A Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Ground Truth

Sankarapandian, Sivaramakrishnan; Kohn, Saul A.; Spurrier, Vaughn; Grullon, Sean; Soans, Rajath E.; Ayyagari, Kameswari D.; Chamarthi, Ramachandra Vikas; Motaparthi, Kiran; Lee, Jason B.; Shon, Wonwoo; Bonham, Michael; Ianni, Julianna D.

doi:10.1109/iccvw54120.2021.00076

Cited by 12 publications

(19 citation statements)

References 21 publications

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“…Overall, 4,888 specimens were included, of which at least 2,715 were melanoma specimens. The diagnostic entities within the datasets varied between studies, with some only containing melanoma deposits 12,21,[24][25][26]33 and others containing more than one pathology 10,11,22,[27][28][29][30][31][32] .…”

Section: Resultsmentioning

confidence: 99%

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Image analysis of cutaneous melanoma histology: a systematic review and meta-analysis

Clarke

Wade

Magee

et al. 2023

Sci Rep

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The current subjective histopathological assessment of cutaneous melanoma is challenging. The application of image analysis algorithms to histological images may facilitate improvements in workflow and prognostication. To date, several individual algorithms applied to melanoma histological images have been reported with variations in approach and reported accuracies. Histological digital images can be created using a camera mounted on a light microscope, or through whole slide image (WSI) generation using a whole slide scanner. Before any such tool could be integrated into clinical workflow, the accuracy of the technology should be carefully evaluated and summarised. Therefore, the objective of this review was to evaluate the accuracy of existing image analysis algorithms applied to digital histological images of cutaneous melanoma. Database searching of PubMed and Embase from inception to 11th March 2022 was conducted alongside citation checking and examining reports from organisations. All studies reporting accuracy of any image analysis applied to histological images of cutaneous melanoma, were included. The reference standard was any histological assessment of haematoxylin and eosin-stained slides and/or immunohistochemical staining. Citations were independently deduplicated and screened by two review authors and disagreements were resolved through discussion. The data was extracted concerning study demographics; type of image analysis; type of reference standard; conditions included and test statistics to construct 2 × 2 tables. Data was extracted in accordance with our protocol and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Diagnostic Test Accuracy (PRISMA-DTA) Statement. A bivariate random-effects meta-analysis was used to estimate summary sensitivities and specificities with 95% confidence intervals (CI). Assessment of methodological quality was conducted using a tailored version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The primary outcome was the pooled sensitivity and specificity of image analysis applied to cutaneous melanoma histological images. Sixteen studies were included in the systematic review, representing 4,888 specimens. Six studies were included in the meta-analysis. The mean sensitivity and specificity of automated image analysis algorithms applied to melanoma histological images was 90% (CI 82%, 95%) and 92% (CI 79%, 97%), respectively. Based on limited and heterogeneous data, image analysis appears to offer high accuracy when applied to histological images of cutaneous melanoma. However, given the early exploratory nature of these studies, further development work is necessary to improve their performance.

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Section: Resultsmentioning

confidence: 99%

“…The generation of digital images of histological tissue has allowed the creation and application of image analysis (IA) algorithms. More recently still, artificial intelligence (AI) models have been applied to these images and yielded promising results [8][9][10][11][12] .…”

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confidence: 99%

Image analysis of cutaneous melanoma histology: a systematic review and meta-analysis

Clarke

Wade

Magee

et al. 2023

Sci Rep

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“…Most deep-learning diagnostic applications for histological images are for the differentiation between melanoma and nevi [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. However, multiple studies show applications in the differentiation between melanoma, nevi, and normal skin [ 34 , 35 ]; and differentiation between melanoma and nonmelanoma skin cancers [ 36 , 37 , 38 ]. Several studies showed deep-learning applications for the segmentation of whole tumor regions [ 39 , 40 , 41 , 42 ] or individual diagnostic markers such as mitotic cells [ 43 , 44 ], melanocytes [ 45 , 46 ], and melanocytic nests [ 47 ].…”

Section: Resultsmentioning

confidence: 99%

“…Initial clustering led to a binary classification of nonmelanoma vs. melanocytic images followed by further classification of melanoma as “high risk” (melanoma) “intermediate risk” (melanoma in situ or severe dysplasia), or “rest” consisting of nonmelanoma skin cancers, nevus, or mild-to-moderate dysplasia. On their two independent validation datasets, their model achieved an AUC of 0.95 and 0.82 [ 38 ].…”

Section: Resultsmentioning

confidence: 99%

Diagnostic and Prognostic Deep Learning Applications for Histological Assessment of Cutaneous Melanoma

Grant

Andrew

Alvarez

et al. 2022

Cancers

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Melanoma is among the most devastating human malignancies. Accurate diagnosis and prognosis are essential to offer optimal treatment. Histopathology is the gold standard for establishing melanoma diagnosis and prognostic features. However, discrepancies often exist between pathologists, and analysis is costly and time-consuming. Deep-learning algorithms are deployed to improve melanoma diagnosis and prognostication from histological images of melanoma. In recent years, the development of these machine-learning tools has accelerated, and machine learning is poised to become a clinical tool to aid melanoma histology. Nevertheless, a review of the advances in machine learning in melanoma histology was lacking. We performed a comprehensive literature search to provide a complete overview of the recent advances in machine learning in the assessment of melanoma based on hematoxylin eosin digital pathology images. In our work, we review 37 recent publications, compare the methods and performance of the reviewed studies, and highlight the variety of promising machine-learning applications in melanoma histology.

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“…Automatic classification of skin lesions utilizing images is a difficult process because of the fine-grained variation in the appearance of skin lesions. A deep convolution neural network (DCNN) shows potential for highly variable and general tasks through several fine-grained object classes [ 10 ]. Outfitted with a deep neural network, the mobile device possibly extends the reach of dermatologists outside of the hospital.…”

Section: Introductionmentioning

confidence: 99%

Computational Intelligence-Based Melanoma Detection and Classification Using Dermoscopic Images

Vaiyapuri

Balaji

Shridevi

et al. 2022

Computational Intelligence and Neuroscience

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Melanoma is a kind of skin cancer caused by the irregular development of pigment-producing cells. Since melanoma detection efficiency is limited to different factors such as poor contrast among lesions and nearby skin regions, and visual resemblance among melanoma and non-melanoma lesions, intelligent computer-aided diagnosis (CAD) models are essential. Recently, computational intelligence (CI) and deep learning (DL) techniques are utilized for effective decision-making in the biomedical field. In addition, the fast-growing advancements in computer-aided surgeries and recent progress in molecular, cellular, and tissue engineering research have made CI an inevitable part of biomedical applications. In this view, the research work here develops a novel computational intelligence-based melanoma detection and classification technique using dermoscopic images (CIMDC-DIs). The proposed CIMDC-DI model encompasses different subprocesses. Primarily, bilateral filtering with fuzzy k-means (FKM) clustering-based image segmentation is applied as a preprocessing step. Besides, NasNet-based feature extractor with stochastic gradient descent is applied for feature extraction. Finally, the manta ray foraging optimization (MRFO) algorithm with a cascaded neural network (CNN) is exploited for the classification process. To ensure the potential efficiency of the CIMDC-DI technique, we conducted a wide-ranging simulation analysis, and the results reported its effectiveness over the existing recent algorithms with the maximum accuracy of 97.50%.

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A Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Ground Truth

Cited by 12 publications

References 21 publications

Image analysis of cutaneous melanoma histology: a systematic review and meta-analysis

Image analysis of cutaneous melanoma histology: a systematic review and meta-analysis

Diagnostic and Prognostic Deep Learning Applications for Histological Assessment of Cutaneous Melanoma

Computational Intelligence-Based Melanoma Detection and Classification Using Dermoscopic Images

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