Breast Cancer

Apparent Diffusion Coefficient Magnetic Resonance Imaging as a Predictive Biomarker for Hypoxia, Treatment De-escalation, and Recurrence in Human Papillomavirus-Associated Oropharyngeal Cancer.

PURPOSE: To evaluate whether quantitative diffusion-weighted magnetic resonance imaging derived apparent diffusion coefficient (ADC) parametric values of nodal disease can identify nodal recurrence (NR) risk and distinguish patients with human papillomavirus-associated oropharyngeal cancer (HPV-OPC) suitable for radiation de-escalation following primary tumor resection. METHODS AND MATERIALS: This...

Deep Learning-Based Motion-Compensated Reconstruction for Accelerating 4-Dimensional Magnetic Resonance Fingerprinting.

PURPOSE: To develop and validate DeepMocor, a deep learning-based method for motion-compensated 4-dimensional magnetic resonance fingerprinting (4D-MRF) reconstruction to accelerate conventional 4D-MRF reconstruction, enabling more efficient clinical treatment planning. METHODS AND MATERIALS: This prospective study enrolled 19 hepatocellular carcinoma patients (mean age, 62 years; 14 males) betwee...

Physics-informed deep learning sharpens nano diagnostics for elusive pancreatic cancer.

Pancreatic disease affects over 10% of the world population, and the most dangerous is pancreatic cancer (PC). The disease is mostly of late age of on...

Fully automated volumetric assessment of tumor burden using artificial intelligence on 68Ga-PSMA-11 PET predicts survival after 177Lu-PSMA therapy in metastatic Castration-resistant prostate cancer.

PURPOSE: Despite the rapid development of artificial intelligence (AI)-powered automated segmentation tools for PET/CT imaging, their prognostic value...

Development and external validation of interpretable machine learning models for personalized multiple treatment recommendations in non-small cell lung cancer.

OBJECTIVE: Treatment decision-making for non-small cell lung cancer (NSCLC) is complex, necessitating individualized decision-support tools to improve...

Astroimmunology: the effects of spaceflight and its associated stressors on the immune system.

As humans embark on longer and deeper missions into space, it is crucial to understand how spaceflight impacts the immune system. Decades of discoveri...

Prognostic prediction of hepatoblastoma in children: development and validation of machine learning models-an SEER-based study.

Hepatoblastoma (HB) is the most common primary malignant liver tumor in children. Although the incidence is low, it is a serious threat to children's ...

Radiation Risk in 2D Mammography Screening: A Scoping Review of Modelling Strategies and Emerging AI Applications.

Breast cancer is the most commonly diagnosed cancer among women worldwide, and concerns regarding radiation exposure from mammography screening remain...

Preferences for Adopting Artificial Intelligence in Radiation Therapy Treatment: A Discrete Choice Experiment.

OBJECTIVES: The integration of artificial intelligence (AI) in radiation therapy offers significant potential to enhance cancer care by improving diag...

Photon-counting detector computed tomography in thoracic oncology: revolutionizing tumor imaging through precision and detail.

Photon-counting detector computed tomography (PCD-CT) is an emerging imaging technology that promises to overcome the limitations of conventional ener...

Performance of a Novel Frameless and Maskless Robotic Head Motion Compensation System for Stereotactic Radiosurgery in a Realistic Clinical Environment with Healthy Volunteers.

PURPOSE: Stereotactic radiosurgery (SRS) is a nonsurgical method for treating brain abnormalities and small tumors. Traditional high-accuracy SRS requ...

Dose Reduction in 4-Dimensional Computed Tomography Imaging: Breathing Signal-Guided Deep Learning-Driven Data Acquisition.

PURPOSE: Four-dimensional computed tomography (4D CT) imaging is essential for radiation therapy planning in thoracic tumors. However, current protoco...

Current State-of-the-Art 3D MRI Sequences for Assessing Bone Morphology with Emphasis on Cranial and Spinal Imaging: A Narrative Review.

Traditionally, CT has been the go-to method for visualizing bone structures, while MRI has been preferred for assessing soft tissues, because structur...

Artificial intelligence-assisted PET imaging for predicting neoadjuvant chemotherapy response in breast cancer: a systematic review and meta-analysis.

PURPOSE: This study aims to evaluate the performance of artificial intelligence (AI)-assisted PET imaging in predicting neoadjuvant chemotherapy (NAC)...

Development and validation of a breast cancer survival prediction model based on perioperative anesthesia-related drug target genes and analysis of immune microenvironment and drug sensitivity.

INTRODUCTION: This study aimed to create a survival prediction model for breast cancer(BC) using perioperative anesthesia - related drug target genes(...

Multimodal deep learning method based on multiple spectra for lung cancer early diagnosis.

Lung cancer remains one of the most lethal malignancies worldwide, and the early and accurate diagnostic is critical. Traditional diagnostic technique...

Prognostic Utility of a Deep Learning Radiomics Nomogram Integrating Ultrasound and Multi-Sequence MRI in Triple-Negative Breast Cancer Treated With Neoadjuvant Chemotherapy.

OBJECTIVE: The aim of this study is to evaluate the prognostic performance of a nomogram integrating clinical parameters with deep learning radiomics ...

Harnessing biomarkers to guide immunotherapy in esophageal cancer: toward precision oncology.

Esophageal cancer (EC) is one of the most serious health issues around the world, ranking seventh among the most lethal types of cancer and eleventh a...

Real-Time Super-Resolution Ultrasound Imaging for Monitoring Tumor Response During Intensive Care Management of Oncologic Emergencies.

BACKGROUND: Oncologic emergencies in critically ill cancer patients frequently require rapid, real-time assessment of tumor responses to therapeutic i...

Novel risk calculator predicting long-term survival in pediatric versus adult patients diagnosed with spinal low-grade glioma: a nationwide analysis.

BACKGROUND CONTEXT: Spinal low-grade gliomas (SLGGs) are rare, slow-growing central nervous system tumors affecting both pediatric and adult populatio...