Literature DB >> 25656269

High-definition optical coherence tomography algorithm for the discrimination of actinic keratosis from normal skin and from squamous cell carcinoma.

M A L M Boone1, A Marneffe1, M Suppa1, M Miyamoto1, I Alarcon2, R Hofmann-Wellenhof3, J Malvehy2, G Pellacani4, V Del Marmol1.   

Abstract

BACKGROUND: Preliminary studies described morphological features of actinic keratosis (AK) and squamous cell carcinoma (SCC) imaged by High-Definition Optical Coherence Tomography (HD-OCT) and suggested that this technique may aid in their diagnosis. However, systematic studies evaluating the accuracy of HD-OCT for the diagnosis of AK and SCC are lacking so far.
OBJECTIVE: In this study, we sought to design an algorithm for AK classification that could (i) distinguish SCC from AK and normal skin, (ii) differentiate AK from normal skin and (iii) discriminate AKs with adnexal involvement from those without.
METHODS: A total of 53 histopathologically confirmed lesions (37 AKs and 16 SCC) were imaged by HD-OCT. Fifty-three HD-OCT images of normal skin of healthy volunteers, with matched age, skin type and anatomic site, were taken as reference. By comparing these 106 en face and cross-sectional HD-OCT images, particular features were selected based on their potential to discriminate AK from normal skin and from SCC, and to assess adnexal involvement in AK. This study represents a training set not a testing set. Severe (>300 μm) hyperkeratotic AKs were not included in this study.
RESULTS: Particular features with high Phi coefficient could be identified. The absence of an outlined dermo-epidermal junction (DEJ) on cross-sectional images allowed discriminating SCC from AK and normal skin (Phi coefficient = 0.84). AK could be discriminated from normal skin in both imaging modes by the presence of alternating hyperkeratosis/parakeratosis in cross-sectional mode and/or variability in shape, size and reflectivity of cells (atypical honeycomb pattern) in en face mode. Adnexal involvement of AK could be assessed by the disappearance of the typical cocarde image of adnexal epithelium in en face mode.
CONCLUSION: This study provides select 3-D HD-OCT features having a potential to discriminate SCC from AK and normal skin. Based on these particular features with high Phi coefficient, a diagnostic algorithm is designed which will be used later in validation studies to determine HD-OCT accuracy in AK/SCC classification.
© 2015 European Academy of Dermatology and Venereology.

Entities:  

Mesh:

Year:  2015        PMID: 25656269     DOI: 10.1111/jdv.12954

Source DB:  PubMed          Journal:  J Eur Acad Dermatol Venereol        ISSN: 0926-9959            Impact factor:   6.166


  9 in total

1.  The value of ultrahigh resolution OCT in dermatology - delineating the dermo-epidermal junction, capillaries in the dermal papillae and vellus hairs.

Authors:  Niels Møller Israelsen; Michael Maria; Mette Mogensen; Sophie Bojesen; Mikkel Jensen; Merete Haedersdal; Adrian Podoleanu; Ole Bang
Journal:  Biomed Opt Express       Date:  2018-04-19       Impact factor: 3.732

2.  Classification of basal cell carcinoma in human skin using machine learning and quantitative features captured by polarization sensitive optical coherence tomography.

Authors:  Tahereh Marvdashti; Lian Duan; Sumaira Z Aasi; Jean Y Tang; Audrey K Ellerbee Bowden
Journal:  Biomed Opt Express       Date:  2016-08-29       Impact factor: 3.732

Review 3.  Non-invasive diagnostic techniques in the diagnosis of squamous cell carcinoma.

Authors:  Olga Warszawik-Hendzel; Małgorzata Olszewska; Małgorzata Maj; Adriana Rakowska; Joanna Czuwara; Lidia Rudnicka
Journal:  J Dermatol Case Rep       Date:  2015-12-31

4.  Microscopy with ultraviolet surface excitation (MUSE): A novel approach to real-time inexpensive slide-free dermatopathology.

Authors:  Amir Qorbani; Farzad Fereidouni; Richard Levenson; Sana Y Lahoubi; Zachary T Harmany; Austin Todd; Maxwell A Fung
Journal:  J Cutan Pathol       Date:  2018-05-08       Impact factor: 1.587

5.  Segmentation of cellular patterns in confocal images of melanocytic lesions in vivo via a multiscale encoder-decoder network (MED-Net).

Authors:  Kivanc Kose; Alican Bozkurt; Christi Alessi-Fox; Melissa Gill; Caterina Longo; Giovanni Pellacani; Jennifer G Dy; Dana H Brooks; Milind Rajadhyaksha
Journal:  Med Image Anal       Date:  2020-10-07       Impact factor: 8.545

6.  Optical coherence tomography for diagnosing skin cancer in adults.

Authors:  Lavinia Ferrante di Ruffano; Jacqueline Dinnes; Jonathan J Deeks; Naomi Chuchu; Susan E Bayliss; Clare Davenport; Yemisi Takwoingi; Kathie Godfrey; Colette O'Sullivan; Rubeta N Matin; Hamid Tehrani; Hywel C Williams
Journal:  Cochrane Database Syst Rev       Date:  2018-12-04

7.  Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms.

Authors:  Saba Adabi; Matin Hosseinzadeh; Shahryar Noei; Silvia Conforto; Steven Daveluy; Anne Clayton; Darius Mehregan; Mohammadreza Nasiriavanaki
Journal:  Sci Rep       Date:  2017-12-20       Impact factor: 4.379

8.  Automatic Segmentation of Laser-Induced Injury OCT Images Based on a Deep Neural Network Model.

Authors:  Tianxin Gao; Shuai Liu; Enze Gao; Ancong Wang; Xiaoying Tang; Yingwei Fan
Journal:  Int J Mol Sci       Date:  2022-09-21       Impact factor: 6.208

9.  Line-field confocal optical coherence tomography for actinic keratosis and squamous cell carcinoma: a descriptive study.

Authors:  E Cinotti; L Tognetti; A Cartocci; A Lamberti; S Gherbassi; C Orte Cano; C Lenoir; G Dejonckheere; G Diet; M Fontaine; M Miyamoto; J Perez-Anker; V Solmi; J Malvehy; V Del Marmol; J L Perrot; P Rubegni; M Suppa
Journal:  Clin Exp Dermatol       Date:  2021-09-24       Impact factor: 4.481

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.