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Melanoma and artificial intelligence

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https://www.readbyqxmd.com/read/30191140/immuno-oncology-emerging-targets-and-combination-therapies
#1
REVIEW
Henry T Marshall, Mustafa B A Djamgoz
Host immunity recognizes and eliminates most early tumor cells, yet immunological checkpoints, exemplified by CTLA-4, PD-1, and PD-L1, pose a significant obstacle to effective antitumor immune responses. T-lymphocyte co-inhibitory pathways influence intensity, inflammation and duration of antitumor immunity. However, tumors and their immunosuppressive microenvironments exploit them to evade immune destruction. Recent PD-1 checkpoint inhibitors yielded unprecedented efficacies and durable responses across advanced-stage melanoma, showcasing potential to replace conventional radiotherapy regimens...
2018: Frontiers in Oncology
https://www.readbyqxmd.com/read/30081540/an-intelligent-system-for-monitoring-skin-diseases
#2
Dawid Połap, Alicja Winnicka, Kalina Serwata, Karolina Kęsik, Marcin Woźniak
The practical increase of interest in intelligent technologies has caused a rapid development of all activities in terms of sensors and automatic mechanisms for smart operations. The implementations concentrate on technologies which avoid unnecessary actions on user side while examining health conditions. One of important aspects is the constant inspection of the skin health due to possible diseases such as melanomas that can develop under excessive influence of the sunlight. Smart homes can be equipped with a variety of motion sensors and cameras which can be used to detect and identify possible disease development...
August 4, 2018: Sensors
https://www.readbyqxmd.com/read/30079779/improving-the-early-diagnosis-of-early-nodular-melanoma-can-we-do-better
#3
Paola Corneli, Iris Zalaudek, Giovanni Magaton Rizzi, Nicola di Meo
Cutaneous melanoma is the sixth most common malignant cancer in the USA. Among different subtypes of melanoma, nodular melanoma (NM) accounts about 14% of all cases but is responsible for more than 40% of melanoma deaths. Early diagnosis is the best method to improve melanoma prognosis. Unfortunately, early diagnosis of NM is particularly challenging given that patients often lack identifiable risk factors such as many moles or freckles. Moreover, early NM may mimic a range of benign skin lesions that are not routinely excised or biopsied in every day practice...
October 2018: Expert Review of Anticancer Therapy
https://www.readbyqxmd.com/read/29846499/artificial-intelligence-for-melanoma-diagnosis-how-can-we-deliver-on-the-promise
#4
V J Mar, H P Soyer
No abstract text is available yet for this article.
August 1, 2018: Annals of Oncology: Official Journal of the European Society for Medical Oncology
https://www.readbyqxmd.com/read/29790922/artificial-intelligence-for-melanoma-diagnosis-how-can-we-deliver-on-the-promise
#5
V J Mar, H P Soyer
No abstract text is available yet for this article.
May 22, 2018: Annals of Oncology: Official Journal of the European Society for Medical Oncology
https://www.readbyqxmd.com/read/29249251/-what-s-new-in-oncodermatology
#6
C Lebbé
This 'What's new in oncodermatology?' addresses the developments in 2017 on the epidemiology and management of skin cancers. We observe a constant increase in carcinomas, risk factors for squamous cell carcinoma, especially in transplant recipients where skin cancer mortality is important. Among epidemiological developments in melanoma are increased mortality despite screening, occupational exposure to UV, second melanoma and higher risk after carcinoma. New classifications that should be considered are AJCC8 for melanoma and carcinoma...
December 2017: Annales de Dermatologie et de Vénéréologie
https://www.readbyqxmd.com/read/29249247/-what-s-new-in-instrumental-dermatology
#7
V Chaussade
This "What's new in instrumental dermatology" dedicated skin surgeryis based upon a 2015-2017 literature analysis. The excision of skin cancers is an important part of surgical dermatology. Will artificial intelligence and new drug be able to face the increasing need for therapy? Wrong-site surgery is due to multiple factors. Photographs of biopsy site and short time between biopsy and surgery decrease postponement of surgery and wrong-site surgery. Noninvasive imaging technologies are improving and help to delineate skin tumors and increase the probability of complete tumor removal...
December 2017: Annales de Dermatologie et de Vénéréologie
https://www.readbyqxmd.com/read/28536262/-in-silico-and-cell-based-analyses-reveal-strong-divergence-between-prediction-and-observation-of-t-cell-recognized-tumor-antigen-t-cell-epitopes
#8
COMPARATIVE STUDY
Julien Schmidt, Philippe Guillaume, Danijel Dojcinovic, Julia Karbach, George Coukos, Immanuel Luescher
Tumor exomes provide comprehensive information on mutated, overexpressed genes and aberrant splicing, which can be exploited for personalized cancer immunotherapy. Of particular interest are mutated tumor antigen T-cell epitopes, because neoepitope-specific T cells often are tumoricidal. However, identifying tumor-specific T-cell epitopes is a major challenge. A widely used strategy relies on initial prediction of human leukocyte antigen-binding peptides by in silico algorithms, but the predictive power of this approach is unclear...
July 14, 2017: Journal of Biological Chemistry
https://www.readbyqxmd.com/read/26885520/automatic-classification-of-specific-melanocytic-lesions-using-artificial-intelligence
#9
Joanna Jaworek-Korjakowska, Paweł Kłeczek
BACKGROUND: Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet...
2016: BioMed Research International
https://www.readbyqxmd.com/read/25561244/non-invasive-estimation-of-skin-thickness-from-hyperspectral-imaging-and-validation-using-echography
#10
Saurabh Vyas, Jon Meyerle, Philippe Burlina
BACKGROUND: The skin is the largest organ and is subject to the greatest exposure to outside elements throughout one׳s lifetime. Current data by the American Academy of Dermatology suggests that approximately ten people die each hour worldwide due to skin related conditions. Cancers such as melanoma are growths that originate in the epidermis. Therefore, an accurate and non-invasive method to estimate skin constitutive elements can play an important clinical role in detecting the early onset of skin tumors...
February 2015: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/25333167/automated-detection-of-new-or-evolving-melanocytic-lesions-using-a-3d-body-model
#11
Federica Bogo, Javier Romero, Enoch Peserico, Michael J Black
Detection of new or rapidly evolving melanocytic lesions is crucial for early diagnosis and treatment of melanoma. We propose a fully automated pre-screening system for detecting new lesions or changes in existing ones, on the order of 2 - 3mm, over almost the entire body surface. Our solution is based on a multi-camera 3D stereo system. The system captures 3D textured scans of a subject at different times and then brings these scans into correspondence by aligning them with a learned, parametric, non-rigid 3D body model...
2014: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/25311811/automatic-diagnosis-of-melanoma-using-machine-learning-methods-on-a-spectroscopic-system
#12
Lin Li, Qizhi Zhang, Yihua Ding, Huabei Jiang, Bruce H Thiers, James Z Wang
BACKGROUND: Early and accurate diagnosis of melanoma, the deadliest type of skin cancer, has the potential to reduce morbidity and mortality rate. However, early diagnosis of melanoma is not trivial even for experienced dermatologists, as it needs sampling and laboratory tests which can be extremely complex and subjective. The accuracy of clinical diagnosis of melanoma is also an issue especially in distinguishing between melanoma and mole. To solve these problems, this paper presents an approach that makes non-subjective judgements based on quantitative measures for automatic diagnosis of melanoma...
October 13, 2014: BMC Medical Imaging
https://www.readbyqxmd.com/read/25137721/four-class-classification-of-skin-lesions-with-task-decomposition-strategy
#13
Kouhei Shimizu, Hitoshi Iyatomi, M Emre Celebi, Kerri-Ann Norton, Masaru Tanaka
This paper proposes a new computer-aided method for the skin lesion classification applicable to both melanocytic skin lesions (MSLs) and nonmelanocytic skin lesions (NoMSLs). The computer-aided skin lesion classification has drawn attention as an aid for detection of skin cancers. Several researchers have developed methods to distinguish between melanoma and nevus, which are both categorized as MSL. However, most of these studies did not focus on NoMSLs such as basal cell carcinoma (BCC), the most common skin cancer and seborrheic keratosis (SK) despite their high incidence rates...
January 2015: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/25066340/comparative-study-of-maximum-likelihood-and-spectral-angle-mapper-algorithms-used-for-automated-detection-of-melanoma
#14
COMPARATIVE STUDY
I Ibraheem
BACKGROUND: Melanoma is a leading fatal illness responsible for 80% of deaths from skin cancer. It originates in the pigment-producing melanocytes in the basal layer of the epidermis. Melanocytes produce the melanin (the dark pigment), which is responsible for the color of skin. As all cancers, melanoma is caused by damage to the DNA of the cells, which causes the cell to grow out of control, leading to a tumor, which is much more dangerous if it cannot be found or detected early. Only biopsy can determine exact malformation diagnosis, although it can rise metastasizing...
February 2015: Skin Research and Technology
https://www.readbyqxmd.com/read/24981466/-melanoma-early-detection-and-automatic-diagnosis-of-pigmented-lesions
#15
EDITORIAL
Wilhelm Stolz
No abstract text is available yet for this article.
July 2014: Journal der Deutschen Dermatologischen Gesellschaft, Journal of the German Society of Dermatology: JDDG
https://www.readbyqxmd.com/read/24505793/automatic-detection-of-blue-white-veil-by-discrete-colour-matching-in-dermoscopy-images
#16
Ali Madooei, Mark S Drew, Maryam Sadeghi, M Stella Atkins
Skin lesions are often comprised of various colours. The presence of multiple colours with an irregular distribution can signal malignancy. Among common colours under dermoscopy, blue-grey (blue-white veil) is a strong indicator of malignant melanoma. Since it is not always easy to visually identify and recognize this feature, a computerised automatic colour analysis method can provide the clinician with an objective second opinion. In this paper, we put forward an innovative method, through colour analysis and computer vision techniques, to automatically detect and segment blue-white veil areas in dermoscopy images...
2013: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/24314859/detection-of-pigment-network-in-dermoscopy-images-using-supervised-machine-learning-and-structural-analysis
#17
Jose Luis García Arroyo, Begoña García Zapirain
By means of this study, a detection algorithm for the "pigment network" in dermoscopic images is presented, one of the most relevant indicators in the diagnosis of melanoma. The design of the algorithm consists of two blocks. In the first one, a machine learning process is carried out, allowing the generation of a set of rules which, when applied over the image, permit the construction of a mask with the pixels candidates to be part of the pigment network. In the second block, an analysis of the structures over this mask is carried out, searching for those corresponding to the pigment network and making the diagnosis, whether it has pigment network or not, and also generating the mask corresponding to this pattern, if any...
January 2014: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/23983813/iterative-reweighted-noninteger-norm-regularizing-svm-for-gene-expression-data-classification
#18
Jianwei Liu, Shuang Cheng Li, Xionglin Luo
Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data. L2 regularization has been commonly used. If the training dataset contains many noise variables, L1 regularization SVM will provide a better performance. However, both L1 and L2 are not the optimal regularization method when handing a large number of redundant values and only a small amount of data points is useful for machine learning...
2013: Computational and Mathematical Methods in Medicine
https://www.readbyqxmd.com/read/22830402/new-diagnostics-for-melanoma-detection-from-artificial-intelligence-to-rna-microarrays
#19
REVIEW
Verena Ahlgrimm-Siess, Martin Laimer, Edith Arzberger, Rainer Hofmann-Wellenhof
Early detection of melanoma remains crucial to ensuring a favorable prognosis. Dermoscopy and total body photography are well-established noninvasive aids that increase the diagnostic accuracy of dermatologists in their daily routine, beyond that of a naked-eye examination. New noninvasive diagnostic techniques, such as reflectance confocal microscopy, multispectral digital imaging and RNA microarrays, are currently being investigated to determine their utility for melanoma detection. This review presents emerging technologies for noninvasive melanoma diagnosis, and discusses their advantages and limitations...
July 2012: Future Oncology
https://www.readbyqxmd.com/read/22093020/computer-aided-pattern-classification-system-for-dermoscopy-images
#20
Qaisar Abbas, M Emre Celebi, Irene Fondón
BACKGROUND: Computer-aided pattern classification of melanoma and other pigmented skin lesions is one of the most important tasks for clinical diagnosis. To differentiate between benign and malignant lesions, the extraction of color, architectural order, symmetry of pattern and homogeneity (CASH) is a challenging task. METHODS: In this article, a novel pattern classification system (PCS) based on the clinical CASH rule is presented to classify among six classes of patterns...
August 2012: Skin Research and Technology
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