- A Call to Improve the Visibility and Access of the American College of Radiology Practice Parameters in Neuroradiology: A Powerful Value Stream Enhancer for Both Neuroradiologists and Patients
The authors suggest that practitioners gain a high degree of familiarity with accessing practice parameters. Doing so will provide additional reference and access to the practice parameters when medical literature searches are undertaken or when questions arise regarding best practices. Such an approach will ensure that future neuroradiology clinical guidelines or technical standards documents are provided as broad an exposure as possible. This effort could enhance the visibility and accessibility of the quality of practice for neuroradiologists, provide needed clinical guidance to practice state-of-the-art neuroradiology/radiology, and ensure the visibility of our valuable contributions to both individual patient care and collective patient outcomes.
- A Deep Learning–Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations
The purpose of this study was to develop a fast, automated method for assessing rescan need in motion-corrupted brain series. A deep learning–based approach was developed, outputting a probability for a series to be clinically useful. Comparison of this per-series probability with a threshold, which can depend on scan indication and reading radiologist, determines whether a series needs to be rescanned. The deep learning classification performance was compared with that of 4 technologists and 5 radiologists in 49 test series with low and moderate motion artifacts. Fast, automated deep learning–based image-quality rating can decrease rescan and recall rates, while rendering them technologist-independent. It was estimated that decreasing rescans and recalls from the technologists' values to the values of deep learning could save hospitals $24,000/scanner/year.
- Treatment Response Prediction of Nasopharyngeal Carcinoma Based on Histogram Analysis of Diffusional Kurtosis Imaging
Thirty-six patients with an initial diagnosis of locoregionally advanced nasopharyngeal carcinoma and diffusional kurtosis imaging acquisitions before and after neoadjuvant chemotherapy were enrolled. Patients were divided into respond-versus-nonrespond groups after neoadjuvant chemotherapy and residual-versus-nonresidual groups after radiation therapy. Receiver operating characteristic analysis indicated that setting pre-D50th = 0.875 x 10-3 mm2/s as the cutoff value could result in optimal diagnostic performance for neoadjuvant chemotherapy response prediction (area under the curve = 0.814, sensitivity = 0.70, specificity = 0.92), while the post-K90th = 1.035 (area under the curve = 0.829, sensitivity = 0.78, specificity = 0.72) was optimal for radiation therapy response prediction. Histogram analysis of diffusional kurtosis imaging may potentially predict the neoadjuvant chemotherapy and short-term radiation therapy response in locoregionally advanced nasopharyngeal carcinoma.
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