Breast Cancer

Deep learning-based thoracic CBCT correction with histogram matching.

Kilovoltage cone-beam computed tomography (CBCT)-based image-guided radiation therapy (IGRT) is used for daily delivery of radiation therapy, especially for stereotactic body radiation therapy (SBRT), which imposes particularly high demands for setup accuracy. The clinical applications of CBCTs are constrained, however, by poor soft tissue contrast, image artifacts, and instability of Hounsfield u...

The mathematics of erythema: Development of machine learning models for artificial intelligence assisted measurement and severity scoring of radiation induced dermatitis.

Although significant advancements in computer-aided diagnostics using artificial intelligence (AI) have been made, to date, no viable method for radiation-induced skin reaction (RISR) analysis and classification is available. The objective of this single-center study was to develop machine learning and deep learning approaches using deep convolutional neural networks (CNNs) for automatic classific...

Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer?

OBJECTIVE: To study the impact of artificial intelligence (AI) on the performance of mammogram with regard to the classification of the detected breas...

Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study.

OBJECTIVES: Breast cancer (BC) is the most common cancer in women worldwide, and neoadjuvant chemotherapy (NAC) is considered the standard of treatmen...

Global processing provides malignancy evidence complementary to the information captured by humans or machines following detailed mammogram inspection.

The information captured by the gist signal, which refers to radiologists' first impression arising from an initial global image processing, is poorly...

Comparative optimization of global solar radiation forecasting using machine learning and time series models.

The increasing use of solar energy as a source of renewable energy has led to increasing the interest in photovoltaic (PV) power outputs forecasting. ...

Coupling of Trastuzumab chromatographic profiling with machine learning tools: A complementary approach for biosimilarity and stability assessment.

Biosimilar products present a growing opportunity to improve the global healthcare systems. The amount of accepted variability during the comparative ...

Natural language processing and machine learning to assist radiation oncology incident learning.

PURPOSE: To develop a Natural Language Processing (NLP) and Machine Learning (ML) pipeline that can be integrated into an Incident Learning System (IL...

Introducing artificial intelligence to the radiation early warning system.

Although radiation level is a serious concern which requires continuous monitoring, many existing systems are designed to perform this task. Radiation...

Deep Learning-based Reconstruction for Lower-Dose Pediatric CT: Technical Principles, Image Characteristics, and Clinical Implementations.

Optimizing the CT acquisition parameters to obtain diagnostic image quality at the lowest possible radiation dose is crucial in the radiosensitive ped...

Outcome-based multiobjective optimization of lymphoma radiation therapy plans.

At its core, radiation therapy (RT) requires balancing therapeutic effects against risk of adverse events in cancer survivors. The radiation oncologis...

Radiation Oncologists' Perceptions of Adopting an Artificial Intelligence-Assisted Contouring Technology: Model Development and Questionnaire Study.

BACKGROUND: An artificial intelligence (AI)-assisted contouring system benefits radiation oncologists by saving time and improving treatment accuracy....

5FU-loaded PCL/Chitosan/FeO Core-Shell Nanofibers Structure: An Approach to Multi-Mode Anticancer System.

5-Fluorouracil (5FU) and FeO nanoparticles were encapsulated in core-shell polycaprolactone (PCL)/chitosan (CS) nanofibers as a multi-mode anticancer...

Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone.

High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence, frequent chemotherapy resistance, and overall 5-...

Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.

The achievement of the pathologic complete response (pCR) has been considered a metric for the success of neoadjuvant chemotherapy (NAC) and a powerfu...

Robot-Assisted Laparoscopic Management of Bladder/Prostate Rhabdomyosarcoma in Children: Initial Series and 1-Year Outcomes.

To present our preliminary experiences of robot-assisted laparoscopic radical or partial cystectomy for bladder/prostate rhabdomyosarcoma (B/P RMS) i...

Deep Learning-Based CT Imaging in Diagnosing Myeloma and Its Prognosis Evaluation.

Imaging examination plays an important role in the early diagnosis of myeloma. The study focused on the segmentation effects of deep learning-based mo...

Deep Learning: a Promising Method for Histological Class Prediction of Breast Tumors in Mammography.

The objective of the study was to determine if the pathology depicted on a mammogram is either benign or malignant (ductal or non-ductal carcinoma) us...

Artificial intelligence in radiation oncology: A review of its current status and potential application for the radiotherapy workforce.

OBJECTIVE: Radiation oncology is a continually evolving speciality. With the development of new imaging modalities and advanced imaging processing tec...

DeepWL: Robust EPID based Winston-Lutz analysis using deep learning, synthetic image generation and optical path-tracing.

Radiation therapy requires clinical linear accelerators to be mechanically and dosimetrically calibrated to a high standard. One important quality ass...