Breast Cancer

Topology-guided hard example mining for cell detection.

Automatic cell detection is a key task in digital pathology, where manual counting remains impractical due to its time-consuming nature and susceptibility to variability and error. Current deep learning approaches still have difficulty achieving accurate detection, particularly in images with crowded cell distributions. In such settings, capturing the global organization of cells within tissue bec...

Semiautomated magnetic resonance imaging-based breast density measurement from routine sequences: comparison with mammography.

AIM: High breast density increases breast cancer risk and decreases mammographic sensitivity, yet prior magnetic resonance imaging (MRI)-based density assessments have relied on specialized sequences or vendor-dependent software. This study aimed to evaluate a semiautomated workflow for MRI-based breast density measurement using routinely acquired MRI sequences and compare its diagnostic performan...

Development and validation of pathomics signature for predicting prognosis of advanced high-grade serous ovarian carcinoma patients after platinum-based chemotherapy.

In patients with advanced high-grade serous ovarian carcinoma (HGSOC), the standard treatment typically involves platinum-based chemotherapy after deb...

Ultrasound-based multiregional radiomics nomogram for predicting recurrence in HER2-positive breast cancer.

PURPOSE: Accurate prediction of recurrence risk is crucial for personalizing therapy in human epidermal growth factor receptor 2-positive (HER2-positi...

Multimodal deep learning integration of clinical, dosiomics, and radiomics features to predict toxicity in breast cancer patients undergoing hypofractionated radiotherapy.

BACKGROUND AND PURPOSE: Radiation dermatitis (RD) and superficial soft tissue fibrosis are common toxicities among the patients with breast cancer rec...

Virtual photon-counting micro-CT platform for simulation of head and neck cancer imaging in mice.

OBJECTIVE: Our goal was to develop a simulation platform for photon-counting CT (PCCT) imaging in mouse models of head and neck squamous cell carcinom...

Hyperspectral Imaging for Breast-Conserving Surgery Margin Assessment: A Systematic Review.

CONTEXT.—: Breast-conserving surgery (BCS) offers comparable overall survival rates to mastectomy. However, 14% to 37% of cases require re-excision ow...

Understanding resistance in glioblastoma: insights into personalized and targeted therapeutic strategies.

Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant brain tumor. Despite combined treatments, including surgical removal...

Peristaltic transport and thermodynamic analysis of hybrid nanofluids in porous media using physics-informed neural networks.

This study presents the wavelet-based physics-informed neural networks (PINNs) simulation to analyse entropy generation in hybrid nanofluid peristalti...

Radiation dose-aware sinogram knowledge library transformer with feature modulation for low-dose medical image segmentation.

Low-dose computed tomography (LDCT) and low-dose positron emission tomography (LDPET) enable shorter acquisition times and lower radiation exposure. H...

Low-kVp techniques for radiation dose optimization in abdominopelvic contrast-enhanced CT: a scoping review.

Contrast-enhanced computed tomography (CECT) of the abdomen and pelvis is widely used for diagnostic imaging but contributes substantially to cumulati...

Biomarker-driven and AI-assisted nanomedicine for breast cancer: advancing precision drug delivery from bench to bedside.

INTRODUCTION: Breast cancer remains the most prevalent malignancy among women worldwide, with its pronounced molecular heterogeneity demanding therape...

Evaluating the VOCORDER device for early disease detection through breath analysis: study protocol for a two-phase clinical study.

INTRODUCTION: The survival rate of patients with life-threatening diseases primarily depends on the speed of diagnosis. Too often, diseases are detect...

Translating lineage-resolved single-cell programs to bulk clinical prognosis: Adversarial generative learning reveals lineage-informed immune - stromal - epithelial risk signatures.

BACKGROUND: Breast cancer transcriptional programs span malignant epithelial cells, and the tumor microenvironment (TME), yet tumor-normal contrasts a...

Machine learning-based identification of high-risk triple-negative breast cancer patients despite pathological complete response after neoadjuvant chemotherapy.

The binary pathological complete response (pCR) status of triple-negative breast cancer (TNBC) patients after neoadjuvant chemotherapy (NAC) inadequat...

Deep learning prediction of pathological complete response in breast cancer using Mamba architecture.

Deep learning is capable of efficiently predicting the therapeutic efficacy of neoadjuvant chemotherapy (NAC) in breast cancer. However, current metho...

Quantifying the AI readiness gap: An international, multidisciplinary assessment of artificial intelligence literacy in the radiation oncology community.

PURPOSE: The rapid integration of artificial intelligence (AI) into imaging-intensive fields like radiation oncology (RO) is transforming the clinical...

Breast cancer survival prediction using machine learning and multimodal data for personalized care plan.

This paper presents a time-stratified breast cancer survival analysis that incorporates tumor characteristics, disease stage, and patient features, us...

Feasibility and Preliminary Effectiveness of the ChulaCancer Mobile Chatbot for Supportive Care of Patients With Breast or Colorectal Cancer Receiving Chemotherapy: Pilot Randomized Controlled Trial.

BACKGROUND: Chemotherapy-related toxicities often lead to unscheduled health care use and diminished quality of life. Digital health interventions, su...

Conditional survival patterns and individualized prognostic prediction in malignant peritoneal mesothelioma.

Malignant peritoneal mesothelioma (MPM) is a rare, aggressive cancer with limited treatment options and extremely poor survival outcomes. Due to the d...