Maya Galperin-Aizenberg

Cardiac CT Angiography in the Emergency Department

OBJECTIVE. Nearly 8 million patients present annually to emergency departments (EDs) in the United States with acute chest pain. Identifying those with a sufficiently low risk of acute coronary syndrome (ACS) remains challenging. Early imaging is important for risk stratification of these individuals. The objective of this article is to discuss the role of cardiac CT angiography (CTA) as a safe, efficient, and cost-effective tool in this setting and review state-of-the-art technology, protocols, advantages, and limitations from the perspective of our institutions 10-year experience. CONCLUSION. Early utilization of cardiac CTA in patients presenting to the ED with chest pain and a low to intermediate risk of ACS quickly identifies a group of particularly low-risk patients (< 1% risk of adverse events within 30 days) and allows safe and expedited discharge. By preventing unnecessary admissions and prolonged lengths of stay, a strategy based on early cardiac CTA has been shown to be efficient, although potential overutilization and other issues require long-term study.

Coronary and cardiac computed tomography in the emergency room: current status and future directions.

In the United States, chest pain is the second leading reason for patients to present to an emergency department (ED). Previously, those patients suspected to have acute coronary syndrome were monitored for 24 hours to determine the presence of serum biomarkers consistent with myocardial injury. However, more recently, imaging has been used to more efficiently triage these individuals and even discharge them directly from the ED. There are multiple cardiac imaging modalities; however, cardiac computed tomography now plays a significant role in the evaluation of patients with suspected acute coronary syndrome who present to the ED. In this review, we discuss the available state-of-the-art techniques for evaluating this cohort of patients, including clinical evaluation, serum biomarkers, and imaging options. Further, we analyze in detail evidence for the use of coronary computed tomography angiography to determine whether these patients can safely be discharged from the ED. Finally, we review some of the related future techniques that may become part of the accepted clinical management of these patients in the future.

Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports

A significant volume of medical data remains unstructured. Natural language processing (NLP) and machine learning (ML) techniques have shown to successfully extract insights from radiology reports. However, the codependent effects of NLP and ML in this context have not been well-studied. Between April 1, 2015 and November 1, 2016, 9418 cross-sectional abdomen/pelvis CT and MR examinations containing our internal structured reporting element for cancer were separated into four categories: Progression, Stable Disease, Improvement, or No Cancer. We combined each of three NLP techniques with five ML algorithms to predict the assigned label using the unstructured report text and compared the performance of each combination. The three NLP algorithms included term frequency-inverse document frequency (TF-IDF), term frequency weighting (TF), and 16-bit feature hashing. The ML algorithms included logistic regression (LR), random decision forest (RDF), one-vs-all support vector machine (SVM), one-vs-all Bayes point machine (BPM), and fully connected neural network (NN). The best-performing NLP model consisted of tokenized unigrams and bigrams with TF-IDF. Increasing N-gram length yielded little to no added benefit for most ML algorithms. With all parameters optimized, SVM had the best performance on the test dataset, with 90.6 average accuracy and F score of 0.813. The interplay between ML and NLP algorithms and their effect on interpretation accuracy is complex. The best accuracy is achieved when both algorithms are optimized concurrently.

Obstructive pulmonary disease in patients with previous tuberculosis : pathophysiology of a community-based cohort

BACKGROUND An association between chronic airflow limitation (CAL) and a history of pulmonary tuberculosis (PTB) has been confirmed in epidemiological studies, but the mechanisms responsible for this association are unclear. It is debated whether CAL in this context should be viewed as chronic obstructive pulmonary disease (COPD) or a separate phenotype. OBJECTIVE To compare lung physiology and high-resolution computed tomography (HRCT) findings in subjects with CAL and evidence of previous (healed) PTB with those in subjects with smoking-related COPD without evidence of previous PTB. METHODS Subjects with CAL identified during a Burden of Obstructive Lung Disease (BOLD) study performed in South Africa were studied. Investigations included questionnaires, lung physiology (spirometry, body plethysmography and diffusing capacity) and quantitative HRCT scans to assess bronchial anatomy and the presence of emphysema (<-950 HU), gas trapping (<-860 HU) and fibrosis (>-200 HU). Findings in subjects with a past history and/or HRCT evidence of PTB were compared with those in subjects without these features. RESULTS One hundred and seven of 196 eligible subjects (54.6%) were enrolled, 104 performed physiology tests and 94 had an HRCT scan. Based on history and HRCT findings, subjects were categorised as no previous PTB (NPTB, n=31), probable previous PTB (n=33) or definite previous PTB (DPTB, n=39). Subjects with DPTB had a lower diffusing capacity (Δ=-17.7%; p=0.001) and inspiratory capacity (Δ=-21.5%; p=0.001) than NPTB subjects, and higher gas-trapping and fibrosis but not emphysema scores (Δ=+6.2% (p=0.021), +0.36% (p=0.017) and +3.5% (p=0.098), respectively). CONCLUSIONS The mechanisms of CAL associated with previous PTB appear to differ from those in the more common smoking-related COPD and warrant further study.

The value of delayed phase enhanced imaging in malignant pleural mesothelioma

BACKGROUND Cross-sectional imaging of malignant pleural mesothelioma (MPM) can underestimate the presence of local tumor invasion. Since accurate staging is vital optimal choice of therapy, techniques that optimize pleural imaging are needed. Here we estimate the optimal timing of MPM enhancement on magnetic resonance imaging (MRI). METHODS All MPM patients with intravenous (IV) contrast enhanced staging MRI between 2000-2016 at our institution were retrospectively selected for image analysis. Patients with incomplete imaging protocol and maximum pleural tumor thickness <1 cm were excluded. Quantitative measurements of tumor signal intensity were obtained on pre-contrast and post-contrast phases where MRI acquisition parameters were fixed. Using best-fit model curves, predicted maximum time points of enhancement were determined using a simulation of predicted values. Additionally, a qualitative assessment of tumor conspicuity was performed at all IV contrast time delays imaged. A statistical analysis assessed for correlation between qualitative lesion conspicuity and quantitative tumor enhancement. RESULTS Of the 42 MPM patients who had undergone staging MRI during the study period, 12 patients met the study criteria. Peak tumor enhancement was between 150 and 300 sec following IV contrast administration. Within this time window, 80% of patients are projected to have reached >80%, >85%, and >90% peak tumor enhancement. There was a statistically significant correlation between increasing tumor enhancement and subjective lesion conspicuity. CONCLUSIONS Optimal MPM enhancement on MRI likely occurs at a time delay between 2.5-5 min following IV contrast administration. Further study of delayed phase enhancement of MPM with dynamic contrast enhanced MRI is warranted.

Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy

BACKGROUND AND PURPOSE To predict treatment response and survival of NSCLC patients receiving stereotactic body radiation therapy (SBRT), we develop an unsupervised machine learning method for stratifying patients and extracting meta-features simultaneously based on imaging data. MATERIAL AND METHODS This study was performed based on an 18F-FDG-PET dataset of 100 consecutive patients who were treated with SBRT for early stage NSCLC. Each patients tumor was characterized by 722 radiomic features. An unsupervised two-way clustering method was used to identify groups of patients and radiomic features simultaneously. The groups of patients were compared in terms of survival and freedom from nodal failure. Meta-features were computed for building survival models to predict survival and free of nodal failure. RESULTS Differences were found between 2 groups of patients when the patients were clustered into 3 groups in terms of both survival (p = 0.003) and freedom from nodal failure (p = 0.038). Average concordance measures for predicting survival and nodal failure were 0.640±0.029 and 0.664±0.063 respectively, better than those obtained by prediction models built upon clinical variables (p < 0.04). CONCLUSIONS The evaluation results demonstrate that our method allows us to stratify patients and predict survival and freedom from nodal failure with better performance than current alternative methods.