Auto-detection of necessity for MRI-guided online adaptive replanning using a machine learning classifier.

Purpose: MRI-guided adaptive radiation therapy (MRgART), particularly daily online adaptive replanning (OLAR) can substantially improve radiation therapy delivery, however, it can be labor-intensive and time-consuming. Currently, the decision to perform OLAR for a treatment fraction is determined subjectively. In this work, we develop a machine learning algorithm based on structural similarity index measure (SSIM) and change in entropy to quickly and objectively determine whether OLAR is necessary for a daily MRI set.

Predicting necessity of daily online adaptive replanning based on wavelet image features for MRI guided adaptive radiation therapy.

Purpose: Online adaptive replanning (OLAR) is generally labor-intensive and time-consuming during MRI-guided adaptive radiation therapy (MRgART). This work aims to develop a method to determine OLAR necessity during MRgART.

Methods: A machine learning classifier was developed to predict OLAR necessity based on wavelet multiscale texture features extracted from daily MRIs and was trained and tested with data from 119 daily MRI datasets acquired during MRgART for 24 pancreatic cancer patients treated on a 1.

A Quantitative Ultrasound-Based Multi-Parameter Classifier for Breast Masses.

This manuscript reports preliminary results obtained by combining estimates of two or three (among seven) quantitative ultrasound (QUS) parameters in a model-free, multi-parameter classifier to differentiate breast carcinomas from fibroadenomas (the most common benign solid tumor). Forty-three patients scheduled for core biopsy of a suspicious breast mass were recruited. Radiofrequency echo signal data were acquired using clinical breast ultrasound systems equipped with linear array transducers.

Three-dimensional Ultrasound Elasticity Imaging on an Automated Breast Volume Scanning System.

Ultrasound elasticity imaging has demonstrated utility in breast imaging, but it is typically performed with handheld transducers and two-dimensional imaging. Two-dimensional (2D) elastography images tissue stiffness of only a plane and hence suffers from errors due to out-of-plane motion, whereas three-dimensional (3D) data acquisition and motion tracking can be used to track out-of-plane motion that is lost in 2D elastography systems. A commercially available automated breast volume scanning system that acquires 3D ultrasound data with precisely controlled elevational movement of the 1D array ultrasound transducer was employed in this study.

Acoustic Properties of Breast Fat.

Objectives: The American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) atlas for ultrasound (US) qualitatively describes the echogenicity and attenuation of a mass, where fat lobules serve as a standard for comparison. This study aimed to estimate acoustic properties of breast fat under clinical imaging conditions to determine the degree to which properties vary among patients.

Methods: Twenty-four women with solid breast masses scheduled for biopsy were scanned with a Siemens S2000 scanner and 18L6 linear array transducer (Siemens Medical Solutions, Malvern, PA).