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Classification of Non-Small Lung Carcinoma Using Ai based algorithm

Classification of Non-small Cell Lung Carcinoma Using Machine Learning Methods Based on CT Radiomic Features

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2024/03/064671
Enrollment
114
Registered
2024-03-22
Start date
Unknown
Completion date
Unknown
Last updated
2024-04-01

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

Conditions

Health Condition 1: J984- Other disorders of lung

Interventions

Control Intervention1: Nil: Nil

Sponsors

Department of Radiodiagnosis and Imaging
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Patients with CT imaging features of Squamous cell carcinoma and Adenocarcinoma.

Exclusion criteria

Exclusion criteria: Patients with histopathologic diagnosis of small cell carcinoma

Design outcomes

Primary

MeasureTime frame
The machine learning methods based on CT radiomic features can be used to classify Non-Small Cell Lung Carcinoma subtypes using a simple, non-invasive, and cost-effective diagnostic approach Timepoint: Scan will be performed after biopsy—

Secondary

MeasureTime frame
Machine learning methods based on CT radiomic features can provide non-invasive diagnosis of classification of Non-Small Cell Lung Carcinoma. Timepoint: scan will be performed after biopsy—

Countries

India

Contacts

Public ContactKaushik Nayak

Kasturba medical College and Hospital

rajagopal.kv@manipal.edu9113933708

Outcome results

None listed

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