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1. A Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic Images

Author

Abder-Rahman Ali, Jingpeng Li, Summrina Kanwal, Guang Yang, Amir Hussain, and Sally Jane O'Shea

Subjects
melanoma, irregularity, dermoscopy, multilayer perceptron, fuzzy logic, Medicine (General), R5-920
Abstract

Skin lesion border irregularity, which represents the B feature in the ABCD rule, is considered one of the most significant factors in melanoma diagnosis. Since signs that clinicians rely on in melanoma diagnosis involve subjective judgment including visual signs such as border irregularity, this deems it necessary to develop an objective approach to finding border irregularity. Increased research in neural networks has been carried out in recent years mainly driven by the advances of deep learning. Artificial neural networks (ANNs) or multilayer perceptrons have been shown to perform well in supervised learning tasks. However, such networks usually don't incorporate information pertaining the ambiguity of the inputs when training the network, which in turn could affect how the weights are being updated in the learning process and eventually degrading the performance of the network when applied on test data. In this paper, we propose a fuzzy multilayer perceptron (F-MLP) that takes the ambiguity of the inputs into consideration and subsequently reduces the effects of ambiguous inputs on the learning process. A new optimization function, the fuzzy gradient descent, has been proposed to reflect those changes. Moreover, a type-II fuzzy sigmoid activation function has also been proposed which enables finding the range of performance the fuzzy neural network is able to attain. The fuzzy neural network was used to predict the skin lesion border irregularity, where the lesion was firstly segmented from the skin, the lesion border extracted, border irregularity measured using a proposed measure vector, and using the extracted border irregularity measures to train the neural network. The proposed approach outperformed most of the state-of-the-art classification methods in general and its standard neural network counterpart in particular. However, the proposed fuzzy neural network was more time-consuming when training the network.

Published
2020
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2. A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

Author

Abder-Rahman Ali, Jingpeng Li, Guang Yang, and Sally Jane O’Shea

Subjects
Machine learning, Dermoscopy, Skin lesion, Melanoma, Segmentation, Electronic computers. Computer science, QA75.5-76.95
Abstract

Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABCD rule. In this article we propose an automated approach for skin lesion border irregularity detection. The approach involves extracting the skin lesion from the image, detecting the skin lesion border, measuring the border irregularity, training a Convolutional Neural Network and Gaussian naive Bayes ensemble, to the automatic detection of border irregularity, which results in an objective decision on whether the skin lesion border is considered regular or irregular. The approach achieves outstanding results, obtaining an accuracy, sensitivity, specificity, and F-score of 93.6%, 100%, 92.5% and 96.1%, respectively.

Published
2020
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3. Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic images.

Author

Abder-Rahman Ali, Jingpeng Li, and Sally Jane O'Shea

Subjects
Medicine, Science
Abstract

Asymmetry, color variegation and diameter are considered strong indicators of malignant melanoma. The subjectivity inherent in the first two features and the fact that 10% of melanomas tend to be missed in the early diagnosis due to having a diameter less than 6mm, deem it necessary to develop an objective computer vision system to evaluate these criteria and aid in the early detection of melanoma which could eventually lead to a higher 5-year survival rate. This paper proposes an approach for evaluating the three criteria objectively, whereby we develop a measure to find asymmetry with the aid of a decision tree which we train on the extracted asymmetry measures and then use to predict the asymmetry of new skin lesion images. A range of colors that demonstrate the suspicious colors for the color variegation feature have been derived, and Feret's diameter has been utilized to find the diameter of the skin lesion. The decision tree is 80% accurate in determining the asymmetry of skin lesions, and the number of suspicious colors and diameter values are objectively identified.

Published
2020
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5. Data from Transcriptomic Analysis Reveals Prognostic Molecular Signatures of Stage I Melanoma

Author

Jérémie Nsengimana, Jennifer H. Barrett, Julia Newton-Bishop, D. Timothy Bishop, Göran Jönsson, Tracey Mell, May Chan, Juliette A. Randerson-Moor, Joanne Gascoyne, Mark Harland, Anastasia Filia, Joanna Poźniak, Sally Jane O'Shea, Joey Mark S. Diaz, Martin Lauss, Jonathan P. Laye, and Rohit Thakur

Abstract

Purpose:Previously identified transcriptomic signatures have been based on primary and metastatic melanomas with relatively few American Joint Committee on Cancer (AJCC) stage I tumors, given difficulties in sampling small tumors. The advent of adjuvant therapies has highlighted the need for better prognostic and predictive biomarkers, especially for AJCC stage I and stage II disease.Experimental Design:A total of 687 primary melanoma transcriptomes were generated from the Leeds Melanoma Cohort (LMC). The prognostic value of existing signatures across all the AJCC stages was tested. Unsupervised clustering was performed, and the prognostic value of the resultant signature was compared with that of sentinel node biopsy (SNB) and tested as a biomarker in three published immunotherapy datasets.Results:Previous Lund and The Cancer Genome Atlas signatures predicted outcome in the LMC dataset (P = 10−8 to 10−4) but showed a significant interaction with AJCC stage (P = 0.04) and did not predict outcome in stage I tumors (P = 0.3–0.7). Consensus-based classification of the LMC dataset identified six classes that predicted outcome, notably in stage I disease. LMC class was a similar indicator of prognosis when compared with SNB, and it added prognostic value to the genes reported by Gerami and colleagues. One particular LMC class consistently predicted poor outcome in patients receiving immunotherapy in two of three tested datasets. Biological characterization of this class revealed high JUN and AXL expression and evidence of epithelial-to-mesenchymal transition.Conclusions:A transcriptomic signature of primary melanoma was identified with prognostic value, including in stage I melanoma and in patients undergoing immunotherapy.

Published
2023
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6. Supplementary Data from Transcriptomic Analysis Reveals Prognostic Molecular Signatures of Stage I Melanoma

Author

Jérémie Nsengimana, Jennifer H. Barrett, Julia Newton-Bishop, D. Timothy Bishop, Göran Jönsson, Tracey Mell, May Chan, Juliette A. Randerson-Moor, Joanne Gascoyne, Mark Harland, Anastasia Filia, Joanna Poźniak, Sally Jane O'Shea, Joey Mark S. Diaz, Martin Lauss, Jonathan P. Laye, and Rohit Thakur

Abstract

Supplementary Figures and tables

Published
2023
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7. Transcriptomic Analysis Reveals Prognostic Molecular Signatures of Stage I Melanoma

Author

Tracey Mell, Sally Jane O'Shea, Jérémie Nsengimana, May Chan, Anastasia Filia, D. Timothy Bishop, Jonathan P. Laye, Joey Mark S. Diaz, Juliette Randerson-Moor, Jennifer H. Barrett, Martin Lauss, Joanne Gascoyne, Julia Newton-Bishop, Mark Harland, Joanna Poźniak, Rohit Thakur, and Göran Jönsson

Subjects
Adult, Male, 0301 basic medicine, Oncology, Cancer Research, medicine.medical_specialty, Skin Neoplasms, Adolescent, medicine.medical_treatment, Disease, Cohort Studies, Transcriptome, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, Biopsy, Biomarkers, Tumor, medicine, Humans, Stage I melanoma, Melanoma, Aged, Neoplasm Staging, Aged, 80 and over, medicine.diagnostic_test, business.industry, Computational Biology, Immunotherapy, Middle Aged, Sentinel node, medicine.disease, 3. Good health, Gene Expression Regulation, Neoplastic, Survival Rate, Treatment Outcome, 030104 developmental biology, 030220 oncology & carcinogenesis, Cohort, Female, business
Abstract

Purpose: Previously identified transcriptomic signatures have been based on primary and metastatic melanomas with relatively few American Joint Committee on Cancer (AJCC) stage I tumors, given difficulties in sampling small tumors. The advent of adjuvant therapies has highlighted the need for better prognostic and predictive biomarkers, especially for AJCC stage I and stage II disease. Experimental Design: A total of 687 primary melanoma transcriptomes were generated from the Leeds Melanoma Cohort (LMC). The prognostic value of existing signatures across all the AJCC stages was tested. Unsupervised clustering was performed, and the prognostic value of the resultant signature was compared with that of sentinel node biopsy (SNB) and tested as a biomarker in three published immunotherapy datasets. Results: Previous Lund and The Cancer Genome Atlas signatures predicted outcome in the LMC dataset (P = 10−8 to 10−4) but showed a significant interaction with AJCC stage (P = 0.04) and did not predict outcome in stage I tumors (P = 0.3–0.7). Consensus-based classification of the LMC dataset identified six classes that predicted outcome, notably in stage I disease. LMC class was a similar indicator of prognosis when compared with SNB, and it added prognostic value to the genes reported by Gerami and colleagues. One particular LMC class consistently predicted poor outcome in patients receiving immunotherapy in two of three tested datasets. Biological characterization of this class revealed high JUN and AXL expression and evidence of epithelial-to-mesenchymal transition. Conclusions: A transcriptomic signature of primary melanoma was identified with prognostic value, including in stage I melanoma and in patients undergoing immunotherapy.

Published
2019
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8. A Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic Images

Author

Sally Jane O’Shea, Jingpeng Li, Amir Hussain, Abder-Rahman Ali, Guang Yang, and Summrina Kanwal

Subjects
020205 medical informatics, Computer science, 02 engineering and technology, Fuzzy logic, 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), melanoma, multilayer perceptron, Original Research, irregularity, lcsh:R5-920, Artificial neural network, business.industry, Deep learning, Supervised learning, Pattern recognition, General Medicine, Perceptron, Multilayer perceptron, Medicine, 020201 artificial intelligence & image processing, Artificial intelligence, fuzzy logic, dermoscopy, business, Gradient descent, lcsh:Medicine (General)
Abstract

Skin lesion border irregularity, which represents the B feature in the ABCD rule, is considered one of the most significant factors in melanoma diagnosis. Since signs that clinicians rely on in melanoma diagnosis involve subjective judgment including visual signs such as border irregularity, this deems it necessary to develop an objective approach to finding border irregularity. Increased research in neural networks has been carried out in recent years mainly driven by the advances of deep learning. Artificial neural networks (ANNs) or multilayer perceptrons have been shown to perform well in supervised learning tasks. However, such networks usually don't incorporate information pertaining the ambiguity of the inputs when training the network, which in turn could affect how the weights are being updated in the learning process and eventually degrading the performance of the network when applied on test data. In this paper, we propose a fuzzy multilayer perceptron (F-MLP) that takes the ambiguity of the inputs into consideration and subsequently reduces the effects of ambiguous inputs on the learning process. A new optimization function, the fuzzy gradient descent, has been proposed to reflect those changes. Moreover, a type-II fuzzy sigmoid activation function has also been proposed which enables finding the range of performance the fuzzy neural network is able to attain. The fuzzy neural network was used to predict the skin lesion border irregularity, where the lesion was firstly segmented from the skin, the lesion border extracted, border irregularity measured using a proposed measure vector, and using the extracted border irregularity measures to train the neural network. The proposed approach outperformed most of the state-of-the-art classification methods in general and its standard neural network counterpart in particular. However, the proposed fuzzy neural network was more time-consuming when training the network.

Published
2020
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9. Amelanotic melanoma

Author

Andy J Muinonen-Martin, Sally Jane O’Shea, and Julia Newton-Bishop

Subjects
Fingers, Skin Neoplasms, Humans, Female, Melanoma, Amelanotic, General Medicine, Diagnostic Errors, Middle Aged, Skin
Published
2018

10. List of Contributors

Author

Anthony Abdullah, Michael Abrouk, Tashmeeta Ahad, Imtiaz Ahmed, Anwar Al Hammadi, Caroline Allen, Amer Ali Almohssen, Wisam Alwan, Mahreen Ameen, Sadegh Amini, Bryan E. Anderson, Grant J. Anhalt, Donald J. Baker, Harini Rajgopal Bala, Julia Baltz, David Banach, Cedric C. Banfield, Robert Baran, Ajoy Bardhan, Melissa C. Barkham, Ysabel M. Bello, Emma Benton, Wilma F. Bergfeld, Eric Berkowitz, Brian Berman, Jeffrey D. Bernhard, Daniel Bernstein, John Berth-Jones, Chinmoy Bhate, Bhavnit K. Bhatia, Jonathan E. Blume, Nevianna Bordet, Catherine Borysiewicz, Gary J. Brauner, Robert T. Brodell, Marc D. Brown, Robert M. Burd, Anne E. Burdick, Niraj Butala, Jeffrey P. Callen, Ivan D. Camacho, Helena Camasmie, Daniel Caplivski, Mitchell S. Cappell, Genevieve A. Casey, Lawrence S. Chan, Loi-Yuen Chan, Jennifer K. Chen, Chen 'Mary' Chen, Nicole Yi Zhen Chiang, Anthony J. Chiaravalloti, Fiona J. Child, Anthony C. Chu, Timothy H. Clayton, Steven R. Cohen, Elizabeth A. Cooper, Susan M. Cooper, Nick Collier, Christina M. Correnti, Ian H. Coulson, M. Laurin Council, Shawn E. Cowper, Nicholas M. Craven, Daniel Creamer, Ponciano D. Cruz, Carrie Ann R. Cusack, Adam Daunton, Mark D.P. Davis, Robert S. Dawe, David P. D’Cruz, David de Berker, Danielle M. DeHoratius, Min Deng, Seemal R. Desai, Georgina Devlin, John J. DiGiovanna, Alexander Doctoroff, Roni P. Dodiuk-Gad, Dawn Z. Eichenfield, Lawrence F. Eichenfield, Drore Eisen, Ure Eke, Dirk M. Elston, Patrick O.M. Emanuel, Clinton W. Enos, Shaheen H. Ensanyat, Anna F. Falabella, Aaron S. Farberg, Lawrence S. Feigenbaum, Kristen Heins Fernandez, Nicole Fett, Andrew Y. Finlay, Bahar F. Firoz, Elnaz F. Firoz, James E. Fitzpatrick, Amy E. Flischel, Kelly A. Foley, Derek Freedman, Georgina A. Fremlin, Richard Fried, Philip Friedlander, Adam Friedman, Amy K. Forrestel, Brian S. Fuchs, Joanna E. Gach, Anjela Galan, Jaya Ganesh, Amit Garg, Lauren Geller, Carlo M. Gelmetti, Elizabeth Ghazi, Sneha Ghunawat, Leonard H. Goldberg, Mark J.D. Goodfield, Marsha L. Gordon, Asha Gowda, Daniel A. Grabell, Matthew Grant, Clive E.H. Grattan, Malcolm W. Greaves, Justin J. Green, Christopher E.M. Griffiths, Charles A. Gropper, Anna L. Grossberg, Aditya K. Gupta, Ali S. Hadi, Suhail M. Hadi, Iris A. Hagans, Bethany R. Hairston, Analisa Vincent Halpern, Caroline Halverstam, Natasha Harper, Matthew J. Harries, John Harris, Shannon Harrison, Michael M. Hatch, Adrian H.M. Heagerty, Adelaide A. Hebert, Stephen E. Helms, Camile L. Hexsel, Doris M. Hexsel, Warren R. Heymann, Elisabeth M. Higgins, Claire L. Higgins, Whitney A. High, Herbert Hönigsmann, Marcelo G. Horenstein, George J. Hruza, Andrea Hui, Ran Huo, Sally H. Ibbotson, Sherrif F. Ibrahim, Andrew Ilchyshyn, Dina Ismail, Stefania Jablonska, Heidi T. Jacobe, William D. James, Aysha Javed, Gregor B.E. Jemec, Graham A. Johnston, Stephen K. Jones, Jacqueline M. Junkins-Hopkins, Jessica Kaffenberger, Kelly R. Kane, Antonios Kanelleas, Ayşe Serap Karadağ, Laura Karas, Ruwani P. Katugampola, Bruce E. Katz, Roselyn Kellen, Murtaza Khan, Hooman Khorasani, Ellen J. Kim, Hee J. Kim, Brian Kirby, Joslyn S. Kirby, Rachel S. Klein, Kate Kleydman, Dimitra Koch, John J. Kohorst, John Y.M. Koo, Sandra A. Kopp, Neil J. Korman, Carrie Kovarik, Kenneth H. Kraemer, Bernice R. Krafchik, Karthik Krishnamurthy, Knut Kvernebo, Charlene Lam, Peter C. Lambert, James A.A. Langtry, Amir A. Larian, Cecilia A. Larocca, E. Frances Lawlor, Clifford M. Lawrence, Mark G. Lebwohl, Oscar Lebwohl, Julia S. Lehman, Tabi A. Leslie, Stuart R. Lessin, Jacob O. Levitt, Fiona M. Lewis, Maryam Liaqat, Kristina J. Liu, Michael P. Loosemore, Thomas A. Luger, Omar Lupi, Boris D. Lushniak, Calum C. Lyon, Andrea D. Maderal, Bassel H. Mahmoud, Slawomir Majewski, Richard B. Mallett, Steven M. Manders, Ranon Mann, Yasaman Mansouri, David J. Margolis, Orit Markowitz, Alexander Marsland, Agustin Martin-Clavijo, Daniela Martinez, Catalina Matiz, Marcus Maurer, Kevin McKerrow, Nekma Meah, Giuseppe Micali, Robert G. Micheletti, Leslie G. Millard, James E. Miller, Jillian W. Wong Millsop, Daniel Mimouni, Ginat W. Mirowski, Sultan A. Mirza, Sonja Molin, Adisbeth Morales-Burgos, Warwick L. Morison, Cato Mørk, Colin A. Morton, Richard J. Motley, Megan Mowbray, Eavan G. Muldoon, Anna E. Muncaster, George J. Murakawa, Jenny E. Murase, Michele E. Murdoch, Adam S. Nabatian, Mio Nakamura, Rajani Nalluri, Zeena Y. Nawas, Glen R. Needham, Glenn C. Newell, Julia Newton-Bishop, Adam V. Nguyen, Rosemary L. Nixon, Jack C. O’Brien, Stephanie Ogden, Suzanne M. Olbricht, Sally Jane O’Shea, Cindy E. Owen, Michael Pan, Lisa Pappas-Taffer, Jennifer L. Parish, Lawrence Charles Parish, Michael Payette, Gary L. Peck, Sandra Pena, Jarad Peranteau, Frederick A. Pereira, William Perkins, Clifford S. Perlis, Robert G. Phelps, Tania J. Phillips, Maureen B. Poh-Fitzpatrick, Miriam Keltz Pomeranz, Samantha R. Pop, Pierluigi Porcu, James B. Powell, Lori D. Prok, Tia M. Pyle, Surod Qazaz, Vikram Rajkomar, Rabia S. Rashid, Mehdi Rashighi, Ravi Ratnavel, Christie G. Regula, Michael Renzi, Jean Revuz, Rachel V. Reynolds, Elisabeth Richard, Gabriele Richard, Darrell S. Rigel, Wanda Sonia Robles, Megan Rogge, Alain H. Rook, Jamie R. Manning, Ted Rosen, Misha Rosenbach, David Rosenfeld, Christopher Rowland Payne, Adam I. Rubin, Courtney Rubin, Malcolm H.A. Rustin, Thomas Ruzicka, Sara Samimi, Lawrence A. Schachner, Noah Scheinfeld, Bethanee J. Schlosser, Rhonda E. Schnur, Robert A. Schwartz, Matthew J. Scorer, Bryan A. Selkin, Jamie Seymour, Christine M. Shaver, Christopher R. Shea, Neil H. Shear, Tang Ngee Shim, Hiroshi Shimizu, Julia Siegel, Elisha Singer, Maral Kibarian Skelsey, Chris Sladden, Michael Sladden, Janellen Smith, Joanne E. Smucker, Najwa Somani, Lacy L. Sommer, Mary Sommerlad, Christine Soon, Jennifer A. Sopkovich, Nicholas A. Soter, James M. Spencer, Richard C.D. Staughton, Jane C. Sterling, Cord Sunderkötter, Saleem M. Taibjee, Deborah Tamura, Eunice Tan, William Y-M. Tang, Lynsey Taylor, Bruce H. Thiers, Lucy J. Thomas, Cody R. Thornton, Anne-Marie Tobin, Rochelle R. Torgerson, Antonella Tosti, Fragkiski Tsatsou, Yukiko Tsuji-Abe, William F.G. Tucker, Stephen K. Tyring, Jeremy Udkoff, Robin H. Unger, Walter P. Unger, Sarah Utz, Martha C. Valbuena, Peter van de Kerkhof, Abby S. Van Voorhees, Ramya Vangipuram, David Veitch, Vanessa Venning, Sarah G. Versteeg, Martha Viera, Carmela C. Vittorio, Ruth Ann Vleugels, Gorav N. Wali, Joanna Wallengren, Joy Wan, Karolyn A. Wanat, Gabriele Weichert, Anja K. Weidmann, Jeffrey M. Weinberg, Victoria P. Werth, Lucile E. White, Adam H. Wiener, Jonathan K. Wilkin, Nathaniel K. Wilkin, Jason Williams, Niall Wilson, Karen Wiss, Joseph A. Witkowski, Lauren E. Wiznia, Henry K. Wong, Junie Li Chun Wong, Andrew L. Wright, Cooper C. Wriston, Benedict C. Wu, Adam Wulkan, Andrea L. Zaenglein, Irshad Zaki, Joshua A. Zeichner, Tian Hao Zhu, John J. Zone, Christos C. Zouboulis, and Torstein Zuberbeir

Published
2018
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11. Which symptoms are linked to a delayed presentation among melanoma patients? A retrospective study

Author

Sally Jane, O'Shea, Zoe, Rogers, Fiona, Warburton, Amanda J, Ramirez, Julia A, Newton-Bishop, and Lindsay J L, Forbes

Subjects
Adult, Aged, 80 and over, Male, Health Knowledge, Attitudes, Practice, Delayed Diagnosis, Time Factors, Incidence, Middle Aged, Patient Acceptance of Health Care, Prognosis, United Kingdom, Young Adult, Risk Factors, Surveys and Questionnaires, Behavioural symptoms, Humans, Female, Melanoma, Aged, Follow-Up Studies, Retrospective Studies, Research Article
Abstract

Background The incidence of melanoma is rising. Early detection is associated with a more favourable outcome. The factors that influence the timing of a patient’s presentation for medical assessment are not fully understood. The aims of the study were to measure the nature and duration of melanoma symptoms in a group of patients diagnosed with melanoma within the preceding 18 months and to identify the symptoms and barriers associated with a delay in presentation. Methods A questionnaire was distributed to a random sample of 200 of the 963 melanoma patients who had participated in the Cancer Patient Experience Survey 2010 and were known to be alive 1 year later. Data were collected on symptoms, duration of symptoms prior to presentation and the reasons for not attending a doctor sooner. Results A total of 159 patients responded to the questionnaire; 74 (47%) were men; mean age was 62 (range 24–90) years. Of the 149 patients who reported a symptom, 40 (27%) had a delayed presentation (i.e. >3 months). A mole growing bigger was the most common symptom and reporting this symptom was significantly associated with a delayed presentation (odds ratio (OR) 2.04, 95% confidence interval (95% CI) 1.14–5.08). Patients aged ≥65 years were less likely to report a barrier to presentation and were less likely to delay than those under 40, although this was of borderline statistical significance (OR 0.28, 95% CI 0.08–1.00). Conclusions This study highlights that an enlarging mole is a significant symptom influencing the timing of presentation. Increasing public awareness of the signs of melanoma and of the importance of early presentation is desirable. Health professionals should take advantage of the opportunity to educate patients on such symptoms and signs where feasible. Further exploration of the barriers to presentation in younger people should be considered. Electronic supplementary material The online version of this article (doi:10.1186/s12885-016-2978-6) contains supplementary material, which is available to authorized users.

Published
2015

12. Chronic Actinic Dermatitis

Author

Sally Jane O’Shea, Christian Gulmann, and Gillian M. Murphy

Subjects
medicine.medical_specialty, Chronic eczema, Hand eczema, business.industry, medicine, Chronic actinic dermatitis, cardiovascular diseases, medicine.disease, business, Contact dermatitis, Allergic contact dermatitis, Dermatology, Cutaneous lymphoma
Abstract

Chronic actinic dermatitis (CAD) is an uncommon photosensitive disorder, which occurs predominantly in elderly men. CAD is characterised by chronic eczema, which is usually severe, is accentuated in light-exposed sites, and persists all year but is worse in summertime. CAD is associated with abnormal responses to ultraviolet (UV) radiation on testing with broad-band or monochromator sources.

Published
2015
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