Skin Cancer

Artificial intelligence for melanoma diagnosis: how can we deliver on the promise?

Artificial Intelligence Estimates the Importance of Baseline Factors in Predicting Response to Anti-PD1 in Metastatic Melanoma.

OBJECTIVE: Prognosis of patients with metastatic melanoma has dramatically improved over recent years because of the advent of antibodies targeting programmed cell death protein-1 (PD1). However, the response rate is ~40% and baseline biomarkers for the outcome are yet to be identified. Here, we aimed to determine whether artificial intelligence might be useful in weighting the importance of basel...

Enhancing Case Capture, Quality, and Completeness of Primary Melanoma Pathology Records via Natural Language Processing.

PURPOSE: Medical records contain a wealth of useful, informative data points valuable for clinical research. Most data points are stored in semistruct...

Convolutional Neural Network Approach to Classify Skin Lesions Using Reflectance Confocal Microscopy.

We propose an approach based on a convolutional neural network to classify skin lesions using the reflectance confocal microscopy (RCM) mosaics. Skin ...

Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers.

INTRODUCTION: Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds...

Programmed cell death protein-1/programmed death-ligand 1 blockade enhances the antitumor efficacy of adoptive cell therapy against non-small cell lung cancer.

BACKGROUND: Cytokine-induced killer (CIK) cells and natural killer (NK) cells are employed by two different approaches to adoptive cell immunotherapy ...

A machine learning approach for somatic mutation discovery.

Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. T...

Rethinking Skin Lesion Segmentation in a Convolutional Classifier.

Melanoma is a fatal form of skin cancer when left undiagnosed. Computer-aided diagnosis systems powered by convolutional neural networks (CNNs) can im...

Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.

BACKGROUND: Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to ...

Neopepsee: accurate genome-level prediction of neoantigens by harnessing sequence and amino acid immunogenicity information.

BACKGROUND: Tumor-specific mutations form novel immunogenic peptides called neoantigens. Neoantigens can be used as a biomarker predicting patient res...

Enhancement of the Immune Function by Titanium Dioxide Nanorods and Their Application in Cancer Immunotherapy.

With the rapid development of nanotherapy, concerns surrounding the possible use of nanomaterials-mediated immunomodulation are growing. Thus, evaluat...

A deep bag-of-features model for the classification of melanomas in dermoscopy images.

Deep learning and unsupervised feature learning have received great attention in past years for their ability to transform input data into high level ...

Conceptual Knowledge Discovery in Databases for Drug Combinations Predictions in Malignant Melanoma.

The worldwide incidence of melanoma is rising faster than any other cancer, and prognosis for patients with metastatic disease is poor. Current target...

A Cloud-Based Infrastructure for Feedback-Driven Training and Image Recognition.

Advanced techniques in machine learning combined with scalable "cloud" computing infrastructure are driving the creation of new and innovative health ...