Breast Cancer

Prediction of breast dose in chest CT examinations using adaptive neuro-fuzzy inference system (ANFIS).

In chest computed tomography (CT), the breasts located within the scan range receive a substantial radiation dose. Due to the risk of breast-related carcinogenesis, analyzing the breast dose for justification of CT examinations seems necessary. The main goal of this study is to overcome the limitations of conventional dosimetry methods, such as thermoluminescent dosimeters (TLDs) by introducing th...

A divide and conquer approach to maximise deep learning mammography classification accuracies.

Breast cancer claims 11,400 lives on average every year in the UK, making it one of the deadliest diseases. Mammography is the gold standard for detecting early signs of breast cancer, which can help cure the disease during its early stages. However, incorrect mammography diagnoses are common and may harm patients through unnecessary treatments and operations (or a lack of treatment). Therefore, s...

Deep learning-based markerless lung tumor tracking in stereotactic radiotherapy using Siamese networks.

BACKGROUND: Radiotherapy (RT) is involved in about 50% of all cancer patients, making it a very important treatment modality. The most common type of ...

Uncertainty estimation for deep learning-based pectoral muscle segmentation via Monte Carlo dropout.

. Deep Learning models are often susceptible to failures after deployment. Knowing when your model is producing inadequate predictions is crucial. In ...

Robot-assisted nipple-sparing mastectomy and immediate breast reconstruction with gel implant and latissimus dorsi muscle flap: Our initial experience.

BACKGROUND: This study reports the preliminary results of da Vinci robot XI robot-assisted nipple-sparing mastectomy immediate breast reconstruction (...

Coronary Computed Tomography Angiography with Deep Learning Image Reconstruction: A Preliminary Study to Evaluate Radiation Exposure Reduction.

Coronary computed tomography angiography (CCTA) is a medical imaging technique that produces detailed images of the coronary arteries. Our work focuse...

Emerging uses of artificial intelligence in breast and axillary ultrasound.

Breast ultrasound is a valuable adjunctive tool to mammography in detecting breast cancer, especially in women with dense breasts. Ultrasound also pla...

Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy.

Deep learning (DL) models for radiation therapy (RT) image segmentation require accurately annotated training data. Multiple organ delineation guideli...

Neural network and spline-based regression for the prediction of salivary hypofunction in patients undergoing radiation therapy.

BACKGROUND: This study leverages a large retrospective cohort of head and neck cancer patients in order to develop machine learning models to predict ...

Distortion-corrected image reconstruction with deep learning on an MRI-Linac.

PURPOSE: MRI is increasingly utilized for image-guided radiotherapy due to its outstanding soft-tissue contrast and lack of ionizing radiation. Howeve...

HER2-low breast cancer: insights on pathological testing.

Human epidermal growth factor receptor 2 (HER2) is an important biomarker for predicting prognosis and effectiveness of HER2-targeted therapy in breas...

Implementation and evaluation of an intelligent automatic treatment planning robot for prostate cancer stereotactic body radiation therapy.

PURPOSE: We previously developed a virtual treatment planner (VTP), an artificial intelligence robot, operating a treatment planning system (TPS). Usi...

Preliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational status.

The detection of tumour gene mutations by DNA or RNA sequencing is crucial for the prescription of effective targeted therapies. Recent developments s...

Deep learning enables MV-based real-time image guided radiation therapy for prostate cancer patients.

. Using MV images for real-time image guided radiation therapy (IGRT) is ideal as it does not require additional imaging equipment, adds no additional...

MutBLESS: A tool to identify disease-prone sites in cancer using deep learning.

Understanding the molecular basis and impact of mutations at different stages of cancer are long-standing challenges in cancer biology. Identification...

Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.

. Radiation therapy for head and neck (H&N) cancer relies on accurate segmentation of the primary tumor. A robust, accurate, and automated gross tumor...

Robot-assisted Laparoscopic Prostatectomy After SpaceOAR Placement.

BACKGROUND: Patients with prostate cancer are offered a wide range of treatments from radiation therapy to chemotherapy to surgery. When giving radiat...

Opportunistic Extraction of Quantitative CT Biomarkers: Turning the Incidental Into Prognostic Information.

The past two decades have seen a significant increase in the use of CT, with a corresponding rise in the mean population radiation dose. This rise in ...

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 essential to determining the chemotherapy regimen for b...

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 targeting of the tumor to avoid damaging the surroundin...