Breast Cancer

Raman Spectroscopy and Exosome-Based Machine Learning Predicts the Efficacy of Neoadjuvant Therapy for HER2-Positive Breast Cancer.

Early prediction of the neoadjuvant therapy efficacy for HER2-positive breast cancer is crucial for personalizing treatment and enhancing patient outcomes. Exosomes, which play a role in tumor development and treatment response, are emerging as potential biomarkers for cancer diagnosis and efficacy prediction. Despite their promise, current exosome detection and isolation methods are cumbersome an...

Hepatoid adenocarcinoma of the stomach with ideal response to neoadjuvant chemo-immunotherapy: a case report.

Hepatoid adenocarcinoma of the stomach (HAS) is a rare subtype of gastric cancer characterized by histological features resembling hepatocellular carcinoma. Surgical intervention remains the preferred treatment modality for eligible patients. However, the efficacy of neoadjuvant therapy and alternative treatment regimens has been found to be suboptimal. Consequently, due to the high metastatic pot...

Artificial Intelligence, Machine Learning and Big Data in Radiation Oncology.

This review explores the applications of artificial intelligence and machine learning (AI/ML) in radiation oncology, focusing on computer vision (CV) ...

Partial-Label Contrastive Representation Learning for Fine-Grained Biomarkers Prediction From Histopathology Whole Slide Images.

In the domain of histopathology analysis, existing representation learning methods for biomarkers prediction from whole slide images (WSIs) face chall...

Automated Quantification of HER2 Amplification Levels Using Deep Learning.

HER2 assessment is necessary for patient selection in anti-HER2 targeted treatment. However, manual assessment of HER2 amplification is time-costly, l...

DIFLF: A domain-invariant features learning framework for single-source domain generalization in mammogram classification.

BACKGROUND AND OBJECTIVE: Single-source domain generalization (SSDG) aims to generalize a deep learning (DL) model trained on one source dataset to mu...

Feasibility of reconstructingpatient 3D dose distributions from 2D EPID image data using convolutional neural networks.

. The primary purpose of this work is to demonstrate the feasibility of a deep convolutional neural network (dCNN) based algorithm that uses two-dimen...

Machine learning Nomogram for Predicting endometrial lesions after tamoxifen therapy in breast Cancer patients.

Objective Endometrial lesions are a frequent complication following breast cancer, and current diagnostic tools have limitations. This study aims to d...

Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model.

Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression l...

Deep learning-based prediction of HER2 status and trastuzumab treatment efficacy of gastric adenocarcinoma based on morphological features.

BACKGROUND: First-line treatment for advanced gastric adenocarcinoma (GAC) with human epidermal growth factor receptor 2 (HER2) is trastuzumab combine...

Insights into AI advances in immunohistochemistry for effective breast cancer treatment: a literature review of ER, PR, and HER2 scoring.

Breast cancer is a significant health challenge, with accurate and timely diagnosis being critical to effective treatment. Immunohistochemistry (IHC) ...

Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer.

BACKGROUND: Programmed cell death (PCD) is closely related to the occurrence, development, and treatment of breast cancer. The aim of this study was t...

Deep Learning for the Accurate Prediction of Triggered Drug Delivery.

The need to mitigate the adverse effects of chemotherapy has driven the exploration of innovative drug delivery approaches. One emerging trend in canc...

Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI.

Automated breast tumor segmentation on the basis of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) has shown great promise in clini...

A GPU-accelerated fuzzy method for real-time CT volume filtering.

During acquisition and reconstruction, medical images may become noisy and lose diagnostic quality. In the case of CT scans, obtaining less noisy imag...

Viscum album, as alternative and bridge to palliative chemotherapy in recurrent gallbladder cancer following laparoscopic radical cholecystectomy: a case report.

A 78-year-old female patient was initially treated with laparoscopic radical cholecystectomy for gallbladder cancer (pT2aN1M0, stage IIIB). The patien...

Integrating immune multi-omics and machine learning to improve prognosis, immune landscape, and sensitivity to first- and second-line treatments for head and neck squamous cell carcinoma.

In recent years, immune checkpoint inhibitors (ICIs) has emerged as a fundamental component of the standard treatment regimen for patients with head a...

Development and external validation of a multi-task feature fusion network for CTV segmentation in cervical cancer radiotherapy.

BACKGROUND AND PURPOSE: Accurate segmentation of the clinical target volume (CTV) is essential to deliver an effective radiation dose to tumor tissues...

Descriptive overview of AI applications in x-ray imaging and radiotherapy.

Artificial intelligence (AI) is transforming medical radiation applications by handling complex data, learning patterns, and making accurate predictio...

Recent Advances and Future Directions in Sonodynamic Therapy for Cancer Treatment.

Deep-tissue solid cancer treatment has a poor prognosis, resulting in a very low 5-year patient survival rate. The primary challenges facing solid tum...