Single probe light reflectance spectroscopy and parameter spectrum feature extraction in experimental skin cancer detection and classification.


Journal

Journal of biophotonics
ISSN: 1864-0648
Titre abrégé: J Biophotonics
Pays: Germany
ID NLM: 101318567

Informations de publication

Date de publication:
08 2023
Historique:
revised: 16 04 2023
received: 02 01 2023
accepted: 16 04 2023
medline: 7 8 2023
pubmed: 20 4 2023
entrez: 20 04 2023
Statut: ppublish

Résumé

Skin cancer, an anomalous development of skin cells in the epidermis, is among the most common types of cancer worldwide. Because of its clinical importance and to improve early diagnosis and patient management, there is an urgent need to develop noninvasive, accurate medical diagnostic tools. To this aim, light reflectance spectroscopy over the visible and near-infrared spectral range (400-1000 nm) based on a single-fiber six-around-one optical probe was applied to extract nine features used for diagnostics. These features include skewness, entropy, energy, kurtosis, scattering amplitude, and others, and are spread over each of four different spectral signatures, namely, light reflectance, absorbance, scattering profile approximation, and absorption/scattering ratio. Our preliminary studies focused on 11 adult patients with diagnoses of malignant melanoma (n = 4), basal cell carcinoma (n = 5), and squamous cell carcinoma (n = 2) in a variety of locations on the body. Measurements were taken first in vivo before surgery, at the site of the lesion and from healthy skin of the same patient, and ex vivo after surgical excision, where the lesion was rinsed in saline solution and measurements of the reflected light from the "inside" facing plane of the tissue were taken in the same manner. Overall, experimental results demonstrate that by examining a variety of wavebands, features, and statistical metrics, we can detect and distinguish cancer from normal tissue and different cancer subtypes. Nevertheless, discrepancies in results between in vivo and ex vivo tissue were observed and explanations for these discrepancies are discussed.

Identifiants

pubmed: 37078262
doi: 10.1002/jbio.202300001
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e202300001

Informations de copyright

© 2023 Wiley-VCH GmbH.

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Auteurs

David Abookasis (D)

Department of Electrical and Electronics Engineering, Ariel University, Ariel, Israel.

David Shemesh (D)

Department of Electrical and Electronics Engineering, Ariel University, Ariel, Israel.

Arik Litwin (A)

Department of Plastic Surgery and Burns, Rabin Medical Center, Petah Tikva, Israel.
The Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.

Hava T Siegelmann (HT)

Department of Computer Science & Program of Neuroscience and Behavior, University of Massachusetts Amherst, Amherst, Massachusetts, USA.
Department of Machine Learning, MBZ University of AI, Abu Dhabi, UAE.

Elena Didkovsky (E)

Department of Plastic Surgery and Burns, Rabin Medical Center, Petah Tikva, Israel.
The Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.

Dean D Ad-El (DD)

Department of Plastic Surgery and Burns, Rabin Medical Center, Petah Tikva, Israel.
The Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.

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