Recent Advances in Skin Cancers

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 10555

Special Issue Editor


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Guest Editor
Department of Dermatology, Indiana University School of Medicine, Indianapolis, IN, USA
Interests: Mohs surgery; cutaneous squamous cell carcinoma; melanoma; facial reconstruction; wound healing; public health epidemiology

Special Issue Information

Dear Colleagues,

While most cutaneous squamous cell carcinomas (cSCCs) are low-risk tumors and treated definitively with surgery, a subset of cSCC is associated with metastasis and death. In recent years, the introduction of the Brigham and Women’s Hospital tumor staging system and the updated American Joint Committee on Cancer guidelines have been instrumental in predicting the risk of poor outcomes from cSCC. Gene expression profile testing of cSCC can provide additional prognostic information to guide the implementation of surveillance strategies and adjuvant therapies. The use of immunotherapy, specifically the PD1 inhibitor cemiplimab, has been a game changer in the treatment of locally advanced or metastatic cSCC.

In this Special Issue, we will highlight major advances in the staging, prognostication, and treatment of cSCC.

Dr. Syril Keena T. Que
Guest Editor

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Keywords

  • cutaneous squamous cell carcinoma
  • staging
  • surgical treatment
  • immunotherapy
  • cemiplimab
  • adjuvant radiation
  • gene expression profile
  • Brigham and Women’s Hospital tumor staging system
  • American Joint Committee on Cancer staging system

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Published Papers (2 papers)

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Research

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15 pages, 4485 KiB  
Article
AI-Driven Enhancement of Skin Cancer Diagnosis: A Two-Stage Voting Ensemble Approach Using Dermoscopic Data
by Tsu-Man Chiu, Yun-Chang Li, I-Chun Chi and Ming-Hseng Tseng
Cancers 2025, 17(1), 137; https://doi.org/10.3390/cancers17010137 - 3 Jan 2025
Cited by 1 | Viewed by 1441
Abstract
Background: Skin cancer is the most common cancer worldwide, with melanoma being the deadliest type, though it accounts for less than 5% of cases. Traditional skin cancer detection methods are effective but are often costly and time-consuming. Recent advances in artificial intelligence have [...] Read more.
Background: Skin cancer is the most common cancer worldwide, with melanoma being the deadliest type, though it accounts for less than 5% of cases. Traditional skin cancer detection methods are effective but are often costly and time-consuming. Recent advances in artificial intelligence have improved skin cancer diagnosis by helping dermatologists identify suspicious lesions. Methods: The study used datasets from two ethnic groups, sourced from the ISIC platform and CSMU Hospital, to develop an AI diagnostic model. Eight pre-trained models, including convolutional neural networks and vision transformers, were fine-tuned. The three best-performing models were combined into an ensemble model, which underwent multiple random experiments to ensure stability. To improve diagnostic accuracy and reduce false negatives, a two-stage classification strategy was employed: a three-class model for initial classification, followed by a binary model for secondary prediction of benign cases. Results: In the ISIC dataset, the false negative rate for malignant lesions was significantly reduced, and the number of malignant cases misclassified as benign dropped from 124 to 45. In the CSMUH dataset, false negatives for malignant cases were completely eliminated, reducing the number of misclassified malignant cases to zero, resulting in a notable improvement in diagnostic precision and a reduction in the false negative rate. Conclusions: Through the proposed method, the study demonstrated clear success in both datasets. First, a three-class AI model can assist doctors in distinguishing between melanoma patients who require urgent treatment, non-melanoma skin cancer patients who can be treated later, and benign cases that do not require intervention. Subsequently, a two-stage classification strategy effectively reduces false negatives in malignant lesions. These findings highlight the potential of AI technology in skin cancer diagnosis, particularly in resource-limited medical settings, where it could become a valuable clinical tool to improve diagnostic accuracy, reduce skin cancer mortality, and reduce healthcare costs. Full article
(This article belongs to the Special Issue Recent Advances in Skin Cancers)
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Review

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17 pages, 358 KiB  
Review
Cutaneous Squamous Cell Carcinoma: An Updated Review
by Rina Jiang, Mike Fritz and Syril Keena T. Que
Cancers 2024, 16(10), 1800; https://doi.org/10.3390/cancers16101800 - 8 May 2024
Cited by 8 | Viewed by 8459
Abstract
Representing the second most common skin cancer, the incidence and disease burden of cutaneous squamous cell carcinoma (cSCC) continues to increase. Surgical excision of the primary site effectively cures the majority of cSCC cases. However, an aggressive subset of cSCC persists with clinicopathological [...] Read more.
Representing the second most common skin cancer, the incidence and disease burden of cutaneous squamous cell carcinoma (cSCC) continues to increase. Surgical excision of the primary site effectively cures the majority of cSCC cases. However, an aggressive subset of cSCC persists with clinicopathological features that are indicative of higher recurrence, metastasis, and mortality risks. Acceleration of these features is driven by a combination of genetic and environmental factors. The past several years have seen remarkable progress in shaping the treatment landscape for advanced cSCC. Risk stratification and clinical management is a top priority. This review provides an overview of the current perspectives on cSCC with a focus on staging, treatment, and maintenance strategies, along with future research directions. Full article
(This article belongs to the Special Issue Recent Advances in Skin Cancers)
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