Breast Cancer

Computed Tomography of the Head : A Systematic Review on Acquisition and Reconstruction Techniques to Reduce Radiation Dose.

In 1971, the first computed tomography (CT) scan was performed on a patient's brain. Clinical CT systems were introduced in 1974 and dedicated to head imaging only. New technological developments, broader availability, and the clinical success of CT led to a steady growth in examination numbers. Most frequent indications for non-contrast CT (NCCT) of the head include the assessment of ischemia and...

Molecular and functional imaging in cancer-targeted therapy: current applications and future directions.

Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather than harming all rapidly dividing cells as in cytotoxic chemotherapy. The Response Evaluation Criteria in Solid Tumor (RECIST) system has been used to assess tumor response to therapy via changes in the size of target lesions as measured by calipers, co...

Computed tomography and radiation dose images-based deep-learning model for predicting radiation pneumonitis in lung cancer patients after radiation therapy.

PURPOSE: To develop a deep learning model that combines CT and radiation dose (RD) images to predict the occurrence of radiation pneumonitis (RP) in l...

Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images.

 Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate segmentation of the primary tumor (GTVp) of oroph...

Evaluation of auto-segmentation for brachytherapy of postoperative cervical cancer using deep learning-based workflow.

. The purpose of this study was to evaluate the accuracy of brachytherapy (BT) planning structures derived from Deep learning (DL) based auto-segmenta...

Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine.

The application of artificial intelligence (AI) is accelerating the paradigm shift towards patient-tailored brain tumor management, achieving optimal ...

Clinical applications of deep learning in breast MRI.

Deep learning (DL) is one of the most powerful data-driven machine-learning techniques in artificial intelligence (AI). It can automatically learn fro...

Development, validation, and evaluation of a deep learning model to screen cyclin-dependent kinase 12 inhibitors in cancers.

Deep learning-based in silico alternatives have been demonstrated to be of significant importance in the acceleration of the drug discovery process an...

A 3D Tumor-Mimicking In Vitro Drug Release Model of Locoregional Chemoembolization Using Deep Learning-Based Quantitative Analyses.

Primary liver cancer, with the predominant form as hepatocellular carcinoma (HCC), remains a worldwide health problem due to its aggressive and lethal...

Neural-Network-Based Immune Optimization Regulation Using Adaptive Dynamic Programming.

This article investigates optimal regulation scheme between tumor and immune cells based on the adaptive dynamic programming (ADP) approach. The thera...

Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network.

BACKGROUND: Cone beam computed tomography (CBCT) plays an increasingly important role in image-guided radiation therapy. However, the image quality of...

Dental innovations which will influence the oral health care of baby boomers.

From the widespread use of smartphones and tablets to the multitude of applications available, older adults are showing an interest in utilizing techn...

Adaptive fuzzy control of drug delivery in cancer treatment using combination of chemotherapy and antiangiogenic therapy.

This paper introduces the adaptive fuzzy control scheme as a promising control technique for cancer treatment from a theoretical point of view. A math...

Reducing both radiation and contrast doses for overweight patients in coronary CT angiography with 80-kVp and deep learning image reconstruction.

PURPOSE: To investigate the use of an 80-kVp tube voltage combined with a deep learning image reconstruction (DLIR) algorithm in coronary CT angiograp...

Synthetic cranial MRI from 3D optical surface scans using deep learning for radiation therapy treatment planning.

BACKGROUND: Optical scanning technologies are increasingly being utilised to supplement treatment workflows in radiation oncology, such as surface-gui...

Strategy for automatic ultrasound (US) probe positioning in robot-assisted ultrasound guided radiation therapy.

. As part of image-guided radiotherapy, ultrasound-guided radiotherapy is currently already in use and under investigation for robot assisted systems ...

Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium.

The era of high-throughput techniques created big data in the medical field and research disciplines. Machine intelligence (MI) approaches can overcom...

Automated Molecular Subtyping of Breast Carcinoma Using Deep Learning Techniques.

OBJECTIVE: Molecular subtyping is an important procedure for prognosis and targeted therapy of breast carcinoma, the most common type of malignancy af...

An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation.

The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in brea...

A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations.

Self-attention mechanism-based algorithms are attractive in digital pathology due to their interpretability, but suffer from computation complexity. T...