Breast Cancer

Deep learning radiomics model based on breast ultrasound video to predict HER2 expression status.

PURPOSE: The detection of human epidermal growth factor receptor 2 (HER2) expression status is essen...

Realistic CT data augmentation for accurate deep-learning based segmentation of head and neck tumors in kV images acquired during radiation therapy.

BACKGROUND: Using radiation therapy (RT) to treat head and neck (H&N) cancers requires precise targe...

Explainable discovery of disease biomarkers: The case of ovarian cancer to illustrate the best practice in machine learning and Shapley analysis.

OBJECTIVE: Ovarian cancer is a significant health issue with lasting impacts on the community. Despi...

Deep learning synthesis of cone-beam computed tomography from zero echo time magnetic resonance imaging.

Cone-beam computed tomography (CBCT) produces high-resolution of hard tissue even in small voxel siz...

Interpretable attention-based deep learning ensemble for personalized ovarian cancer treatment without manual annotations.

Inhibition of pathological angiogenesis has become one of the first FDA approved targeted therapies ...

ChatGPT: Can a Natural Language Processing Tool Be Trusted for Radiation Oncology Use?

No prostate? No problem: robotic inguinal hernia repair after prostatectomy.

Traditional teaching suggests that prior pelvic operations, including prostatectomy, are a contraind...

Trustworthy Deep Neural Network for Inferring Anticancer Synergistic Combinations.

The lack of a gold standard synergy quantification method for chemotherapeutic drug combinations war...

BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images.

Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United...

[Robot assisted prophylactic mastectomy].

Review of Robot-Assisted HIFU Therapy.

This paper provides an overview of current robot-assisted high-intensity focused ultrasound (HIFU) s...

Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.

OBJECTIVES: To investigate the predictive performance of the deep learning radiomics (DLR) model int...

An End-to-End Natural Language Processing System for Automatically Extracting Radiation Therapy Events From Clinical Texts.

PURPOSE: Real-world evidence for radiation therapy (RT) is limited because it is often documented on...

Deep learning-based fast volumetric imaging using kV and MV projection images for lung cancer radiotherapy: A feasibility study.

PURPOSE: The long acquisition time of CBCT discourages repeat verification imaging, therefore increa...

Application of Deep Learning-Based Denoising Technique for Radiation Dose Reduction in Dynamic Abdominal CT: Comparison with Standard-Dose CT Using Hybrid Iterative Reconstruction Method.

The purpose is to evaluate whether deep learning-based denoising (DLD) algorithm provides sufficient...

Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: prospective, randomized, double-blind study.

OBJECTIVE: To investigate the image quality and lesion conspicuity of a deep-learning-based contrast...

Deep Learning-Based Segmentation of Locally Advanced Breast Cancer on MRI in Relation to Residual Cancer Burden: A Multi-Institutional Cohort Study.

BACKGROUND: While several methods have been proposed for automated assessment of breast-cancer respo...

Equivalent radiation exposure with robotic total hip replacement using a novel, fluoroscopic-guided (CT-free) system: case-control study versus manual technique.

Accurate and precise positioning of the acetabular cup remains a prevalent challenge in total hip ar...

Deep learning-based dominant index lesion segmentation for MR-guided radiation therapy of prostate cancer.

BACKGROUND: Dose escalation radiotherapy enables increased control of prostate cancer (PCa) but requ...

Validation of a Fully Automated Hybrid Deep Learning Cardiac Substructure Segmentation Tool for Contouring and Dose Evaluation in Lung Cancer Radiotherapy.

BACKGROUND AND PURPOSE: Accurate and consistent delineation of cardiac substructures is challenging....

Unpaired low-dose computed tomography image denoising using a progressive cyclical convolutional neural network.

BACKGROUND: Reducing the radiation dose from computed tomography (CT) can significantly reduce the r...