Radiomics for Distinguishing Benign and Malignant Lung Nodules
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.
Aperçu de l'étude
Statut
Type d'étude
Inscription (Estimé)
Critères de participation
Critère d'éligibilité
Âges éligibles pour étudier
Accepte les volontaires sains
Sexes éligibles pour l'étude
Méthode d'échantillonnage
Population étudiée
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.
La 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.
Plan d'étude
Comment l'étude est-elle conçue ?
Détails de conception
- Modèles d'observation: COHORT
- Perspectives temporelles: CROSS_SECTIONAL
Cohortes et interventions
Groupe / Cohorte |
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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. |
Que mesure l'étude ?
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
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Area Under the Receiver Operating Characteristic Curve (AUC) of the CT Radiomics Model
Délai: Baseline
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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
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Collaborateurs et enquêteurs
Parrainer
Dates d'enregistrement des études
Dates principales de l'étude
Début de l'étude (Estimé)
Achèvement primaire (Estimé)
Achèvement de l'étude (Estimé)
Dates d'inscription aux études
Première soumission
Première soumission répondant aux critères de contrôle qualité
Première publication (Réel)
Mises à jour des dossiers d'étude
Dernière mise à jour publiée (Réel)
Dernière mise à jour soumise répondant aux critères de contrôle qualité
Dernière vérification
Plus d'information
Termes liés à cette étude
Mots clés
Termes MeSH pertinents supplémentaires
Autres numéros d'identification d'étude
- CT Radiomics for Lung Nodules
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