AIMC Topic: Neoplasm Invasiveness

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Propofol affects the growth and metastasis of pancreatic cancer via ADAM8.

Pharmacological reports : PR Dec 20, 2019
BACKGROUND: Anesthesia is a major component of surgery and recently considered an important regulator of cell phenotypes. Here we aimed to investigate propofol, an anesthesia drug, in suppressing pancreatic cancer (PDAC), focusing on A disintegrin an...
Neoplasm Invasiveness Membrane Proteins Neoplasm Metastasis Cell Proliferation Pancreatic Neoplasms MAP Kinase Signaling System Propofol Matrix Metalloproteinase 9 ADAM Proteins Cell Movement Matrix Metalloproteinase 2 Mice, SCID Humans Apoptosis Xenograft Model Antitumor Assays Animals Antineoplastic Agents Cell Line, Tumor
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A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.

European radiology Dec 6, 2019
OBJECTIVE: To develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme wi...
Adult Male Middle Aged Disease Progression Deep Learning Tomography, X-Ray Computed Female Aged Aged, 80 and over Young Adult Neoplasm Invasiveness Radiologists Feasibility Studies Adenocarcinoma of Lung Adenocarcinoma in Situ Adolescent ROC Curve Image Processing, Computer-Assisted Lung Neoplasms Solitary Pulmonary Nodule Humans Neural Networks, Computer Artificial Intelligence Retrospective Studies
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Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study.

Gut Nov 28, 2019
OBJECTIVE: Diagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools.
Lymphocytes, Tumor-Infiltrating Image Interpretation, Computer-Assisted Humans Neoplasm Invasiveness Artificial Intelligence Neoplasm Staging Prognosis Colonic Neoplasms Antineoplastic Combined Chemotherapy Protocols Disease-Free Survival Adenocarcinoma
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Machine Learning Approaches to Radiogenomics of Breast Cancer using Low-Dose Perfusion Computed Tomography: Predicting Prognostic Biomarkers and Molecular Subtypes.

Scientific reports Nov 28, 2019
Radiogenomics investigates the relationship between imaging phenotypes and genetic expression. Breast cancer is a heterogeneous disease that manifests complex genetic changes and various prognosis and treatment response. We investigate the value of m...
Aged Aged, 80 and over Adult Breast Neoplasms Middle Aged Humans Machine Learning Tomography, X-Ray Computed Female Neoplasm Invasiveness Neoplasm Grading ErbB Receptors Receptors, Estrogen Ki-67 Antigen
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Breast cancer histopathology image classification through assembling multiple compact CNNs.

BMC medical informatics and decision making Oct 22, 2019
BACKGROUND: Breast cancer causes hundreds of thousands of deaths each year worldwide. The early stage diagnosis and treatment can significantly reduce the mortality rate. However, the traditional manual diagnosis needs intense workload, and diagnosti...
Neoplasm Invasiveness Breast Datasets as Topic Data Compression Female Carcinoma in Situ Humans Sensitivity and Specificity Neural Networks, Computer Breast Neoplasms Algorithms Image Processing, Computer-Assisted Computer Simulation
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Endoscopic detection and differentiation of esophageal lesions using a deep neural network.

Gastrointestinal endoscopy Oct 1, 2019
BACKGROUND AND AIMS: Diagnosing esophageal squamous cell carcinoma (SCC) depends on individual physician expertise and may be subject to interobserver variability. Therefore, we developed a computerized image-analysis system to detect and differentia...
Observer Variation Precancerous Conditions Optical Imaging Esophageal Squamous Cell Carcinoma Esophageal Diseases Deep Learning Image Processing, Computer-Assisted Neoplasm Invasiveness Esophagus Esophageal Neoplasms Narrow Band Imaging Humans Neural Networks, Computer Female Reproducibility of Results Aged Aged, 80 and over Adult Sensitivity and Specificity Male Middle Aged
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Machine learning-based prediction of breast cancer growth rate in vivo.

British journal of cancer Aug 9, 2019
BACKGROUND: Determining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the r...
Humans Early Detection of Cancer Machine Learning Neoplasm Invasiveness Carcinoma, Lobular Algorithms Survival Rate Mammography Breast Neoplasms Prognosis Image Interpretation, Computer-Assisted Middle Aged Retrospective Studies Adenocarcinoma, Mucinous Nomograms Female Follow-Up Studies Aged
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Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists.

Gastrointestinal endoscopy May 8, 2019
BACKGROUND AND AIMS: Cancer invasion depth is a critical factor affecting the choice of treatment in patients with superficial squamous cell carcinoma (SCC). However, the diagnosis of invasion depth is currently subjective and liable to interobserver...
Male Middle Aged Deep Learning Neoplasm Invasiveness Esophageal Neoplasms Esophageal Squamous Cell Carcinoma Esophagoscopy Gastroenterologists Neural Networks, Computer Female Aged Aged, 80 and over Adult Humans
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Five simultaneous artificial intelligence data challenges on ultrasound, CT, and MRI.

Diagnostic and interventional imaging Mar 15, 2019
PURPOSE: The goal of this data challenge was to create a structured dynamic with the following objectives: (1) teach radiologists the new rules of General Data Protection Regulation (GDPR), while building a large multicentric prospective database of ...
Tibial Meniscus Injuries Humans Interprofessional Relations Thyroid Cartilage Artificial Intelligence Kidney Cortex Liver Neoplasms Tomography, X-Ray Computed Magnetic Resonance Imaging Breast Neoplasms Communication Thyroid Neoplasms Ultrasonography Neoplasm Invasiveness Datasets as Topic Computer Security
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Detecting abnormal thyroid cartilages on CT using deep learning.

Diagnostic and interventional imaging Feb 25, 2019
PURPOSE: The purpose of this study was to evaluate the performance of a deep learning algorithm in detecting abnormalities of thyroid cartilage from computed tomography (CT) examination.
Neoplasm Invasiveness Datasets as Topic Thyroid Cartilage Algorithms Humans Tomography, X-Ray Computed Sensitivity and Specificity Deep Learning Thyroid Neoplasms
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