Relevant bibliographies by topics / Skin cancer

Academic literature on the topic 'Skin cancer'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 September 2023

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Journal articles on the topic "Skin cancer"

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Benkirane, Selma. "Skin metastases revealing lung cancer." Clinical Medical Reviews and Reports 2, no. 4 (August 10, 2020): 01–02. http://dx.doi.org/10.31579/2690-8794/024.

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Shope, Chelsea, Laura Andrews, Courtney Linkous, Pelin Sagut, and Lara Wine Lee. "Predicting Skin Cancer Development after Liver Transplant." SKIN The Journal of Cutaneous Medicine 7, no. 1 (January 10, 2023): 602–7. http://dx.doi.org/10.25251/skin.7.1.8.

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Introduction: As the number of solid organ transplants (SOTs) continues to increase and post-transplant therapies improve, SOT recipients (SOTRs) live longer and thus, are increasingly affected by post-transplant sequala such as skin cancer. Research investigating risk factors associated with skin cancer development in SOTRs has largely been conducted in kidney recipients. Methods: We performed a retrospective chart review of SOTRs seen by dermatology from January 1, 2012 – June 1, 2022. Data was analyzed using Pearson chi-square testing and Classification and Regression Tree (CART) modeling. Results: Of 530 patients meeting inclusion criteria, 80 received liver transplants. Among liver recipients, a total of 155 skin cancers and five recurrences developed following transplant among 37 patients (46.25%). Patients who developed skin cancer were Caucasian (94.6%, p-value=0.186) and were significantly more likely to be male (78.4%, p-value=0.045) and former smokers (59.5%, p-value=0.038). CART showed age greater than 43 years was the biggest predictor for later skin cancer development. Patients most frequently developed squamous cell carcinoma (60.87%) of the head and neck (51.35%) or upper extremities (29.73%). Conclusion: Risk factors associated with skin cancer development in liver transplant recipients include increased age at transplant, white race, male sex, and smoking status. Stratification of referrals to dermatology based on these factors should be considered.
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Bhattacharya, Kaustuv, Namita Joshi, Ruchit Shah, and Vinayak K. Nahar. "Impact of Depression on Health-Related Quality of Life among Skin Cancer Survivors." SKIN The Journal of Cutaneous Medicine 3, no. 6 (December 2, 2019): 381–94. http://dx.doi.org/10.25251/skin.3.6.3.

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Introduction: Skin cancers are one of the most common cancers in the United States (US). Studies have reported depression to be a common comorbid condition among individuals with skin cancer. This study aimed to evaluate the relationship of depression with health-related quality of life (HRQOL) among individuals with a skin cancer diagnosis.Methods: A cross-sectional study design using the 2017 Behavioral Risk Factor Surveillance System (BRFSS) data, a nationally representative sample of non-institutionalized US adults, was utilized for the study. Multivariable logistic regression was used to assess the relationship between depression and the HRQOL domains (general health status, physical health, mental health, and activity limitations due to poor physical or mental health) among survivors of skin cancer.Results: Comorbid depression was identified in 20% of skin cancer survivors. After adjusting for covariates, skin cancer survivors with depression had higher odds of having poor general health status (Odds Ratio [OR] = 1.67, 95% Confidence Interval [CI] 1.41-1.98) as compared to skin cancer survivors without depression. Skin cancer survivors with depression also had greater odds of having poor physical HRQOL (OR = 1.82, 95% CI 1.53-2.15), poor mental HRQOL (OR = 6.38, 95% CI 5.26-7.74), and activity limitations (OR = 2.42, 95% CI 2.03-2.89) as compared to those without depression.Conclusion: This study highlights the significant negative impact of comorbid depression on HRQOL in a nationally representative sample of skin cancer survivors, and serves as evidence for the need for more active surveillance and management of depression in this population.
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A, Soujanya. "A Review on Melanoma Skin Cancer Detection Methods." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1525–33. http://dx.doi.org/10.5373/jardcs/v12sp7/20202255.

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Marson, Justin, Aaron Farberg, Alex Glazer, Graham Litchman, Ryan Svoboda, Richard Winkelmann, and Darrell Rigel. "Expert Consensus on Sunscreen for the Primary Prevention of Skin Cancer: Results of the Skin Cancer Prevention Working Group Conference." SKIN The Journal of Cutaneous Medicine 5, no. 3 (May 21, 2021): 190–202. http://dx.doi.org/10.25251/skin.5.3.1.

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Background: Melanoma and non-melanoma skin cancers (NMSC) are the overall most common type of malignancy. Despite this fact, the use of sunscreen as a primary preventative measure for skin cancer is not ubiquitous. Objective: To review the literature regarding efficacy and safety of sunscreens and to process and condense data into overarching principles to provide guidance to the general public and improve outcomes for melanoma NMSC. Methods: A systematic review of the literature pertaining to sunscreen efficacy in the primary prevention of melanoma and non-melanoma skin cancer, safety in humans and environmental impact was conducted. Following a thorough review of the literature, the Skin Cancer Prevention Working Group (SCPWG), an expert panel consisting of dermatologists with specialized training in melanoma and NMSC diagnosis and management, employed a modified Delphi technique to reach consensus over the development of statements regarding the current level of evidence for sunscreen efficacy and safety. Final statements were only adopted after achieving a supermajority vote >80%. Results: 96 articles were identified for further review and discussion. The SCPWG developed 7 consensus statements regarding the efficacy and safety of sunscreens and their role in the prevention of melanoma and NMSC. Conclusion: The proven benefits of primary skin cancer prevention outweigh the potential/hypothetical risks of sunscreen use, especially given insufficient real-world, prospective data for the discussed risks. As experts in skin health and skin cancer pathophysiology, the SCPWG believes dermatologists are uniquely qualified to lead future studies investigating sunscreen efficacy and safety and should counsel patients and the public on skin cancer primary prevention strategies.
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Becerril, Sara, Roberto Corchado-Cobos, Natalia García-Sancha, Leonor Revelles, David Revilla, Tatiana Ugalde, Concepción Román-Curto, Jesús Pérez-Losada, and Javier Cañueto. "Viruses and Skin Cancer." International Journal of Molecular Sciences 22, no. 10 (May 20, 2021): 5399. http://dx.doi.org/10.3390/ijms22105399.

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Advances in virology and skin cancer over recent decades have produced achievements that have been recognized not only in the field of dermatology, but also in other areas of medicine. They have modified the therapeutic and preventive solutions that can be offered to some patients and represent a significant step forward in our knowledge of the biology of skin cancer. In this paper, we review the viral agents responsible for different types of skin cancer, especially for solid skin tumors. We focus on human papillomavirus and squamous cell cancers, Merkel cell polyomavirus and Merkel cell carcinoma, and human herpesvirus 8 and Kaposi’s sarcoma.
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Berry, Lisa. "Skin cancer." Cancer Nursing Practice 15, no. 8 (October 10, 2016): 11. http://dx.doi.org/10.7748/cnp.15.8.11.s11.

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Buchanan, Pauline J. "Skin cancer." Nursing Standard 15, no. 45 (July 25, 2001): 45–52. http://dx.doi.org/10.7748/ns2001.07.15.45.45.c3063.

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Marks, Ronald, and Richard J. Motley. "Skin Cancer." Drugs 50, no. 1 (July 1995): 48–61. http://dx.doi.org/10.2165/00003495-199550010-00005.

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Chambers, Spencer. "Skin cancer." University of Western Ontario Medical Journal 85, no. 2 (November 6, 2016): 38–40. http://dx.doi.org/10.5206/uwomj.v85i2.2240.

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Skin cancer is the most common type of neoplasm worldwide. Skin cancer can be classified as non-melanoma skin cancer (NMSC) or melanoma skin cancer (MSC). NMSCs are more common lesions, and typically carry a good prognosis. MSCs are rarer, but cause the majority of skin cancer-related death and morbidity. The biggest causative factor for any skin cancer is ultraviolet radiation exposure (UVR). UVR comes from the sun or synthetic sources such as tanning beds, making it highly avoidable through behavioral change. Despite this, the incidence of skin cancer in Canada has risen over the past decades at an alarming rate. There are many levels of preventive medicine currently in place attempting to change this trend. Primary strategies include wearing hats and protective clothing, as well as avoiding peak daytime hours and tanning beds. At a secondary level, clinical skin exams and public education work to identify disease at earlier stages to make treatments more effective. Both surgical and destructive management can be effective, but success is highly dependent on the stage of disease. Even with appropriate treatment there is a risk of complications including deformity, recurrence and even death. This emphasizes the best treatment for skin cancer is prevention and further underscores the need for behavioural changes at the population level.
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Dissertations / Theses on the topic "Skin cancer"

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Andrews, S. G. "Skin cancer image recognition." Thesis, Lancaster University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.421835.

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Primm, Aaron N. "Immunosuppression, inflammation, and skin cancer will eczema treatment enhance ultraviolet light-induced skin cancer? /." Connect to resource, 2006. http://hdl.handle.net/1811/6433.

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Thesis (Honors)--Ohio State University, 2006.
Title from first page of PDF file. Document formatted into pages: contains 45 p.; also includes graphics. Includes bibliographical references (p. 41-45). Available online via Ohio State University's Knowledge Bank.
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Moustafa, Ahmed. "Skin cancer Detection byTemperature VariationAnalysis." Thesis, KTH, Skolan för teknik och hälsa (STH), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-107422.

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In the medical field new technologies are incorporated for the sole purpose to enhance the quality of life for patients and even for the normal persons. Infrared technology is one of the technologies that has some applications in both the medical and biological fields. In this work, the thermal infrared (IR) measurement is used to investigate its potential in skin cancer detection. IR enjoys a non-invasive and non-contact advantages as well as favorable cost, apparently. It is also very well developed regarding the technological and methodological aspects. IR radiation, per se, is an electromagnetic radiation that all objects emit when their temperature is above the absolute zero. Human body is not different. The IR range extends, ideally, to cover wavelengths from 800 nanometer to few hundreds micrometer. Cancer, in modern life, has grown tangibly due to many factors apparently such life expectancies increase, personal habits, and ultraviolet radiation (UV) exposures among others. Moreover, the significant enhancement of technologies has helped identifying more types of cancers than before. The purpose of this work is to investigate further another method and application of IR technology not yet matured in detection of skin cancer to enhance detection ability that is accompanied with higher level of safety. An extensive research project was designed to use two laboratory animals injected with cancer cells subcutaneously and two IR radiation sensors able to detect wavelengths in the range 8 – 14 μm which proved to be a favorable range to measure the temperature of the skin. Data collection performed using two lab animals as subjects that formed a double blind investigation process. An analysis of the observations was conducted both in qualitative as well as quantitative approaches. The analysis and discussion revealed the potential of the thermal IR radiation in detecting skin cancer existence. The thesis was supported with significant evidence and achieved its target. Furthermore, it was clear that the functional nature of thermal IR detection constitutes another advantage for this method that can be used in the future to develop an objective and automated method for detection of skin cancer in a straight forward and cost effective manner.
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McHenry, Pamela Margaret. "Skin cancer in the elderly." Thesis, Queen's University Belfast, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282040.

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Soerjomataram, Isabelle. "Multiple primary cancers in patients with breast ans skin cancer." [S.l.] : Rotterdam : [The Author] ; Erasmus University [Host], 2007. http://hdl.handle.net/1765/10779.

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Kerr, Peter Joseph. "A study of skin homeostasis and skin tumorigenesis using transgenic mice." Thesis, University of Glasgow, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366197.

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Dolfe, Rafael, and Keivan Matinzadeh. "Investigating Skin Cancer with Unsupervised Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259363.

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Skin cancer is one of the most commonly diagnosed cancers in the world. Diagnosis of skin cancer is commonly performed by analysing skin lesions on the patient’s body. Today’s medical diagnostics use a established set of labels for different types of skin lesions. Another way of categorising skin lesions could be to let a computer perform the analysis without any prior knowledge of the data, where the data is a data set of skin lesion images. This categorisation could then be compared to the already existing medical labels assigned to each image. This categorisation and comparison could provide insight into underlying structures of skin lesion data. To investigate this, three unsupervised learning algorithms; K-means, agglomerative clustering, and spectral clustering, have been used to produce cluster partitionings on a data set of skin lesion images. We found no clear cluster partitionings and no connection to the already existing medical labels. The highest scoring partitioning was produced by spectral clustering when the number of clusters was set to two. Further investigation into the structure of this partitioning revealed that one cluster contained essentially every image. Although relatively low, the score does indicate that the underlying structure may be best represented by a single cluster.
Hudcancer är en av de mest förekommande typerna av cancer i världen. Det vanligaste sättet att diagnosticera hudcancer är för en dermatolog att analysera hudsår på en patients kropp. Dagens medicinsk diagnostik använder en etablerad mängd beteckningar för olika typer av hudsår. Ett alternativ till denna typ av diagnostisering skulle kunna vara att låta en dator utan förkunskap om datan (bilder på hudsår) sköta analysen. Denna katogorisering skulle sedan kunna jämföras med de existerande medicinska katogorierna som varje bild fått. För att undersöka detta användes tre algoritmer av typen oövervakat lärande för att producera kluster-indelningar på ett dataset innehållandes bilder på hudsår. Dessa algoritmer var K-means, agglomerative clustering, och spectral clustering. Vi fann inga uppenbara kluster-indelningar och ingen koppling mellan de nuvarande medicinska beteckningarna. Den indelning av kluster som fick högst poäng när den evaluaredes internt var den indelning av kluster genererad av spectral clustering. Detta skedde när antalet kluster som algoritmen skulle dela upp datan i var satt till två. En djupare undersökning i strukturen av denna indelning visade att ett av klustrerna i princip innehöll varje bild. Även fast Silhouette-värdet för denna indelning var låg, pekar värdet på att den underliggande strukturen bäst kan representeras av ett enda kluster.
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Åberg, Peter. "Skin cancer as seen by electrical impedance /." Stockholm, 2004. http://diss.kib.ki.se/2004/91-7140-103-2/.

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Campbell, Charlotte, Allen Ashley Van, and Erin Vincent. "Skin Cancer Knowledge and Prevention Counseling among Arizona Pharmacists." The University of Arizona, 2009. http://hdl.handle.net/10150/623972.

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Class of 2009 Abstract
OBJECTIVES: Skin cancer is particularly prevalent in Arizona, with incidence rates ranking number two worldwide. Pharmacists are useful advocates for educating patients about the risks of skin cancer and methods of prevention. This study was conducted to assess pharmacists’ knowledge of skin cancer and their demographics and to evaluate how these factors impact skin cancer prevention patient counseling. METHODS: Participants were recruited using a listserv from pharmacists that were members of the Arizona Pharmacy Alliance or preceptors of the University of Arizona College of Pharmacy. Subjects completed an online questionnaire consisting of knowledge- based questions, questions about patient counseling preferences and subject demographics. RESULTS: The average score by pharmacists on the Skin Cancer and Sun Exposure Knowledge Indicator was 5.8 + 1.9. Pharmacists living in Arizona for longer times were more likely to know the minimum recommended SPF of sunscreen for adults to use when outdoors (p=0.003) and the factors associated with malignant melanoma prognosis/survival (p=0.004), but were less likely to know the definition of ABCD acronym (p=0.027). Having a family or friend diagnosed with any form of skin cancer or precancerous skin condition led to more pharmacists knowing the risk factors for developing melanoma (p=0.046) and knowing how often to apply water resistant sunscreen (p=0.035). CONCLUSIONS: The length of pharmacy practice in Arizona and having a family member or close friend affected by skin cancer significantly impacted a pharmacists’ knowledge of skin cancer.
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Urosevic, Mirjana. "Exploring immunotherapeutic strategies to treat skin cancer /." Zürich, 2005. http://opac.nebis.ch/cgi-bin/showAbstract.pl?sys=000253356.

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Books on the topic "Skin cancer"

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Schwartz, Robert A. Skin Cancer. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4612-3790-7.

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Schwartz, Robert A., ed. Skin Cancer. Oxford, UK: Blackwell Publishing Ltd, 2008. http://dx.doi.org/10.1002/9780470696347.

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Fredericks, Carrie. Skin cancer. Detroit: Greenhaven Press, 2010.

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Baldi, Alfonso, Paola Pasquali, and Enrico P. Spugnini, eds. Skin Cancer. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7357-2.

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Fredericks, Carrie. Skin cancer. Detroit: Greenhaven Press, 2010.

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G, Haluska Frank, ed. Skin cancer. Hamilton, Ont: B C Decker, 2001.

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Goldsmith, Connie. Skin cancer. Minneapolis: Twenty-First Century Books, 2011.

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Cushion, Jane. Skin cancer. [London]: House of Commons Library, 1996.

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Juettner, Bonnie. Skin cancer. Detroit: Lucent Books, 2008.

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Carrie, Fredericks, ed. Skin cancer. Detroit: Greenhaven Press, 2010.

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Book chapters on the topic "Skin cancer"

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Theisler, Charles. "Cancer (Skin Cancer)." In Adjuvant Medical Care, 52. New York: CRC Press, 2022. http://dx.doi.org/10.1201/b22898-61.

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Beyzadeoglu, Murat, Gokhan Ozyigit, Ugur Selek, and Ugur Selek. "Skin Cancer." In Radiation Oncology, 419–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27988-1_12.

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Singer, Lisa, and Sue S. Yom. "Skin Cancer." In Handbook of Evidence-Based Radiation Oncology, 3–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-62642-0_1.

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Joshi, Nikhil P., and Martin C. Tom. "Skin Cancer." In Practical Radiation Oncology, 263–68. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0073-2_42.

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Jensen, Lindsay G., Loren K. Mell, Christin A. Knowlton, Michelle Kolton Mackay, Filip T. Troicki, Jaganmohan Poli, Edward J. Gracely, et al. "Skin Cancer." In Encyclopedia of Radiation Oncology, 792–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-540-85516-3_118.

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Telfer, M. "Skin Cancer." In A Manual of Oral and Maxillofacial Surgery for Nurses, 231–47. Oxford, UK: Blackwell Science Ltd, 2008. http://dx.doi.org/10.1002/9780470698914.ch11b.

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Kaprealian, Tania, James Rembert, Lawrence W. Margolis, and Sue S. Yom. "Skin Cancer." In Handbook of Evidence-Based Radiation Oncology, 3–25. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-0-387-92988-0_1.

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Friedman, R., and C. D. Sherman. "Skin Cancer." In Manual of Clinical Oncology, 235–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85159-9_14.

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Yom, Sue S., and Sarah Arron. "Skin Cancer." In Skin Care in Radiation Oncology, 187–98. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31460-0_14.

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Emmert, Steffen. "Skin Cancer." In Encyclopedia of Cancer, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-27841-9_5339-8.

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Conference papers on the topic "Skin cancer"

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Junayed, Masum Shah, Nipa Anjum, Abu Noman Sakib, and Baharul Islam. "A Deep CNN Model for Skin Cancer Detection and Classification." In WSCG'2021 - 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision'2021. Západočeská univerzita, 2021. http://dx.doi.org/10.24132/csrn.2021.3002.8.

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Skin cancer is one of the most dangerous types of cancers that affect millions of people every year. The detection ofskin cancer in the early stages is an expensive and challenging process. In recent studies, machine learning-basedmethods help dermatologists in classifying medical images. This paper proposes a deep learning-based modelto detect and classify skin cancer using the concept of deep Convolution Neural Network (CNN). Initially, wecollected a dataset that includes four skin cancer image data before applying them in augmentation techniques toincrease the accumulated dataset size. Then, we designed a deep CNN model to train our dataset. On the test data,our model receives 95.98% accuracy that exceeds the two pre-train models, GoogleNet by 1.76% and MobileNetby 1.12%, respectively. The proposed deep CNN model also beats other contemporaneous models while beingcomputationally comparable.
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Kumar, Ayushi, Ari Kapelyan, and Avimanyou K. Vatsa. "Classification of Skin Phenotype: Melanoma Skin Cancer." In 2021 IEEE Integrated STEM Education Conference (ISEC). IEEE, 2021. http://dx.doi.org/10.1109/isec52395.2021.9763999.

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Alharbi, Wafa Matar A. "Skin Cancer Monitoring Project." In 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS). IEEE, 2019. http://dx.doi.org/10.1109/cais.2019.8769479.

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Lee, Tim K., Lioudmila Tchvialeva, Haishan Zeng, David I. McLean, and Harvey Lui. "Laser speckle and skin cancer: skin roughness assessment." In Correlation Optics 2009. SPIE, 2009. http://dx.doi.org/10.1117/12.854001.

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Marquez, Guillermo, Lihong V. Wang, Mehrube Mehrubeoglu, and Nasser Kehtarnavaz. "Imaging obliquely illuminated skin lesions for skin cancer detection." In Biomedical Optical Spectroscopy and Diagnostics. Washington, D.C.: OSA, 2000. http://dx.doi.org/10.1364/bosd.2000.sug1.

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Nugroho, Ardan Adi, Isnandar Slamet, and Sugiyanto. "Skins cancer identification system of HAMl0000 skin cancer dataset using convolutional neural network." In INTERNATIONAL CONFERENCE ON SCIENCE AND APPLIED SCIENCE (ICSAS) 2019. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5141652.

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Dubal, Pratik, Sankirtan Bhatt, Chaitanya Joglekar, and Sonali Patil. "Skin cancer detection and classification." In 2017 6th International Conference on Electrical Engineering and Informatics (ICEEI). IEEE, 2017. http://dx.doi.org/10.1109/iceei.2017.8312419.

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Gouda, Niharika, and J. Amudha. "Skin Cancer Classification using ResNet." In 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA). IEEE, 2020. http://dx.doi.org/10.1109/iccca49541.2020.9250855.

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Panjehpour, Masoud, Clark E. Julius, and Donald L. Hartman. "Photodynamic therapy for skin cancer." In Photonics West '96, edited by Thomas J. Dougherty. SPIE, 1996. http://dx.doi.org/10.1117/12.237545.

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Sudhamsu, Gadug, and Mahesh TR. "ANN Based Skin Cancer Detection." In 2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC). IEEE, 2022. http://dx.doi.org/10.1109/iihc55949.2022.10060456.

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Reports on the topic "Skin cancer"

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Paul, Satashree. Skin Cancer: Prevalent in Caucasians. Science Repository, December 2020. http://dx.doi.org/10.31487/sr.blog.21.

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More number of melanized melanosomes present in darker-skinned groups absorb and scatter more energy than do the smaller number of melanosomes. The color of the skin is a primary reason behind the occurrence of skin cancer.
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Janda, Monika, and Rachel Neale. Skin Cancer Prevention Queensland: towards a future of reduced skin cancer burden for Queenslanders. Brisbane, QLD, Australia: The University of Queensland, January 2023. http://dx.doi.org/10.14264/2753bd0.

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Smit, Amelia, Kate Dunlop, Nehal Singh, Diona Damian, Kylie Vuong, and Anne Cust. Primary prevention of skin cancer in primary care settings. The Sax Institute, August 2022. http://dx.doi.org/10.57022/qpsm1481.

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Overview Skin cancer prevention is a component of the new Cancer Plan 2022–27, which guides the work of the Cancer Institute NSW. To lessen the impact of skin cancer on the community, the Cancer Institute NSW works closely with the NSW Skin Cancer Prevention Advisory Committee, comprising governmental and non-governmental organisation representatives, to develop and implement the NSW Skin Cancer Prevention Strategy. Primary Health Networks and primary care providers are seen as important stakeholders in this work. To guide improvements in skin cancer prevention and inform the development of the next NSW Skin Cancer Prevention Strategy, an up-to-date review of the evidence on the effectiveness and feasibility of skin cancer prevention activities in primary care is required. A research team led by the Daffodil Centre, a joint venture between the University of Sydney and Cancer Council NSW, was contracted to undertake an Evidence Check review to address the questions below. Evidence Check questions This Evidence Check aimed to address the following questions: Question 1: What skin cancer primary prevention activities can be effectively administered in primary care settings? As part of this, identify the key components of such messages, strategies, programs or initiatives that have been effectively implemented and their feasibility in the NSW/Australian context. Question 2: What are the main barriers and enablers for primary care providers in delivering skin cancer primary prevention activities within their setting? Summary of methods The research team conducted a detailed analysis of the published and grey literature, based on a comprehensive search. We developed the search strategy in consultation with a medical librarian at the University of Sydney and the Cancer Institute NSW team, and implemented it across the databases Embase, MEDLINE, PsycInfo, Scopus, Cochrane Central and CINAHL. Results were exported and uploaded to Covidence for screening and further selection. The search strategy was designed according to the SPIDER tool for Qualitative and Mixed-Methods Evidence Synthesis, which is a systematic strategy for searching qualitative and mixed-methods research studies. The SPIDER tool facilitates rigour in research by defining key elements of non-quantitative research questions. We included peer-reviewed and grey literature that included skin cancer primary prevention strategies/ interventions/ techniques/ programs within primary care settings, e.g. involving general practitioners and primary care nurses. The literature was limited to publications since 2014, and for studies or programs conducted in Australia, the UK, New Zealand, Canada, Ireland, Western Europe and Scandinavia. We also included relevant systematic reviews and evidence syntheses based on a range of international evidence where also relevant to the Australian context. To address Question 1, about the effectiveness of skin cancer prevention activities in primary care settings, we summarised findings from the Evidence Check according to different skin cancer prevention activities. To address Question 2, about the barriers and enablers of skin cancer prevention activities in primary care settings, we summarised findings according to the Consolidated Framework for Implementation Research (CFIR). The CFIR is a framework for identifying important implementation considerations for novel interventions in healthcare settings and provides a practical guide for systematically assessing potential barriers and facilitators in preparation for implementing a new activity or program. We assessed study quality using the National Health and Medical Research Council (NHMRC) levels of evidence. Key findings We identified 25 peer-reviewed journal articles that met the eligibility criteria and we included these in the Evidence Check. Eight of the studies were conducted in Australia, six in the UK, and the others elsewhere (mainly other European countries). In addition, the grey literature search identified four relevant guidelines, 12 education/training resources, two Cancer Care pathways, two position statements, three reports and five other resources that we included in the Evidence Check. Question 1 (related to effectiveness) We categorised the studies into different types of skin cancer prevention activities: behavioural counselling (n=3); risk assessment and delivering risk-tailored information (n=10); new technologies for early detection and accompanying prevention advice (n=4); and education and training programs for general practitioners (GPs) and primary care nurses regarding skin cancer prevention (n=3). There was good evidence that behavioural counselling interventions can result in a small improvement in sun protection behaviours among adults with fair skin types (defined as ivory or pale skin, light hair and eye colour, freckles, or those who sunburn easily), which would include the majority of Australians. It was found that clinicians play an important role in counselling patients about sun-protective behaviours, and recommended tailoring messages to the age and demographics of target groups (e.g. high-risk groups) to have maximal influence on behaviours. Several web-based melanoma risk prediction tools are now available in Australia, mainly designed for health professionals to identify patients’ risk of a new or subsequent primary melanoma and guide discussions with patients about primary prevention and early detection. Intervention studies have demonstrated that use of these melanoma risk prediction tools is feasible and acceptable to participants in primary care settings, and there is some evidence, including from Australian studies, that using these risk prediction tools to tailor primary prevention and early detection messages can improve sun-related behaviours. Some studies examined novel technologies, such as apps, to support early detection through skin examinations, including a very limited focus on the provision of preventive advice. These novel technologies are still largely in the research domain rather than recommended for routine use but provide a potential future opportunity to incorporate more primary prevention tailored advice. There are a number of online short courses available for primary healthcare professionals specifically focusing on skin cancer prevention. Most education and training programs for GPs and primary care nurses in the field of skin cancer focus on treatment and early detection, though some programs have specifically incorporated primary prevention education and training. A notable example is the Dermoscopy for Victorian General Practice Program, in which 93% of participating GPs reported that they had increased preventive information provided to high-risk patients and during skin examinations. Question 2 (related to barriers and enablers) Key enablers of performing skin cancer prevention activities in primary care settings included: • Easy access and availability of guidelines and point-of-care tools and resources • A fit with existing workflows and systems, so there is minimal disruption to flow of care • Easy-to-understand patient information • Using the waiting room for collection of risk assessment information on an electronic device such as an iPad/tablet where possible • Pairing with early detection activities • Sharing of successful programs across jurisdictions. Key barriers to performing skin cancer prevention activities in primary care settings included: • Unclear requirements and lack of confidence (self-efficacy) about prevention counselling • Limited availability of GP services especially in regional and remote areas • Competing demands, low priority, lack of time • Lack of incentives.
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Author, Not Given. DNA Repair Enzyme-Liposomes: Human Skin Cancer Prevention. Office of Scientific and Technical Information (OSTI), September 1999. http://dx.doi.org/10.2172/770453.

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Hona, Telena, Rachael Neale, Jodie Antrobus, Caitlin Horsham, and Monika Janda. Skin Cancer Prevention Queensland: Sunscreen Industry Forum Report. Brisbane, QLD, Australia: Skin Cancer Prevention Queensland and The University of Queensland, August 2023. http://dx.doi.org/10.14264/3d35dc1.

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Liu, Ruolin, Qianyi Wan, and Rui Zhao. Non-melanoma skin cancer risk in patients receiving biological therapy for common inflammatory diseases. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, July 2021. http://dx.doi.org/10.37766/inplasy2021.7.0005.

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Ransom, Monica. Determining the Location of DNA Modification and Mutation Caused by UVB Light in Skin Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada599203.

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Morris, Rebecca J. A Novel Mechanism for the Pathogenesis of Nonmelanoma Skin Cancer Resulting from Early Exposure to Ultraviolet Light. Fort Belvoir, VA: Defense Technical Information Center, September 2014. http://dx.doi.org/10.21236/ada614400.

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Morris, Rebecca. A Novel Mechanism for the Pathogenesis of Nonmelanoma Skin Cancer Resulting from Early Exposure to Ultraviolet Light. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada600686.

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Murphy, Adam B. Vitamin D Levels and Related Genetic Polymorphisms, Sun Exposure, Skin Color, and Risk of Aggressive Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, July 2011. http://dx.doi.org/10.21236/ada549597.

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