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.
Studieoversigt
Status
Status
Betingelser
Betingelser
Undersøgelsestype
Undersøgelsestype
Tilmelding (Anslået)
Tilmelding
Deltagelseskriterier
Berettigelseskriterier
Berettigelseskriterier
Aldre berettiget til at studere
Tager imod sunde frivillige
Køn, der er berettiget til at studere
Prøveudtagningsmetode
Studiebefolkning
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.
Beskrivelse
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.
Studieplan
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
- Observationsmodeller: COHORT
- Tidsperspektiver: CROSS_SECTIONAL
Antal grupper/kohorter
Kohorter og interventioner
Gruppe / kohorteGruppe / kohorte |
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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. |
Hvad måler undersøgelsen?
Primære resultatmål
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
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Area Under the Receiver Operating Characteristic Curve (AUC) of the CT Radiomics Model
Tidsramme: 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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Samarbejdspartnere og efterforskere
Sponsor
Sponsor
Datoer for undersøgelser
Studer store datoer
Studiestart (Anslået)
Studiestart
Primær færdiggørelse (Anslået)
Primær færdiggørelse
Studieafslutning (Anslået)
Studieafslutning
Datoer for studieregistrering
Først indsendt
Først indsendt
Først indsendt, der opfyldte QC-kriterier
Først indsendt, der opfyldte QC-kriterier
Først opslået (Faktiske)
Først opslået
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
Sidste opdatering sendt
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidst verificeret
Sidst verificeret
Mere information
Begreber relateret til denne undersøgelse
Nøgleord
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
Andre undersøgelses-id-numre
- CT Radiomics for Lung Nodules
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