AJR. American journal of roentgenology
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AJR Am J Roentgenol · Feb 2019
Repeat CT Performed Within One Month of CT Conducted in the Emergency Department for Abdominal Pain: A Secondary Analysis of Data From a Prospective Multicenter Study.
The purpose of this study is to determine both the frequency of repeat CT performed within 1 month after a patient visits the emergency department (ED) and undergoes CT evaluation for abdominal pain and the frequency of worsened or new CT-based diagnoses. ⋯ Short-term, repeat abdominal CT was performed for 10% of patients who underwent CT in the ED for abdominal pain, and it yielded new or worse findings for 30% of those patients.
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AJR Am J Roentgenol · Jan 2019
Comparative StudyGadolinium-Based Blood Volume Mapping From MRI With Ultrashort TE Versus CT and SPECT for Predicting Postoperative Lung Function in Patients With Non-Small Cell Lung Cancer.
The purpose of this study is to directly compare the capability of gadolinium-based blood volume (BV) mapping from MRI (BV-MRI) with ultrashort TE (UTE) with that of CT and perfusion SPECT in predicting the postoperative lung function of patients with non-small cell lung cancer (NSCLC). ⋯ BV-MRI with UTE has the potential to predict the postoperative lung function of patients with NSCLC more accurately than qualitatively assessed CT and SPECT, and it can be considered to be at least as useful as quantitatively assessed CT.
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AJR Am J Roentgenol · Jan 2019
Multiparametric MRI Features and Pathologic Outcome of Wedge-Shaped Lesions in the Peripheral Zone on T2-Weighted Images of the Prostate.
This study investigates the multiparametric MRI (mpMRI) characteristics and pathologic outcome of wedge-shaped lesions observed on T2-weighted images. ⋯ This study shows that a greater percentage of wedge-shaped features are malignant than was previously thought. Of importance, mpMRI (specifically, ADC maps) can distinguish between malignant and benign wedge-shaped features.
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AJR Am J Roentgenol · Jan 2019
ReviewPeering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods.
Machine learning (ML) and artificial intelligence (AI) are rapidly becoming the most talked about and controversial topics in radiology and medicine. Over the past few years, the numbers of ML- or AI-focused studies in the literature have increased almost exponentially, and ML has become a hot topic at academic and industry conferences. However, despite the increased awareness of ML as a tool, many medical professionals have a poor understanding of how ML works and how to critically appraise studies and tools that are presented to us. Thus, we present a brief overview of ML, explain the metrics used in ML and how to interpret them, and explain some of the technical jargon associated with the field so that readers with a medical background and basic knowledge of statistics can feel more comfortable when examining ML applications. ⋯ Attention to sample size, overfitting, underfitting, cross validation, as well as a broad knowledge of the metrics of machine learning, can help those with little or no technical knowledge begin to assess machine learning studies. However, transparency in methods and sharing of algorithms is vital to allow clinicians to assess these tools themselves.
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AJR Am J Roentgenol · Jan 2019
MRI for Pediatric Appendicitis in an Adult-Focused General Hospital: A Clinical Effectiveness Study-Challenges and Lessons Learned.
The objective of our study was to determine the feasibility and accuracy of MRI for pediatric appendicitis in an adult-predominant general hospital setting where non-pediatric-trained radiologists routinely interpret the studies. ⋯ Our data show that unenhanced MRI for suspected appendicitis in pediatric patients is clinically effective when performed in a nonpediatric hospital setting with nonpediatric radiologists, emergency physicians, and surgeons.