Oncology/Hematology

CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept.

INTRODUCTION: To assess the performance of pre-ablation computed tomography texture features of adrenal metastases to predict post-treatment local progression and survival in patients who underwent ablation using machine learning as a prediction tool.

A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies.

BACKGROUND: Predicting lung adenocarcinoma (LUAD) risk is crucial in determining further treatment strategies. Molecular biomarkers may improve risk stratification for LUAD.

Gabor wavelet-based deep learning for skin lesion classification.

Skin cancer cases are increasing and becoming one of the main problems worldwide. Skin cancer is known as a malignant type of skin lesion, and early d...

Range and dose verification in proton therapy using proton-induced positron emitters and recurrent neural networks (RNNs).

Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proto...

Profiling of Exosomal Biomarkers for Accurate Cancer Identification: Combining DNA-PAINT with Machine- Learning-Based Classification.

Exosomes are endosome-derived vesicles enriched in body fluids such as urine, blood, and saliva. So far, they have been recognized as potential biomar...

Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis.

Histopathology image analysis serves as the gold standard for cancer diagnosis. Efficient and precise diagnosis is quite critical for the subsequent t...

Automated Counting of Cancer Cells by Ensembling Deep Features.

High-content and high-throughput digital microscopes have generated large image sets in biological experiments and clinical practice. Automatic image ...

[Coexistence of mycosis fungoides and essential thrombocythemia with JAK2V617F].

BACKGROUND: The coexistence of myeloproliferative neoplasms (MPNs), specifically essential thrombocythemia and lymphoproliferative neoplasms, are a ve...

Spatial Radiation Dose Influence on Xerostomia Recovery and Its Comparison to Acute Incidence in Patients With Head and Neck Cancer.

PURPOSE: Radiation-induced xerostomia is one of the most prevalent symptoms during and after head and neck cancer radiation therapy (RT). We aimed to ...

Early Esophageal Cancer: A Gastroenterologist's Disease.

Traditionally, early esophageal cancer (i.e., cancer limited to the mucosa or superficial submucosa) was managed surgically; the gastroenterologist's ...

Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds.

Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and pharmaceutical chemistry need and consider it a huge goal to d...

Radiomics of small renal masses on multiphasic CT: accuracy of machine learning-based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat.

OBJECTIVE: To investigate the discriminative capabilities of different machine learning-based classification models on the differentiation of small (<...

Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study.

Medical images such as magnetic resonance (MR) imaging provide valuable information for cancer detection, diagnosis, and prognosis. In addition to the...

A Hierarchical Neural Network for Sleep Stage Classification Based on Comprehensive Feature Learning and Multi-Flow Sequence Learning.

Automatic sleep staging methods usually extract hand-crafted features or network trained features from signals recorded by polysomnography (PSG), and ...

Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules.

PURPOSE: To enhance clinician's decision-making by diagnosing hepatocellular carcinoma (HCC) in cirrhotic patients with indeterminate liver nodules us...

Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network.

BACKGROUND: Visual inspection, lesion detection, and differentiation between malignant and benign features are key aspects of an endoscopist's role. T...

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA.

BACKGROUND: Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage ...

The future of endoscopy: Advances in endoscopic image innovations.

The latest state of the art technological innovations have led to a palpable progression in endoscopic imaging and may facilitate standardisation of p...

Recognizing basal cell carcinoma on smartphone-captured digital histopathology images with a deep neural network.

BACKGROUND: Pioneering effort has been made to facilitate the recognition of pathology in malignancies based on whole-slide images (WSIs) through deep...

DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Network.

This paper introduces a deep neural network based method, i.e., DeepOrganNet, to generate and visualize fully high-fidelity 3D / 4D organ geometric mo...