Radiomics for Distinguishing Benign and Malignant Lung Nodules

September 25, 2026 updated by: Abdelrahman mohamed farid abdelrahman ali, Assiut University

Radiomics as a Non-Invasive Adjunct to Chest CT in Distinguishing Benign and Malignant Lung Nodules

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.

Study Overview

Status

Not yet recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

255

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

N/A

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patients presenting to the Department of Diagnostic and Interventional Radiology at Assiut University Hospitals and the South Egypt Cancer Institute who have confirmed pulmonary nodules measuring less than 5 cm on chest computed tomography (CT) scans acquired within the preceding year.

Description

Inclusion Criteria:

  • Patients presenting with pulmonary nodules measuring less than 5 cm in diameter.
  • Chest computed tomography (CT) scan acquired within the preceding 1 year.

Exclusion Criteria:

  • Patients with pulmonary nodules measuring greater than 5 cm in diameter.
  • Chest computed tomography (CT) scan obtained more than 3 years prior.
  • Low-quality or artifact-compromised CT images unsuitable for radiomic feature extraction.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: COHORT
  • Time Perspectives: CROSS_SECTIONAL

Cohorts and Interventions

Group / Cohort
Patients with Pulmonary Nodules

Patients presenting with pulmonary nodules measuring less than 5 cm on chest computed tomography (CT) images obtained within the preceding year. Participants undergo comprehensive clinical history taking, clinical examination, and CT image evaluation. Radiomic features, including nodule intensity, shape, texture analysis, and wavelet decompositions, are extracted from segmented nodule volumes and classified using machine learning algorithms to differentiate benign from malignant pulmonary lesions.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Area Under the Receiver Operating Characteristic Curve (AUC) of the CT Radiomics Model
Time Frame: Baseline

The primary outcome is the discriminatory performance of the machine learning-based radiomics model in differentiating benign from malignant pulmonary nodules, quantified by the Area Under the Receiver Operating Characteristic Curve (AUC). AUC values range from 0.5 (no discrimination / performance equal to chance) to 1.0 (perfect diagnostic discrimination). Classification accuracy will be validated against histopathological confirmation or established clinical and radiological reference standards.
Baseline

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Assiut University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

October 1, 2026

Primary Completion (Estimated)

October 1, 2027

Study Completion (Estimated)

November 1, 2027

Study Registration Dates

First Submitted

September 25, 2026

First Submitted That Met QC Criteria

September 25, 2026

First Posted (Actual)

October 1, 2026

Study Record Updates

Last Update Posted (Actual)

October 1, 2026

Last Update Submitted That Met QC Criteria

September 25, 2026

Last Verified

September 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • CT Radiomics for Lung Nodules

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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