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Prediction of EGFR Mutation Status in Non-small-cell Lung Carcinoma with Computed Tomography using Deep Learning Technique

Prediction of EGFR Mutation Status in Non-small-cell Lung Carcinoma with Computed Tomography using Deep Learning Technique

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR1800019891
Enrollment
Unknown
Registered
2018-12-07
Start date
2019-01-01
Completion date
Unknown
Last updated
2018-12-10

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

Conditions

Non-small-cell lung carcinoma

Interventions

Gold Standard:Histopathological diagnosis
Index test:Deep&#32
learning&#32
prediction&#32
model

Sponsors

Shanghai Pulmonary Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
19 Years to 70 Years

Inclusion criteria

Inclusion criteria: 1. Histologically confirmed primary NSCLC; 2. Pathologic examination of tumor specimens been carried out with proven records of EGFR mutation status; 3. Preoperative contrast-enhanced CT data obtained; 4. Layer thickness of preoperative CT scan thinner than 1.25mm; 5. Patients volunteered to participate in this study and signed informed consent.

Exclusion criteria

Exclusion criteria: 1. Patient with incomplete clinical data; 2. Preoperative treatment is received; 3. The duration between CT examination and subsequent surgery exceeded one month; 4. Suspected to have been transferred elsewhere; 5. Patients with other malignancies.

Design outcomes

Primary

MeasureTime frame
EGFR mutation status;Accuracy;—

Secondary

MeasureTime frame
Sensitivity;Specificity;—

Countries

China

Contacts

Public ContactJingyun Shi

Shanghai Pulmonary Hospital

almondjj@163.com+86 17621143663

Outcome results

None listed

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