AIMC Topic: Melanoma

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Machine learning-based integrative analysis of methylome and transcriptome identifies novel prognostic DNA methylation signature in uveal melanoma.

Briefings in bioinformatics Jul 20, 2021
Uveal melanoma (UVM) is the most common primary intraocular human malignancy with a high mortality rate. Aberrant DNA methylation has rapidly emerged as a diagnostic and prognostic signature in many cancers. However, such DNA methylation signature av...
Gene Expression Profiling Transcriptome Gene Expression Regulation, Neoplastic Survival Rate Male Middle Aged Disease-Free Survival DNA, Neoplasm Uveal Neoplasms Uveal Melanoma DNA Methylation Neoplasm Proteins Predictive Value of Tests Humans Machine Learning Female Aged, 80 and over Adult Melanoma Aged
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Using deep learning for dermatologist-level detection of suspicious pigmented skin lesions from wide-field images.

Science translational medicine Feb 17, 2021
A reported 96,480 people were diagnosed with melanoma in the United States in 2019, leading to 7230 reported deaths. Early-stage identification of suspicious pigmented lesions (SPLs) in primary care settings can lead to improved melanoma prognosis an...
Sensitivity and Specificity Humans Deep Learning Melanoma Dermatologists Skin Neoplasms
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Dermoscopic Features of Giant Molluscum Contagiosum in a Patient with Acquired Immunodeficiency Syndrome.

Acta dermatovenerologica Croatica : ADC Dec 1, 2020
Giant molluscum contagiosum (MC) is a peculiar variant of the disease with the presence of multiple or single lesions larger than 5 mm. In contrast to typical molluscum contagiosum, dermoscopic features of giant lesions have been poorly described, an...
Humans Acquired Immunodeficiency Syndrome Female Skin Neoplasms Imiquimod Adult Melanoma Molluscum Contagiosum Child, Preschool
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Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma.

JAMA Nov 17, 2020
IMPORTANCE: Less than 10% of patients with cancer have detectable pathogenic germline alterations, which may be partially due to incomplete pathogenic variant detection.
Germ-Line Mutation Genetic Testing High-Throughput Nucleotide Sequencing DNA Mutational Analysis Cross-Sectional Studies Male Middle Aged Predictive Value of Tests Prostatic Neoplasms Deep Learning Genetic Predisposition to Disease Female Melanoma Sensitivity and Specificity Humans Neural Networks, Computer
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Bioinformatics, Sequencing Accuracy, and the Credibility of Clinical Genomics.

JAMA Nov 17, 2020
Humans Melanoma Computational Biology Prostatic Neoplasms Genetic Testing Reference Standards Male Deep Learning Genomics
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[Determination of dacarbazine in the urine of mice with melanoma by high performance liquid chromatography].

Se pu = Chinese journal of chromatography Nov 8, 2020
Dacarbazine (DTIC) is a first-line chemotherapy drug that is widely used in clinical practice for malignant melanoma. DTIC is metabolized by the liver . Some drugs are excreted in urine in the form of a prototype. Hence, DTIC in urine can be monitore...
Mice, Inbred C57BL Chromatography, High Pressure Liquid Melanoma Dacarbazine Animals Mice
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Deep learning-level melanoma detection by interpretable machine learning and imaging biomarker cues.

Journal of biomedical optics Nov 1, 2020
SIGNIFICANCE: Melanoma is a deadly cancer that physicians struggle to diagnose early because they lack the knowledge to differentiate benign from malignant lesions. Deep machine learning approaches to image analysis offer promise but lack the transpa...
Cues Machine Learning Humans Dermoscopy Algorithms Melanoma Deep Learning Biomarkers Skin Neoplasms
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Deep learning-based, computer-aided classifier developed with dermoscopic images shows comparable performance to 164 dermatologists in cutaneous disease diagnosis in the Chinese population.

Chinese medical journal Sep 5, 2020
BACKGROUND: Diagnoses of Skin diseases are frequently delayed in China due to lack of dermatologists. A deep learning-based diagnosis supporting system can facilitate pre-screening patients to prioritize dermatologists' efforts. We aimed to evaluate ...
Humans Computers Melanoma China Dermatologists Skin Neoplasms Dermoscopy Deep Learning
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[Computer-assisted skin cancer diagnosis : Is it time for artificial intelligence in clinical practice?].

Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete Sep 1, 2020
BACKGROUND: Artificial intelligence (AI) is increasingly being used in medical practice. Especially in the image-based diagnosis of skin cancer, AI shows great potential. However, there is a significant discrepancy between expectations and true relev...
Image Processing, Computer-Assisted Mass Screening Skin Neoplasms Dermoscopy Diagnosis, Computer-Assisted Artificial Intelligence Algorithms Melanoma Humans Neural Networks, Computer
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[Artificial intelligence and smartphone program applications (Apps) : Relevance for dermatological practice].

Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete Sep 1, 2020
ADVANTAGES OF ARTIFICIAL INTELLIGENCE (AI): With responsible, safe and successful use of artificial intelligence (AI), possible advantages in the field of dermato-oncology include the following: (1) medical work can focus on skin cancer patients, (2)...
Humans Skin Neoplasms Smartphone Artificial Intelligence Melanoma Image Interpretation, Computer-Assisted Medical Oncology Mobile Applications Telemedicine Dermatology
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