-cancer therapy received before surgery. 3. No history of other malignancy. 4. All clinical information could be acquired. 5. The pulmonary nodules were confirmed AIS, MIA or IAC pathologically. 6. The sizes of pulmonary nodules were less than 3cm after CT or 18F-FDG PET/CT scan. 7. The images under standardized lighting condition were jpg format.

Exclusion criteria

Exclusion criteria: 1. Images couldn't be acquired because of tissue hemorrhages. 2. Images with poor quality or low resolution that precluded proper classification. 3. No frozen pathology after surgery.

Design outcomes

Primary

MeasureTime frame
AUC;—

Secondary

MeasureTime frame
Accuracy;Sensitivity;Specificity;—

Countries

China

Contacts

Public ContactZhou Haiyu

Guangdong Provincial People's Hospital

zhouhaiyu@gdph.org.cn+86 137 1034 2002

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 6, 2026