Single-blind Clinical Trial Assessing the Validity of Optical Coherence Tomography (OCT) in Diagnosing Potentially Malignant Oral Lesions and Oral Cancer
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 200
- 试验地点
- 1
- 主要终点
- Phase I: Standardization of Biopsy and OCT Imaging Techniques
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 99 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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.
结局指标
主要结局
Phase I: Standardization of Biopsy and OCT Imaging Techniques
时间窗: This outcome will be assessed during the first year of study period.
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.
Phase III: Development and Large-Scale Validation of Diagnostic OCT Software
时间窗: this outcome will be assessed during the third year of study period.
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.
Phase II: Development of Standardized OCT Patterns, Creation of Comprehensive Image Repository, and Training Algorithms
时间窗: this outcome will be assessed during the second year of study period.
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.
次要结局
未报告次要终点
研究者
Vera Panzarella
Assistant Professor (RTDb)
University of Palermo