AI Medical Compendium Journal:
European journal of cancer (Oxford, England : 1990)

Showing 41 to 50 of 58 articles

Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients.

European journal of cancer (Oxford, England : 1990) Dec 26, 2020
BACKGROUND: CDK4/6 inhibitors plus endocrine therapies are the current standard of care in the first-line treatment of HR+/HER2-negative metastatic breast cancer, but there are no well-established clinical or molecular predictive factors for patient ...
Young Adult Adult Survival Rate Breast Neoplasms Electronic Health Records Humans Female Machine Learning Aged Prognosis Aged, 80 and over Retrospective Studies Natural Language Processing Receptor, ErbB-2 Receptors, Estrogen Receptors, Progesterone Middle Aged Disease Progression Antineoplastic Combined Chemotherapy Protocols Follow-Up Studies
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Past and present of computer-assisted dermoscopic diagnosis: performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesions.

European journal of cancer (Oxford, England : 1990) Jun 10, 2020
BACKGROUND: Convolutional neural networks (CNNs) have shown a dermatologist-level performance in the classification of skin lesions. We aimed to deliver a head-to-head comparison of a conventional image analyser (CIA), which depends on segmentation a...
Humans Melanoma Skin Neoplasms Dermoscopy Reproducibility of Results Databases, Factual Prospective Studies Prognosis Female Deep Learning Adolescent Predictive Value of Tests Image Interpretation, Computer-Assisted Cross-Sectional Studies Male Middle Aged Diagnosis, Computer-Assisted Aged Aged, 80 and over Young Adult Adult
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Reply to the letter to the editor: 'Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images'.

European journal of cancer (Oxford, England : 1990) Jan 30, 2020
Deep Learning Pathologists Humans Melanoma Skin Neoplasms
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Melanoma recognition by a deep learning convolutional neural network-Performance in different melanoma subtypes and localisations.

European journal of cancer (Oxford, England : 1990) Jan 20, 2020
BACKGROUND: Deep learning convolutional neural networks (CNNs) show great potential for melanoma diagnosis. Melanoma thickness at diagnosis among others depends on melanoma localisation and subtype (e.g. advanced thickness in acrolentiginous or nodul...
Deep Learning ROC Curve Follow-Up Studies Aged Male Middle Aged Case-Control Studies Humans Neural Networks, Computer Prognosis Retrospective Studies Female Melanoma
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Re: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.

European journal of cancer (Oxford, England : 1990) Oct 23, 2019
Pathologists Humans Melanoma Skin Neoplasms Deep Learning
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Superior skin cancer classification by the combination of human and artificial intelligence.

European journal of cancer (Oxford, England : 1990) Sep 10, 2019
BACKGROUND: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. Howe...
Algorithms Prognosis Skin Neoplasms Dermoscopy Deep Learning Dermatologists Image Interpretation, Computer-Assisted Observer Variation Humans Neural Networks, Computer
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Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks.

European journal of cancer (Oxford, England : 1990) Aug 14, 2019
BACKGROUND: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer s...
Male Melanoma Humans Surveys and Questionnaires Skin Neoplasms Neural Networks, Computer Diagnosis, Differential Dermoscopy Algorithms Biopsy Female Sensitivity and Specificity Dermatologists Hospitals, University Nevus
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Machine learning defined diagnostic criteria for differentiating pituitary metastasis from autoimmune hypophysitis in patients undergoing immune checkpoint blockade therapy.

European journal of cancer (Oxford, England : 1990) Aug 12, 2019
PURPOSE: New-onset pituitary gland lesions are observed in up to 18% of cancer patients undergoing treatment with immune checkpoint blockers (ICB). We aimed to develop and validate an imaging-based decision-making algorithm for use by the clinician t...
B7-H1 Antigen Humans Pituitary Neoplasms Diagnosis, Differential Programmed Cell Death 1 Receptor Autoimmune Hypophysitis Machine Learning CTLA-4 Antigen Algorithms Radioimmunotherapy Neoplasms ROC Curve
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Prediction of melanoma evolution in melanocytic nevi via artificial intelligence: A call for prospective data.

European journal of cancer (Oxford, England : 1990) Aug 8, 2019
Recent research revealed the superiority of artificial intelligence over dermatologists to diagnose melanoma from images. However, 30-50% of all melanomas and more than half of those in young patients evolve from initially benign lesions. Despite its...
Mutation Skin Cell Transformation, Neoplastic Nevus, Pigmented Skin Neoplasms Dermoscopy Sensitivity and Specificity Artificial Intelligence Early Detection of Cancer Algorithms Prospective Studies Melanoma Humans
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Deep neural networks are superior to dermatologists in melanoma image classification.

European journal of cancer (Oxford, England : 1990) Aug 8, 2019
BACKGROUND: Melanoma is the most dangerous type of skin cancer but is curable if detected early. Recent publications demonstrated that artificial intelligence is capable in classifying images of benign nevi and melanoma with dermatologist-level preci...
Humans Neural Networks, Computer Algorithms Melanoma Skin Neoplasms Dermoscopy Reproducibility of Results Surveys and Questionnaires Image Interpretation, Computer-Assisted ROC Curve Nevus Biopsy Skin Dermatologists
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