Chelsea A Boyd1, Mahesh V Jayaraman1,2,3,4, Grayson L Baird5,6, William S Einhorn1, Matthew T Stib1, Michael K Atalay1, Jerrold L Boxerman1, Ana P Lourenco1, Gaurav Jindal1, Douglas T Hidlay1, Eleanor L DiBiasio1, Ryan A McTaggart1,2,3,4. 1. Department of Diagnostic Imaging, Warren Alpert School of Medicine at Brown University, 593 Eddy Street, Room 302, Providence, RI, 02903, USA. 2. Department of Neurology, Warren Alpert School of Medicine at Brown University, Providence, RI, USA. 3. Department of Neurosurgery, Warren Alpert School of Medicine at Brown University, Providence, RI, USA. 4. The Norman Prince Neuroscience Institute, Rhode Island Hospital Providence, Providence, RI, USA. 5. Department of Diagnostic Imaging, Warren Alpert School of Medicine at Brown University, 593 Eddy Street, Room 302, Providence, RI, 02903, USA. GBaird@Lifespan.org. 6. Lifespan Biostatistics Core, Rhode Island Hospital Providence, Providence, RI, USA. GBaird@Lifespan.org.
Abstract
OBJECTIVES: CT angiography (CTA) is essential in acute stroke to detect emergent large vessel occlusions (ELVO) and must be interpreted by radiologists with and without subspecialized training. Additionally, grayscale inversion has been suggested to improve diagnostic accuracy in other radiology applications. This study examines diagnostic performance in ELVO detection between neuroradiologists, non-neuroradiologists, and radiology residents using standard and grayscale inversion viewing methods. METHODS: A random, counterbalanced experimental design was used, where 18 radiologists with varying experiences interpreted the same patient images with and without grayscale inversion. Confirmed positive and negative ELVO cases were randomly ordered using a balanced design. Sensitivity, specificity, positive and negative predictive values as well as confidence, subjective assessment of image quality, time to ELVO detection, and overall interpretation time were examined between grayscale inversion (on/off) by experience level using generalized mixed modeling assuming a binary, negative binomial, and binomial distributions, respectively. RESULTS: All groups of radiologists had high sensitivity and specificity for ELVO detection (all > .94). Neuroradiologists were faster than non-neuroradiologists and residents in interpretation time, with a mean of 47 s to detect ELVO, as compared with 59 and 74 s, respectively. Residents were subjectively less confident than attending physicians. With respect to grayscale inversion, no differences were observed between groups with grayscale inversion vs. standard viewing for diagnostic performance (p = 0.30), detection time (p = .45), overall interpretation time (p = .97), and confidence (p = .20). CONCLUSIONS: Diagnostic performance in ELVO detection with CTA was high across all levels of radiologist training level. Grayscale inversion offered no significant detection advantage. KEY POINTS: • Stroke is an acute vascular syndrome that requires acute vascular imaging. • Proximal large vessel occlusions can be identified quickly and accurately by radiologists across all training levels. • Grayscale inversion demonstrated minimal detectable benefit in the detection of proximal large vessel occlusions.
OBJECTIVES: CT angiography (CTA) is essential in acute stroke to detect emergent large vessel occlusions (ELVO) and must be interpreted by radiologists with and without subspecialized training. Additionally, grayscale inversion has been suggested to improve diagnostic accuracy in other radiology applications. This study examines diagnostic performance in ELVO detection between neuroradiologists, non-neuroradiologists, and radiology residents using standard and grayscale inversion viewing methods. METHODS: A random, counterbalanced experimental design was used, where 18 radiologists with varying experiences interpreted the same patient images with and without grayscale inversion. Confirmed positive and negative ELVO cases were randomly ordered using a balanced design. Sensitivity, specificity, positive and negative predictive values as well as confidence, subjective assessment of image quality, time to ELVO detection, and overall interpretation time were examined between grayscale inversion (on/off) by experience level using generalized mixed modeling assuming a binary, negative binomial, and binomial distributions, respectively. RESULTS: All groups of radiologists had high sensitivity and specificity for ELVO detection (all > .94). Neuroradiologists were faster than non-neuroradiologists and residents in interpretation time, with a mean of 47 s to detect ELVO, as compared with 59 and 74 s, respectively. Residents were subjectively less confident than attending physicians. With respect to grayscale inversion, no differences were observed between groups with grayscale inversion vs. standard viewing for diagnostic performance (p = 0.30), detection time (p = .45), overall interpretation time (p = .97), and confidence (p = .20). CONCLUSIONS: Diagnostic performance in ELVO detection with CTA was high across all levels of radiologist training level. Grayscale inversion offered no significant detection advantage. KEY POINTS: • Stroke is an acute vascular syndrome that requires acute vascular imaging. • Proximal large vessel occlusions can be identified quickly and accurately by radiologists across all training levels. • Grayscale inversion demonstrated minimal detectable benefit in the detection of proximal large vessel occlusions.
Authors: Martijne H C Duvekot; Adriaan C G M van Es; Esmee Venema; Lennard Wolff; Anouk D Rozeman; Walid Moudrous; Frédérique H Vermeij; Hester F Lingsma; Jeannette Bakker; Aarnout S Plaisier; Jan-Hein J Hensen; Geert J Lycklama À Nijeholt; Pieter Jan van Doormaal; Diederik W J Dippel; Henk Kerkhoff; Bob Roozenbeek; Aad van der Lugt Journal: Eur Stroke J Date: 2021-11-12
Authors: T Truc My Nguyen; Ido R van den Wijngaard; Jan Bosch; Eduard van Belle; Erik W van Zwet; Tamara Dofferhoff-Vermeulen; Dion Duijndam; Gaia T Koster; Els L L M de Schryver; Loet M H Kloos; Karlijn F de Laat; Leo A M Aerden; Stas A Zylicz; Marieke J H Wermer; Nyika D Kruyt Journal: JAMA Neurol Date: 2021-02-01 Impact factor: 18.302
Authors: Sven P R Luijten; Lennard Wolff; Martijne H C Duvekot; Pieter-Jan van Doormaal; Walid Moudrous; Henk Kerkhoff; Geert J Lycklama A Nijeholt; Reinoud P H Bokkers; Lonneke S F Yo; Jeannette Hofmeijer; Wim H van Zwam; Adriaan C G M van Es; Diederik W J Dippel; Bob Roozenbeek; Aad van der Lugt Journal: J Neurointerv Surg Date: 2021-08-19 Impact factor: 8.572