Breast Cancer

Feasibility of single-phase DECT as an alternative to triple-phase CT for imaging-defined risk factors assessment in neuroblastoma for dose reduction.

OBJECTIVE: To investigate the feasibility of evaluating Imaging-Defined Risk Factors (IDRFs) in Neuroblastoma (NB) patients using venous-phase (VP)-only images in dual-energy CT for radiation dose reduction and examination workflow optimization, compared with conventional triple-phase CECT. MATERIALS AND METHODS: Ninety-four pediatric NB patients (ages 4.92 ± 3.45 y, range 0-17 y), who underwent t...

Education as a Tool to Navigate Changing Technology in Radiation Oncology.

While technological innovation in radiation therapy (RT) continues to accelerate, safe and equitable adoption of emerging tools is reliant on the readiness of the workforce and the robustness of associated educational frameworks. Early experience with disruptive technologies such as intensity-modulated RT (IMRT) has taught us that fragmented or insufficient education can create an implementation b...

Multi-cohort and single-cell profiling of aging genes reveals prognostic and therapeutic targets in breast cancer.

Aging-related transcriptional programs shape breast cancer progression, immune regulation, and therapeutic response. We integrated curated aging-assoc...

Construction of a risk model based on exosome-related genes predict clinical prognosis and therapeutic response and revealing TIMP1 as a promising target in colorectal cancer.

Various exosome-derived proteins have been reported to play essential roles in regulating colorectal cancer progression and affecting the prognosis of...

HRD-Informed Digital Histology Model for Predicting Platinum Chemo-Response and Prognosis in High-Grade Serous Ovarian Cancer.

Homologous recombination deficiency (HRD) is a critical biomarker in high-grade serous ovarian cancer for the clinical benefit from platinum-based che...

Novel "No-Lo" imaging techniques to minimize intraoperative radiation exposure in vascular and endovascular surgery.

INTRODUCTION: The rapid expansion in endovascular techniques has placed vascular surgeons among those most exposed to occupational medical radiation. ...

High reliability Ag/Ni/NiO nanowire-based SERS for cancer detection: a study on breast cancer.

Over the past decade, substantial research has focused on identifying cancer biomarkers using Raman spectroscopy. However, no commercial Raman-based d...

Comparative study on predicting postoperative distant metastasis of lung cancer based on machine learning models.

Lung cancer remains the leading cause of cancer-related incidence and mortality worldwide. Its tendency for postoperative distant metastasis significa...

Predicting and interpreting protein and phosphoprotein abundance from pan-cancer and single-cell transcriptomes.

Proteins that impact phenotype and disease are often approximated by RNA expression, which poorly infers protein abundance. We developed DeepGxP, a de...

Hierarchical image pyramid transformer framework for automated breast cancer molecular subtyping using tissue microarrays.

The heterogeneity of breast cancer at molecular and histological levels poses significant challenges for precise diagnosis and treatment. Current mole...

[Transformation of gastrointestinal surgery diagnosis and treatment models].

In recent years, with the rapid development of medical device and biopharmaceutical technologies, the treatment model of gastric cancer surgery has be...

MRI-based multilevel radiomics and transformer features for predicting radiation-induced carotid artery injury after nasopharyngeal carcinoma radiotherapy: A multicenter study.

PURPOSE: To develop and validate an MRI-based fusion model (Rad-SRad-SwinT) integrating conventional radiomics (Rad), subregional radiomics (SRad), an...

Breast cancer frontiers: mapping molecular signals, intelligent diagnostics, and adaptive therapies.

Breast cancer continues to be a significant worldwide health concern, requiring ongoing improvements in early detection, therapeutic approaches, and c...

Utilization of machine learning algorithms for the identification of the RLN associated prognostic model and feature biomarkers of RLN-related subtypes in breast cancer.

BACKGROUND: Breast cancer (BC) is the most common malignancy afflicting women worldwide, yet the role of relaxin-related genes (RLN) in BC progression...

Recent advances in redox-responsive polysaccharide-based nanostructures for cancer therapy.

Conventional tumor chemotherapy faces limitations including drug resistance, high toxicity, non-selectivity, and side effects. Nano-drug delivery syst...

Sorafenib nanomedicine in HCC: nano-bio interactions and combination therapies.

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with systemic therapies offering only limited benefit. S...

Predicting the Efficacy of Breast Cancer Neoadjuvant Chemotherapy Using Ultrasonography and Machine Learning.

OBJECTIVE: This study aimed to develop a machine learning model based on ultrasonography (US) and clinicopathological features to predict pathological...

On the transfer learning behavior of domain-specific vision-language models in screening mammography.

Vision-Language models have shown remarkable performance for natural images and text. Given the homology of the anatomy, high gray-scale image dimensi...

Lung scintigraphy for the diagnosis of acute pulmonary embolism.

Pulmonary embolism (PE) remains a major diagnostic challenge due to its potentially life-threatening nature and the clinical burden associated with an...

Amplifying image quality gain in x-ray phase contrast imaging of mastectomy samples with deep learning denoising.

Phase-contrast computed tomography (PCT) of the breast has previously been shown to produce higher-quality images at lower radiation doses without the...