Breast Cancer

Predicting late radiation-associated neurocognitive and endocrine toxicity in patients with brain tumors.

PURPOSE: Neurocognitive and endocrine dysfunction are potential complications of cranial irradiation. However, risk factors are poorly understood, impeding accurate prognostication and exploration of potential preventive interventions. The objective of this study was to evaluate the prognostic value of various vascular and genotypic risk factors for the development of radiation-related toxicities....

Swarm intelligence-guided ROI selection for deep learning assessment of HER2 in colorectal cancer.

Accurate assessment of Human Epidermal Growth Factor Receptor 2 (HER2) status in colorectal cancer (CRC) is pivotal for precision therapy, yet the gigapixel resolution of Whole Slide Images (WSIs) presents a significant computational bottleneck for traditional deep learning workflows that rely on exhaustive sliding-window tiling. Addressing this challenge, we propose a novel coarse-to-fine framewo...

CAR Practice Guidelines on Breast Imaging and Interventions: Mammography and Digital Breast Tomosynthesis.

This guideline presents Part I of the Canadian Association of Radiologists (CAR) Practice Guidelines on Breast Imaging and Intervention and focuses on...

Efficient in vivo assembly of DNA encoded multivalent BTEs for dual antigen targeting for broadening therapeutic impact in ovarian cancer.

The clinical potential of bispecific T cell engagers (BTEs) is limited by their short serum half-life and the complexity and cost of recombinant prote...

Predicting neoadjuvant breast cancer therapy response using BRIDGE from tumor transcriptomics and histopathology.

BACKGROUND: While expression-based signatures inform adjuvant therapy in breast cancer (BC), no approved molecular biomarkers exist for the neoadjuvan...

Beyond the Coronaries: Utility of Extra-Coronary Calcification for Cardiovascular Risk Assessment in Women.

PURPOSE OF REVIEW: Globally, cardiovascular disease (CVD) is the leading cause of mortality in women. Traditional risk calculators underestimate ather...

DREAMER-S: Deep leaRning-Enabled Attention-based Multiple-instance approaches with Explainable Representations for Spatial biology.

Identifying image features that associate strongly with diagnostic or prognostic classes in large-scale, multi-channel spatial imaging is challenging ...

Online adaptive radiotherapy: International strategies for AI-enabled workflow efficiency and radiation therapist-led delivery for sustainable practice.

Online adaptive radiotherapy (oART) represents a significant advancement in personalised radiation cancer treatment, offering improved daily dose to t...

Exploring osteoporosis risk in breast cancer patients after comprehensive treatment via explainable artificial intelligence algorithms.

BACKGROUND: Osteoporosis is a common complication among long-term breast cancer (BC) survivors. Assessment of osteoporosis risk of patients before the...

Development of a clinical prediction model using digital device data for interstitial lung disease in patients with unresectable stage III non-small cell lung cancer treated with durvalumab.

BACKGROUND: Early detection of interstitial lung disease including radiation pneumonitis (ILD/RP) is crucial in consolidative durvalumab therapy after...

Leveraging the non-contrast CT component of PET/CT: an AI-driven delta-radiomics approach to monitor treatment response in metastatic breast cancer.

PURPOSE: 18 F-FDG PET/CT is the standard modality for monitoring treatment response in metastatic breast cancer. This study aims to evaluate the predi...

Generating Labeled Low-Heterogeneity Transcriptomes Using CRISPRa and CRISPRi Can Improve Phenotype Prediction by Deep Learning.

The development of deep learning (DL) methods in biology holds great promise for fundamental knowledge, biomedicine, and agriculture. The most global ...

Evaluating artificial intelligence-generated synthesized mammography as a standalone alternative to digital mammography with or without tomosynthesis.

PURPOSE: To evaluate whether standalone synthesized mammography (SM) can maintain or improve diagnostic accuracy while reducing reading time and radia...

Whole-body reduced-dose dual-energy CT with deep learning image reconstruction for detection of osteolytic lesions in multiple myeloma.

INTRODUCTION: Deep learning image reconstruction (DLIR) has been incorporated into dual-energy CT (DECT) to improve image quality. However, its applic...

Explainable machine learning-guided integrated multiomics analysis reveals macrophage-driven immune suppression in breast cancer.

Despite thorough characterizations of cellular compositions within the breast tumor microenvironment (TME), their implications for disease progression...

Development of a model for design radiation shielding composite aprons using machine learning.

This study presents the development and evaluation of a novel lead-free composite for radiation shielding, designed using an artificial neural network...

Predicting response to neoadjuvant chemotherapy combined with immunotherapy in gastric cancer based on habitat imaging and peritumoral radiomics: a two-center study.

BACKGROUND: Predicting pathological response to neoadjuvant chemotherapy combined with immunotherapy (NACI) in locally advanced gastric cancer (LAGC) ...

Predicting fire consequences with the transformer model based on multimodal feature fusion.

Accurate prediction of fire consequences is fundamental to process safety management and quantitative risk assessment in the chemical process industri...

Machine learning-based prediction of 3-month mortality in elderly patients with non-small cell lung cancer and bone metastases.

Elderly patients with non-small cell lung cancer (NSCLC) and bone metastases face a dire prognosis, creating an urgent need for accurate short-term mo...

Molecular and spatial differences in the tumor microenvironment of high-grade serous ovarian cancers with short versus long-term survival.

OBJECTIVE: Survival of patients diagnosed with advanced-stage high-grade serous ovarian cancer (HGSOC) varies widely. Understanding the biological and...