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NCT05565677: LungCaMa3D

Prognostic Value of Lung Cancer MicroAnatomy in 3D

Status unknown Last updated 4 October 2022
What this trial tests

trial testing micro-computed tomography scanning of lung tissue specimens in Lung Cancer in 50 participants. Status unknown.

Timeline
1 October 2022
Primary endpoint
1 October 2024
1 October 2025

Quick facts

Lead sponsorAristotle University Of Thessaloniki
StatusStatus unknown
Study typeOBSERVATIONAL
Enrollment50
Start date1 October 2022
Primary completion1 October 2024
Estimated completion1 October 2025

Drugs / interventions tested

Conditions studied

Sponsor

Aristotle University Of Thessaloniki

Who can join

18 and older, any sex, with Lung Cancer or Adenocarcinoma of Lung. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Micro-computed tomography (micro-CT) is a novel biomedical non-destructive, slide-free digital imaging modality, which enables the rapid acquisition of accurate high-resolution, volumetric images of intact surgical tissue specimens. This imaging modality provides microscopic level of detail of intact tissues in three-dimensions without requiring any specimen preparation. Its non-destructive nature and the ongoing enhancement of imaging resolution and contrast renders micro-CT imaging particularly well suited for microanatomic studies in basic research across a wide range of interventional medical disciplines, including oncology. Our proposal concerns a multidisciplinary basic research effort which aims to facilitate the effective identification of different -and maybe challenging to differentiate- lung cancer patterns based on 3D X-ray histology. As an alternative for the use of hematoxylin \& eosin (H\&E) slides, optimized micro-CT scanning of soft tissues emerges as a promising tool to enable non-invasive 3D X-ray histology of formalin-fixed and paraffin-embedded (FFPE) lung cancer specimens. The objective of our proposal is to offer novel insights into the complex architecture of each lung cancer subtype after imaging FFPE surgical specimens, resected from lung cancer surgeries. The investigators aim to generate 3D datasets of FFPE lung cancer tissues which will be combined with the corresponding conventional 2D histology slides. Our study will be also adequately empowered to identify particular differences in morphometric measurements according to each particular lung cancer growth pattern. Finally, this proposal aims to delineate the different 3D microanatomy and morphology of some patterns that are challenging to interpret and differentiate through traditional 2D histological evaluation, such as papillary and lepidic adenocarcinoma growth patterns. Classification of the histological subtypes based on 2D histology sections can be ambiguous, as shown by suboptimal inter-observer consensus when determining predominant histological subtypes in FFPE lung adenocarcinoma tissue specimens. Hence, micro-CT-based 3D imaging of the lung specimens could aid classification of histological subtypes by providing more comprehensive sampling of the entire tissue block and yielding detail relevant for subtype classification that might not be visible in 2D sections alone.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.

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Other recruiting trials for Lung Cancer

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Data sources for this page

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