Clinical and Spectral-Domain Optical Coherence Tomography Appearance of Optic Disc Melanocytoma: A New Classification and Differentiation from Pigmented Choroidal Lesions

Apinyawasisuk, Supanut; McCannel, Tara A.; Arnold, Anthony C.

doi:10.1159/000453110

Cited by 8 publications

(3 citation statements)

References 19 publications

(48 reference statements)

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“…Type 1 ODM was a typical dense hyperpigmented lesion with abrupt posterior shadowing. Type 2 lesions are less pigmented and as a result have fewer signal-blocking pigmented cells within the tumor, resulting in less hyporeflective shadow and more visible intralesional structures on SD-OCT. 5 In our case, the SD-OCT scans was of the type 1 variety. Scans through the IR hypoautofluorescence showed multiple hyperreflective spots in the tumour substance and the overlying retina.…”

Section: Discussionmentioning

confidence: 54%

Insights Into Infrared Autofluorescence for Monitoring of Optic Disc Melanocytoma

Ayachit¹

2019

DJO

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A case report describing infrared autofluorescence findings in a case of optic disc melanocytoma (ODM): A 42 year old asymptomatic woman was diagnosed to have ODM in the left eye. Two years later, growth in the lesion was noted clinically. On infrared autofluorescence (IR-AF) imaging the lesion showed hypo-autofluorescence corresponding to the area of the pre-existing lesion surrounded by a more hyperautofluorescent arc temporally corresponding to the area of growth. We hypothesize that melanophagosome activity depleted the melanin content of the tumor which led to hypo IR-AF in a melanocytoma. Optical coherence tomography also contributed to the understanding of the infrared autofluorescence findings.

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Section: Discussionmentioning

confidence: 54%

Insights Into Infrared Autofluorescence for Monitoring of Optic Disc Melanocytoma

Ayachit¹

2019

DJO

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“…Automated textural analysis has also been demonstrated for different ophthalmologic pathologies for image segmentation, quantification, and separation of layers for diagnostic purposes [104,105]. Similar approaches have been used to discriminate different types of skin dysplasia [106], ovarian cancer [59], esophageal malignancies [107] and coronary artery diseases [108].…”

Section: Texture Featuresmentioning

confidence: 99%

Extracting Morphological and Sub-Resolution Features from Optical Coherence Tomography Images, a Review with Applications in Cancer Diagnosis

2023

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Before they become invasive, early cancer cells exhibit specific and characteristic changes that are routinely used by a histopathologist for diagnosis. Currently, these early abnormalities are only detectable ex vivo by histopathology or, non-invasively and in vivo, by optical modalities that have not been clinically implemented due to their complexity and their limited penetration in tissues. Optical coherence tomography (OCT) is a noninvasive medical imaging technology with increasing clinical applications in areas such as ophthalmology, cardiology, gastroenterology, etc. In addition to imaging the tissue micro-structure, OCT can also provide additional information, describing the constituents and state of the cellular components of the tissue. Estimates of the nuclear size, sub-cellular morphological variations, dispersion and index of refraction can be extracted from the OCT images and can serve as diagnostically useful biomarkers. Moreover, the development of fully automated algorithms for tissue segmentation and feature extraction and the application of machine learning, can further enhance the clinical potential of OCT. When fully exploited, OCT has the potential to lead to accurate and sensitive, image-derived, biomarkers for disease diagnosis and treatment monitoring of cancer.

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“…Furthermore, the OCT textural analysis and image classification are used in the processing of OCT images [36][37][38][39]. Similar processing has been to illuminate samples for classifying skin dysplasia [40,41], ovarian tissue lesions [42,43], esophageal cancer [44] and coronary arteries [45]. Some machine learning-based algorithms have been applied into the identification of cancerous tissue.…”

Section: Laser Physics Lettersmentioning

confidence: 99%

Classification of human stomach cancer using morphological feature analysis from optical coherence tomography images

et al. 2019

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Optical coherence tomography is radiation-free, and it is considered a tool of optical biopsy. Classification of normal and cancerous tissues is very important for the guidance of surgeons. Here, we develop the morphological feature analysis-based classification (MFAC) method, combining it with machine learning to identify cancerous tissues. We extract five quantitative morphological features from one OCT image through the structured analysis. Five classifiers are involved to make a classification: the support vector machine, the K-nearest neighbor, the random forest, logic regression, and the conventional threshold method. Sensitivity, specificity, and accuracy are used to evaluate these classifiers and are compared with each other. We launched the experimental research of the imaging of ex vivo patients' stomach cancerous tissue with the OCT system. The results showed the three additional features specially designed for stomach cancer are remarkably better than the traditional image feature. The best feature demonstrated over 95% accuracy under all five classifiers. The designed feature based on the layer structure of the stomach tissue is significantly effective. This MFAC method will be used to image the in vivo tissue in clinical applications in the future.

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Clinical and Spectral-Domain Optical Coherence Tomography Appearance of Optic Disc Melanocytoma: A New Classification and Differentiation from Pigmented Choroidal Lesions

Cited by 8 publications

References 19 publications

Insights Into Infrared Autofluorescence for Monitoring of Optic Disc Melanocytoma

Insights Into Infrared Autofluorescence for Monitoring of Optic Disc Melanocytoma

Extracting Morphological and Sub-Resolution Features from Optical Coherence Tomography Images, a Review with Applications in Cancer Diagnosis

Classification of human stomach cancer using morphological feature analysis from optical coherence tomography images

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