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Radiomics for Distinguishing Benign and Malignant Lung Nodules

25. september 2026 opdateret af: 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.

Studieoversigt

Status

Ikke rekrutterer endnu

Betingelser

Undersøgelsestype

Observationel

Tilmelding (Anslået)

255

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

18 år og ældre (Voksen, Ældre voksen)

Tager imod sunde frivillige

N/A

Køn, der er berettiget til at studere

Alle

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

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

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

  • Observationsmodeller: COHORT
  • Tidsperspektiver: CROSS_SECTIONAL

Kohorter og interventioner

Gruppe / kohorte
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

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Area Under the Receiver Operating Characteristic Curve (AUC) of the CT Radiomics Model
Tidsramme: 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

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Anslået)

1. oktober 2026

Primær færdiggørelse (Anslået)

1. oktober 2027

Studieafslutning (Anslået)

1. november 2027

Datoer for studieregistrering

Først indsendt

25. september 2026

Først indsendt, der opfyldte QC-kriterier

25. september 2026

Først opslået (Faktiske)

1. oktober 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

1. oktober 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

25. september 2026

Sidst verificeret

1. september 2026

Mere information

Begreber relateret til denne undersøgelse

Andre undersøgelses-id-numre

  • CT Radiomics for Lung Nodules

Disse oplysninger blev hentet direkte fra webstedet clinicaltrials.gov uden ændringer. Hvis du har nogen anmodninger om at ændre, fjerne eller opdatere dine undersøgelsesoplysninger, bedes du kontakte register@clinicaltrials.gov. Så snart en ændring er implementeret på clinicaltrials.gov, vil denne også blive opdateret automatisk på vores hjemmeside .