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Cancers
Special Issues
Diagnosis of Melanoma and Non-melanoma Skin Cancer
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Diagnosis of Melanoma and Non-melanoma Skin Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: closed (5 November 2023) | Viewed by 3010

Special Issue Editors

Dr. Alexander Witkowski

E-Mail Website
Guest Editor
Department of Dermatology, Oregon Health and Science University, Portland, OR, USA
Interests: artificial intelligence; smartphone; digital health; skin cancer; melanoma; basal cell carcinoma; dermoscopy; reflectance confocal microscopy; RCM; genetic expression profiling; GEP; optical coherence tomography; OCT
Dr. Joanna Łudzik

E-Mail Website
Guest Editor
Department of Dermatology, Oregon Health and Science University, Portland, OR, USA
Interests: melanoma; non-melanoma skin cancer; screening; early detection; teledermoscopy; dermoscopy; reflectance confocal microscopy; GEP; at- home dermoscopy; PLA test

Special Issue Information

Dear Colleagues, 

With the global rise of skin cancer incidence, early detection plays a key role in improving patient care. Precision medical tools such as dermoscopy and reflectance confocal microscopy enable dermatologists to properly triage, identify, diagnose and manage patients. Moreover, they help medical professionals to push the threshold of early diagnosis, which not only reduces costs and patient scarring, but may also improve patient outcomes. With the increased use of virtual visits post-pandemic via smartphone cameras and attachments, health-conscious individuals and patients have become increasingly connected with their doctors. We have entered a new realm in which artificial intelligence is applied to enable improved diagnosis by providers and home triage by patients. This special issue will explore all facets of in-office and at-home skin cancer detection and upcoming technologies that may revolutionize dermatology practice for decades to come.

Dr. Alexander Witkowski
Dr. Joanna Łudzik
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • melanoma
  • non-melanoma skin cancer
  • screening
  • early detection
  • teledermoscopy
  • dermoscopy
  • reflectance confocal microscopy
  • GEP
  • artificial intelligence
  • optical coherence tomography

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Published Papers (2 papers)

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Research

19 pages, 3915 KiB  
Article
Detection of Malignant Skin Lesions Based on Decision Fusion of Ensembles of Neural Networks
by Loretta Ichim, Razvan-Ionut Mitrica, Madalina-Oana Serghei and Dan Popescu
Cancers 2023, 15(20), 4946; https://doi.org/10.3390/cancers15204946 - 11 Oct 2023
Cited by 1 | Viewed by 1186
Abstract
Today, skin cancer, and especially melanoma, is an increasing and dangerous health disease. The high mortality rate of some types of skin cancers needs to be detected in the early stages and treated urgently. The use of neural network ensembles for the detection [...] Read more.
Today, skin cancer, and especially melanoma, is an increasing and dangerous health disease. The high mortality rate of some types of skin cancers needs to be detected in the early stages and treated urgently. The use of neural network ensembles for the detection of objects of interest in images has gained more and more interest due to the increased performance of the results. In this sense, this paper proposes two ensembles of neural networks, based on the fusion of the decisions of the component neural networks for the detection of four skin lesions (basal cancer cell, melanoma, benign keratosis, and melanocytic nevi). The first system is based on separate learning of three neural networks (MobileNet V2, DenseNet 169, and EfficientNet B2), with multiple weights for the four classes of lesions and weighted overall prediction. The second system is made up of six binary models (one for each pair of classes) for each network; the fusion and prediction are conducted by weighted summation per class and per model. In total, 18 such binary models will be considered. The 91.04% global accuracy of this set of binary models is superior to the first system (89.62%). Separately, only for the binary classifications within the system was the individual accuracy better. The individual F1 score for each class and the global system varied from 81.36% to 94.17%. Finally, a critical comparison is made with similar works from the literature. Full article
(This article belongs to the Special Issue Diagnosis of Melanoma and Non-melanoma Skin Cancer)
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10 pages, 1117 KiB  
Article
Introduction of a Polyethylene Glycol Linker Improves Uptake of 67Cu-NOTA-Conjugated Lactam-Cyclized Alpha-Melanocyte-Stimulating Hormone Peptide in Melanoma
by Zheng Qiao, Jingli Xu, Darrell R. Fisher, Rene Gonzalez and Yubin Miao
Cancers 2023, 15(10), 2755; https://doi.org/10.3390/cancers15102755 - 14 May 2023
Cited by 1 | Viewed by 1439
Abstract
The aim of this study was to evaluate the effect of linker on tumor targeting and biodistribution of 67Cu-NOTA-PEG2Nle-CycMSHhex {67Cu-1,4,7-triazacyclononane-1,4,7-triyl-triacetic acid-polyethylene glycol-Nle-c[Asp-His-DPhe-Arg-Trp-Lys]-CONH2} and 67Cu-NOTA-GGNle-CycMSHhex {67Cu-NOTA-GlyGlyNle-CycMSHhex} on melanoma-bearing mice. NOTA-PEG [...] Read more.
The aim of this study was to evaluate the effect of linker on tumor targeting and biodistribution of 67Cu-NOTA-PEG2Nle-CycMSHhex {67Cu-1,4,7-triazacyclononane-1,4,7-triyl-triacetic acid-polyethylene glycol-Nle-c[Asp-His-DPhe-Arg-Trp-Lys]-CONH2} and 67Cu-NOTA-GGNle-CycMSHhex {67Cu-NOTA-GlyGlyNle-CycMSHhex} on melanoma-bearing mice. NOTA-PEG2Nle-CycMSHhex and NOTA-GGNle-CycMSHhex were synthesized and purified by HPLC. The biodistribution of 67Cu-NOTA-PEG2Nle-CycMSHhex and 67Cu-NOTA-GGNle-CycMSHhex was determined in B16/F10 melanoma-bearing C57 mice. The melanoma imaging property of 67Cu-NOTA-PEG2Nle-CycMSHhex was further examined in B16/F10 melanoma-bearing C57 mice. 67Cu-NOTA-PEG2Nle-CycMSHhex exhibited higher tumor uptake than 67Cu-NOTA-GGNle-CycMSHhex at 2, 4, and 24 h post-injection. The tumor uptake of 67Cu-NOTA-PEG2Nle-CycMSHhex was 27.97 ± 1.98, 24.10 ± 1.83, and 9.13 ± 1.66% ID/g at 2, 4, and 24 h post-injection, respectively. Normal organ uptake of 67Cu-NOTA-PEG2Nle-CycMSHhex was lower than 2.6% ID/g at 4 h post-injection, except for kidney uptake. The renal uptake of 67Cu-NOTA-PEG2Nle-CycMSHhex was 6.43 ± 1.31, 2.60 ± 0.79, and 0.90 ± 0.18% ID/g at 2, 4, and 24 h post-injection, respectively. 67Cu-NOTA-PEG2Nle-CycMSHhex showed high tumor to normal organ uptake ratios after 2 h post-injection. The B16/F10 melanoma lesions could be clearly visualized by single photon emission computed tomography (SPECT) using 67Cu-NOTA-PEG2Nle-CycMSHhex as an imaging probe at 4 h post-injection. The favorable tumor targeting and biodistribution properties of 67Cu-NOTA-PEG2Nle-CycMSHhex underscored its potential as an MC1R-targeted therapeutic peptide for melanoma treatment. Full article
(This article belongs to the Special Issue Diagnosis of Melanoma and Non-melanoma Skin Cancer)
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