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Predictive Radiomics Model Using Machine Learning for Pulmonary Nodule Classification: An External Validation Cohort Study

Predictive Radiomics Model Using Machine Learning for Pulmonary Nodule Classification: An External Validation Cohort Study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500107055
Enrollment
Unknown
Registered
2025-08-01
Start date
2025-08-01
Completion date
Unknown
Last updated
2025-08-18

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

Conditions

Lung Nodules

Interventions

Index test:The radiomics-based Machine Learning Model

Sponsors

Huai'an First People's Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 75 Years

Inclusion criteria

Inclusion criteria: 1.CT layer thickness <=1.5 mm; 2.Nodule diameter 5-30 mm; 3.Pathology results are clear (benign/malignant); 4.No history of prior lung surgery or radiotherapy;

Exclusion criteria

Exclusion criteria: 1.CT image artifacts affect nodal segmentation; 2.Multiple nodules which difficult to match pathologically; 3.Incomplete clinical information;

Design outcomes

Primary

MeasureTime frame
AUC for predicting malignant pulmonary nodules;Accuracy;

Secondary

MeasureTime frame
The sensitivity, specificity, precision, and recall for the prediction of malignant pulmonary small nodules.;

Countries

China

Contacts

Public ContactGu Biao

Huai'an First People's Hospital

eargood@163.com+86 517 8087 2611

Outcome results

None listed

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