Breast Cancer

Real-time respiratory motion forecasting with online learning of recurrent neural networks for accurate targeting in externally guided radiotherapy.

BACKGROUND AND OBJECTIVE: In lung radiotherapy, infrared cameras can track reflective objects on the chest to estimate tumor motion due to breathing. However, treatment system latencies hinder radiation beam precision. Real-time recurrent learning (RTRL), the conventional online learning approach for training recurrent neural networks (RNNs), is a potential solution that can learn patterns within ...

Non-orthogonal kV imaging guided patient position verification in non-coplanar radiation therapy with dataset-free implicit neural representation.

BACKGROUND: Cone-beam CT (CBCT) is crucial for patient alignment and target verification in radiation therapy (RT). However, for non-coplanar beams, potential collisions between the treatment couch and the on-board imaging system limit the range that the gantry can be rotated. Limited-angle measurements are often insufficient to generate high-quality volumetric images for image-domain registration...

Study on the relationship between vaginal dose and radiation-induced vaginal injury following cervical cancer radiotherapy, and model development.

OBJECTIVE: This study investigates the relationship between vaginal radiation dose and radiation-induced vaginal injury in cervical cancer patients, w...

Artificial Intelligence as a Potential Tool for Predicting Surgical Margin Status in Early Breast Cancer Using Mammographic Specimen Images.

: Breast cancer is the most common malignancy among women globally, with an increasing incidence, particularly in younger populations. Achieving compl...

Radiomics of Dynamic Contrast-Enhanced MRI for Predicting Radiation-Induced Hepatic Toxicity After Intensity Modulated Radiotherapy for Hepatocellular Carcinoma: A Machine Learning Predictive Model Based on the SHAP Methodology.

OBJECTIVE: To develop an interpretable machine learning (ML) model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic data,...

Construction of Sonosensitizer-Drug Co-Assembly Based on Deep Learning Method.

Drug co-assemblies have attracted extensive attention due to their advantages of easy preparation, adjustable performance and drug component co-delive...

Deep learning predicts HER2 status in invasive breast cancer from multimodal ultrasound and MRI.

The preoperative human epidermal growth factor receptor type 2 (HER2) status of breast cancer is typically determined by pathological examination of a...

2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction.

Positron Emission Tomography (PET) is an important clinical imaging tool but inevitably introduces radiation exposure to patients and healthcare provi...

A Prospective Comparative Study of the Effectiveness of Flap Fixation by Suture Versus Conventional Closure in Reducing Seroma Formation After Modified Radical Mastectomy.

Background Seroma formation is a common postoperative complication of modified radical mastectomy (MRM), leading to delayed wound healing, increased i...

Efficacy and safety of first-line CDK4/6 inhibitors plus AI therapy for patients with HR +/HER2- advanced breast cancer: a network meta-analysis.

BACKGROUND: CDK4/6 inhibitors plus aromatase inhibitors (AI) significantly improve the therapeutic effect of initial treatment for HR + /HER2- advance...

An integrated machine learning-based prognostic model in head and neck cancer using the systemic inflammatory response index and correlations with patient reported financial toxicity.

OBJECTIVE: To investigate the prognostic utility of systemic inflammatory response index (SIRI) as a biological readout of stress associated immune mo...

Stacking classifiers based on integrated machine learning model: fusion of CT radiomics and clinical biomarkers to predict lymph node metastasis in locally advanced gastric cancer patients after neoadjuvant chemotherapy.

BACKGROUND: The early prediction of lymph node positivity (LN+) after neoadjuvant chemotherapy (NAC) is crucial for optimizing individualized treatmen...

Identification of Npas4 as a biomarker for CICI by transcriptomics combined with bioinformatics and machine learning approaches.

Chemotherapy is one of the most successful strategies for treating cancer. Unfortunately, up to 70 % of cancer survivors develop cognitive impairment ...

Frozen Large-Scale Pretrained Vision-Language Models are the Effective Foundational Backbone for Multimodal Breast Cancer Prediction.

Breast cancer is a pervasive global health concern among women. Leveraging multimodal data from enterprise patient databases-including Picture Archivi...

Development and Validation of a Novel Prediction Model for Hearing Loss From Cisplatin Chemotherapy.

PURPOSE: Cisplatin treats many common tumors but causes permanent and debilitating hearing loss (HL). The objective of this study was to develop and e...

An efficient patient's response predicting system using multi-scale dilated ensemble network framework with optimization strategy.

The forecasting of a patient's response to radiotherapy and the likelihood of experiencing harmful long-term health impacts would considerably enhance...

Evaluation of Artificial Intelligence Models for Nutritional Symptom Management in Breast Cancer Patients Undergoing Chemotherapy.

The purpose of the study is to determine if artificial intelligence (AI) models could provide dietary recommendations to manage chemotherapy-induced n...

Artificial intelligence-based muscle analysis risk assessment of treatment-related toxicity in metastatic colorectal cancer.

IntroductionUp to 60% of adults with metastatic colorectal cancer (mCRC) receiving combination cytotoxic chemotherapy may experience loss of skeletal ...

Multi-objective optimization framework to plan laser ablation procedure for prostate tumors through a genetic algorithm.

BACKGROUND AND OBJECTIVES: Prostate cancer is the most common form of cancer in the male population. While the survival rate is high, many patients un...

Detecting the left atrial appendage in CT localizers using deep learning.

Patients with cardioembolic stroke often undergo CT of the left atrial appendage (LAA), for example, to determine whether thrombi are present in the L...