Health TrialsbyHealthPanel

SYsteMatical Trained learnIng aLgorithms for Oral carcInogenesiS Interpretation by Optical Coherence Tomography

ConditionsOral Squamous Cell CarcinomaOral Potentially Malignant DisorderOral LeukoplakiaProliferative Verrucous LeukoplakiaOral Lichen PlanusOral Lichenoid LesionGraft-versus-host-diseaseOral ErythroplakiaActinic KeratosesActinic CheilitisOral CancerOral Disease
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-99
SponsorUniversity of Palermo

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

Adult patients with clinical suspicion of potentially malignant oral disorders (OPMDs) and oral squamous cell carcinoma (OSCC).

Patients able to provide informed consent for participation in the study.

Availability of complete clinical data and medical records.

Disqualifiers

Patients with a previous diagnosis of OSCC/OPMDs and/or who have already undergone treatment.

Patients with contraindications to the OCT examination for nonpermissive oral localization using the probe.

Pregnant or breastfeeding women.

Patients with disabilities, reluctance or difficulties of understanding to follow the procedures of the study and who have not provided a consent.

Trial population

The study population will be enrolled at the Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.) and at the Oral Medicine Clinic of the A.O.U.P. 'Paolo Giaccone' of Palermo, which serves as a primary care clinic. This includes patients visiting the clinic for either their initial consultation or follow-up appointments.

Trial design

Design model

Case-control

Time perspective

Prospective

Treatments tested in this trial

  • OCT (Optical Coherence Tomography)

    Device

    OCT diagnosis in oral carcinogenesis

Treatment groups

No treatment groups listed

Trial outcomes

Primary outcomes

1

Phase I: Standardization of Biopsy and OCT Imaging Techniques

In Phase I, the focus will be on developing and implementing standardized protocols for biopsy acquisition and OCT imaging. This phase aims to optimize tissue preservation, ensure alignment with OCT imaging parameters, and enhance diagnostic yield through the standardization of site and dimension of optical and surgical sampling. Detailed protocols will be established for both OCT imaging and histological processing of biopsy specimens, laying the foundation for reliable correlation between imaging modalities.

Time frame
This outcome will be assessed during the first year of study period.
2

Phase II: Development of Standardized OCT Patterns, Creation of Comprehensive Image Repository, and Training Algorithms

A meticulous analysis of OCT images will be conducted to standardize patterns reflective of various oral lesions. These standardized OCT patterns will not only enhance diagnostic precision but will also serve as the foundation for training algorithms. Concurrently, a robust dataset comprising OCT images and corresponding histological data will be meticulously curated. This comprehensive repository will facilitate the training and validation of machine learning algorithms, aimed at developing sophisticated diagnostic software. By incorporating standardized OCT patterns into algorithm training, clinicians can benefit from automated assistance in interpreting OCT images, thereby improving diagnostic accuracy and efficiency in oral cancer detection. This integrated approach represents a significant advancement in diagnostic methodologies, providing clinicians with robust software tool for early detection and intervention, ultimately enhancing patient outcomes and clinical practice.

Time frame
this outcome will be assessed during the second year of study period.
3

Phase III: Development and Large-Scale Validation of Diagnostic OCT Software

In Phase III, the focus shifts towards the development and validation of diagnostic software empowered by the standardized OCT patterns and the comprehensive image dataset. Leveraging machine learning algorithms trained on this dataset, sophisticated diagnostic software will be meticulously designed to detect early signs of oral cancer with high sensitivity and specificity. This software will enable clinicians to efficiently interpret OCT images, providing automated assistance in diagnosis. Furthermore, extensive validation on a large scale will be conducted to ensure the robustness and reliability of the software across diverse clinical settings. By empowering clinicians with this advanced digital tool, Phase III aims to revolutionize oral cancer diagnosis, ultimately leading to improved patient outcomes and the transformation of clinical practice on a global scale.

Time frame
this outcome will be assessed during the third year of study period.

Secondary outcomes

Other outcomes

Sponsors and contacts

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University of Palermo

Lead sponsor

Source ClinicalTrials.gov
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