Virtual Lung Screening Trial (VLST): An Replica of the National Lung Screening Trial for Lung Cancer Detection.

Importance: Clinical imaging trials are crucial for evaluation of medical innovations, but the process is inefficient, expensive, and ethically-constrained. Virtual imaging trial (VIT) approach addresses these limitations by emulating the components of a clinical trial. An rendition of the National Lung Screening Trial (NCLS) via Virtual Lung Screening Trial (VLST) demonstrates the promise of VITs to expedite clinical trials, reduce risks to subjects, and facilitate the optimal use of imaging technologies in clinical settings.

Massive Mysteries.

Papillary fibroelastomas are benign masses often originating from the endocardium of the aortic and mitral valves. Rarely, these neoplasms are found in areas of the heart embryonically distinct from the aortic and mitral valves. Diagnosis of a papillary fibroelastoma relies on multimodal imaging as well as histologic assessment.

Preoperative Planning for Structural Heart Disease.

Preoperative assessment with computed tomography (CT) is critical before transcatheter interventions for structural heart disease. CT provides information for device selection, device sizing, and vascular access approach. The interpreting radiologist must have knowledge of appropriate CT protocols, how and where to obtain the important measurements, and know additional imaging characteristics that are important to describe for optimal support of the interventionalist.

Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.

Purpose: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the diagnosis of ductal carcinoma in situ (DCIS) by core needle biopsy.

Materials And Methods: Our study is retrospective. The data was collected from 2000 to 2014.

Deep learning for identifying radiogenomic associations in breast cancer.

Rationale And Objectives: To determine whether deep learning models can distinguish between breast cancer molecular subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Materials And Methods: In this institutional review board-approved single-center study, we analyzed DCE-MR images of 270 patients at our institution. Lesions of interest were identified by radiologists.

Association of distant recurrence-free survival with algorithmically extracted MRI characteristics in breast cancer.

Background: While important in diagnosis of breast cancer, the scientific assessment of the role of imaging in prognosis of outcomes and treatment planning is limited.

Purpose: To evaluate the potential of using quantitative imaging variables for stratifying risk of distant recurrence in breast cancer patients.

Study Type: Retrospective.

Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.

Purpose: To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer patients.

Methods: Institutional review board approval was obtained for this retrospective study of 288 breast cancer patients at our institution who received NAT and had a pre-treatment breast MRI. A comprehensive set of 529 radiomic features was extracted from each patient's pre-treatment MRI.

A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.

Background: Recent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship.

Methods: We analysed a set of 922 patients with invasive breast cancer and pre-operative MRI.

Intra-tumor molecular heterogeneity in breast cancer: definitions of measures and association with distant recurrence-free survival.

Purpose: The purpose of the study was to define quantitative measures of intra-tumor heterogeneity in breast cancer based on histopathology data gathered from multiple samples on individual patients and determine their association with distant recurrence-free survival (DRFS).

Methods: We collected data from 971 invasive breast cancers, from 1st January 2000 to 23rd March 2014, that underwent repeat tumor sampling at our institution. We defined and calculated 31 measures of intra-tumor heterogeneity including ER, PR, and HER2 immunohistochemistry (IHC), proliferation, EGFR IHC, grade, and histology.

Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors.

Purpose: To review features used in MRI radiomics of breast cancer and study the inter-reader stability of the features.

Methods: We implemented 529 algorithmic features that can be extracted from tumor and fibroglandular tissue (FGT) in breast MRIs. The features were identified based on a review of the existing literature with consideration of their usage, prognostic ability, and uniqueness.

A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models.

Purpose: To determine whether multivariate machine learning models of algorithmically assessed magnetic resonance imaging (MRI) features from breast cancer patients are associated with Oncotype DX (ODX) test recurrence scores.

Methods: A set of 261 female patients with invasive breast cancer, pre-operative dynamic contrast enhanced magnetic resonance (DCE-MR) images and available ODX score at our institution was identified. A computer algorithm extracted a comprehensive set of 529 features from the DCE-MR images of these patients.

Can algorithmically assessed MRI features predict which patients with a preoperative diagnosis of ductal carcinoma in situ are upstaged to invasive breast cancer?

Purpose: To assess the ability of algorithmically assessed magnetic resonance imaging (MRI) features to predict the likelihood of upstaging to invasive cancer in newly diagnosed ductal carcinoma in situ (DCIS).

Materials And Methods: We identified 131 patients at our institution from 2000-2014 with a core needle biopsy-confirmed diagnosis of pure DCIS, a 1.5 or 3T preoperative bilateral breast MRI with nonfat-saturated T -weighted MRI sequences, no preoperative therapy before breast MRI, and no prior history of breast cancer.

Algorithms for prediction of the Oncotype DX recurrence score using clinicopathologic data: a review and comparison using an independent dataset.

Purpose: Given the potential savings in cost and resource utilization, several algorithms have been proposed to predict Oncotype DX recurrence score (ODX RS) using commonly acquired histopathologic variables. Although it is promising, additional independent validation of these surrogate markers is needed prior to guide the patient management.

Methods: In this retrospective study, we analyzed 305 patients with invasive breast cancer at our institution who had ODX RS available.

Predictive parameters for the antecedent development of hip pathology associated with long segment fusions to the pelvis for the treatment of adult spinal deformity.

Background: The surgical treatment of adult scoliosis frequently involves long segment fusions across the lumbosacral joints that redistribute tremendous amounts of force to the remaining mobile spinal segments as well as to the pelvis and hip joints. Whether or not these forces increase the risk of femoral bone pathology remains unknown. The aim of this study is to determine the correlation between long segment spinal fusions to the pelvis and the antecedent development of degenerative hip pathologies as well as what predictive patient characteristics, if any, correlate with their development.

Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics.

Purpose: To assess the interobserver variability of readers when outlining breast tumors in MRI, study the reasons behind the variability, and quantify the effect of the variability on algorithmic imaging features extracted from breast MRI.

Methods: Four readers annotated breast tumors from the MRI examinations of 50 patients from one institution using a bounding box to indicate a tumor. All of the annotated tumors were biopsy proven cancers.