Research Techniques Made Simple: Emerging Imaging Technologies for Noninvasive Optical Biopsy of Human Skin

Lentsch, Griffin; Baugh, Erica G.; Lee, Bonnie; Aszterbaum, Michelle; Zachary, Christopher B.; Kelly, Kristen M.; Balu, Mihaela

doi:10.1016/j.jid.2022.01.016

Cited by 21 publications

(30 citation statements)

References 33 publications

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“…Dermoscopy visualizes the disease only by the contour and color of the skin surface and it is hard to assess disease in the dermis 17 . In contrast, US and OCT generate vertical images of skin which can observe deep tumors' borders, size, and blood flow 3,18 . RCM and MPM provide subcellular and submicron structural features of the skin, not only observing 3D images of the skin obtained by scanning the skin from epidermis to dermis (z‐stack) but also observing the characteristics of each layer of skin cells 19 .…”

Section: Discussionmentioning

confidence: 99%

“…Although non‐invasive optical imaging detection such as dermoscopy, reflectance confocal microscopy (RCM), multiphoton microscopy (MPM), and high‐frequency ultrasonography (US) were widely used in skin diseases and have reduced the need for invasive skin pathology biopsy, 3 only RCM and MPM are able to assess each layer of the skin and view the morphology of epidermal cells at high resolution 4 . RCM uses a near‐infrared diode laser as a light source and utilizes refractive indices of different cell structures in melanin and keratin to image skin.…”

Section: Introductionmentioning

confidence: 99%

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In vivo detection of healthy skin: Comparison of multiphoton microscopy and reflectance confocal microscopy

Chen

Wang

Han

et al. 2023

Skin Research and Technology

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Background: Noninvasive skin examination evolved rapidly in recent years, with multiphoton microscopy (MPM) and reflectance confocal microscopy (RCM) being used to image in-vivo skin at high resolution. The aim of this study is to compare the imaging clarity between the two techniques and measure the thickness of the epidermis in different body sites. We also measured the degree of skin aging with noninvasive tools.Methods: Fifty-six volunteers were evaluated and measured at three different body sites, including the cheek, volar forearm, and back. We used RCM and MPM to evaluate the clarity of each skin layer, including stratum corneum, stratum granulosum, stratum spinosum, dermo-epidermal junction, and dermis. We measured epidermal thickness (ET) at the three body sites in individuals of different ages and genders. We assessed skin aging by the second harmonic to autofluorescence aging index of dermis (SAAID), and multiple linear regression was used to analyze the factors affecting SAAID.Results: MPM had advantages in observation of stratum granulosum, collagen fiber, and elastic fiber (p < 0.001), but RCM provided better observation in dermo-epidermal junction layer (p < 0.001). The epidermis was thicker in the cheek area than the volar forearm and back in both RCM and MPM detection, and the average ET measured by MPM was lower than RCM. ET varied among the three body sites with significant differences (p < 0.05). ET was significantly lower at almost all sites in individuals above 40y (p < 0.05). SAAID decreased with age, and more rapidly in women. Cheeks have lower SAAID scores than other body sites. Conclusion:MPM and RCM provide noninvasive methods for imaging skin and each method has its own advantages. Epidermal thickness and SAAID correlated with age, gender, and different body sites. MPM could also assess the degree of skin aging, which could guide the clinical treatment of patients with diffferent ages and genders in the above body sites.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In vivo detection of healthy skin: Comparison of multiphoton microscopy and reflectance confocal microscopy

Chen

Wang

Han

et al. 2023

Skin Research and Technology

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“…Further validation is required to test the generalizability of our findings to the in vivo setting. Unfortunately, the existing in vivo MPM devices provide a small field of view images and have slow image acquisition speed, 29 making them impractical for clinical use. There has been some recent progress in overcoming these limitations.…”

Section: Discussionmentioning

confidence: 99%

Quantitative collagen analysis using second harmonic generation images for the detection of basal cell carcinoma with ex vivo multiphoton microscopy

Sendín‐Martin

Posner

Harris

et al. 2022

Experimental Dermatology

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Basal cell carcinoma (BCC) is the most common skin cancer, and its incidence is rising. Millions of benign biopsies are performed annually for BCC diagnosis, increasing morbidity, and healthcare costs. Non‐invasive in vivo technologies such as multiphoton microscopy (MPM) can aid in diagnosing BCC, reducing the need for biopsies. Furthermore, the second harmonic generation (SHG) signal generated from MPM can classify and prognosticate cancers based on extracellular matrix changes, especially collagen type I. We explored the potential of MPM to differentiate collagen changes associated with different BCC subtypes compared to normal skin structures and benign lesions. Quantitative analysis such as frequency band energy analysis in Fourier domain, CurveAlign and CT‐FIRE fibre analysis was performed on SHG images from 52 BCC and 12 benign lesions samples. Our results showed that collagen distribution is more aligned surrounding BCCs nests compared to the skin's normal structures (p < 0.001) and benign lesions (p < 0.001). Also, collagen was orientated more parallelly surrounding indolent BCC subtypes (superficial and nodular) versus those with more aggressive behaviour (infiltrative BCC) (p = 0.021). In conclusion, SHG signal from type I collagen can aid not only in the diagnosis of BCC but could be useful for prognosticating these tumors. Our initial results are limited to a small number of samples, requiring large‐scale studies to validate them. These findings represent the groundwork for future in vivo MPM for diagnosis and prognosis of BCC.

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“…Moreover, the acquisition of clinical images (photographs of the lesions) can now be complemented by dermatoscopy examination, currently an important tool to help dermatologists distinguish melanomas from other pigmented skin lesions [ 2 ]. Consequently, software based on artificial intelligence (AI) is being developed to automate the analysis of images in order to facilitate early diagnosis of melanoma, and to help dermatologists in both hospital environments and in primary care consultations [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Melanoma Clinical Decision Support System: An Artificial Intelligence-Based Tool to Diagnose and Predict Disease Outcome in Early-Stage Melanoma Patients

Díaz-Ramón

Gardeazábal

Izu

et al. 2023

Cancers

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This study set out to assess the performance of an artificial intelligence (AI) algorithm based on clinical data and dermatoscopic imaging for the early diagnosis of melanoma, and its capacity to define the metastatic progression of melanoma through serological and histopathological biomarkers, enabling dermatologists to make more informed decisions about patient management. Integrated analysis of demographic data, images of the skin lesions, and serum and histopathological markers were analyzed in a group of 196 patients with melanoma. The interleukins (ILs) IL-4, IL-6, IL-10, and IL-17A as well as IFNγ (interferon), GM-CSF (granulocyte and macrophage colony-stimulating factor), TGFβ (transforming growth factor), and the protein DCD (dermcidin) were quantified in the serum of melanoma patients at the time of diagnosis, and the expression of the RKIP, PIRIN, BCL2, BCL3, MITF, and ANXA5 proteins was detected by immunohistochemistry (IHC) in melanoma biopsies. An AI algorithm was used to improve the early diagnosis of melanoma and to predict the risk of metastasis and of disease-free survival. Two models were obtained to predict metastasis (including “all patients” or only patients “at early stages of melanoma”), and a series of attributes were seen to predict the progression of metastasis: Breslow thickness, infiltrating BCL-2 expressing lymphocytes, and IL-4 and IL-6 serum levels. Importantly, a decrease in serum GM-CSF seems to be a marker of poor prognosis in patients with early-stage melanomas.

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Research Techniques Made Simple: Emerging Imaging Technologies for Noninvasive Optical Biopsy of Human Skin

Cited by 21 publications

References 33 publications

In vivo detection of healthy skin: Comparison of multiphoton microscopy and reflectance confocal microscopy

In vivo detection of healthy skin: Comparison of multiphoton microscopy and reflectance confocal microscopy

Quantitative collagen analysis using second harmonic generation images for the detection of basal cell carcinoma with ex vivo multiphoton microscopy

Melanoma Clinical Decision Support System: An Artificial Intelligence-Based Tool to Diagnose and Predict Disease Outcome in Early-Stage Melanoma Patients

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