Clinical Trial Details
— Status: Recruiting
Administrative data
NCT number |
NCT06321003 |
Other study ID # |
OCT01 |
Secondary ID |
|
Status |
Recruiting |
Phase |
|
First received |
|
Last updated |
|
Start date |
March 13, 2024 |
Est. completion date |
April 1, 2027 |
Study information
Verified date |
March 2024 |
Source |
University of Palermo |
Contact |
Vera Panzarella |
Phone |
091 6554612 |
Email |
vera.panzarella[@]unipa.it |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
This clinical trial aims to assess the efficacy of Optical Coherence Tomography (OCT) in the
early diagnosis of oral cancer. It focuses on Oral Potentially Malignant Disorders (OPMDs) as
precursors to Oral Squamous Cell Carcinoma (OSCC). Despite the availability of oral
screening, diagnostic delays persist, underscoring the importance of exploring non-invasive
methodologies. The OCT technology provides cross-sectional analysis of biological tissues,
enabling a detailed evaluation of ultrastructural oral mucosal features.
The trial aims to compare OCT preliminary evaluation with traditional histology, considered
the gold standard in oral lesion diagnosing. It seeks to create a database of pathological
OCT data, facilitating the non invasive identification of carcinogenic processes. The goal is
to develop a diagnostic algorithm based on OCT, enhancing its ability to detect
characteristic patterns such as the keratinized layer, squamous epithelium, basement
membrane, and lamina propria in oral tissues affected by OPMDs and OSCC.
Furthermore, the trial aims to implement Artificial Intelligence (AI) in OCT image analysis.
The use of machine learning algorithms could contribute to a faster and more accurate
assessment of images, aiding in early diagnosis. The trial aims to standardize the comparison
between in vivo OCT images and histological analysis, adopting a site-specific approach in
biopsies to improve correspondence between data collected by both methods.
In summary, the trial not only evaluates OCT as a diagnostic tool but also aims to integrate
AI to develop a standardized approach that enhances the accuracy of oral cancer diagnosis,
providing a significant contribution to clinical practice.
Description:
Background and needs:
Despite advancements in oral screening techniques, diagnostic delays persist, necessitating
the exploration of non-invasive methodologies for early detection of oral cancer. The current
standard diagnostic method, histological analysis, often requires invasive biopsies and can
be time-consuming, leading to delays in treatment initiation. Moreover, traditional screening
methods may not always detect early-stage oral lesions accurately. Therefore, there is a
critical need to enhance diagnostic capabilities through the adoption of innovative
technologies.
In this context, Optical Coherence Tomography (OCT) emerges as a promising technology
warranting investigation. OCT offers several advantages over conventional diagnostic
approaches. Its non-invasive nature allows for real-time and non-invasive imaging of tissue
morphology with high resolution, enabling clinicians to visualize structural changes in oral
tissues. By providing cross-sectional images of tissue layers, OCT has the potential to
identify subtle alterations indicative of early-stage oral lesions, including potentially
malignant disorders (OPMDs) and squamous cell carcinoma (OSCC). Additionally, OCT can
facilitate early detection by enabling repeated examinations over time, thereby monitoring
lesion progression or regression without the need for repeated biopsies.
The exploration of OCT as a diagnostic tool aligns with the urgent need to improve the
efficiency and accuracy of oral cancer diagnosis. By leveraging the capabilities of OCT,
clinicians can potentially expedite the identification of suspicious lesions, leading to
timely intervention and improved patient outcomes. Moreover, the integration of OCT into
routine clinical practice has the potential to reduce the burden associated with invasive
procedures and diagnostic delays, ultimately enhancing the quality of care for individuals at
risk of oral cancer.
However, despite these potential benefits, several challenges remain. Currently, there is a
lack of precise definition of OCT patterns specific to various oral lesions. This hinders the
consistent interpretation of OCT images and limits its diagnostic utility. Additionally, the
accurate alignment of OCT findings with histological analysis is essential for validation and
clinical applicability. Yet, there is still a need for standardized protocols to ensure
proper overlay of OCT images with corresponding histopathological features.
Furthermore, while computerized OCT analysis holds promise for enhancing diagnostic accuracy,
existing methodologies may be prone to biases. These biases must be addressed to develop
robust algorithms capable of reliably detecting early signs of oral cancer, trained on
standardized techniques of comparison between OCT and histology.
Therefore, addressing these challenges through the standardization of OCT imaging protocols,
the establishment of consistent OCT patterns, and the development of unbiased computerized
analysis methods is imperative. Doing so will not only advance the clinical utility of OCT in
oral cancer diagnosis but also improve patient outcomes by enabling earlier detection and
intervention.
Aims and approach:
1. Standardization of technique for OCT scans and biopsy of oral lesions:
- Objective: To standardize the biopsy acquisition technique for both OCT and
histological analysis, ensuring a reliable correlation between imaging modalities.
- Approach: We will develop and implement a standardized biopsy acquisition protocol,
optimizing tissue preservation and alignment with OCT imaging parameters. This may
involve specialized instrumentation and procedural guidelines tailored to maximize
diagnostic yield, focusing on standardization of site and dimension of optical and
surgical sampling. Detailed protocols will be established for OCT imaging, ensuring
consistent acquisition parameters across all sites. Similarly, histological
processing of biopsy specimens will adhere to standardized protocols to maintain
integrity and facilitate accurate correlation with OCT findings. A novel optical
and histological procedure of Target biopsy will be performed and assessed.
2. Standardization of OCT patterns of oral carcinogenesis:
- Objective: To establish standardized patterns for OCT imaging of OPMDs and OSCCs,
enhancing diagnostic accuracy.
- Approach: The evaluation of OCT images will entail meticulous analysis to identify
consistent patterns reflective of various oral lesions. By correlating these
patterns with histological findings, we aim to develop a comprehensive reference
guide for interpreting OCT images with precision and consistency.
3. Creation of Image Dataset for the Development of Diagnostic Software:
- Objective: To collect a comprehensive repository of OCT images, facilitating the
development of digital diagnostic tools.
- Approach: A robust dataset comprising OCT images and corresponding histological
data will be meticulously curated. This dataset will serve as the foundation for
training and validating machine learning algorithms aimed at developing
sophisticated diagnostic software capable of detecting early signs of oral cancer
with high sensitivity and specificity.
By pursuing these objectives, we aim to not only evaluate the efficacy of OCT in early oral
cancer diagnosis but also contribute to the standardization of diagnostic methodologies and
pave the way for the integration of advanced technologies into clinical practice.