Breast Cancer

Semiautomated Extraction of Research Topics and Trends From National Cancer Institute Funding in Radiological Sciences From 2000 to 2020.

PURPOSE: Investigators and funding organizations desire knowledge on topics and trends in publicly funded research but current efforts for manual categorization have been limited in breadth and depth of understanding. We present a semiautomated analysis of 21 years of R-type National Cancer Institute (NCI) grants to departments of radiation oncology and radiology using natural language processing.

Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma.

To integrate machine learning and multiomic data on lactylation-related genes (LRGs) for molecular typing and prognosis prediction in lung adenocarcinoma (LUAD). LRG mRNA and long non-coding RNA transcriptomes, epigenetic methylation data, and somatic mutation data from The Cancer Genome Atlas LUAD cohort were analyzed to identify lactylation cancer subtypes (CSs) using 10 multiomics ensemble clus...

Non-parametric Bayesian deep learning approach for whole-body low-dose PET reconstruction and uncertainty assessment.

Positron emission tomography (PET) imaging plays a pivotal role in oncology for the early detection of metastatic tumors and response to therapy asses...

Breast radiation therapy fluence painting with multi-agent deep reinforcement learning.

BACKGROUND: The electronic compensation (ECOMP) technique for breast radiation therapy provides excellent dose conformity and homogeneity. However, th...

A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images.

Neoadjuvant chemotherapy (NAC) is a systemic and systematic chemotherapy regimen for breast cancer patients before surgery. However, NAC is not effect...

Embracing the Future of Clinical Trials in Radiation Therapy: An NRG Oncology CIRO Technology Retreat Whitepaper on Pioneering Technologies and AI-Driven Solutions.

This white paper examines the potential of pioneering technologies and artificial intelligence-driven solutions in advancing clinical trials involving...

Predicting Postoperative Infection After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy with Splenectomy.

BACKGROUND: Hematologic changes after splenectomy and hyperthermic intraperitoneal chemotherapy (HIPEC) can complicate postoperative assessment of inf...

Multimodal integration using a machine learning approach facilitates risk stratification in HR+/HER2- breast cancer.

Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most common type of breast cancer, with...

Development of a machine learning tool to predict deep inspiration breath hold requirement for locoregional right-sided breast radiation therapy patients.

. This study presents machine learning (ML) models that predict if deep inspiration breath hold (DIBH) is needed based on lung dose in right-sided bre...

A Machine Learning Model for Predicting the HER2 Positive Expression of Breast Cancer Based on Clinicopathological and Imaging Features.

RATIONALE AND OBJECTIVES: To develop a machine learning (ML) model based on clinicopathological and imaging features to predict the Human Epidermal Gr...

Predicting doxorubicin-induced cardiotoxicity in breast cancer: leveraging machine learning with synthetic data.

Doxorubicin (DOXO) is a primary treatment for breast cancer but can cause cardiotoxicity in over 25% of patients within the first year post-chemothera...

Artificial Neural Network-Based Validation, DFT, Thermal and Biological Evaluation of 4-Aminoantipyrine-Derived Ru(III) Complexes.

New methodologies have been evaluated for validating analytical characterization with artificial neural networks (ANNs). Compared to previous machine ...

Artificial Intelligence-Powered Human Epidermal Growth Factor Receptor 2 and Tumor Microenvironment Analysis in Human Epidermal Growth Factor Receptor 2-Amplified Metastatic Colorectal Cancer: Exploratory Analysis of Phase II TRIUMPH Trial.

PURPOSE: Human epidermal growth factor receptor 2 (HER2)-targeted therapies have shown promise in treating -amplified metastatic colorectal cancer (mC...

MOCapsNet: Multiomics Data Integration for Cancer Subtype Analysis Based on Dynamic Self-Attention Learning and Capsule Networks.

: With the rapid development of the accumulation of large-scale multiomics data sets, integrating various omics data to provide a thorough study from ...

Evaluation of a Deep Learning Denoising Algorithm for Dose Reduction in Whole-Body Photon-Counting CT Imaging: A Cadaveric Study.

RATIONALE AND OBJECTIVES: Photon Counting CT (PCCT) offers advanced imaging capabilities with potential for substantial radiation dose reduction; howe...

Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer.

BACKGROUND: This study explores the clinical value of a machine learning (ML) model based on ultrasound radiomics features of primary foci, combined w...

Application of deep learning in automated localization and interpretation of coronary artery calcification in oncological PET/CT scans.

Coronary artery calcification (CAC) is a key marker of coronary artery disease (CAD) but is often underreported in cancer patients undergoing non-gate...

Multimodal deep-learning model using pre-treatment endoscopic images and clinical information to predict efficacy of neoadjuvant chemotherapy in esophageal squamous cell carcinoma.

BACKGROUND: Neoadjuvant chemotherapy is standard for advanced esophageal squamous cell carcinoma, though often ineffective. Therefore, predicting the ...

Artificial Intelligence for Predicting HER2 Status of Gastric Cancer Based on Whole-Slide Histopathology Images: A Retrospective Multicenter Study.

Human epidermal growth factor receptor 2 (HER2) positive gastric cancer (GC) shows a robust response to the combined therapy based HER2-targeted thera...

Noninvasive identification of HER2 status by integrating multiparametric MRI-based radiomics model with the vesical imaging-reporting and data system (VI-RADS) score in bladder urothelial carcinoma.

PURPOSE: HER2 expression is crucial for the application of HER2-targeted antibody-drug conjugates. This study aims to construct a predictive model by ...