Cardiovascular

A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk.

BACKGROUND: Breast ductal carcinoma in situ (DCIS) represent approximately 20% of screen-detected breast cancers. The overall risk for DCIS patients treated with breast-conserving surgery stems almost exclusively from local recurrence. Although a mastectomy or adjuvant radiation can reduce recurrence risk, there are significant concerns regarding patient over-/under-treatment. Current clinicopatho...

Prediction of Chemotherapy Response of Osteosarcoma Using Baseline F-FDG Textural Features Machine Learning Approaches with PCA.

PURPOSE: Patients with high-grade osteosarcoma undergo several chemotherapy cycles before surgical intervention. Response to chemotherapy, however, is affected by intratumor heterogeneity. In this study, we assessed the ability of a machine learning approach using baseline F-fluorodeoxyglucose (F-FDG) positron emitted tomography (PET) textural features to predict response to chemotherapy in osteos...

Younger tamoxifen-treated breast cancer patients also had higher risk of endometrial cancer and the risk could be reduced by sequenced aromatase inhibitor use: A population-based study in Taiwan.

OBJECTIVE: Previous Western studies reported that older (≥50 years) breast cancer survivors with tamoxifen treatment had higher risk of endometrial ca...

MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method.

Magnetic resonance imaging (MRI) has been widely used in combination with computed tomography (CT) radiation therapy because MRI improves the accuracy...

Incorporating imaging information from deep neural network layers into image guided radiation therapy (IGRT).

BACKGROUND AND PURPOSE: To investigate a novel markerless prostate localization strategy using a pre-trained deep learning model to interpret routine ...

Characterization and potential antitumor effect of a heteropolysaccharide produced by the red alga .

Taking into account the rising trend of the incidence of cancers of various organs, effective therapies are urgently needed to control human malignanc...

Future of Radiotherapy in Nasopharyngeal Carcinoma.

Nasopharyngeal carcinoma (NPC) is a malignancy with unique clinical biological profiles such as associated Epstein-Barr virus infection and high radio...

Relevant Word Order Vectorization for Improved Natural Language Processing in Electronic Health Records.

Electronic health records (EHR) represent a rich resource for conducting observational studies, supporting clinical trials, and more. However, much of...

Clinical Documentation and Patient Care Using Artificial Intelligence in Radiation Oncology.

Detailed clinical documentation is required in the patient-facing specialty of radiation oncology. The burden of clinical documentation has increased ...

Automatic gas detection in prostate cancer patients during image-guided radiation therapy using a deep convolutional neural network.

PURPOSE: The detection of intestinal/rectal gas is very important during image-guided radiation therapy (IGRT) of prostate cancer patients because int...

Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms.

Background Computational models on the basis of deep neural networks are increasingly used to analyze health care data. However, the efficacy of tradi...

Markerless Pancreatic Tumor Target Localization Enabled By Deep Learning.

PURPOSE: Deep learning is an emerging technique that allows us to capture imaging information beyond the visually recognizable level of a human being....

A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial.

PURPOSE: Xerostomia commonly occurs in patients who undergo head and neck radiation therapy and can seriously affect patients' quality of life. In thi...

Applications and limitations of machine learning in radiation oncology.

Machine learning approaches to problem-solving are growing rapidly within healthcare, and radiation oncology is no exception. With the burgeoning inte...

An Intelligent DNA Nanorobot with Enhanced Protein Lysosomal Degradation of HER2.

DNA nanorobots have emerged as new tools for nanomedicine with the potential to ameliorate the delivery and anticancer efficacy of various drugs. DNA ...

Development and Validation of a Bayesian Network Method to Detect External Beam Radiation Therapy Physician Order Errors.

PURPOSE: To investigate a Bayesian network (BN)-based method to detect errors in external beam radiation therapy physician orders.

Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction.

Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model fo...

Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network.

The uncontrollable growth of cells in the breast tissue causes breast cancer which is the second most common type of cancer affecting women in the Uni...

Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer.

Inverse treatment planning in radiation therapy is formulated as solving optimization problems. The objective function and constraints consist of mult...

Reduction of operator radiation exposure using a passive robotic device during fluoroscopy-guided arterial puncture: an experimental study in a swine model.

BACKGROUND: Vascular interventions imply radiation exposure to the operating physician (OP). To reduce radiation exposure, we propose a novel passive ...