Deep learning prediction of axillary lymph node status using ultrasound images.

Objective: To investigate the ability of our convolutional neural network (CNN) to predict axillary lymph node metastasis using primary breast cancer ultrasound (US) images.

Methods: In this IRB-approved study, 338 US images (two orthogonal images) from 169 patients from 1/2014-12/2016 were used. Suspicious lymph nodes were seen on US and patients subsequently underwent core-biopsy.

Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom.

Recent innovations in quantitative magnetic resonance imaging (MRI) measurement methods have led to improvements in accuracy, repeatability, and acquisition speed, and have prompted renewed interest to reevaluate the medical value of quantitative T1. The purpose of this study was to determine the bias and reproducibility of T1 measurements in a variety of MRI systems with an eye toward assessing the feasibility of applying diagnostic threshold T1 measurement across multiple clinical sites. We used the International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) system phantom to assess variations of T1 measurements, using a slow, reference standard inversion recovery sequence and a rapid, commonly-available variable flip angle sequence, across MRI systems at 1.

Convolutional Neural Network Detection of Axillary Lymph Node Metastasis Using Standard Clinical Breast MRI.

Background: Axillary lymph node status is important for breast cancer staging and treatment planning as the majority of breast cancer metastasis spreads through the axillary lymph nodes. There is currently no reliable noninvasive imaging method to detect nodal metastasis associated with breast cancer.

Materials And Methods: Magnetic resonance imaging (MRI) data were those from the peak contrast dynamic image from 1.

How Many Radiographs Does It Take to Screen for Femoral Cam Morphology?: A Noninferiority Study.

Purpose: To compare a 2-view radiograph series (AP of the pelvis and 45° Dunn of the hip) with a 5-view radiograph series for sensitivity in identifying femoral cam morphology.

Materials And Methods: This is a retrospective review of consecutive patients with a 5-view radiograph series (AP pelvis and AP, 45° Dunn, frog lateral, and false profile of the affected hip) from 2016 to 2017. Three fellowship trained radiologists blindly and independently evaluated 2 views (AP pelvis and Dunn) for a femoral cam lesion, acetabular rim calcification, Tonnis grade, and important incidental findings.

Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network-Based Machine Learning Approach Using Mammographic Image Data.

The purpose of this study was to test the hypothesis that convolutional neural networks can be used to predict which patients with pure atypical ductal hyperplasia (ADH) may be safely monitored rather than undergo surgery. A total of 298 unique images from 149 patients were used for our convolutional neural network algorithm. A total of 134 images from 67 patients with ADH that had been diagnosed by stereotactic-guided biopsy of calcifications but had not been upgraded to ductal carcinoma in situ or invasive cancer at the time of surgical excision.

Multicenter Repeatability Study of a Novel Quantitative Diffusion Kurtosis Imaging Phantom.

Quantitative kurtosis phantoms are sought by multicenter clinical trials to establish accuracy and precision of quantitative imaging biomarkers on the basis of diffusion kurtosis imaging (DKI) parameters. We designed and evaluated precision, reproducibility, and long-term stability of a novel isotropic (i)DKI phantom fabricated using four families of chemicals based on vesicular and lamellar mesophases of liquid crystal materials. The constructed iDKI phantoms included negative control monoexponential diffusion materials to independently characterize noise and model-induced bias in quantitative kurtosis parameters.

Repeatability of Quantitative Diffusion-Weighted Imaging Metrics in Phantoms, Head-and-Neck and Thyroid Cancers: Preliminary Findings.

The aim of this study was to establish the repeatability measures of quantitative Gaussian and non-Gaussian diffusion metrics using diffusion-weighted imaging (DWI) data from phantoms and patients with head-and-neck and papillary thyroid cancers. The Quantitative Imaging Biomarker Alliance (QIBA) DWI phantom and a novel isotropic diffusion kurtosis imaging phantom were scanned at 3 different sites, on 1.5T and 3T magnetic resonance imaging systems, using standardized multiple b-value DWI acquisition protocol.

Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm.

To develop a convolutional neural network (CNN) algorithm that can predict the molecular subtype of a breast cancer based on MRI features. An IRB-approved study was performed in 216 patients with available pre-treatment MRIs and immunohistochemical staining pathology data. First post-contrast MRI images were used for 3D segmentation using 3D slicer.

Advanced MR Imaging of the Temporal Bone.

Temporal bone pathologies are challenging to discern because of their small size and subtle contrast. MR imaging is one of the key modalities in evaluating otologic diseases. Current advancement in MR techniques provide multiparametric information for evaluation of these pathologies.

Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset.

We hypothesize that convolutional neural networks (CNN) can be used to predict neoadjuvant chemotherapy (NAC) response using a breast MRI tumor dataset prior to initiation of chemotherapy. An institutional review board-approved retrospective review of our database from January 2009 to June 2016 identified 141 locally advanced breast cancer patients who (1) underwent breast MRI prior to the initiation of NAC, (2) successfully completed adriamycin/taxane-based NAC, and (3) underwent surgical resection with available final surgical pathology data. Patients were classified into three groups based on their NAC response confirmed on final surgical pathology: complete (group 1), partial (group 2), and no response/progression (group 3).

Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score.

Background: Oncotype Dx is a validated genetic analysis that provides a recurrence score (RS) to quantitatively predict outcomes in patients who meet the criteria of estrogen receptor positive / human epidermal growth factor receptor-2 negative (ER+/HER2-)/node negative invasive breast carcinoma. Although effective, the test is invasive and expensive, which has motivated this investigation to determine the potential role of radiomics.

Hypothesis: We hypothesized that convolutional neural network (CNN) can be used to predict Oncotype Dx RS using an MRI dataset.

Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement.

The aim of this study is to develop a fully automated convolutional neural network (CNN) method for quantification of breast MRI fibroglandular tissue (FGT) and background parenchymal enhancement (BPE). An institutional review board-approved retrospective study evaluated 1114 breast volumes in 137 patients using T1 precontrast, T1 postcontrast, and T1 subtraction images. First, using our previously published method of quantification, we manually segmented and calculated the amount of FGT and BPE to establish ground truth parameters.

Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset.

Rationale And Objectives: We propose a novel convolutional neural network derived pixel-wise breast cancer risk model using mammographic dataset.

Materials And Methods: An institutional review board approved retrospective case-control study of 1474 mammographic images was performed in average risk women. First, 210 patients with new incidence of breast cancer were identified.

Predicting Post Neoadjuvant Axillary Response Using a Novel Convolutional Neural Network Algorithm.

Objectives: In the postneoadjuvant chemotherapy (NAC) setting, conventional radiographic complete response (rCR) is a poor predictor of pathologic complete response (pCR) of the axilla. We developed a convolutional neural network (CNN) algorithm to better predict post-NAC axillary response using a breast MRI dataset.

Methods: An institutional review board-approved retrospective study from January 2009 to June 2016 identified 127 breast cancer patients who: (1) underwent breast MRI before the initiation of NAC; (2) successfully completed Adriamycin/Taxane-based NAC; and (3) underwent surgery, including sentinel lymph node evaluation/axillary lymph node dissection with final surgical pathology data.

Insulin Hexamer-Caged Gadolinium Ion as MRI Contrast-o-phore.

High-relaxivity protein-complexes of Gd are being pursued as MRI contrast agents in hope that they can be used at much lower doses that would minimize toxic-side effects of Gd release from traditional contrast agents. We construct here a new type of protein-based MRI contrast agent, a proteinaceous cage based on a stable insulin hexamer in which Gd is captured inside a water filled cavity. The macromolecular structure and the large number of "free" Gd coordination sites available for water binding lead to exceptionally high relaxivities per one Gd ion.

Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset.

The aim of this study is to evaluate the role of convolutional neural network (CNN) in predicting axillary lymph node metastasis, using a breast MRI dataset. An institutional review board (IRB)-approved retrospective review of our database from 1/2013 to 6/2016 identified 275 axillary lymph nodes for this study. Biopsy-proven 133 metastatic axillary lymph nodes and 142 negative control lymph nodes were identified based on benign biopsies (100) and from healthy MRI screening patients (42) with at least 3 years of negative follow-up.

Can diffusion-weighted imaging serve as a biomarker of fibrosis in pancreatic adenocarcinoma?

Purpose: To assess the relationship between diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM)-derived quantitative parameters (apparent diffusion coefficient [ADC], perfusion fraction [f], D , diffusion coefficient [D], and D , pseudodiffusion coefficient [D*]) and histopathology in pancreatic adenocarcinoma (PAC).

Materials And Methods: Subjects with suspected surgically resectable PAC were prospectively enrolled in this Health Insurance Portability and Accountability Act (HIPAA)-compliant, Institutional Review Board-approved study. Imaging was performed at 1.

Evaluation of neonatal brain myelination using the T1- and T2-weighted MRI ratio.

Purpose: To validate the T1- and T2-weighted (T1w/T2w) MRI ratio technique in evaluating myelin in the neonatal brain.

Materials And Methods: T1w and T2w MR images of 10 term neonates with normal-appearing brain parenchyma were obtained from a single 1.5 Tesla MRI and retrospectively analyzed.