AIMC Topic: Carcinoma, Squamous Cell

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Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer.

Medical image analysis Nov 21, 2020
We apply for the first-time interpretable deep learning methods simultaneously to the most common skin cancers (basal cell carcinoma, squamous cell carcinoma and intraepidermal carcinoma) in a histological setting. As these three cancer types constit...
Algorithms Skin Neoplasms Humans Carcinoma, Squamous Cell Diagnosis, Computer-Assisted Deep Learning
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Machine learning-based FDG PET-CT radiomics for outcome prediction in larynx and hypopharynx squamous cell carcinoma.

Clinical radiology Oct 6, 2020
AIM: To determine whether machine learning-based radiomic feature analysis of baseline integrated 2-[F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET) computed tomography (CT) predicts disease progression in patients with locally a...
Positron Emission Tomography Computed Tomography Neoplasm Recurrence, Local Radiopharmaceuticals Fluorodeoxyglucose F18 Adult Male Middle Aged Predictive Value of Tests Chemoradiotherapy Laryngeal Neoplasms Hypopharynx Female Carcinoma, Squamous Cell Aged Humans Machine Learning
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Detecting mouse squamous cell carcinoma from submicron full-field optical coherence tomography images by deep learning.

Journal of biophotonics Sep 21, 2020
The standard medical practice for cancer diagnosis requires histopathology, which is an invasive and time-consuming procedure. Optical coherence tomography (OCT) is an alternative that is relatively fast, noninvasive, and able to capture three-dimens...
Deep Learning Mice Tomography, Optical Coherence Intestinal Neoplasms Algorithms Carcinoma, Squamous Cell Animals
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Machine Learning-Based MRI Texture Analysis to Predict the Histologic Grade of Oral Squamous Cell Carcinoma.

AJR. American journal of roentgenology Sep 15, 2020
This study aimed to explore the performance of machine learning (ML)-based MRI texture analysis in discriminating between well-differentiated (WD) oral squamous cell carcinoma (OSCC) and moderately or poorly differentiated OSCC. The study enrolled ...
Humans Aged Young Adult Adult Male Middle Aged Machine Learning Retrospective Studies Female Carcinoma, Squamous Cell Mouth Neoplasms Magnetic Resonance Imaging Diagnosis, Differential Predictive Value of Tests Neoplasm Grading
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Fully-Connected Neural Networks with Reduced Parameterization for Predicting Histological Types of Lung Cancer from Somatic Mutations.

Biomolecules Aug 28, 2020
Several challenges appear in the application of deep learning to genomic data. First, the dimensionality of input can be orders of magnitude greater than the number of samples, forcing the model to be prone to overfitting the training dataset. Second...
Adenocarcinoma Diagnosis, Differential Mutation Carcinoma, Squamous Cell Reproducibility of Results Diagnosis, Computer-Assisted Lung Neoplasms Humans Neural Networks, Computer
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Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models.

The British journal of radiology Jun 19, 2020
OBJECTIVES: High throughput pre-treatment imaging features may predict radiation treatment outcome and guide individualized treatment in radiotherapy (RT). Given relatively small patient sample (as compared with high dimensional imaging features), id...
Tongue Neoplasms Probability Tongue Carcinoma, Squamous Cell Reproducibility of Results Principal Component Analysis Humans Mouth Neoplasms Algorithms Prognosis Tomography, X-Ray Computed Neural Networks, Computer Machine Learning
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Automatic detection of cervical lymph nodes in patients with oral squamous cell carcinoma using a deep learning technique: a preliminary study.

Oral radiology Jun 6, 2020
OBJECTIVE: To apply a deep learning object detection technique to CT images for detecting cervical lymph nodes metastasis in patients with oral cancers, and to clarify the detection performance.
Carcinoma, Squamous Cell Deep Learning Lymph Nodes Squamous Cell Carcinoma of Head and Neck Mouth Neoplasms Head and Neck Neoplasms Lymphatic Metastasis Humans
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A deep learning system for differential diagnosis of skin diseases.

Nature medicine May 18, 2020
Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of sk...
Melanoma Warts Skin Neoplasms Hispanic or Latino Middle Aged Humans Carcinoma, Basal Cell Telemedicine Dermatitis, Seborrheic Deep Learning Female White People Folliculitis Acne Vulgaris Indians, North American Carcinoma, Squamous Cell Physicians, Primary Care Adult Asian Psoriasis Alaska Natives Photography Male Diagnosis, Differential Native Hawaiian or Pacific Islander Nurse Practitioners Dermatologists Black or African American Keratosis, Seborrheic Skin Diseases Eczema
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Assessment of associations between clinical and immune microenvironmental factors and tumor mutation burden in resected nonsmall cell lung cancer by applying machine learning to whole-slide images.

Cancer medicine May 12, 2020
BACKGROUND: It is unclear whether clinical factors and immune microenvironment (IME) factors are associated with tumor mutation burden (TMB) in patients with nonsmall cell lung cancer (NSCLC).
Aged, 80 and over Adult Tumor Microenvironment Male B7-H1 Antigen Antigens, Neoplasm Keratin-19 Adenocarcinoma Ex-Smokers Middle Aged Non-Smokers Lung Neoplasms Machine Learning Forkhead Transcription Factors Carcinoma, Non-Small-Cell Lung Female Carcinoembryonic Antigen Humans Lymphocytes, Tumor-Infiltrating Smokers Regression Analysis Exome Sequencing Mutation Carcinoma, Squamous Cell Aged
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Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network.

Neural networks : the official journal of the International Neural Network Society May 7, 2020
The analysis of tissue of a tumor in the oral cavity is essential for the pathologist to ascertain its grading. Recent studies using biopsy images reveal computer-aided diagnosis for oral sub-mucous fibrosis (OSF) carried out using machine learning a...
Epithelial Cells Humans Carcinoma, Squamous Cell Mouth Neoplasms Diagnosis, Computer-Assisted Deep Learning
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