Breast Cancer

Unlocking the Power of ChatGPT, Artificial Intelligence, and Large Language Models: Practical Suggestions for Radiation Oncologists.

Recent advances in artificial intelligence (AI), such as generative AI and large language models (LL...

Classification of laterality and mastectomy/lumpectomy for breast cancer patients for improved performance of deep learning auto segmentation.

Circulating Plasma Exosomal PD-L1 Predicts Prognosis of Head and Neck Squamous Cell Carcinoma After Radiation Therapy.

PURPOSE: Radiation therapy is widely used to treat head and neck squamous cell carcinoma (HNSCC). Th...

Enhanced breast mass mammography classification approach based on pre-processing and hybridization of transfer learning models.

BACKGROUND AND OBJECTIVE: The second most prevalent cause of death among women is now breast cancer,...

Deep Learning for Detecting BRCA Mutations in High-Grade Ovarian Cancer Based on an Innovative Tumor Segmentation Method From Whole Slide Images.

BRCA1 and BRCA2 genes play a crucial role in repairing DNA double-strand breaks through homologous r...

Deep learning-based scan range optimization can reduce radiation exposure in coronary CT angiography.

OBJECTIVES: Cardiac computed tomography (CT) is essential in diagnosing coronary heart disease. Howe...

Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics.

The tumor microenvironment (TME) plays a critical role in disease progression and is a key determina...

The Clinical Added Value of Breast Cancer Imaging Using Hybrid PET/MR Imaging.

Dedicated MR imaging is highly performant for the evaluation of the primary lesion and should regula...

Deep learning for the prediction of the chemotherapy response of metastatic colorectal cancer: comparing and combining H&E staining histopathology and infrared spectral histopathology.

Colorectal cancer is a global public health problem with one of the highest death rates. It is the s...

Suitability of DNN-based vessel segmentation for SIRT planning.

PURPOSE: The segmentation of the hepatic arteries (HA) is essential for state-of-the-art pre-interve...

Post-chemotherapy robot-assisted retroperitoneal lymph node dissection for metastatic germ cell tumors: safety and perioperative outcomes.

PURPOSE: To evaluate the feasibility, safety, and early oncologic outcomes after post-chemotherapy r...

Automatic prediction of hepatic arterial infusion chemotherapy response in advanced hepatocellular carcinoma with deep learning radiomic nomogram.

OBJECTIVES: Hepatic arterial infusion chemotherapy (HAIC) using the FOLFOX regimen (oxaliplatin plus...

Hardware deployment of deep learning model for classification of breast carcinoma from digital mammogram images.

Cancer is an illness that instils fear in many individuals throughout the world due to its lethal na...

Ultra-low-dose computed tomography with deep learning reconstruction for craniosynostosis at radiation doses comparable to skull radiographs: a pilot study.

BACKGROUND: Craniofacial computed tomography (CT) is the diagnostic investigation of choice for cran...

An AI Predictive Model to Determine Who Benefits from ADT with Radiation: Working Smarter, Not Harder.

Whether you are a surgical, medical, or radiation oncologist, the care goals remain the same, that i...

A deep-learning assisted bioluminescence tomography method to enable radiation targeting in rat glioblastoma.

. A novel solution is required for accurate 3D bioluminescence tomography (BLT) based glioblastoma (...

Machine learning to predict environmental dose rates from a radionuclide therapy service - a proof of concept study.

The Ionising Radiation Regulations 2017 requires prior risk assessment calculations and regular envi...

A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre-treatment tumor biopsies.

BACKGROUND: Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) has demonstrated ...

A deep learning approach to estimate x-ray scatter in digital breast tomosynthesis: From phantom models to clinical applications.

BACKGROUND: Digital breast tomosynthesis (DBT) has gained popularity as breast imaging modality due ...

The prediction of drug sensitivity by multi-omics fusion reveals the heterogeneity of drug response in pan-cancer.

Cancer drug response prediction based on genomic information plays a crucial role in modern pharmaco...

Deep learning radio-clinical signatures for predicting neoadjuvant chemotherapy response and prognosis from pretreatment CT images of locally advanced gastric cancer patients.

BACKGROUND: Early noninvasive screening of patients who would benefit from neoadjuvant chemotherapy ...