Automated melanoma detection with a novel multispectral imaging system: results of a prospective study

Melanin Stacking Differences in Pigmented and Non-Pigmented Melanomas: Quantitative Differentiation between Pigmented and Non-Pigmented Melanomas Based on Light-Scattering Properties

Cutaneous melanoma is a cancer with metastatic potential characterized by varying amounts of pigment-producing melanocytes, and it is one of the most aggressive and fatal forms of skin malignancy, with several hundreds of thousands of cases each year. Early detection and therapy can lead to decreased morbidity and decreased cost of therapy. In the clinic, this often translates to annual skin screenings, especially for high-risk patients, and generous use of the ABCDE (asymmetry, border irregularity, color, diameter, evolving) criteria. We have used a new technique termed vibrational optical coherence tomography (VOCT) to non-invasively differentiate between pigmented and non-pigmented melanomas in a pilot study. The VOCT results reported in this study indicate that both pigmented and non-pigmented melanomas have similar characteristics, including new 80, 130, and 250 Hz peaks. Pigmented melanomas have larger 80 Hz peaks and smaller 250 Hz peaks than non-pigmented cancers. The 80 and 250 Hz peaks can be used to quantitative characterize differences between different melanomas. In addition, infrared light penetration depths indicated that melanin in pigmented melanomas has higher packing densities than in non-pigmented lesions. Using machine learning techniques, the sensitivity and specificity of differentiating skin cancers from normal skin are shown to range from about 78% to over 90% in this pilot study. It is proposed that using AI on both lesion histopathology and mechanovibrational peak heights may provide even higher specificity and sensitivity for differentiating the metastatic potential of different melanocytic lesions.

Diagnostics Using Non-Invasive Technologies in Dermatological Oncology

The growing incidence of skin cancer, with its associated mortality and morbidity, has in recent years led to the developing of new non-invasive technologies, which allow an earlier and more accurate diagnosis. Some of these, such as digital photography, 2D and 3D total-body photography and dermoscopy are now widely used and others, such as reflectance confocal microscopy and optical coherence tomography, are limited to a few academic and referral skin cancer centers because of their cost or the long training period required. Health care professionals involved in the treatment of patients with skin cancer need to know the implications and benefits of new non-invasive technologies for dermatological oncology. In this article we review the characteristics and usability of the main diagnostic imaging methods available today.

Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions

Malignant melanoma poses a clinical diagnostic problem, since a large number of benign lesions are excised to find a single melanoma. This study assessed the accuracy of a novel non-invasive diagnostic technology, hyperspectral imaging, for melanoma detection. Lesions were imaged prior to excision and histopathological analysis. A deep neural network algorithm was trained twice to distinguish between histopathologically verified malignant and benign melanocytic lesions and to classify the separate subgroups. Furthermore, 2 different approaches were used: a majority vote classification and a pixel-wise classification. The study included 325 lesions from 285 patients. Of these, 74 were invasive melanoma, 88 melanoma in situ, 115 dysplastic naevi, and 48 non-dysplastic naevi. The study included a training set of 358,800 pixels and a validation set of 7,313 pixels, which was then tested with a training set of 24,375 pixels. The majority vote classification achieved high overall sensitivity of 95% and a specificity of 92% (95% confidence interval (95% CI) 0.024-0.029) in differentiating malignant from benign lesions. In the pixel-wise classification, the overall sensitivity and specificity were both 82% (95% CI 0.005-0.005). When divided into 4 subgroups, the diagnostic accuracy was lower. Hyperspectral imaging provides high sensitivity and specificity in distinguishing between naevi and melanoma. This novel method still needs further validation.

Artificial intelligence in melanoma: A systematic review

BACKGROUND

Melanoma accounts for the majority of skin cancer deaths. Artificial intelligence has been applied in many types of cancers, and in melanoma in recent years. However, no systematic review summarized the application of artificial intelligence in melanoma.

AIMS

This study aims to systematically review previously published articles to explore the application of artificial intelligence in melanoma.

MATERIALS & METHODS

PubMed database was used to search the eligible publications on August 1, 2020. The query term was "artificial intelligence" and "melanoma."

RESULTS

A total of 51 articles were included in this review. Artificial intelligence technique is mainly used in the evaluation of dermoscopic images, other image segmentation and processing, and artificial intelligence diagnosis system.

DISCUSSION

Artificial intelligence is also applied in metastasis prediction, drug response prediction, and prognosis of melanoma. Besides, patients' perspectives of artificial intelligence and collaboration of human and artificial intelligence in melanoma also attracted attention. The query term might not include all articles, and we could not examine the algorithms that were built without publication.

CONCLUSION

The performance of artificial intelligence in melanoma is satisfactory and the future for potential applications is enormous.

Translating Molecules into Imaging—The Development of New PET Tracers for Patients with Melanoma

Melanoma is a deadly disease that often exhibits relentless progression and can have both early and late metastases. Recent advances in immunotherapy and targeted therapy have dramatically increased patient survival for patients with melanoma. Similar advances in molecular targeted PET imaging can identify molecular pathways that promote disease progression and therefore offer physiological information. Thus, they can be used to assess prognosis, tumor heterogeneity, and identify instances of treatment failure. Numerous agents tested preclinically and clinically demonstrate promising results with high tumor-to-background ratios in both primary and metastatic melanoma tumors. Here, we detail the development and testing of multiple molecular targeted PET-imaging agents, including agents for general oncological imaging and those specifically for PET imaging of melanoma. Of the numerous radiopharmaceuticals evaluated for this purpose, several have made it to clinical trials and showed promising results. Ultimately, these agents may become the standard of care for melanoma imaging if they are able to demonstrate micrometastatic disease and thus provide more accurate information for staging. Furthermore, these agents provide a more accurate way to monitor response to therapy. Patients will be able to receive treatment based on tumor uptake characteristics and may be able to be treated earlier for lesions that with traditional imaging would be subclinical, overall leading to improved outcomes for patients.

Multispectral Imaging Algorithm Predicts Breslow Thickness of Melanoma

Breslow thickness is a major prognostic factor for melanoma. It is based on histopathological evaluation, and thus it is not available to aid clinical decision making at the time of the initial melanoma diagnosis. In this work, we assessed the efficacy of multispectral imaging (MSI) to predict Breslow thickness and developed a classification algorithm to determine optimal safety margins of the melanoma excision. First, we excluded nevi from the analysis with a novel quantitative parameter. Parameter s’ could differentiate nevi from melanomas with a sensitivity of 89.60% and specificity of 88.11%. Following this step, we have categorized melanomas into three different subgroups based on Breslow thickness (≤1 mm, 1–2 mm and >2 mm) with a sensitivity of 78.00% and specificity of 89.00% and a substantial agreement (κ = 0.67; 95% CI, 0.58–0.76). We compared our results to the performance of dermatologists and dermatology residents who assessed dermoscopic and clinical images of these melanomas, and reached a sensitivity of 60.38% and specificity of 80.86% with a moderate agreement (κ = 0.41; 95% CI, 0.39–0.43). Based on our findings, this novel method may help predict the appropriate safety margins for curative melanoma excision.

Quantitative Multispectral Imaging Differentiates Melanoma from Seborrheic Keratosis

Melanoma is a melanocytic tumor that is responsible for the most skin cancer-related deaths. By contrast, seborrheic keratosis (SK) is a very common benign lesion with a clinical picture that may resemble melanoma. We used a multispectral imaging device to distinguish these two entities, with the use of autofluorescence imaging with 405 nm and diffuse reflectance imaging with 525 and 660 narrow-band LED illumination. We analyzed intensity descriptors of the acquired images. These included ratios of intensity values of different channels, standard deviation and minimum/maximum values of intensity of the lesions. The pattern of the lesions was also assessed with the use of particle analysis. We found significantly higher intensity values in SKs compared with melanoma, especially with the use of the autofluorescence channel. Moreover, we found a significantly higher number of particles with high fluorescence in SKs. We created a parameter, the SK index, using these values to differentiate melanoma from SK with a sensitivity of 91.9% and specificity of 57.0%. In conclusion, this imaging technique is potentially applicable to distinguish melanoma from SK based on the analysis of various quantitative parameters. For this application, multispectral imaging could be used as a screening tool by general physicians and non-experts in the everyday practice.

Label-free spectral imaging to study drug distribution and metabolism in single living cells

During drug development, evaluation of drug and its metabolite is an essential process to understand drug activity, stability, toxicity and distribution. Liquid chromatography (LC) coupled with mass spectrometry (MS) has become the standard analytical tool for screening and identifying drug metabolites. Unlike LC/MS approach requiring liquifying the biological samples, we showed that spectral imaging (or spectral microscopy) could provide high-resolution images of doxorubicin (dox) and its metabolite doxorubicinol (dox’ol) in single living cells. Using this new method, we performed measurements without destroying the biological samples. We calculated the rate constant of dox translocating from extracellular moiety into the cell and the metabolism rate of dox to dox’ol in living cells. The translocation rate of dox into a single cell for spectral microscopy and LC/MS approaches was similar (~ 1.5 pM min⁻¹ cell⁻¹). When compared to spectral microscopy, the metabolism rate of dox was underestimated for about every 500 cells using LC/MS. The microscopy approach further showed that dox and dox’ol translocated to the nucleus at different rates of 0.8 and 0.3 pM min⁻¹, respectively. LC/MS is not a practical approach to determine drug translocation from cytosol to nucleus. Using various methods, we confirmed that when combined with a high-resolution imaging, spectral characteristics of a molecule could be used as a powerful approach to analyze drug metabolism. We propose that spectral microscopy is a new method to study drug localization, translocation, transformation and identification with a resolution at a single cell level, while LC/MS is more appropriate for drug screening at an organ or tissue level.

Curve-Based Classification Approach for Hyperspectral Dermatologic Data Processing

This paper shows new contributions in the detection of skin cancer, where we present the use of a customized hyperspectral system that captures images in the spectral range from 450 to 950 nm. By choosing a 7 × 7 sub-image of each channel in the hyperspectral image (HSI) and then taking the mean and standard deviation of these sub-images, we were able to make fits of the resulting curves. These fitted curves had certain characteristics, which then served as a basis of classification. The most distinct fit was for the melanoma pigmented skin lesions (PSLs), which is also the most aggressive malignant cancer. Furthermore, we were able to classify the other PSLs in malignant and benign classes. This gives us a rather complete classification method for PSLs with a novel perspective of the classification procedure by exploiting the variability of each channel in the HSI.

Multispectral imaging detects gastritis consistently in mouse model and in humans

Gastritis constitutes the initial step of the gastric carcinogenesis process. Gastritis diagnosis is based on histological examination of biopsies. Non-invasive real-time methods to detect mucosal inflammation are needed. Tissue optical properties modify reemitted light, i.e. the proportion of light that is emitted by a tissue after stimulation by a light flux. Analysis of light reemitted by gastric tissue could predict the inflammatory state. The aim of our study was to investigate a potential association between reemitted light and gastric tissue inflammation. We used two models and three multispectral analysis methods available on the marketplace. We used a mouse model of Helicobacter pylori infection and included patients undergoing gastric endoscopy. In mice, the reemitted light was measured using a spectrometer and a multispectral camera. We also exposed patient’s gastric mucosa to specific wavelengths and analyzed reemitted light. In both mouse model and humans, modifications of reemitted light were observed around 560 nm, 600 nm and 640 nm, associated with the presence of gastritis lesions. These results pave the way for the development of improved endoscopes in order to detect real-time gastritis without the need of biopsies. This would allow a better prevention of gastric cancer alongside with cost efficient endoscopies.

Callico G. Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support

Skin cancer is one of the most common forms of cancer worldwide and its early detection its key to achieve an effective treatment of the lesion. Commonly, skin cancer diagnosis is based on dermatologist expertise and pathological assessment of biopsies. Although there are diagnosis aid systems based on morphological processing algorithms using conventional imaging, currently, these systems have reached their limit and are not able to outperform dermatologists. In this sense, hyperspectral (HS) imaging (HSI) arises as a new non-invasive technology able to facilitate the detection and classification of pigmented skin lesions (PSLs), employing the spectral properties of the captured sample within and beyond the human eye capabilities. This paper presents a research carried out to develop a dermatological acquisition system based on HSI, employing 125 spectral bands captured between 450 and 950 nm. A database composed of 76 HS PSL images from 61 patients was obtained and labeled and classified into benign and malignant classes. A processing framework is proposed for the automatic identification and classification of the PSL based on a combination of unsupervised and supervised algorithms. Sensitivity and specificity results of 87.5% and 100%, respectively, were obtained in the discrimination of malignant and benign PSLs. This preliminary study demonstrates, as a proof-of-concept, the potential of HSI technology to assist dermatologists in the discrimination of benign and malignant PSLs during clinical routine practice using a real-time and non-invasive hand-held device.

Optical Radiomic Signatures Derived from Optical Coherence Tomography Images Improve Identification of Melanoma

The current gold standard for clinical diagnosis of melanoma is excisional biopsy and histopathologic analysis. Approximately 15-30 benign lesions are biopsied to diagnose each melanoma. In addition, biopsies are invasive and result in pain, anxiety, scarring, and disfigurement of patients, which can add additional burden to the health care system. Among several imaging techniques developed to enhance melanoma diagnosis, optical coherence tomography (OCT), with its high-resolution and intermediate penetration depth, can potentially provide required diagnostic information noninvasively. Here, we present an image analysis algorithm, "optical properties extraction (OPE)," which improves the specificity and sensitivity of OCT by identifying unique optical radiomic signatures pertinent to melanoma detection. We evaluated the performance of the algorithm using several tissue-mimicking phantoms and then tested the OPE algorithm on 69 human subjects. Our data show that benign nevi and melanoma can be differentiated with 97% sensitivity and 98% specificity. These findings suggest that the adoption of OPE algorithm in the clinic can lead to improvements in melanoma diagnosis and patient experience. SIGNIFICANCE: This study describes a noninvasive, safe, simple-to-implement, and accurate method for the detection and differentiation of malignant melanoma versus benign nevi.

Differentiating melanoma and healthy tissues based on elasticity-specific Brillouin microspectroscopy

The main objective of the present study is to evaluate the use of Brillouin microspectroscopy for differentiation of melanoma and normal tissues based on elasticity measurements. Previous studies of malignant melanoma show that the lesion is stiffer than the surrounding healthy tissue. We hypothesize that elasticity-specific Brillouin spectroscopy can be used to distinguish between healthy and cancerous regions of an excised melanoma from a Sinclair miniature swine. Brillouin measurements of non-regressing and regressing melanomas and the surrounding healthy tissues were performed. Based on the Brillouin measurements, the melanomas and healthy tissues can be successfully differentiated. The stiffness of both tumors is found to be significantly greater than the healthy tissues. Notably, we found that the elasticity of regressing melanoma is closer to that of the normal tissue. The results indicate that Brillouin spectroscopy can be utilized as a tool for elasticity-based differentiation between malignant melanoma and surrounding healthy tissue, with potential use for melanoma boundary identification, monitoring tumor progression, or response to treatment.

Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults

BACKGROUND

Early accurate detection of all skin cancer types is essential to guide appropriate management and to improve morbidity and survival. Melanoma and cutaneous squamous cell carcinoma (cSCC) are high-risk skin cancers which have the potential to metastasise and ultimately lead to death, whereas basal cell carcinoma (BCC) is usually localised with potential to infiltrate and damage surrounding tissue. Anxiety around missing early curable cases needs to be balanced against inappropriate referral and unnecessary excision of benign lesions. Computer-assisted diagnosis (CAD) systems use artificial intelligence to analyse lesion data and arrive at a diagnosis of skin cancer. When used in unreferred settings ('primary care'), CAD may assist general practitioners (GPs) or other clinicians to more appropriately triage high-risk lesions to secondary care. Used alongside clinical and dermoscopic suspicion of malignancy, CAD may reduce unnecessary excisions without missing melanoma cases.

OBJECTIVES

To determine the accuracy of CAD systems for diagnosing cutaneous invasive melanoma and atypical intraepidermal melanocytic variants, BCC or cSCC in adults, and to compare its accuracy with that of dermoscopy.

SEARCH METHODS

We undertook a comprehensive search of the following databases from inception up to August 2016: Cochrane Central Register of Controlled Trials (CENTRAL); MEDLINE; Embase; CINAHL; CPCI; Zetoc; Science Citation Index; US National Institutes of Health Ongoing Trials Register; NIHR Clinical Research Network Portfolio Database; and the World Health Organization International Clinical Trials Registry Platform. We studied reference lists and published systematic review articles.

SELECTION CRITERIA

Studies of any design that evaluated CAD alone, or in comparison with dermoscopy, in adults with lesions suspicious for melanoma or BCC or cSCC, and compared with a reference standard of either histological confirmation or clinical follow-up.

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted all data using a standardised data extraction and quality assessment form (based on QUADAS-2). We contacted authors of included studies where information related to the target condition or diagnostic threshold were missing. We estimated summary sensitivities and specificities separately by type of CAD system, using the bivariate hierarchical model. We compared CAD with dermoscopy using (a) all available CAD data (indirect comparisons), and (b) studies providing paired data for both tests (direct comparisons). We tested the contribution of human decision-making to the accuracy of CAD diagnoses in a sensitivity analysis by removing studies that gave CAD results to clinicians to guide diagnostic decision-making.

MAIN RESULTS

We included 42 studies, 24 evaluating digital dermoscopy-based CAD systems (Derm-CAD) in 23 study cohorts with 9602 lesions (1220 melanomas, at least 83 BCCs, 9 cSCCs), providing 32 datasets for Derm-CAD and seven for dermoscopy. Eighteen studies evaluated spectroscopy-based CAD (Spectro-CAD) in 16 study cohorts with 6336 lesions (934 melanomas, 163 BCC, 49 cSCCs), providing 32 datasets for Spectro-CAD and six for dermoscopy. These consisted of 15 studies using multispectral imaging (MSI), two studies using electrical impedance spectroscopy (EIS) and one study using diffuse-reflectance spectroscopy. Studies were incompletely reported and at unclear to high risk of bias across all domains. Included studies inadequately address the review question, due to an abundance of low-quality studies, poor reporting, and recruitment of highly selected groups of participants.Across all CAD systems, we found considerable variation in the hardware and software technologies used, the types of classification algorithm employed, methods used to train the algorithms, and which lesion morphological features were extracted and analysed across all CAD systems, and even between studies evaluating CAD systems. Meta-analysis found CAD systems had high sensitivity for correct identification of cutaneous invasive melanoma and atypical intraepidermal melanocytic variants in highly selected populations, but with low and very variable specificity, particularly for Spectro-CAD systems. Pooled data from 22 studies estimated the sensitivity of Derm-CAD for the detection of melanoma as 90.1% (95% confidence interval (CI) 84.0% to 94.0%) and specificity as 74.3% (95% CI 63.6% to 82.7%). Pooled data from eight studies estimated the sensitivity of multispectral imaging CAD (MSI-CAD) as 92.9% (95% CI 83.7% to 97.1%) and specificity as 43.6% (95% CI 24.8% to 64.5%). When applied to a hypothetical population of 1000 lesions at the mean observed melanoma prevalence of 20%, Derm-CAD would miss 20 melanomas and would lead to 206 false-positive results for melanoma. MSI-CAD would miss 14 melanomas and would lead to 451 false diagnoses for melanoma. Preliminary findings suggest CAD systems are at least as sensitive as assessment of dermoscopic images for the diagnosis of invasive melanoma and atypical intraepidermal melanocytic variants. We are unable to make summary statements about the use of CAD in unreferred populations, or its accuracy in detecting keratinocyte cancers, or its use in any setting as a diagnostic aid, because of the paucity of studies.

AUTHORS' CONCLUSIONS

In highly selected patient populations all CAD types demonstrate high sensitivity, and could prove useful as a back-up for specialist diagnosis to assist in minimising the risk of missing melanomas. However, the evidence base is currently too poor to understand whether CAD system outputs translate to different clinical decision-making in practice. Insufficient data are available on the use of CAD in community settings, or for the detection of keratinocyte cancers. The evidence base for individual systems is too limited to draw conclusions on which might be preferred for practice. Prospective comparative studies are required that evaluate the use of already evaluated CAD systems as diagnostic aids, by comparison to face-to-face dermoscopy, and in participant populations that are representative of those in which the test would be used in practice.

On the spectral signature of melanoma: a non-parametric classification framework for cancer detection in hyperspectral imaging of melanocytic lesions

Early detection and diagnosis is a must in secondary prevention of melanoma and other cancerous lesions of the skin. In this work, we present an online, reservoir-based, non-parametric estimation and classification model that allows for this functionality on pigmented lesions, such that detection thresholding can be tuned to maximize accuracy and/or minimize overall false negative rates. This system has been tested in a dataset consisting of 116 patients and a total of 124 hyperspectral images of nevi, raised nevi and melanomas, detecting up to 100% of the suspicious lesions at the expense of some false positives.

Skin cancer detection using non-invasive techniques

Skin cancer is the most common form of cancer and is globally rising. Historically, the diagnosis of skin cancers has depended on various conventional techniques which are of an invasive manner. A variety of commercial diagnostic tools and auxiliary techniques are available to detect skin cancer. This article explains in detail the principles and approaches involved for non-invasive skin cancer diagnostic methods such as photography, dermoscopy, sonography, confocal microscopy, Raman spectroscopy, fluorescence spectroscopy, terahertz spectroscopy, optical coherence tomography, the multispectral imaging technique, thermography, electrical bio-impedance, tape stripping and computer-aided analysis. The characteristics of an ideal screening test are outlined, and the authors pose several points for clinicians and scientists to consider in the evaluation of current and future studies of skin cancer detection and diagnosis. This comprehensive review critically analyses the literature associated with the field and summarises the recent updates along with their merits and demerits.

Visible and Extended Near-Infrared Multispectral Imaging for Skin Cancer Diagnosis

With the goal of diagnosing skin cancer in an early and noninvasive way, an extended near infrared multispectral imaging system based on an InGaAs sensor with sensitivity from 995 nm to 1613 nm was built to evaluate deeper skin layers thanks to the higher penetration of photons at these wavelengths. The outcomes of this device were combined with those of a previously developed multispectral system that works in the visible and near infrared range (414 nm⁻995 nm). Both provide spectral and spatial information from skin lesions. A classification method to discriminate between melanomas and nevi was developed based on the analysis of first-order statistics descriptors, principal component analysis, and support vector machine tools. The system provided a sensitivity of 78.6% and a specificity of 84.6%, the latter one being improved with respect to that offered by silicon sensors.

Performance of a computer-aided digital dermoscopic image analyzer for melanoma detection in 1,076 pigmented skin lesion biopsies

BACKGROUND

Digital dermoscopic image analysis of pigmented skin lesions (PSLs) has become increasingly popular, despite its unclear clinical utility. Unbiased, high-powered studies investigating the efficacy of commercially available systems are limited.

OBJECTIVE

To investigate the diagnostic performance of the FotoFinder Mole-Analyzer in assessing PSLs for cutaneous melanoma.

METHODS

In this 15-year retrospective study, the histopathologies of 1076 biopsied PSLs among a total of 2500 imaged PSLs were collected. The biopsied PSLs were categorized as benign or malignant (cutaneous melanoma) based on histopathology. Analyzer scores (0-1.00) for these PSLs were obtained and grouped according to histopathology.

RESULTS

At an optimized cutoff score of 0.50, a sensitivity of 56% and a specificity of 74% were achieved. The area under the receiver operating characteristics curve was 0.698, indicating poor accuracy as a diagnostic tool.

LIMITATIONS

This study had a retrospective design and involved only a single institution.

CONCLUSION

Our study reveals a low sensitivity of the scoring function of this digital dermoscopic image analyzer for detecting cutaneous melanomas. Physicians must apply keen clinical judgment when using such devices in the screening of suspicious PSLs.

In Vivo Evaluation of Melanoma Thickness by Multispectral Imaging and An Artificial Neural Network. A Retrospective Study on 250 Cases of Cutaneous Melanoma

Aims and Background

Noninvasive diagnostic methods such as dermoscopy, sonography, palpation or combined approaches have been developed in an attempt to preoperatively estimate melanoma thickness. However, the clinical presentation is often complex and the evaluation subjective. Multispectral image analysis of melanomas allows selection of features related to the content and distribution of absorbers, mainly melanin and hemoglobin, present within the lesion. Hence, it is reasonable to assume that the same features might be useful to predict melanoma thickness.

Methods

A multispectral image system was used to analyze in vivo 1939 pigmented skin lesions. The lesion selection was based on clinical and/or dermoscopic features that supported a suspicion for melanoma. All the lesions were then subjected to surgery for the histopathological diagnosis, and 250 were melanomas. From the multispectral images of the melanomas, we selected 12 features, seven of which were used to train and test an artificial neural network on 155 and 95 melanomas, respectively.

Results

Sensitivity (i.e., melanoma ≥0.75 mm thick correctly classified) and specificity (i.e., melanoma <0.75 mm thick correctly classified) evaluated from the receiving operating characteristic curves ranged from 76 to 90% and from 91 to 74%, respectively.

Conclusions

Our approach provides results similar to those obtained with other methods and has the advantage that it is not related to the expertise of the clinician. In addition, the physical interpretation of the selected features suggests a possible role of spectrophotometry as an objective method to study the natural history of the early phases of the disease.

Using Raman Spectroscopy to Detect and Diagnose Skin Cancer In Vivo

Raman spectroscopy provides a noninvasive bedside tool that captures unique optical signals via molecular vibrations in tissue samples. Raman theory was discovered at the beginning of the twentieth century, but it was not until the past few decades that it has been used to differentiate skin neoplasms. We provide a brief description of Raman spectroscopy for in vivo skin cancer diagnosis, including the physical principles underlying Raman spectroscopy, its advantages, typical spectra of skin pathologies, and its clinical application for aiding skin cancer diagnosis.

Uniqueness in multispectral constant-wave epi-illumination imaging

Multispectral tissue imaging based on optical cameras and continuous-wave tissue illumination is commonly used in medicine and biology. Surprisingly, there is a characteristic absence of a critical look at the quantities that can be uniquely characterized from optically diffuse matter by multispectral imaging. Here, we investigate the fundamental question of uniqueness in epi-illumination measurements from turbid media obtained at multiple wavelengths. By utilizing an analytical model, tissue-mimicking phantoms, and an in vivo imaging experiment we show that independent of the bands employed, spectral measurements cannot uniquely retrieve absorption and scattering coefficients. We also establish that it is, nevertheless, possible to uniquely quantify oxygen saturation and the Mie scattering power-a previously undocumented uniqueness condition.

Practical application of new technologies for melanoma diagnosis: Part I. Noninvasive approaches

Confirming a diagnosis of cutaneous melanoma requires obtaining a skin biopsy specimen. However, obtaining numerous biopsy specimens-which often happens in patients with increased melanoma risk-is associated with significant cost and morbidity. While some melanomas are easily recognized by the naked eye, many can be difficult to distinguish from nevi, and therefore there is a need and opportunity to develop new technologies that can facilitate clinical examination and melanoma diagnosis. In part I of this 2-part continuing medical education article, we will review the practical applications of emerging technologies for noninvasive melanoma diagnosis, including mobile (smartphone) applications, multispectral imaging (ie, MoleMate and MelaFind), and electrical impedance spectroscopy (Nevisense).

Skin parameter map retrieval from a dedicated multispectral imaging system applied to dermatology/cosmetology

In vivo quantitative assessment of skin lesions is an important step in the evaluation of skin condition. An objective measurement device can help as a valuable tool for skin analysis. We propose an explorative new multispectral camera specifically developed for dermatology/cosmetology applications. The multispectral imaging system provides images of skin reflectance at different wavebands covering visible and near-infrared domain. It is coupled with a neural network-based algorithm for the reconstruction of reflectance cube of cutaneous data. This cube contains only skin optical reflectance spectrum in each pixel of the bidimensional spatial information. The reflectance cube is analyzed by an algorithm based on a Kubelka-Munk model combined with evolutionary algorithm. The technique allows quantitative measure of cutaneous tissue and retrieves five skin parameter maps: melanin concentration, epidermis/dermis thickness, haemoglobin concentration, and the oxygenated hemoglobin. The results retrieved on healthy participants by the algorithm are in good accordance with the data from the literature. The usefulness of the developed technique was proved during two experiments: a clinical study based on vitiligo and melasma skin lesions and a skin oxygenation experiment (induced ischemia) with healthy participant where normal tissues are recorded at normal state and when temporary ischemia is induced.

3-D volume reconstruction of skin lesions for melanin and blood volume estimation and lesion severity analysis

Subsurface information about skin lesions, such as the blood volume beneath the lesion, is important for the analysis of lesion severity towards early detection of skin cancer such as malignant melanoma. Depth information can be obtained from diffuse reflectance based multispectral transillumination images of the skin. An inverse volume reconstruction method is presented which uses a genetic algorithm optimization procedure with a novel population initialization routine and nudge operator based on the multispectral images to reconstruct the melanin and blood layer volume components. Forward model evaluation for fitness calculation is performed using a parallel processing voxel-based Monte Carlo simulation of light in skin. Reconstruction results for simulated lesions show excellent volume accuracy. Preliminary validation is also done using a set of 14 clinical lesions, categorized into lesion severity by an expert dermatologist. Using two features, the average blood layer thickness and the ratio of blood volume to total lesion volume, the lesions can be classified into mild and moderate/severe classes with 100% accuracy. The method therefore has excellent potential for detection and analysis of pre-malignant lesions.

SEGMENTATION OF DERMATOSCOPIC IMAGES USED FOR COMPUTER-AIDED DIAGNOSIS OF MELANOMA

In this paper, a methodological approach to the segmentation of tumours skin lesions in dermoscopy images is presented. Melanoma is the most malignant skin tumor, growing in melanocytes, the cells responsible for pigmentation. This type of cancer is nowadays increasing rapidly, its related mortality rate increases by more modest and inversely proportional to the thickness of the tumor. This rate can be decreased by an earlier detection and better prevention. In dermatoscopic images, the segmentation is essential to characterize the information shape of the lesion and also to locate the tumor for analysis. In this domain, we have evaluated several techniques for the segmentation of dermatoscopic images. All these methods do not exactly separate the lesion from the background. In this work a fast approach in border detection of dermoscopy pigmented skin lesions images based on the region growing algorithm is presented. This method is tested on a set of 60 dermoscopy images. The obtained results show that the presented method achieves both fast and accurate border detection.

In vivo diagnosis of melanoma and nonmelanoma skin cancer using oblique incidence diffuse reflectance spectrometry

Early detection and treatment of skin cancer can significantly improve patient outcome. However, present standards for diagnosis require biopsy and histopathologic examinations that are relatively invasive, expensive, and difficult for patients with many early-stage lesions. Here, we show an oblique incidence diffuse reflectance spectroscopic (OIDRS) system that can be used for rapid skin cancer detection in vivo. This system was tested under clinical conditions by obtaining spectra from pigmented and nonpigmented skin lesions, including melanomas, differently staged dysplastic nevi, and common nevi that were validated by standard pathohistologic criteria. For diagnosis of pigmented melanoma, the data obtained achieved 90% sensitivity and specificity for a blinded test set. In a second analysis, we showed that this spectroscopy system can also differentiate nonpigmented basal cell or squamous cell carcinomas from noncancerous skin abnormalities, such as actinic keratoses and seborrheic keratoses, achieving 92% sensitivity and specificity. Taken together, our findings establish how OIDRS can be used to more rapidly and easily diagnose skin cancer in an accurate and automated manner in the clinic.

A possible melanoma discrimination index based on hyperspectral data: a pilot study

BACKGROUND

Early detection and proper excision of the primary lesions of malignant melanoma (MM) are crucial for reducing melanoma-related deaths. To support the early detection of melanoma, automated melanoma screening systems have been extensively studied and developed. In this article, we present a hyperspectral melanoma screening system and propose a possible melanoma discrimination index derived from the characteristics of the pigment molecules in the skin, both of which have been derived from hyperspectral data (HSD).

METHODS

The index expresses the disordered nature of each lesion including variegation in color based on variation in spectral information obtained from each lesion. Performance of the index in discriminating melanomas from other pigmented skin lesions has been studied in five cases of melanoma (41 HSD sets), one case of Spitz nevus (13 HSD sets), 10 cases of seborrheic keratosis (78 HSD sets), three cases of basal cell carcinoma (16 HSD sets), and nine cases of melanocytic nevus (21 HSD sets), obtained from patients and volunteers, all of whom were Japanese.

RESULTS

Performance of the index, which reflects the disordered nature of a lesion, discriminates melanomas with a sensitivity of 90%, a specificity of 84%, and an area under the receiver operating characteristic curve of 0.93, on resubstitution.

CONCLUSION

An objective melanoma discrimination index at a molecular pigmentary level, derived from HSD, has been proposed, and its performance evaluated. This index was highly successful in discriminating MM from non-melanoma, although the statistical population was small.

Ratiometric spectral imaging for fast tumor detection and chemotherapy monitoring in vivo

We report a novel in vivo spectral imaging approach to cancer detection and chemotherapy assessment. We describe and characterize a ratiometric spectral imaging and analysis method and evaluate its performance for tumor detection and delineation by quantitatively monitoring the specific accumulation of targeted gallium corrole (HerGa) into HER2-positive (HER2 +) breast tumors. HerGa temporal accumulation in nude mice bearing HER2 + breast tumors was monitored comparatively by a. this new ratiometric imaging and analysis method; b. established (reflectance and fluorescence) spectral imaging; c. more commonly used fluorescence intensity imaging. We also tested the feasibility of HerGa imaging in vivo using the ratiometric spectral imaging method for tumor detection and delineation. Our results show that the new method not only provides better quantitative information than typical spectral imaging, but also better specificity than standard fluorescence intensity imaging, thus allowing enhanced in vivo outlining of tumors and dynamic, quantitative monitoring of targeted chemotherapy agent accumulation into them.

Towards noncontact skin melanoma selection by multispectral imaging analysis

A clinical trial comprising 334 pigmented and vascular lesions has been performed in three Riga clinics by means of multispectral imaging analysis. The imaging system Nuance 2.4 (CRi) and self-developed software for mapping of the main skin chromophores were used. Specific features were observed and analyzed for malignant skin melanomas: notably higher absorbance (especially as the difference of optical density relative to the healthy skin), uneven chromophore distribution over the lesion area, and the possibility to select the "melanoma areas" in the correlation graphs of chromophores. The obtained results indicate clinical potential of this technology for noncontact selection of melanoma from other pigmented and vascular skin lesions.

Reconstruction of hyperspectral cutaneous data from an artificial neural network-based multispectral imaging system

The development of an integrated MultiSpectral Imaging (MSI) system yielding hyperspectral cubes by means of artificial neural networks is described. The MSI system is based on a CCD camera, a rotating wheel bearing a set of seven interference filters, a light source and a computer. The resulting device has been elaborated for in vivo imaging of skin lesions. It provides multispectral images and is coupled with a software reconstructing hyperspectral cubes from multispectral images. Reconstruction is performed by a neural network-based algorithm using heteroassociative memories. The resulting hyperspectral cube provides skin optical reflectance spectral data combined with bidimensional spatial information. This combined information will hopefully improve diagnosis and follow-up in a range of skin disorders from skin cancer to inflammatory diseases.

In vivo optical properties of melanocytic skin lesions: common nevi, dysplastic nevi and malignant melanoma

We present an in vivo study of the optical properties of common nevi, dysplastic nevi and malignant melanoma skin lesions in human subjects. Reflectance spectra were measured on 1379 skin lesions, in the visible and near-infrared spectral regions, using a spectral imaging system, in a clinical setting. Analysis of the data using a reflectance model revealed differences between the optical properties of melanin present in nevi and melanoma lesions. These differences, which are in agreement with our previous observations on average reflectance spectra, may be potentially useful for the noninvasive characterization of pigmented skin lesions and the early diagnosis of melanoma.

In vivo characterization of melanin in melanocytic lesions: spectroscopic study on 1671 pigmented skin lesions

The purpose of this study is to determine the role of melanin in the various steps of progression of melanocytic neoplasia. To this aim, we perform a retrospective analysis on 1671 multispectral images of in vivo pigmented skin lesions previously recruited in the framework of a study focused on the computer-assisted diagnosis of melanoma. The series included 288 melanomas in different phases of progression, i.e., in situ, horizontal and vertical growth phase invasive melanomas, 424 dysplastic nevi, and other 957 melanocytic lesions. Analysis of the absorbance spectra in the different groups shows that the levels of eumelanin and pheomelanin increase and decrease, respectively, from dysplastic nevi to invasive melanomas. In both cases, the trend of melanin levels is associated to the progression from dysplastic nevi to vertical growth phase melanomas, reflecting a possible hierarchy in the natural history of the early phases of the disease. Our results suggest that diffuse reflectance spectroscopy used to differentiate eumelanin and pheomelanin in in vivo lesions is a promising technique useful to develop better strategies for the characterization of various melanocytic lesions, for instance, by monitoring melanin in a time-lapse study of a lesion that was supposed to be benign.

Detection limits of multi-spectral optical imaging under the skin surface

The present work shows that the optical/biological information contained in a typical spectral image mainly reflects the properties of a small (conic like) volume of tissue situated vertically under each individual pixel. The objects appearing on a spectral image reasonably reproduce the correct geometrical shape and size (like a non-deformed shadow) of underlying inclusions of pathological tissue. The information contained in a spectral image comes from a depth that does not exceed approximately 2-3 mm. The number of photons that visit a given tissue voxel situated at a depth larger than approximately 2 mm represents less than the 1% of the total number of photons reaching the corresponding detection pixel (forming the image). A pathological inclusion (e.g. a pool of blood or vascular tumor) situated at a depth of approximately 0.5 mm with a thickness of 0.5 mm produces an image intensity contrast of approximately 5% (for images taken at wavelengths in the 600-1000 nm range) when compared to the normal skin background. The same inclusion at a depth of 20 microm provides a contrast decreasing from 55 to 20% with respect to an increase in wavelength. The dermis/hypodermis interface behaves as a partial barrier for the photons, limiting their access to deeper skin regions. The image contrast depends on the depth and the type of chromophore contained in the inclusion. An increase in the concentration of a given molecule may produce different contrast, independently of the depth, depending on the characteristics of the skin layer where this change occurs.

The use of a spectrophotometric intracutaneous analysis device in the real-time diagnosis of melanoma in the setting of a melanoma screening clinic

BACKGROUND

Skin imaging devices to aid melanoma diagnosis have been developed in recent years but few have been assessed clinically.

OBJECTIVES

To investigate if a spectrophotometric skin imaging device, the SIAscope, could increase a dermatologist's ability to distinguish melanoma from nonmelanoma in a melanoma screening clinic.

METHODS

Eight hundred and eighty-one pigmented lesions from 860 patients were prospectively assessed clinically and with the aid of the spectrophotometric device by a dermatologist. Assessment before and after spectrophotometric imaging was made and compared with histology, where available, or with the clinical diagnosis of a dermatologist with 20 years of experience.

RESULTS

One hundred and seventy-nine biopsies were performed, with 31 melanomas diagnosed. Sensitivity and specificity for melanoma diagnosis before and after spectrophotometry were 94% and 91% vs. 87% and 91%, respectively, with no significant difference in the area under the receiver operating characteristic curves (0.932 and 0.929).

CONCLUSIONS

Our study provides no evidence for the use of SIAscope by dermatologists to help distinguish melanoma from benign lesions.

Skin and cutaneous melanocytic lesion simulation in biomedical optics with multilayered phantoms

The complex inner layered structure of skin influences the photon diffusion inside the cutaneous tissues and determines the reflectance spectra formation. Phantoms are very useful tools to understand the biophysical meaning of parameters involved in light propagation through the skin. To simulate the skin reflectance spectrum, we realized a multilayered skin-like phantom and a multilayered skin phantom with a melanoma-like phantom embedded inside. Materials used were Al(2)O(3) particles, melanin of sepia officinalis and a calibrator for haematology systems dispersed in transparent silicon. Components were optically characterized with indirect techniques. Reflectance phantom spectra were compared with average values of in vivo spectra acquired on a sample of 573 voluntary subjects and 132 pigmented lesions. The phantoms' reflectance spectra agreed with those measured in vivo, mimicking the optical behaviour of the human skin. Further, the phantoms were optically stable and easily manageable, and represented a valid resource in spectra formation comprehension, in diagnostic laser applications and simulation model implementation, such as the Monte Carlo code for non-homogeneous media.

Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions

Various instruments based on acquisition and elaboration of images of pigmented skin lesions have been developed in an attempt to in vivo establish whether a lesion is a melanoma or not. Although encouraging, the response of these instruments, e.g. epiluminescence microscopy, reflectance spectrophotometry and fluorescence imaging, cannot currently replace the well-established diagnostic procedures. However, in place of the approach to instrumentally assess the diagnosis of the lesion, recent studies suggest that instruments should rather reproduce the assessment by an expert clinician of whether a lesion has to be excised or not. The aim of this study was to evaluate the performance of a spectrophotometric system to mimic such a decision. The study involved 1794 consecutively recruited patients with 1966 doubtful cutaneous pigmented lesions excised for histopathological diagnosis and 348 patients with 1940 non-excised lesions because clinically reassuring. Images of all these lesions were acquired in vivo with a multispectral imaging system. The data set was randomly divided into a train (802 reassuring and 1003 excision-needing lesions, including 139 melanomas), a verify (464 reassuring and 439 excision-needing lesions, including 72 melanomas) and a test set (674 reassuring and 524 excision-needing lesions, including 76 melanomas). An artificial neural network (ANN(1)) was set up to perform the classification of the lesions as excision-needing or reassuring, according to the expert clinicians' decision on how to manage each examined lesion. In the independent test set, the system was able to emulate the clinicians with a sensitivity of 88% and a specificity of 80%. Of the 462 correctly classified as excision-needing lesions, 72 (95%) were melanomas. No major variations in receiver operating characteristic curves were found between the test and the train/verify sets. On the same data set, a further artificial neural network (ANN(2)) was then architected to perform classification of the lesions as melanoma or non-melanoma, according to the histological diagnosis. Having set the sensitivity in recognizing melanoma to 95%, ANN(1) resulted to be significantly better in the classification of reassuring lesions than ANN(2). This study suggests that multispectral image analysis and artificial neural networks could be used to support primary care physicians or general practitioners in identifying pigmented skin lesions that require further investigations.

Optical devices used for image analysis of pigmented skin lesions: a proposal for quality assurance protocol using tissue-like phantoms

Different technological tools have been developed to aid in the diagnosis of pigmented skin lesions, including cameras working with conventional RGB colour systems, epiluminescence microscopy and spectrophotometric methods using visible and near infrared wavelengths. All the different procedures should provide in an objective and reproducible fashion quantitative measurements of the colour and shape features of a given skin mole. At present, many devices have been introduced in experimental stages for clinical diagnosis, mainly used to provide to the clinicians an objective, computer-assisted second opinion. As for any diagnostic instruments, optical devices should also be subjected to a dedicated quality assurance protocol in order to evaluate the response repeatability of each device (intra-instrument agreement) and to check the accordance among the responses of different devices (inter-instrument agreement). The aim of this study was to design a quality assurance protocol for optical devices dedicated to image analysis of pigmented skin lesions and, in case, to detect cutaneous melanoma by using suitable tissue-like phantoms as standard references that enable testing of both hardware and software components. As an example, we report the results of intra-instrument and inter-instrument agreement when the protocol was applied on a series of 30 SpectroShade instruments, a novel optical device based on multi-spectral image analysis of colour and shape features of pigmented skin lesion.

Micro-melanoma detection: a clinical study on 206 consecutive cases of pigmented skin lesions with a diameter ≤ 3 mm

BACKGROUND

Very small pigmented lesions may represent an important diagnostic challenge to the clinician.

OBJECTIVES

The aim of the present study was to establish the diagnostic value, in terms of sensitivity and specificity, of both clinical and dermoscopic examinations in a population of patients with unselected consecutive pigmented lesions with a maximum clinical diameter of 3 mm.

PATIENTS AND METHODS

Two hundred and four consecutive patients bearing 206 pigmented skin lesions with a maximum diameter of 3 mm were seen and operated on. Twenty-three of these lesions were melanomas. Each lesion was subjected to both clinical and dermoscopic evaluation before surgery. The results were expressed in terms of sensitivity and specificity of both kinds of evaluation.

RESULTS

Clinical evaluation produced a diagnostic sensitivity of 43% and a specificity of 91%. Dermoscopy resulted in a sensitivity of 83% and in a specificity of 69%. The comparison between the sensitivity values of the two diagnostic methods showed a significant difference (P < 0.01). A high value of significance was also obtained comparing the respective specificity values (P < 0.001).

CONCLUSIONS

Detection of very small melanomas is feasible by accurate visual inspection. Dermoscopy appears to be an important aid to diagnosis, provided that physicians are aware of this type of lesion and maintain the index of suspicion at a high level.

Automated segmentation of pigmented skin lesions in multispectral imaging

The aim of this study was to develop an algorithm for the automatic segmentation of multispectral images of pigmented skin lesions. The study involved 1700 patients with 1856 cutaneous pigmented lesions, which were analysed in vivo by a novel spectrophotometric system, before excision. The system is able to acquire a set of 15 different multispectral images at equally spaced wavelengths between 483 and 951 nm. An original segmentation algorithm was developed and applied to the whole set of lesions and was able to automatically contour them all. The obtained lesion boundaries were shown to two expert clinicians, who, independently, rejected 54 of them. The 97.1% contour accuracy indicates that the developed algorithm could be a helpful and effective instrument for the automatic segmentation of skin pigmented lesions.