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

Diagnostic Accuracy of Novel Optical Imaging Techniques for Melanoma Detection: A Systematic Review and Meta-Analysis

The incidence of melanoma is increasing worldwide, requiring early detection to improve survival rates. Although dermoscopy is the standard non-invasive tool for diagnosing melanoma, it relies on experience and skill. Advances in optical imaging technologies and artificial intelligence have the potential to improve diagnostic accuracy. Our objective was to compare the diagnostic accuracy of novel non-invasive optical imaging techniques for melanoma detection. A systematic literature search was conducted in three databases (Medline, Embase, and CENTRAL) on November 15, 2023. Inclusion criteria focused on studies comparing the accuracy of optical imaging methods against histopathology. Outcomes consisted of measures of diagnostic accuracy. Random-effects meta-analyses were performed for each method with 95% confidence intervals to summarize all relevant effect sizes. Of the 16,239 records, 141 articles met the inclusion criteria, of which 138 articles were eligible for the meta-analysis. Reflectance confocal microscopy (RCM) and dermoscopy combined with artificial intelligence (DSC + AI) had the highest sensitivity (0.93), with DSC + AI showing higher specificity (0.77 [0.70-0.83]) than RCM (0.749 [0.7475-0.7504]). Multispectral imaging combined with AI also showed high sensitivity (0.92 [0.82-0.97]) and relatively high specificity (0.80 [0.67-0.89]). Standalone dermoscopy exhibited balanced sensitivity (0.87 [0.84-0.90]) and specificity (0.82 [0.78-0.86]). In melanoma diagnosis, both RCM and DSC + AI can serve as second-step optical evaluation methods for suspicious lesions following initial screening with DSC. By maintaining a strong emphasis on multimodal imaging, healthcare providers could improve early detection and outcomes for patients with melanoma.

Towards reliable hyperspectral imaging biomarkers of CT26 murine tumor model

The non-invasive monitoring of tumor growth can offer invaluable diagnostic insights and enhance our understanding of tumors and their microenvironment. Integrating hyperspectral imaging (HSI) with three-dimensional optical profilometry (3D OP) makes contactless and non-invasive tumor diagnosis possible by utilizing the inherent tissue contrast provided by visible (VIS) and near-infrared (NIR) light. Consequently, valuable information regarding tumors and healthy tissues can be extracted from the acquired hyperspectral images. Until now, very few methods have been used to monitor tumor models in vivo daily and non-invasively. In this research, we conducted a 14-day study monitoring BALB/c mice with subcutaneously grown CT26 murine colon carcinomas in vivo, commencing on the day of tumor cell injection. We extracted physiological properties such as total hemoglobin (THB) and tissue oxygenation (StO 2 ) using the inverse adding-doubling (IAD) algorithm and manually segmented the tissues. We then selected the ten most relevant features describing tumors using the Max-Relevance Min-Redundancy (MRMR) algorithm and utilized 30 classic and advanced machine learning (ML) algorithms to discriminate tumors from healthy tissues. Finally, we tested the robustness of feature selection and model performance by smoothing tissue parameter maps extracted by IAD with a variable kernel and omitting selected training data. We could discriminate CT26 tumor models from surrounding healthy tissues with an area under the curve (AUC) of up to 1 for models based on the gradient boosting method, linear discriminant analysis, and random forests. Our findings help pave the way for precise and robust imaging biomarkers that could aid tumor diagnosis and advance clinical practice.

Spectrum-based deep learning framework for dermatological pigment analysis and simulation

BACKGROUND

Deep learning in dermatology presents promising tools for automated diagnosis but faces challenges, including labor-intensive ground truth preparation and a primary focus on visually identifiable features. Spectrum-based approaches offer professional-level information like pigment distribution maps, but encounter practical limitations such as complex system requirements.

METHODS

This study introduces a spectrum-based framework for training a deep learning model to generate melanin and hemoglobin distribution maps from skin images. This approach eliminates the need for manually prepared ground truth by synthesizing output maps into skin images for regression analysis. The framework is applied to acquire spectral data, create pigment distribution maps, and simulate pigment variations.

RESULTS

Our model generated reflectance spectra and spectral images that accurately reflect pigment absorption properties, outperforming spectral upsampling methods. It produced pigment distribution maps with correlation coefficients of 0.913 for melanin and 0.941 for hemoglobin compared to the VISIA system. Additionally, the model's simulated images of pigment variations exhibited a proportional correlation with adjustments made to pigment levels. These evaluations are based on pigment absorption properties, the Individual Typology Angle (ITA), and pigment indices.

CONCLUSION

The model produces pigment distribution maps comparable to those from specialized clinical equipment and simulated images with numerically adjusted pigment variations. This approach demonstrates significant promise for developing professional-level diagnostic tools for future clinical applications.

Hyperspectral and multispectral image processing for gross-level tumor detection in skin lesions: a systematic review

SIGNIFICANCE

Skin cancer is one of the most prevalent cancers worldwide. In the advent of medical digitization and telepathology, hyper/multispectral imaging (HMSI) allows for noninvasive, nonionizing tissue evaluation at a macroscopic level.

AIM

We aim to summarize proposed frameworks and recent trends in HMSI-based classification and segmentation of gross-level skin tissue.

APPROACH

A systematic review was performed, targeting HMSI-based systems for the classification and segmentation of skin lesions during gross pathology, including melanoma, pigmented lesions, and bruises. The review adhered to the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. For eligible reports published from 2010 to 2020, trends in HMSI acquisition, preprocessing, and analysis were identified.

RESULTS

HMSI-based frameworks for skin tissue classification and segmentation vary greatly. Most reports implemented simple image processing or machine learning, due to small training datasets. Methodologies were evaluated on heavily curated datasets, with the majority targeting melanoma detection. The choice of preprocessing scheme influenced the performance of the system. Some form of dimension reduction is commonly applied to avoid redundancies that are inherent in HMSI systems.

CONCLUSIONS

To use HMSI for tumor margin detection in practice, the focus of system evaluation should shift toward the explainability and robustness of the decision-making process.

Repeatability and reproducibility of a hyperspectral imaging system for in vivo color evaluation

BACKGROUND

Color imaging is a tried and true method for the evaluation of cosmetic and dermatological effects, but it fails to capture all the information in a scene's spectral reflectance. For this reason, there has been in recent years increasing interest in the use of imaging spectrometers for clinical studies and product evaluation.

MATERIAL AND METHODS

We developed a novel HyperSpectral Imager (HSI) able to take in vivo full-face format images as a next generation instrument for skin color measurement and beyond. Here, we report part of the results of our first full-scale validation test of the HSI. We replicated a make-up foundation screening test by applying three products to a panel of 9 models and evaluated the product L^∗ , a^∗ , b^∗ , and ∆E effect immediately after application relative to the bare skin condition. We repeated this test twice in order to study the repeatability of the HSI as an evaluation instrument and during each test two different operators duplicated the data acquisition so we can assess the reproducibility of the measurements.

RESULTS

We find that the measurements from the HSI provide repeatability and reproducibility as good or better than those of our previous benchmark devices.

CONCLUSION

From these results, we conclude that not only is the HSI suitable for use in color evaluation studies, but also that it gives operational advantages over the previous generation of evaluation instruments, as it provides a spectral measurement combined with good spatial resolution. This allows for analysis of color over an area and post hoc selection of study regions and so opens new possibilities for studies of complex in vivo phenomena which neither non-imaging spectrometers nor conventional cameras can pursue. This study also raises points for future work concerning proper inclusion of instrument uncertainty in comparisons of results between instruments and handling of systematic uncertainties from analyses based on a single area.

FPI Based Hyperspectral Imager for the Complex Surfaces—Calibration, Illumination and Applications

Hyperspectral imaging (HSI) applications for biomedical imaging and dermatological applications have been recently under research interest. Medical HSI applications are non-invasive methods with high spatial and spectral resolution. HS imaging can be used to delineate malignant tumours, detect invasions, and classify lesion types. Typical challenges of these applications relate to complex skin surfaces, leaving some skin areas unreachable. In this study, we introduce a novel spectral imaging concept and conduct a clinical pre-test, the findings of which can be used to develop the concept towards a clinical application. The SICSURFIS spectral imager concept combines a piezo-actuated Fabry–Pérot interferometer (FPI) based hyperspectral imager, a specially designed LED module and several sizes of stray light protection cones for reaching and adapting to the complex skin surfaces. The imager is designed for the needs of photometric stereo imaging for providing the skin surface models (3D) for each captured wavelength. The captured HS images contained 33 selected wavelengths (ranging from 477 nm to 891 nm), which were captured simultaneously with accordingly selected LEDs and three specific angles of light. The pre-test results show that the data collected with the new SICSURFIS imager enable the use of the spectral and spatial domains with surface model information. The imager can reach complex skin surfaces. Healthy skin, basal cell carcinomas and intradermal nevi lesions were classified and delineated pixel-wise with promising results, but further studies are needed. The results were obtained with a convolutional neural network.

Design and performance of a hyperspectral camera for full-face in vivo imaging

Red, green, blue color photography is a mature technology and a powerful tool for the evaluation and understanding of the way an object reflects light and its related optical properties, but color photography fails to give a complete picture of these effects due to its inherent lack of spectral resolution. In this work, we update the L'OREAL reference device for skin color measurement, the Chromasphere, by replacing its current color camera system with an imaging spectrometer. This imaging spectrometer must provide a spatial resolution on par with the previous color cameras and a spectral resolution commensurate with a spectroradiometer while also achieving a time resolution suitable for in vivo studies of the human face. Due to these requirements, common spatial scanning techniques are not suitable for this application, and so we utilized a spectral-scanning approach based on a tunable liquid-crystal birefringent filter. We present the design and performance tests of a working prototype that is capable of measuring the spectrum in each of 4 MP with a nominal spectral resolution of 10 nm across the wavelength range from 420 to 730 nm in a total imaging time of less than 10 s. We cross-compared the spectral and color measurements obtained with this prototype, an industry-standard spectroradiometer, and a charge-coupled device color camera in order to assess the prototype's performance, and the results of this comparison show that our prototype is capable of taking spectral measurements near enough in quality to those of a spectroradiometer to successfully bridge the divide between such devices and conventional color cameras. Doing so, this instrument opens new possibilities for studies of complex in vivo phenomena that neither non-imaging spectrometers nor conventional cameras can pursue.

Clinical performance of a novel hyperspectral imaging device for cutaneous melanoma and pigmented skin lesions in Caucasian skin

BACKGROUND

The quest for diagnostic tools for the detection of cutaneous malignant melanoma (cMM) is ongoing. A challenge in cMM care is not overlooking cMM at an early stage, while simultaneously avoiding unnecessary biopsies or excisions of benign pigmented skin lesions (PSLs). A novel hyperspectral imaging (HSI) device is shown to have potential for differentiating equivocal PSLs in Asian skin types. Our objective was to assess the accuracy of the HSI device in distinguishing between cMM and benign PSLs in patients with Caucasian skin types.

METHODS

Patients with Caucasian skin types (Fitzpatrick I-II), enrolled for excisional biopsies of PSLs were included and examined using the HSI device. The discrimination index (DI) was used to demonstrate the sensitivity (SE) and specificity (SP) in comparison with the re-evaluated histopathology diagnoses.

RESULTS

In 186 patients, 202 pigmented skin lesions were included. The sensitivity to detect cMM was 96.7% (87/90), and the specificity for benign lesions was 42.1% (45/107). The AUC was 0.800 (95% confidence interval (CI): 0.740-0.861).

CONCLUSIONS

Our novel HSI device showed a high sensitivity in detecting malignant lesions in patients with Caucasian skin types. Compared with analogous technologies, as multispectral imaging or electrical impedance spectroscopy, our device showed similar or better accuracy in differentiating cMM from benign PSLs. Therefore, it might be a useful clinical tool in skin types I-IV and where further triage of pigmented skin lesions is important.

Automatic diagnosis of melanoma using hyperspectral data and GoogLeNet

BACKGROUND

Melanoma is a type of superficial tumor. As advanced melanoma has a poor prognosis, early detection and therapy are essential to reduce melanoma-related deaths. To that end, there is a need to develop a quantitative method for diagnosing melanoma. This paper reports the development of such a diagnostic system using hyperspectral data (HSD) and a convolutional neural network, which is a type of machine learning.

MATERIALS AND METHODS

HSD were acquired using a hyperspectral imager, which is a type of spectrometer that can simultaneously capture information about wavelength and position. GoogLeNet pre-trained with Imagenet was used to model the convolutional neural network. As many CNNs (including GoogLeNet) have three input channels, the HSD (involving 84 channels) could not be input directly. For that reason, a "Mini Network" layer was added to reduce the number of channels from 84 to 3 just before the GoogLeNet input layer. In total, 619 lesions (including 278 melanoma lesions and 341 non-melanoma lesions) were used for training and evaluation of the network.

RESULTS AND CONCLUSION

The system was evaluated by 5-fold cross-validation, and the results indicate sensitivity, specificity, and accuracy of 69.1%, 75.7%, and 72.7% without data augmentation, 72.3%, 81.2%, and 77.2% with data augmentation, respectively. In future work, it is intended to improve the Mini Network and to increase the number of lesions.

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.

Intraoperative multispectral and hyperspectral label-free imaging: A systematic review of in vivo clinical studies

Multispectral and hyperspectral imaging (HSI) are emerging optical imaging techniques with the potential to transform the way surgery is performed but it is not clear whether current systems are capable of delivering real-time tissue characterization and surgical guidance. We conducted a systematic review of surgical in vivo label-free multispectral and HSI systems that have been assessed intraoperatively in adult patients, published over a 10-year period to May 2018. We analysed 14 studies including 8 different HSI systems. Current in-vivo HSI systems generate an intraoperative tissue oxygenation map or enable tumour detection. Intraoperative tissue oxygenation measurements may help to predict those patients at risk of postoperative complications and in-vivo intraoperative tissue characterization may be performed with high specificity and sensitivity. All systems utilized a line-scanning or wavelength-scanning method but the spectral range and number of spectral bands employed varied significantly between studies and according to the system's clinical aim. The time to acquire a hyperspectral cube dataset ranged between 5 and 30 seconds. No safety concerns were reported in any studies. A small number of studies have demonstrated the capabilities of intraoperative in-vivo label-free HSI but further work is needed to fully integrate it into the current surgical workflow.

Use of Hyperspectral/Multispectral Imaging in Gastroenterology. Shedding Some⁻Different⁻Light into the Dark

Hyperspectral/Multispectral imaging (HSI/MSI) technologies are able to sample from tens to hundreds of spectral channels within the electromagnetic spectrum, exceeding the capabilities of human vision. These spectral techniques are based on the principle that every material has a different response (reflection and absorption) to different wavelengths. Thereby, this technology facilitates the discrimination between different materials. HSI has demonstrated good discrimination capabilities for materials in fields, for instance, remote sensing, pollution monitoring, field surveillance, food quality, agriculture, astronomy, geological mapping, and currently, also in medicine. HSI technology allows tissue observation beyond the limitations of the human eye. Moreover, many researchers are using HSI as a new diagnosis tool to analyze optical properties of tissue. Recently, HSI has shown good performance in identifying human diseases in a non-invasive manner. In this paper, we show the potential use of these technologies in the medical domain, with emphasis in the current advances in gastroenterology. The main aim of this review is to provide an overview of contemporary concepts regarding HSI technology together with state-of-art systems and applications in gastroenterology. Finally, we discuss the current limitations and upcoming trends of HSI in gastroenterology.

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.

Reflectance confocal microscopy of skin in vivo: From bench to bedside

Following more than two decades of effort, reflectance confocal microscopy (RCM) imaging of skin was granted codes for reimbursement by the US Centers for Medicare and Medicaid Services. Dermatologists in the USA have started billing and receiving reimbursement for the imaging procedure and for the reading and interpretation of images. RCM imaging combined with dermoscopic examination is guiding the triage of lesions into those that appear benign, which are being spared from biopsy, against those that appear suspicious, which are then biopsied. Thus far, a few thousand patients have been spared from biopsy of benign lesions. The journey of RCM imaging from bench to bedside is certainly a success story, but still much more work lies ahead toward wider dissemination, acceptance, and adoption. We present a brief review of RCM imaging and highlight key challenges and opportunities. The success of RCM imaging paves the way for other emerging optical technologies, as well-and our bet for the future is on multimodal approaches. Lasers Surg. Med. 49:7-19, 2017. © 2016 Wiley Periodicals, Inc.

Portable hyperspectral imager with continuous wave green laser for identification and detection of untreated latent fingerprints on walls

Untreated latent fingerprints are known to exhibit fluorescence under UV laser excitation. Previously, the hyperspectral imager (HSI) has been primarily evaluated in terms of its potential to enhance the sensitivity of latent fingerprint detection following treatment by conventional chemical methods in the forensic science field. In this study however, the potential usability of the HSI for the visualization and detection of untreated latent fingerprints by measuring their inherent fluorescence under continuous wave (CW) visible laser excitation was examined. Its potential to undertake spectral separation of overlapped fingerprints was also evaluated. The excitation wavelength dependence of fluorescent images was examined using an untreated palm print on a steel based wall, and it was found that green laser excitation is superior to blue and yellow lasers' excitation for the production of high contrast fluorescence images. In addition, a spectral separation method for overlapped fingerprints/palm prints on a plaster wall was proposed using new images converted by the division and subtraction of two single wavelength images constructed based on measured hyperspectral data (HSD). In practical tests, the relative isolation of two overlapped fingerprints/palm prints was successful in twelve out of seventeen cases. Only one fingerprint/palm print was extracted for an additional three cases. These results revealed that the feasibility of overlapped fingerprint/palm print spectral separation depends on the difference in the temporal degeneration of each fluorescence spectrum. The present results demonstrate that a combination of a portable HSI and CW green laser has considerable potential for the identification and detection of untreated latent fingerprints/palm prints on the walls under study, while the use of HSD makes it practically possible for doubly overlapped fingerprints/palm prints to be separated spectrally.

Hyperspectroscopic imager for baby fibers

Hyperspectral imaging system for diagnosing digestive diseases was newly developed in order to obtain information on pathology beyond morphology of lesions. In order to guide light reflected from a lesion, a baby fiber, which can be inserted in a forceps channel of the electronic endoscope, was also developed. The performance of the system was evaluated by animal experiment. Obtained hyperspectral data were found to have sufficient quality endurable to practical use. Harmful phenomena to a living body were not observed within the experiment. It was considered from the animal experiment that the present system could be practically used for humans.

Noninvasive diagnostics supporting system for choroidal melanoma: a pilot study

PURPOSE

To examine the usefulness of a near-infrared hyperspectral imager (NIR-HSI) system in discriminating uveal melanoma from other intraocular tumors.

METHOD

The NIR-HSI, which had been developed as a screening system for age-related macular degeneration, was used to measure near-infrared hyperspectral data (NIR-HSD) of a lesion located at the ocular fundus of 17 Japanese patients, including 5 with choroidal melanoma and 12 with other intraocular tumors. The index was derived from each NIR-HSD. Non-parametric statistical analysis was performed.

RESULTS

Diagnostic accuracy of 94.1% was achieved when the threshold value of the index was set to minimize the average value of false-positive and -negative fractions.

CONCLUSIONS

The NIR-HSI system is useful as a noninvasive diagnostic supporting system for choroidal melanoma.

Modification of a melanoma discrimination index derived from hyperspectral data: a clinical trial conducted in 2 centers between March 2011 and December 2013

BACKGROUND

The morphology of pigmented skin lesions (PSLs) is predominantly a result of varying concentrations and distributions of pigmented molecules such as melanin and hemoglobin. Based on these differences and the fact that their information is contained in cutaneous spectra, a hyperspectral imager (HSI) for pigmented melanoma and a single discrimination index derived from the resultant hyperspectral data are proposed.

OBJECTIVE

To develop and evaluate a new discrimination index for melanomas, compared to the previous index.

METHODS

A HSI, which is convenient for both patients and clinicians, was newly developed and used in a clinical trial conducted in 2 centers with 80 patients with primary lesions and 17 volunteers between March 2011 and December 2013. There were 24 melanomas and 110 other PSLs. A previously proposed discrimination index was used without modifications. A new index, which emphasized the essential features of melanoma, was proposed, and its performance was examined. For each index, a threshold value was set to minimize the average value of the false positive and false negative fractions. The performances of both indices were compared.

RESULTS

The sensitivity and specificity of the old index were 75% and 97%, respectively, while those of the new index were 96% and 87%.

CONCLUSION

The new index had a higher sensitivity and adequate specificity, indicating that it is more useful than the old index.

Melanoma screening system using hyperspectral imager attached to imaging fiberscope

Early detection and proper excision of the primary lesions of melanoma are crucial for reducing melanoma-related deaths. In order to support the early detection of melanoma, melanoma screening systems have been extensively studied and developed. Recently we have proposed a melanoma discrimination index derived from hyperspectral data (HSD) in the visible-near infrared wavelength region. The index represents variegation in spectra over a lesion and works well in discriminating melanoma from other pigmented lesions. However the previous hyperspectral imager did not have an enough allowance for measurement of lesions. To overcome the problem with it, we have developed a hyperspectral imager attached to imaging fiberscope. This equipment has been able to accumulate HSD in a view field of φ40 mm within about 10 seconds, from which the above-mentioned melanoma discrimination index has been calculated. Performance of the system has been studied in nine cases of melanoma and 18 cases of non-melanoma, obtained from patients and volunteers, all of whom were Japanese. The index has achieved a sensitivity of 100 % and a specificity of 94.4 %.

Novel automated screening of age-related macular degeneration

PURPOSE

To determine the objective and quantitative hyperspectral parameters for distinguishing between age-related macular degeneration (AMD) and a normal macula.

METHODS

Near-infrared hyperspectral images were taken of 71 eyes of 62 AMD patients with exudative AMD and 21 eyes of 12 control subjects without AMD. The spatial information included a 480 × 321-pixel image in a 50° field located at the ocular fundus and a 720-950-nm-per-pixel reflectance spectrum. Macular vectors were determined as the average spectrum for each macula, and reference vectors were used as average macular vectors for healthy volunteers. Variations in vector length and angle were calculated based on comparison with the reference vector. The AMD differentiation index was a parameter that minimized the plot overlap between AMD patients and controls.

RESULTS

Statistically significant differences between the AMD patients and controls were noted. Receiver-operating characteristic curve analysis revealed an area under the curve of 0.888. The appropriate threshold values were attained for the proposed discrimination index, including 68 % sensitivity, 95 % specificity and 74 % accuracy.

CONCLUSIONS

This study presents a simplified diagnostic index for the determination of age-related macular degeneration based on near-infrared spectra.