Latest AI and machine learning research in breast cancer for healthcare professionals.
PURPOSE: To evaluate C-11 choline positron emission tomography/computed tomography (CholPET) in staging and determining patterns of recurrence in prostate cancer patients with rising prostate-specific antigen levels after prostatectomy radiation therapy (RT).
Perirectal space segmentation in computed tomography images aids in quantifying radiation dose received by healthy tissues and toxicity during the course of radiation therapy treatment of the prostate. Radiation dose normalised by tissue volume facilitates predicting outcomes or possible harmful side effects of radiation therapy treatment. Manual segmentation of the perirectal space is time consum...
PURPOSE: The aim of this study was to determine the effect of single fraction (SF) and multiple fraction (MF) radiation therapy (RT) on bone mineral d...
PURPOSE: NRG Oncology RTOG 9202 was a randomized trial testing long-term adjuvant androgen deprivation (LTAD) versus initial androgen deprivation only...
OBJECTIVE: Conventional mammogram enhancement methods use transform-domain filtering, which possibly produce some artifacts or not well highlight all ...
PURPOSE: To assess the efficacy of robotic-assisted laparoscopic sentinel lymph node (SLN) dissection (SLND) to select those patients with prostate ca...
PURPOSE: The purpose of this study was to evaluate freedom from biochemical failure (FFBF), freedom from androgen deprivation therapy (FFADT), freedom...
We are developing a cooperatively controlled robot system for image-guided radiation therapy (IGRT) in which a clinician and robot share control of a ...
MOTIVATION: Chemotherapy or targeted therapy are two of the main treatment options for many types of cancer. Due to the heterogeneous nature of cancer...
We have proposed a computer-assisted framework for machine-learning-based delineation of gross tumor volumes (GTVs) following an optimum contour selec...
Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The pre...
BACKGROUND: The high degree of heterogeneity observed in breast cancers makes it very difficult to classify the cancer patients into distinct clinical...
OBJECTIVE: To design a precision medicine approach aimed at exploiting significant patterns in data, in order to produce venous thromboembolism (VTE) ...
PURPOSE: A phase 2 protocol was designed and implemented to assess the toxicity and efficacy of hypofractionated image guided intensity modulated radi...
The study of radiation entrance and exit dynamics and energy consumption in a system is important for understanding the environmental processes that r...
To develop a patient-specific 'big data' clinical decision tool to predict pneumonitis in stage I non-small cell lung cancer (NSCLC) patients after st...
In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnos...
PURPOSE: The purpose of this study was to evaluate a receiver operating characteristic (ROC) curve method to determine dose thresholds with late genit...
The dose from ionizing radiation exposure can be interpolated from a calibration curve fit to the frequency of dicentric chromosomes (DCs) at multiple...
Using macrophage recruitment in tumors, we develop active, transportable, cancer theragnostic macrophage-based microrobots as vector to deliver therap...