Cardiovascular

A deep learning-based interpretable decision tool for predicting high risk of chemotherapy-induced nausea and vomiting in cancer patients prescribed highly emetogenic chemotherapy.

OBJECTIVE: This study aims to develop a risk prediction model for chemotherapy-induced nausea and vo...

Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function.

BACKGROUND: In those receiving chemotherapy, renal and hepatic dysfunction can increase the risk of ...

Biology-guided deep learning predicts prognosis and cancer immunotherapy response.

Substantial progress has been made in using deep learning for cancer detection and diagnosis in medi...

Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer.

OBJECTIVE: This study aimed to establish a MRI-based deep learning radiomics (DLR) signature to pred...

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...

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 ...