Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Evidence for the Collective Nature of Radial Flow in Pb+Pb Collisions with the ATLAS Detector.

Physical review letters·2026

Same author

Evidence for the Dimuon Decay of the Higgs Boson in pp Collisions with the ATLAS Detector.

Physical review letters·2025

Same author

Evidence for Longitudinally Polarized W Bosons in the Electroweak Production of Same-Sign W Boson Pairs in Association with Two Jets in pp Collisions at sqrt[s]=13 TeV with the ATLAS Detector.

Physical review letters·2025

Same author

Observation of tt[over ¯] Production in Pb+Pb Collisions at sqrt[s_{NN}]=5.02 TeV with the ATLAS Detector.

Physical review letters·2025

Same author

Search for Dark Matter Produced in Association with a Dark Higgs Boson in the bb[over ¯] Final State Using pp Collisions at sqrt[s]=13 TeV with the ATLAS Detector.

Physical review letters·2025

Same author

Search for Magnetic Monopole Pair Production in Ultraperipheral Pb+Pb Collisions at sqrt[s_{NN}]=5.36 TeV with the ATLAS Detector at the LHC.

Physical review letters·2025

Same journal

Broad-Spectrum Antibiotics are Associated with worse Survival After Radical Treatment for Glioblastoma Multiforme: A Multicentre Study.

Clinical oncology (Royal College of Radiologists (Great Britain))·2026

Same journal

Exploring the Need for Plan Adaptation in Robustly Optimised Head and Neck Intensity Modulated Proton Therapy: A Dosimetric Perspective From Initial Institutional Experience.

Clinical oncology (Royal College of Radiologists (Great Britain))·2026

Same journal

Lifting the Lid on Best Practice in a Case of Oligometastatic Breast Cancer.

Clinical oncology (Royal College of Radiologists (Great Britain))·2026

Same journal

Standardisation of Imaging Surveillance for Patients With Metastatic Melanoma Stopping Immune Checkpoint Inhibitors.

Clinical oncology (Royal College of Radiologists (Great Britain))·2026

Same journal

Valuing Public Sector Data Assets: Methodological Perspectives From the Add-Aspirin Trial's Use of Healthcare Systems Data for Clinical Follow-Up.

Clinical oncology (Royal College of Radiologists (Great Britain))·2026

Same journal

Autologous Cell Therapy in Oncology: Current Landscape and Future Directions.

Clinical oncology (Royal College of Radiologists (Great Britain))·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 2, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

A New Approach to Quantify and Grade Radiation Dermatitis Using Deep-Learning Segmentation in Skin Photographs.

Y I Park¹, S H Choi², C-S Hong³

¹Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, South Korea; Medical Physics and Biomedical Engineering Lab (MPBEL), Yonsei University College of Medicine, Seoul, South Korea.

Clinical Oncology (Royal College of Radiologists (Great Britain))

|August 2, 2022

Summary

This summary is machine-generated.

A novel convolutional neural network (CNN) system accurately segments radiation dermatitis from skin photographs and dose data. This tool aids in objective grading and analyzing correlations between radiation dose and dermatitis severity.

Keywords:

Convolutional neural networks dermatitis grading scale radiation dermatitis radiation therapy skin toxicity skin-dose distribution

More Related Videos

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

Related Experiment Videos

Last Updated: Sep 2, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

Area of Science:

Medical Imaging
Radiotherapy Oncology
Artificial Intelligence in Medicine

Background:

Objective evaluation of radiation dermatitis is crucial for clinical practice and research.
Current methods may lack consistency in assessing dermatitis severity and its correlation with radiation dose distribution.

Purpose of the Study:

To develop and evaluate a novel radiation dermatitis segmentation system using convolutional neural networks (CNNs).
To enable consistent and objective evaluation of radiation dermatitis severity and patterns.

Main Methods:

Developed a CNN architecture with dilated convolution and skip connections for segmentation.
Trained the network using skin photographs and skin-dose distribution data from 73 radiotherapy patients.
Input data combinations included RGB, RGB+CIELAB, and RGB+CIELAB+skin-dose distribution (RGBLAB_D).
Evaluated performance using Dice Similarity Coefficient (DSC), sensitivity, specificity, and normalized Matthews Correlation Coefficient (nMCC).

Main Results:

The RGBLAB_D input combination demonstrated optimal performance in segmenting radiation dermatitis.
Achieved average DSC of 0.62 for faint and 0.69 for severe dermatitis.
Reported average sensitivity of 0.61 (faint) and 0.78 (severe), specificity of 0.91 (faint) and 0.96 (severe), and nMCC of 0.77 (faint) and 0.83 (severe).

Conclusions:

CNN-based segmentation of radiation dermatitis from skin photographs is feasible and effective.
The system can objectively describe radiation dermatitis severity and patterns.
This approach can improve the analysis of correlations between dosimetric factors and dermatitis morphology.