Cardiovascular

System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation.

PURPOSE: Patients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited prospective studies investigating the clinical impact of ML in health care. The objective of this study was to determine whether ML can identify high...

Multi-Responsive Bottlebrush-Like Unimolecules Self-Assembled Nano-Riceball for Synergistic Sono-Chemotherapy.

Improved drug loading content, bioavailability, and controlled release in targeted tissue have been major bottlenecks in the design of precision nanomedicine. Herein, a tumor-specific and multiple-stimuli responsive nano-riceball is proposed and validated for enhanced sono-chemotherapy. The nano-riceball (NGR@DDP) possesses a well-designed core-shell structure, formed by an inner core assembly tha...

A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database.

PURPOSE: To apply our convolutional neural network (CNN) algorithm to predict neoadjuvant chemotherapy (NAC) response using the I-SPY TRIAL breast MRI...

Use of artificial intelligence in computed tomography dose optimisation.

The field of artificial intelligence (AI) is transforming almost every aspect of modern society, including medical imaging. In computed tomography (CT...

Accurate 3D-dose-based generation of MLC segments for robotic radiotherapy.

Radiotherapy treatment planning requires accurate modeling of the delivered patient dose, including radiation scatter effects, multi-leaf collimator (...

Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review.

Radiation oncology for prostate cancer is important as it can decrease the morbidity and mortality associated with this disease. Planning for this mod...

Knowledge Models as Teaching Aid for Training Intensity Modulated Radiation Therapy Planning: A Lung Cancer Case Study.

Artificial intelligence (AI) employs knowledge models that often behave as a black-box to the majority of users and are not designed to improve the s...

Artificial intelligence in radiation oncology.

Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is practised. AI platforms excel in recognizing complex pattern...

A knowledge-based intensity-modulated radiation therapy treatment planning technique for locally advanced nasopharyngeal carcinoma radiotherapy.

BACKGROUND: To investigate the feasibility of a knowledge-based automated intensity-modulated radiation therapy (IMRT) planning technique for locally ...

Using deep learning to predict beam-tunable Pareto optimal dose distribution for intensity-modulated radiation therapy.

PURPOSE: Many researchers have developed deep learning models for predicting clinical dose distributions and Pareto optimal dose distributions. Models...

Synergistic drug combinations and machine learning for drug repurposing in chordoma.

Chordoma is a devastating rare cancer that affects one in a million people. With a mean-survival of just 6 years and no approved medicines, the primar...

Artificial intelligence in radiotherapy: a technological review.

Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-...

Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy.

For patients with locally advanced rectal cancer (LARC), achieving a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (CRT) pr...

Correlation Between the Trajectory of the Center of Pressure and Thermography of Cancer Patients Undergoing Chemotherapy.

OBJECTIVE: To correlate the potential of the stabilometric parameters of baropodometry with the superficial temperature of the legs of cancer patients...

Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives.

Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last decade due to numerous technological breakthroughs. Im...

Feasibility of Multiparametric Positron Emission Tomography/Magnetic Resonance Imaging as a One-Stop Shop for Radiation Therapy Planning for Patients with Head and Neck Cancer.

PURPOSE: Multiparametric positron emission tomography (PET)/magnetic resonance imaging (MRI) as a one-stop shop for radiation therapy (RT) planning ha...

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm.

The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of ...

Radiomics in radiation oncology-basics, methods, and limitations.

Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased sub...

A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning.

Response to Neoadjuvant chemotherapy (NAC) has demonstrated a high correlation to survival in locally advanced breast cancer (LABC) patients. An early...

An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm.

Non-small-cell lung cancer (NSCLC) patients often develop bone metastases (BM), and the overall survival for these patients is usually perishing. Howe...