Hyperspectral imaging in automated digital dermoscopy screening for melanoma

Abstract

Objectives: Early melanoma detection decreases morbidity and mortality. Early detection classically involves dermoscopy to identify suspicious lesions for which biopsy is indicated. Biopsy and histological examination then diagnose benign nevi, atypical nevi, or cancerous growths. With current methods, a considerable number of unnecessary biopsies are performed as only 11% of all biopsied, suspicious lesions are actually melanomas. Thus, there is a need for more advanced noninvasive diagnostics to guide the decision of whether or not to biopsy. Artificial intelligence can generate screening algorithms that transform a set of imaging biomarkers into a risk score that can be used to classify a lesion as a melanoma or a nevus by comparing the score to a classification threshold. Melanoma imaging biomarkers have been shown to be spectrally dependent in Red, Green, Blue (RGB) color channels, and hyperspectral imaging may further enhance diagnostic power. The purpose of this study was to use the same melanoma imaging biomarkers previously described, but over a wider range of wavelengths to determine if, in combination with machine learning algorithms, this could result in enhanced melanoma detection.

Methods: We used the melanoma advanced imaging dermatoscope (mAID) to image pigmented lesions assessed by dermatologists as requiring a biopsy. The mAID is a 21-wavelength imaging device in the 350-950 nm range. We then generated imaging biomarkers from these hyperspectral dermoscopy images, and, with the help of artificial intelligence algorithms, generated a melanoma Q-score for each lesion (0 = nevus, 1 = melanoma). The Q-score was then compared to the histopathologic diagnosis.

Results: The overall sensitivity and specificity of hyperspectral dermoscopy in detecting melanoma when evaluated in a set of lesions selected by dermatologists as requiring biopsy was 100% and 36%, respectively.

Conclusion: With widespread application, and if validated in larger clinical trials, this non-invasive methodology could decrease unnecessary biopsies and potentially increase life-saving early detection events. Lasers Surg. Med. 51:214-222, 2019. © 2019 The Authors. Lasers in Surgery and Medicine Published by Wiley Periodicals, Inc.

Keywords: artificial intelligence; dermoscopy; hyperspectral imaging; machine learning; melanoma.

Figure 1

This figure highlights two imaging biomarkers on one sample lesion. The two most diagnostic RGB melanoma imaging biomarkers (from our previous study 4) were evaluated on each hyperspectral gray scale image of a pigmented lesion. They illustrate two classes of hyperspectral imaging biomarkers where the trend is either (A) the maximum is within the spectral range or (B) a constant decrease with a maximum in the ultraviolet (UV) and minimum in the infrared (IR). The data show that hyperspectral melanoma imaging biomarkers can be evaluated at a wavelength where they have values outside the visible (RGB) values (illustrated for imaging biomarker B in magenta).

Figure 2

Hyperspectral imaging camera (A) and spectra of the individual imaging wavelengths normalized so the area under the curve equals unity (B). (C) Shows the inner components of the melanoma advanced imaging dermatoscope.

Figure 3

Hyperspectral melanoma imaging biomarkers were derived from spectral analysis (e.g., blood distribution and oxygenation). RGB image (A) shows a visual representative sampling of the hyperspectral image. The correlating blood volume fraction (B), oxygen saturation (C) and melanin factor (D) maps are produced by fitting the spectrum at each pixel. The absorption effect of melanin is added to attenuate the spectrum as shown (green). The final added absorber in our diffuse reflectance spectral simulation is hemoglobin, which we added a reasonable volume fraction and oxygen saturation. The values shown in red text are spectral oximetry biomarkers which are shown as solved for in one pixel of the image but are available in the whole lateral field of view (i.e., at any pixel). These maps, which demonstrate the type of image data hyperspectral melanoma imaging biomarkers are to be derived from, are created by fitting the spectrum (E) produced by the investigational device at each pixel. The spectrum shown (E) is a single pixel in the image (shown in green in A), evaluated across the 21 colors of the LEDs in the hyperspectral camera.

Figure 4

Receiver operator characteristic (ROC) curve for melanoma detection in hyperspectral images. Thin lines represent the individual machine learning approaches used while the thick line represents the “wisdom of the crowds” diagnostic that averaged the risk scores produced by the individual machine learning approaches.