Solitary Pulmonary NoduleNCT07852000
Radiomics for Distinguishing Benign and Malignant Lung Nodules
An observational study of Solitary Pulmonary Nodule and Lung Neoplasms, run by Assiut University.
Trial summary
Differentiating between benign (non-cancerous) and malignant (cancerous) pulmonary nodules is a critical step in determining appropriate patient management and cancer treatment planning. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Solitary Pulmonary Nodule, Lung Neoplasms, Multiple Pulmonary Nodules
- How many people
- 255 participants
- Where
- No sites listed yet
- Study dates
- Oct 2026to Nov 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Adult, Older adult
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.
Who may be able to join
- Patients presenting with pulmonary nodules measuring less than 5 cm in diameter.
- Chest computed tomography (CT) scan acquired within the preceding 1 year.
Who cannot join
- 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.
About this study
Official title: 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.
- Keywords
- Radiomics, Computed Tomography, Pulmonary Nodule
- Medical subject headings
- Solitary Pulmonary Nodule, Lung Neoplasms, Multiple Pulmonary Nodules
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
Area Under the Receiver Operating Characteristic Curve (AUC) of the CT Radiomics Model
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.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Oct 2026
- Study startOct 2026 (estimated)
- Primary completionOct 2027 (estimated)
- Study completionNov 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Solitary Pulmonary NoduleStudies of the same condition, open and under way.
- More trials run by Assiut UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 255 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Assiut University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Abdelrahman mohamed farid abdelrahman ali, Assiut University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07852000
- Sponsor's protocol ID
- CT Radiomics for Lung Nodules
- Status last verified
- Sep 2026
- First submitted
- 25 Sep 2026
- Last updated
- 1 Oct 2026
- Results posted
- No
Study at a glance
From the registry record, last updated by the sponsor on 1 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Radiomics as a Non-Invasive Adjunct to Chest CT in Distinguishing Benign and Malignant Lung Nodules
- ClinicalTrials.gov ID
- NCT07852000
- Acronym / sponsor ID
- CT Radiomics for Lung Nodules
- Keywords
- Radiomics, Computed Tomography, Pulmonary Nodule
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 255 participants (estimated)
- Lead sponsor
- Assiut UniversityUniversity, hospital or other
- Study start
- Oct 2026 (estimated)
- Primary completion
- Oct 2027 (estimated)
- Study completion
- Nov 2027 (estimated)
- First posted
- 1 Oct 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (2 listed)
- Patients presenting with pulmonary nodules measuring less than 5 cm in diameter.
- Chest computed tomography (CT) scan acquired within the preceding 1 year.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Oct 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Solitary Pulmonary Nodule and Lung Neoplasms, run by Assiut University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 1 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Receiver Operating Characteristic Curve (AUC) of the CT Radiomics Model (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Oct 2026.