Melanin Stacking Differences in Pigmented and Non-Pigmented Melanomas: Quantitative Differentiation between Pigmented and Non-Pigmented Melanomas Based on Light-Scattering Properties

Sep 24, 2026

Uploaded by Liam

Cutaneous melanoma is an aggressive skin cancer with metastatic potential, driven by differing amounts of pigment-producing melanocytes. Early screening and treatment reduce morbidity and therapy cost, commonly using ABCDE lesion evaluation. This study applies vibrational optical coherence tomography (VOCT) to non-invasively distinguish pigmented from non-pigmented melanomas in a pilot setting. VOCT shows comparable features, including characteristic 80, 130, and 250 Hz peaks, with pigmented tumors having higher 80 Hz and lower 250 Hz peaks. Infrared penetration suggests greater melanin packing density in pigmented lesions, while machine learning yields sensitivity and specificity roughly spanning 78% to above 90%.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Abstract

Introduction

Clinical importance of early melanoma detection

Limitations of current classification methods

Need for non-invasive diagnostic screening

Study approach and reported findings

life Article Melanin Stacking Differences in Pigmented and Non-Pigmented Melanomas: Quantitative Differentiation between Pigmented and Non-Pigmented Melanomas Based on Light-Scattering Properties Frederick H. Silver 1,2,*, Tanmay Deshmukh 2, Hari Nadiminti 3 and Isabella Tan 1 Citation: Silver, F.H.; Deshmukh, T.; Nadiminti, H.; Tan, I. Melanin Stacking Differences in Pigmented and Non-Pigmented Melanomas: Quantitative Differentiation between Pigmented and Non-Pigmented Melanomas Based on LightScattering Properties. Life 2023, 13, 1004. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)life13041004 Academic Editors: Paola Nieri and Jacek C. Szepietowski Received: 1 March 2023 Revised: 4 April 2023 Accepted: 11 April 2023 Published: 13 April 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Department of Pathology and Laboratory Medicine, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA 2 OptoVibronex, LLC, Bethlehem, PA 18015, USA 3 Summit Health, Dermatology Department, Berkeley Heights, NJ 07922, USA * Correspondence: [silverfr@rutgers.edu](silverfr@rutgers.edu) Abstract: Cutaneous melanoma is a cancer with metastatic potential characterized by varying amounts of pigment-producing melanocytes, and it is one of the most aggressive and fatal forms of skin malignancy, with several hundreds of thousands of cases each year. Early detection and therapy can lead to decreased morbidity and decreased cost of therapy. In the clinic, this often translates to annual skin screenings, especially for high-risk patients, and generous use of the ABCDE (asymmetry, border irregularity, color, diameter, evolving) criteria. We have used a new technique termed vibrational optical coherence tomography (VOCT) to non-invasively differentiate between pigmented and non-pigmented melanomas in a pilot study. The VOCT results reported in this study indicate that both pigmented and non-pigmented melanomas have similar characteristics, including new 80, 130, and 250 Hz peaks. Pigmented melanomas have larger 80 Hz peaks and smaller 250 Hz peaks than non-pigmented cancers. The 80 and 250 Hz peaks can be used to quantitative characterize differences between different melanomas. In addition, infrared light penetration depths indicated that melanin in pigmented melanomas has higher packing densities than in non-pigmented lesions. Using machine learning techniques, the sensitivity and speciﬁcity of differentiating skin cancers from normal skin are shown to range from about 78% to over 90% in this pilot study. It is proposed that using AI on both lesion histopathology and mechanovibrational peak heights may provide even higher speciﬁcity and sensitivity for differentiating the metastatic potential of different melanocytic lesions. Keywords: skin cancer; basal cell carcinoma; squamous cell carcinoma; melanoma; fibrosis; blood vessels; VOCT; OCT; pigmented melanomas; non-pigmented melanomas; melanin stacking; light scattering 1. Introduction Cutaneous melanoma is a cancer with metastatic potential characterized by varying amounts of pigment-producing melanocytes displaying new friable vessels and stiff ﬁbrotic extracellular matrix [1] . It is one of the most aggressive and fatal forms of skin malignancy, with 351,880 new cases in 2015 [2] . There is great importance in diagnosing melanoma in its early stage since prognosis is directly proportional to the severity of the neoplasm [3] . Early detection and therapy can lead to decreased morbidity and decreased cost of therapy [4] . In the clinic, this often translates into annual sk

FAQs

What technique is used to differentiate pigmented and non-pigmented melanomas in the study?

The study uses vibrational optical coherence tomography (VOCT) to non-invasively differentiate between pigmented and non-pigmented melanomas.

Which VOCT frequency peaks show differences between pigmented and non-pigmented melanomas?

Pigmented melanomas have larger 80 Hz peaks and smaller 250 Hz peaks than non-pigmented melanomas, while both share similar characteristic peaks including 80, 130, and 250 Hz.

How do the reported machine learning results perform in the pilot study?

Machine learning-based differentiation of skin cancers from normal skin shows sensitivity and specificity ranging from about 78% to over 90% in the pilot study.

Sep 24, 2026

Uploaded by Liam