Radiomics for Distinguishing Benign and Malignant Lung Nodules

Brief summary (registry, in English)

Differentiating between benign (non-cancerous) and malignant (cancerous) pulmonary nodules is a critical step in determining appropriate patient management and cancer treatment planning. Conventional evaluation using chest computed tomography (CT) relies on visual inspection of features such as size, shape, and borders; however, benign and malignant nodules frequently exhibit overlapping characteristics, which often necessitates invasive biopsy procedures. Radiomics is an emerging analytical technique that extracts high-dimensional quantitative data from standard medical images, including tissue texture, density patterns, and complex spatial features, that cannot be detected by visual inspection alone. The purpose of this observational study is to evaluate the utility of CT-derived radiomics combined with machine learning algorithms to non-invasively differentiate between benign and malignant pulmonary nodules. Researchers will extract quantitative imaging features from chest CT scans of patients presenting with pulmonary nodules measuring less than 5 cm to train and validate predictive machine learning models, with the goal of improving non-invasive diagnostic accuracy and reducing unnecessary biopsy procedures.

What you commit to

• Study type: Observational
• Planned participants: 255
• From Oct 2026 to the primary completion date, Oct 2027
• No sites listed in no country
• Visits and tests: ask the trial team, the registry does not list them

Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.

Who cannot take part

Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.

Who the study is looking for

Age in the registry: 18 years to - · Sex: All

Full criteria on ClinicalTrials.gov