Breast Cancer

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy.

PURPOSE: The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non-small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre-treatment computed tomography (CT) with features from the biologically effective dose (BED) distribution.

Chemotherapeutic potential of Cayratia trifolia L nhexane extract on A2780 cells.

It is of interest to report the chemotherapeutic (drug target based) potential of n-hexane Cayratia trifolia L. (C.trifolia) extract on A2780 cell lines. mRNA and protein expression analysis of the human chemokine receptor (CXCR4) and human epidermal growth factor receptors-2 (HER2) were studied using RT-PCR analysis and western blot analysis. The results show significant cell growth inhibition wi...

A deep learning-based dual-omics prediction model for radiation pneumonitis.

PURPOSE: Radiation pneumonitis (RP) is the main source of toxicity in thoracic radiotherapy. This study proposed a deep learning-based dual-omics mode...

Artificial intelligence: The opinions of radiographers and radiation therapists in Ireland.

INTRODUCTION: Implementation of Artificial Intelligence (AI) into medical imaging is much debated. Diagnostic Radiographers (DRs) and Radiation Therap...

Deep learning method for prediction of patient-specific dose distribution in breast cancer.

BACKGROUND: Patient-specific dose prediction improves the efficiency and quality of radiation treatment planning and reduces the time required to find...

Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer.

BACKGROUND: Pathological complete response (pCR) is considered a surrogate endpoint for favorable survival in breast cancer patients treated with neoa...

Clinical implementation of deep-learning based auto-contouring tools-Experience of three French radiotherapy centers.

Deep-learning (DL)-based auto-contouring solutions have recently been proposed as a convincing alternative to decrease workload of target volumes and ...

Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis.

BACKGROUND: Patients with urolithiasis undergo radiation overexposure from computed tomography (CT) scans. Improvement of image reconstruction is nece...

Personalized brachytherapy dose reconstruction using deep learning.

BACKGROUND AND PURPOSE: Accurate calculation of the absorbed dose delivered to the tumor and normal tissues improves treatment gain factor, which is t...

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease pro...

Applications of machine and deep learning to patient-specific IMRT/VMAT quality assurance.

In order to deliver accurate and safe treatment to cancer patients in radiation therapy using advanced techniques such as intensity modulated radiatio...

Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images.

Breast cancer is a commonly occurring disease in women all over the world. Mammogram is an efficient technique used for screening and identification o...

MTT assay based inhibition analysis of A2780 cells proliferation using Lam. extract with CXCR4 and HER2 expression.

It is of interest to document the inhibition of A2780 cell proliferation using Lam.() extract by MTT assay and by monitoring the CXCR4 and HER2 expre...

xDeep-AcPEP: Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning.

Cancer is one of the leading causes of death worldwide. Conventional cancer treatment relies on radiotherapy and chemotherapy, but both methods bring ...

Modeling Texture in Deep 3D CNN for Survival Analysis.

Radiomics has shown remarkable potential for predicting the survival outcome for various types of cancers such as pancreatic ductal adenocarcinoma (PD...

Deep learning for segmentation in radiation therapy planning: a review.

Segmentation of organs and structures, as either targets or organs-at-risk, has a significant influence on the success of radiation therapy. Manual se...

Joint fully convolutional and graph convolutional networks for weakly-supervised segmentation of pathology images.

Tissue/region segmentation of pathology images is essential for quantitative analysis in digital pathology. Previous studies usually require full supe...

A model of modified -iodobenzylguanidine conjugated gold nanoparticles for neuroblastoma treatment.

Iodine-131 -iodobenzylguanidine (I-IBG) has been utilized as a standard treatment to minimize adverse side effects by targeting therapies to bind to t...

Can AI-assisted microscope facilitate breast HER2 interpretation? A multi-institutional ring study.

The level of human epidermal growth factor receptor-2 (HER2) protein and gene expression in breast cancer is an essential factor in judging the progno...

Convolutional Neural Network of Multiparametric MRI Accurately Detects Axillary Lymph Node Metastasis in Breast Cancer Patients With Pre Neoadjuvant Chemotherapy.

BACKGROUND: Accurate assessment of the axillary lymph nodes (aLNs) in breast cancer patients is essential for prognosis and treatment planning. Curren...