High-fidelity detection, subtyping, and localization of five skin neoplasms using supervised and semi-supervised learning

Journal of Pathology Informatics - Tập 14 - Trang 100159 - 2023
James Requa1, Tuatini Godard1, Rajni Mandal1, Bonnie Balzer2, Darren Whittemore1, Eva George1, Frenalyn Barcelona1, Chalette Lambert3, Jonathan Lee4, Allison Lambert1, April Larson1, Gregory Osmond5
1Pathology Watch, 497 West 4800 South, Suite 201, Murray, UT 84123, USA
2Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
3Kirk Kerkorian School of Medicine at UNLV, University of Nevada, Las Vegas, Mail Stop: 3070, 2040 W Charleston Blvd., Las Vegas, NV 89102-2244, USA
4Bethesda Dermatopathology Laboratory, 1730 Elton Road, Silver Spring, MD 20903, USA
5Intermountain Healthcare, Saint George Regional Hospital, Department of Pathology, 1380 East Medical Center Drive, Saint George, Utah 84790, USA

Tài liệu tham khảo

Ciążyńska, 2021, The incidence and clinical analysis of non-melanoma skin cancer, Sci Rep., 11, 4337, 10.1038/s41598-021-83502-8

Rees, 2014, Non melanoma skin cancer and subsequent cancer risk, PLoS One., 9, 10.1371/journal.pone.0099674

Fitzmaurice, 2019, Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2017: a systematic analysis for the global burden of disease study, JAMA Oncol., 5, 1749, 10.1001/jamaoncol.2019.2996

Gilchrest, 1999, The pathogenesis of melanoma induced by ultraviolet radiation, New Engl J Med., 340, 1341, 10.1056/NEJM199904293401707

Mohan, 2014, Advanced basal cell carcinoma: epidemiology and therapeutic innovations, Curr Derm Rep., 3, 40, 10.1007/s13671-014-0069-y

Mitsis, 2015, Trends in demographics, incidence, and survival in children, adolescents and young adults (AYA) with melanoma: A Surveillance, Epidemiology and End Results (SEER) population-based analysis, JCO., 33, 9058, 10.1200/jco.2015.33.15_suppl.9058

Melanoma Skin Cancer Statistics

Melanoma - Statistics

Gordon, 2013, Skin cancer: an overview of epidemiology and risk factors, Semin Oncol Nurs., 29, 160, 10.1016/j.soncn.2013.06.002

Narayanan, 2010, Review: ultraviolet radiation and skin cancer, Int J Dermatol., 49, 978, 10.1111/j.1365-4632.2010.04474.x

Cancer Facts & Figures 2022. Published online 2022:80.

Koh, 1998, Public health interventions for melanoma: prevention, early detection, and education, Hematol/Oncol Clin North Am., 12, 903, 10.1016/S0889-8588(05)70030-7

Elmore, 2015, Diagnostic concordance among pathologists interpreting breast biopsy specimens, JAMA., 313, 1122, 10.1001/jama.2015.1405

van der Wel, 2020, Histopathologist features predictive of diagnostic concordance at expert level among a large international sample of pathologists diagnosing Barrett’s dysplasia using digital pathology, Gut., 69, 811, 10.1136/gutjnl-2019-318985

Tschandl, 2019, Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study, Lancet Oncol., 20, 938, 10.1016/S1470-2045(19)30333-X

Gerami, 2014, Histomorphologic assessment and interobserver diagnostic reproducibility of atypical spitzoid melanocytic neoplasms with long-term follow-up, Am J Surg Pathol., 38, 934, 10.1097/PAS.0000000000000198

Shoo, 2010, Discordance in the histopathologic diagnosis of melanoma at a melanoma referral center, J Am Acad Dermatol., 62, 751, 10.1016/j.jaad.2009.09.043

Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature., 542, 115, 10.1038/nature21056

Heal, 2009, Agreement between histological diagnosis of skin lesions by histopathologists and a dermato-histopathologist, Int J Dermatol., 48, 1366, 10.1111/j.1365-4632.2009.04185.x

Bush, 2015, Utilizing the frequency of Benign, Atypical and Malignant diagnoses for quality improvement in the histopathologic diagnosis of melanocytic neoplasms, J Cutan Pathol., 42, 712, 10.1111/cup.12566

Litjens, 2016, Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis, Sci Rep., 6, 26286, 10.1038/srep26286

Musumeci, 2014, Past, present and future: overview on histology and histopathology, J Histol Histopathol., 1, 5, 10.7243/2055-091X-1-5

Bi, 2019, Artificial intelligence in cancer imaging: clinical challenges and applications, CA Cancer J Clin., 69, 127, 10.3322/caac.21552

Aeffner, 2019, Introduction to digital image analysis in whole-slide imaging: a white paper from the digital pathology association, J Pathol Inform., 10, 9, 10.4103/jpi.jpi_82_18

Zhang, 2019, Pathologist-level interpretable whole-slide cancer diagnosis with deep learning, Nat Mach Intel., 1, 236, 10.1038/s42256-019-0052-1

Yu, 2021, Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images, Nat Commun., 12, 6311, 10.1038/s41467-021-26643-8

Coudray, 2018, Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning, Nat Med., 24, 1559, 10.1038/s41591-018-0177-5

Holten-Rossing, 2017, Application of automated image analysis reduces the workload of manual screening of sentinel lymph node biopsies in breast cancer, Histopathology., 71, 866, 10.1111/his.13305

Zadeh Shirazi, 2020, The application of deep convolutional neural networks to brain cancer images: a survey, J Personal Med., 10, 224, 10.3390/jpm10040224

Han, 2017, Breast cancer multi-classification from histopathological images with structured deep learning model, Sci Rep., 7, 4172, 10.1038/s41598-017-04075-z

Tschandl, 2019, Expert-level diagnosis of nonpigmented skin cancer by combined convolutional neural networks, JAMA Dermatol., 155, 58, 10.1001/jamadermatol.2018.4378

Campanella, 2019, Clinical-grade computational pathology using weakly supervised deep learning on whole slide images, Nat Med., 25, 1301, 10.1038/s41591-019-0508-1

Han, 2018, Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm, J Investig Dermatol., 138, 1529, 10.1016/j.jid.2018.01.028

Jiang, 2020, Recognizing basal cell carcinoma on smartphone-captured digital histopathology images with a deep neural network, Brit J Dermatol., 182, 754, 10.1111/bjd.18026

Ianni, 2020, Tailored for real-world: a whole slide image classification system validated on uncurated multi-site data emulating the prospective pathology workload, Sci Rep., 10, 3217, 10.1038/s41598-020-59985-2

Wang, 2020, Weakly supervised deep learning for whole slide lung cancer image analysis, IEEE Trans Cybernet., 50, 3950, 10.1109/TCYB.2019.2935141

Rashidi, 2019, Artificial intelligence and machine learning in pathology: the present landscape of supervised methods, Acad Pathol., 6, 10.1177/2374289519873088

Olsen, 2018, Diagnostic performance of deep learning algorithms applied to three common diagnoses in dermatopathology, J Pathol Inform., 9, 32, 10.4103/jpi.jpi_31_18

Gao, 2019, Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: an overview, MBE., 16, 6536, 10.3934/mbe.2019326

Bankhead, 2017, QuPath: open source software for digital pathology image analysis, Sci Rep., 7, 16878, 10.1038/s41598-017-17204-5

Liu, 2022, A ConvNet for the 2020s, 11976

He, 2016, Identity mappings in deep residual networks, 630

de Boer, 2005, A tutorial on the cross-entropy method, Ann Oper Res., 134, 19, 10.1007/s10479-005-5724-z

Loshchilov I, Hutter F. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101. Published online 2017.

Lin

React – A JavaScript library for building user interfaces

Seth, 2017, Global burden of skin disease: inequities and innovations, Curr Derm Rep., 6, 204, 10.1007/s13671-017-0192-7

Elder, 2018, Pathologist characteristics associated with accuracy and reproducibility of melanocytic skin lesion interpretation, J Am Acad Dermatol., 79, 52, 10.1016/j.jaad.2018.02.070

Chang, 2022, Characterization of multiple diagnostic terms in melanocytic skin lesion pathology reports, J Cutan Pathol., 49, 153, 10.1111/cup.14126

Baidoshvili, 2018, Evaluating the benefits of digital pathology implementation: time savings in laboratory logistics, Histopathology., 73, 784, 10.1111/his.13691