1
|
Shugar AL, Konger RL, Rohan CA, Travers JB, Kim YL. Mapping cutaneous field carcinogenesis of nonmelanoma skin cancer using mesoscopic imaging of pro-inflammation cues. Exp Dermatol 2024; 33:e15076. [PMID: 38610095 PMCID: PMC11034840 DOI: 10.1111/exd.15076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 03/24/2024] [Accepted: 03/30/2024] [Indexed: 04/14/2024]
Abstract
Nonmelanoma skin cancers remain the most widely diagnosed types of cancers globally. Thus, for optimal patient management, it has become imperative that we focus our efforts on the detection and monitoring of cutaneous field carcinogenesis. The concept of field cancerization (or field carcinogenesis), introduced by Slaughter in 1953 in the context of oral cancer, suggests that invasive cancer may emerge from a molecularly and genetically altered field affecting a substantial area of underlying tissue including the skin. A carcinogenic field alteration, present in precancerous tissue over a relatively large area, is not easily detected by routine visualization. Conventional dermoscopy and microscopy imaging are often limited in assessing the entire carcinogenic landscape. Recent efforts have suggested the use of noninvasive mesoscopic (between microscopic and macroscopic) optical imaging methods that can detect chronic inflammatory features to identify pre-cancerous and cancerous angiogenic changes in tissue microenvironments. This concise review covers major types of mesoscopic optical imaging modalities capable of assessing pro-inflammatory cues by quantifying blood haemoglobin parameters and hemodynamics. Importantly, these imaging modalities demonstrate the ability to detect angiogenesis and inflammation associated with actinically damaged skin. Representative experimental preclinical and human clinical studies using these imaging methods provide biological and clinical relevance to cutaneous field carcinogenesis in altered tissue microenvironments in the apparently normal epidermis and dermis. Overall, mesoscopic optical imaging modalities assessing chronic inflammatory hyperemia can enhance the understanding of cutaneous field carcinogenesis, offer a window of intervention and monitoring for actinic keratoses and nonmelanoma skin cancers and maximise currently available treatment options.
Collapse
Affiliation(s)
- Andrea L. Shugar
- Department of Pharmacology & Toxicology, Wright State University Boonshoft School of Medicine, Dayton, Ohio, USA
| | - Raymond L. Konger
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Dermatology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Pathology, Richard L. Roudebush Veterans Administration Hospital, Indianapolis, Indiana, USA
| | - Craig A. Rohan
- Department of Pharmacology & Toxicology, Wright State University Boonshoft School of Medicine, Dayton, Ohio, USA
- Department of Dermatology, Wright State University Boonshoft School of Medicine, Dayton, Ohio, USA
- Department of Medicine, Dayton Veterans Affairs Medical Center, Dayton, Ohio, USA
| | - Jeffrey B. Travers
- Department of Pharmacology & Toxicology, Wright State University Boonshoft School of Medicine, Dayton, Ohio, USA
- Department of Dermatology, Wright State University Boonshoft School of Medicine, Dayton, Ohio, USA
- Department of Medicine, Dayton Veterans Affairs Medical Center, Dayton, Ohio, USA
| | - Young L. Kim
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
- Purdue Institute for Cancer Research, Purdue University, West Lafayette, Indiana, USA
| |
Collapse
|
2
|
Kye S, Lee O. Hyperspectral imaging-based erythema classification in atopic dermatitis. Skin Res Technol 2024; 30:e13631. [PMID: 38390997 PMCID: PMC10885178 DOI: 10.1111/srt.13631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 02/11/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND/PURPOSE Among the characteristics that appear in the epidermis of the skin, erythema is primarily evaluated through qualitative scales, such as visual assessment (VA). However, VA is not ideal because it relies on the experience and skill of dermatologists. In this study, we propose a new evaluation method based on hyperspectral imaging (HSI) to improve the accuracy of erythema diagnosis in clinical settings and investigate the applicability of HSI to skin evaluation. METHODS For this study, 23 subjects diagnosed with atopic dermatitis were recruited. The inside of the right arm is selected as the target area and photographed using a hyperspectral camera (HS). Subsequently, based on the erythema severity visually assessed by a dermatologist, the severity classification performance of the RGB and HS images is compared. RESULTS Erythema severity is classified as high when using (i) all reflectances of the entire HSI band and (ii) a combination of color features (R of RGB, a* of CIEL*a*b*) and five selected bands through band selection. However, as the number of features increases, the amount of calculation increases and becomes inefficient; therefore, (ii), which uses only seven features, is considered to perform classification more efficiently than (i), which uses 150 features. CONCLUSION In conclusion, we demonstrate that HSI can be applied to erythema severity classification, which can further increase the accuracy and reliability of diagnosis when combined with other features observed in erythema. Additionally, the scope of its application can be expanded to various studies related to skin pigmentation.
Collapse
Affiliation(s)
- Seula Kye
- Department of Software ConvergenceGraduate SchoolSoonchunhyang UniversityAsan CityChungcheongnam‐doRepublic of Korea
| | - Onseok Lee
- Department of Software ConvergenceGraduate SchoolSoonchunhyang UniversityAsan CityChungcheongnam‐doRepublic of Korea
- Department of Medical IT EngineeringCollege of Medical SciencesSoonchunhyang UniversityAsan CityChungcheongnam‐doRepublic of Korea
| |
Collapse
|
3
|
McNeil AJ, Parks K, Liu X, Jiang B, Coco J, McCool K, Fabbri D, Duhaime EP, Dawant BM, Tkaczyk ER. Crowdsourcing Skin Demarcations of Chronic Graft-Versus-Host Disease in Patient Photographs: Training Versus Performance Study. JMIR DERMATOLOGY 2023; 6:e48589. [PMID: 38147369 PMCID: PMC10777279 DOI: 10.2196/48589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 10/02/2023] [Accepted: 10/24/2023] [Indexed: 12/27/2023] Open
Abstract
BACKGROUND Chronic graft-versus-host disease (cGVHD) is a significant cause of long-term morbidity and mortality in patients after allogeneic hematopoietic cell transplantation. Skin is the most commonly affected organ, and visual assessment of cGVHD can have low reliability. Crowdsourcing data from nonexpert participants has been used for numerous medical applications, including image labeling and segmentation tasks. OBJECTIVE This study aimed to assess the ability of crowds of nonexpert raters-individuals without any prior training for identifying or marking cGHVD-to demarcate photos of cGVHD-affected skin. We also studied the effect of training and feedback on crowd performance. METHODS Using a Canfield Vectra H1 3D camera, 360 photographs of the skin of 36 patients with cGVHD were taken. Ground truth demarcations were provided in 3D by a trained expert and reviewed by a board-certified dermatologist. In total, 3000 2D images (projections from various angles) were created for crowd demarcation through the DiagnosUs mobile app. Raters were split into high and low feedback groups. The performances of 4 different crowds of nonexperts were analyzed, including 17 raters per image for the low and high feedback groups, 32-35 raters per image for the low feedback group, and the top 5 performers for each image from the low feedback group. RESULTS Across 8 demarcation competitions, 130 raters were recruited to the high feedback group and 161 to the low feedback group. This resulted in a total of 54,887 individual demarcations from the high feedback group and 78,967 from the low feedback group. The nonexpert crowds achieved good overall performance for segmenting cGVHD-affected skin with minimal training, achieving a median surface area error of less than 12% of skin pixels for all crowds in both the high and low feedback groups. The low feedback crowds performed slightly poorer than the high feedback crowd, even when a larger crowd was used. Tracking the 5 most reliable raters from the low feedback group for each image recovered a performance similar to that of the high feedback crowd. Higher variability between raters for a given image was not found to correlate with lower performance of the crowd consensus demarcation and cannot therefore be used as a measure of reliability. No significant learning was observed during the task as more photos and feedback were seen. CONCLUSIONS Crowds of nonexpert raters can demarcate cGVHD images with good overall performance. Tracking the top 5 most reliable raters provided optimal results, obtaining the best performance with the lowest number of expert demarcations required for adequate training. However, the agreement amongst individual nonexperts does not help predict whether the crowd has provided an accurate result. Future work should explore the performance of crowdsourcing in standard clinical photos and further methods to estimate the reliability of consensus demarcations.
Collapse
Affiliation(s)
- Andrew J McNeil
- Dermatology Service and Research Service, Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN, United States
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Kelsey Parks
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Xiaoqi Liu
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Bohan Jiang
- Dermatology Service and Research Service, Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN, United States
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Joseph Coco
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nasvhille, TN, United States
| | | | - Daniel Fabbri
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nasvhille, TN, United States
| | | | - Benoit M Dawant
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Eric R Tkaczyk
- Dermatology Service and Research Service, Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN, United States
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nasvhille, TN, United States
| |
Collapse
|
4
|
Jung G, Kim S, Lee J, Yoo S. Deep learning-based pigment analysis model trained with optical approach and ground truth assistance. JOURNAL OF BIOPHOTONICS 2023; 16:e202300231. [PMID: 37602740 DOI: 10.1002/jbio.202300231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/14/2023] [Accepted: 08/15/2023] [Indexed: 08/22/2023]
Abstract
This study introduces an integrated training method combining the optical approach with ground truth for skin pigment analysis. Deep learning is increasingly applied to skin pigment analysis, primarily melanin and hemoglobin. While regression analysis is a widely used training method to predict ground truth-like outputs, the input image resolution is restricted by computational resources. The optical approach-based regression method can alleviate this problem, but compromises performance. We propose a strategy to overcome the limitation of image resolution while preserving performance by incorporating ground truth within the optical approach-based learning structure. The proposed model decomposes skin images into melanin, hemoglobin, and shading maps, reconstructing them by solving the forward problem with reference to the ground truth for pigments. Evaluation against the VISIA system, a professional diagnostic equipment, yields correlation coefficients of 0.978 for melanin and 0.975 for hemoglobin. Furthermore, our model can produce pigment-modified images for applications like simulating treatment effects.
Collapse
Affiliation(s)
- Geunho Jung
- AI R&D Center, Lululab Inc., Seoul, Republic of Korea
| | - Semin Kim
- AI R&D Center, Lululab Inc., Seoul, Republic of Korea
| | - Jongha Lee
- AI R&D Center, Lululab Inc., Seoul, Republic of Korea
| | - Sangwook Yoo
- AI R&D Center, Lululab Inc., Seoul, Republic of Korea
| |
Collapse
|