Oncology/Hematology

Serum markers improve current prediction of metastasis development in early-stage melanoma patients: a machine learning-based study.

Metastasis development represents an important threat for melanoma patients, even when diagnosed at early stages and upon removal of the primary tumor. In this scenario, determination of prognostic biomarkers would be of great interest. Serum contains information about the general status of the organism and therefore represents a valuable source for biomarkers. Thus, we aimed to define serological...

IConMHC: a deep learning convolutional neural network model to predict peptide and MHC-I binding affinity.

Tumor-specific neoantigens are mutated self-peptides presented by tumor cell major histocompatibility complex (MHC) molecules and are necessary to elicit host's anti-cancer cytotoxic T cell responses. It could be specifically recognized by neoantigen-specific T cell receptors (TCRs). However, current wet-lab assays for identifying peptide MHC binding are too expensive and time-consuming to meet th...

Artificial intelligence-based collaborative filtering method with ensemble learning for personalized lung cancer medicine without genetic sequencing.

In personalized medicine, many factors influence the choice of compounds. Hence, the selection of suitable medicine for patients with non-small-cell l...

Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells.

A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses. We in...

An Approach to Biomarker Discovery of Cannabis Use Utilizing Proteomic, Metabolomic, and Lipidomic Analyses.

Relatively little is known about the molecular pathways influenced by cannabis use in humans. We used a multi-omics approach to examine protein, meta...

Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models.

OBJECTIVES: High throughput pre-treatment imaging features may predict radiation treatment outcome and guide individualized treatment in radiotherapy ...

Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks.

Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and...

Ensemble Deep Learning Model for Multicenter Classification of Thyroid Nodules on Ultrasound Images.

BACKGROUND Thyroid nodules are extremely common and typically diagnosed with ultrasound whether benign or malignant. Imaging diagnosis assisted by Art...

Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens.

Automatically detecting and grading cancerous regions on radical prostatectomy (RP) sections facilitates graphical and quantitative pathology reportin...

Deep learning detection of prostate cancer recurrence with F-FACBC (fluciclovine, Axumin®) positron emission tomography.

PURPOSE: To evaluate the performance of deep learning (DL) classifiers in discriminating normal and abnormal F-FACBC (fluciclovine, Axumin®) PET scans...

Accuracy of artificial intelligence-assisted detection of upper GI lesions: a systematic review and meta-analysis.

BACKGROUND AND AIMS: Artificial intelligence (AI)-assisted detection is increasingly used in upper endoscopy. We performed a meta-analysis to determin...

First robot-assisted radical prostatectomy in a client-owned Bernese mountain dog with prostatic adenocarcinoma.

OBJECTIVE: To describe robot-assisted radical prostatectomy (RARP) and report the short-term outcome of a dog with prostatic cancer treated with RARP.

Deep learning-based survival prediction for multiple cancer types using histopathology images.

Providing prognostic information at the time of cancer diagnosis has important implications for treatment and monitoring. Although cancer staging, his...

Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.

PURPOSE: Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to...

The use of artificial intelligence, machine learning and deep learning in oncologic histopathology.

BACKGROUND: Recently, there has been a momentous drive to apply advanced artificial intelligence (AI) technologies to diagnostic medicine. The introdu...

A machine learning model for detecting invasive ductal carcinoma with Google Cloud AutoML Vision.

OBJECTIVES: This study is aimed to assess the feasibility of AutoML technology for the identification of invasive ductal carcinoma (IDC) in whole slid...

Deep-Learning F-FDG Uptake Classification Enables Total Metabolic Tumor Volume Estimation in Diffuse Large B-Cell Lymphoma.

Total metabolic tumor volume (TMTV), calculated from F-FDG PET/CT baseline studies, is a prognostic factor in diffuse large B-cell lymphoma (DLBCL) wh...

Predicting direct hepatocyte toxicity in humans by combining high-throughput imaging of HepaRG cells and machine learning-based phenotypic profiling.

Accurate prediction of drug- and chemical-induced hepatotoxicity remains to be a problem for pharmaceutical companies as well as other industries and ...

Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States.

Prediction of the COVID-19 incidence rate is a matter of global importance, particularly in the United States. As of 4 June 2020, more than 1.8 millio...

Decoding whole-genome mutational signatures in 37 human pan-cancers by denoising sparse autoencoder neural network.

Millions of somatic mutations have recently been discovered in cancer genomes. These mutations in cancer genomes occur due to internal and external mu...