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Improving oral cavity cancer diagnosis and treatment with fluorescence molecular imaging.

ABSTRACT: Early diagnosis and radical surgical excision of oral squamous cell carcinomas are essential for achieving optimal treatment outcomes. To date, diagnostic tools that rely on anatomical anomalies provide limited information and resolution in clinical practice. As a result, oral cancer is often detected in an advanced stage. Also, no reliable real-time intraoperative tools are readily available for the evaluation of surgical resection margins. Fluorescence imaging visualises biological processes that occur in early carcinogenesis and could, therefore, enable detection of small tumours in early stages. Furthermore, due to the high sensitivity and spatial resolution, fluorescence imaging could assist in resection margin assessment during surgery. In this review, we discuss several techniques that employ fluorescence for early diagnosis and surgical guidance in oral squamous cell carcinoma and present future perspectives on the potential of fluorescence imaging in oral cancer in the near future.

SUBMITTER: Vonk J

PROVIDER: S-EPMC7818506 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Improving oral cavity cancer diagnosis and treatment with fluorescence molecular imaging.

Vonk Jasper J de Wit Jaron Gérard JG Voskuil Floris Jan FJ Witjes Max Johannes Hendrikus MJH

Oral diseases 20200313 1

Early diagnosis and radical surgical excision of oral squamous cell carcinomas are essential for achieving optimal treatment outcomes. To date, diagnostic tools that rely on anatomical anomalies provide limited information and resolution in clinical practice. As a result, oral cancer is often detected in an advanced stage. Also, no reliable real-time intraoperative tools are readily available for the evaluation of surgical resection margins. Fluorescence imaging visualises biological processes t ...[more]

PMID: 32072691

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Project description:Early diagnosis is the most important determinant of oral and oropharyngeal squamous cell carcinoma (OPSCC) outcomes, yet most of these cancers are detected late, when outcomes are poor. Typically, nonspecialists such as dentists screen for oral cancer risk, and then they refer high-risk patients to specialists for biopsy-based diagnosis. Because the clinical appearance of oral mucosal lesions is not an adequate indicator of their diagnosis, status, or risk level, this initial triage process is inaccurate, with poor sensitivity and specificity. The objective of this study is to provide an overview of emerging optical imaging modalities and novel artificial intelligence-based approaches, as well as to evaluate their individual and combined utility and implications for improving oral cancer detection and outcomes. The principles of image-based approaches to detecting oral cancer are placed within the context of clinical needs and parameters. A brief overview of artificial intelligence approaches and algorithms is presented, and studies that use these 2 approaches singly and together are cited and evaluated. In recent years, a range of novel imaging modalities has been investigated for their applicability to improving oral cancer outcomes, yet none of them have found widespread adoption or significantly affected clinical practice or outcomes. Artificial intelligence approaches are beginning to have considerable impact in improving diagnostic accuracy in some fields of medicine, but to date, only limited studies apply to oral cancer. These studies demonstrate that artificial intelligence approaches combined with imaging can have considerable impact on oral cancer outcomes, with applications ranging from low-cost screening with smartphone-based probes to algorithm-guided detection of oral lesion heterogeneity and margins using optical coherence tomography. Combined imaging and artificial intelligence approaches can improve oral cancer outcomes through improved detection and diagnosis.

Project description:SignificanceIndia has one of the highest rates of oral cancer incidence in the world, accounting for 30% of reported cancers. In rural areas, a lack of adequate medical infrastructure contributes to unchecked disease progression and dismal mortality rates. Photodynamic therapy (PDT) has emerged as an effective modality with potential for treating early stage disease in resource-limited settings, while photosensitizer fluorescence can be leveraged for treatment guidance.AimOur aim was to assess the capability of a simple smartphone-based device for imaging 5-aminolevulinic acid (ALA)-induced protoporphyrin IX (PpIX) fluorescence for treatment guidance and monitoring as part of an ongoing clinical study evaluating low-cost technology for ALA-based PDT treatment of early oral cancer.ApproachA total of 29 subjects with <2 cm diameter moderately/well-differentiated microinvasive ( < 5 mm depth) oral squamous cell carcinoma lesions (33 lesions total, mean area ∼1.23 cm2) were administered 60 mg / kg ALA in oral solution and imaged before and after delivery of 100 J / cm2 total light dose to the lesion surface. Smartphone-based fluorescence and white light (WL) images were analyzed and compared with ultrasound (US) imaging of the same lesions.ResultsWe present a comparative analysis of pre- and post-treatment fluorescence, WL, and US images of oral lesions. There was no significant difference in the distribution of lesion widths measured by fluorescence and US (mean widths of 14.5 and 15.3 mm, respectively) and linear regression shows good agreement (R2 = 0.91). In general, PpIX fluorescence images obtained prior to therapeutic light delivery are able to resolve lesion margins while dramatic photobleaching (∼42 % ) is visible post-treatment. Segmentation of the photobleached area confirms the boundaries of the irradiated zone.ConclusionsA simple smartphone-based approach for imaging oral lesions is shown to agree in most cases with US, suggesting that this approach may be a useful tool to aid in PDT treatment guidance and monitoring photobleaching as part of a low-cost platform for intraoral PDT.

Dataset Information

Improving oral cavity cancer diagnosis and treatment with fluorescence molecular imaging.

Publications

Improving oral cavity cancer diagnosis and treatment with fluorescence molecular imaging.

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