Olivia Goodkin1,2, Hugh G Pemberton3,4, John S Thornton4,5, Tarek A Yousry4,5, Frederik Barkhof3,4,5,6, Sjoerd B Vos3,4,7, Ferran Prados3,8, Ravi K Das9, James Moggridge4,5, Bianca De Blasi10, Philippa Bartlett7,11, Elaine Williams12, Thomas Campion5, Lukas Haider13,14, Kirsten Pearce5, Nuria Bargallό15, Esther Sanchez6, Sotirios Bisdas4,5, Mark White16, Sebastien Ourselin10,17, Gavin P Winston7,11,18, John S Duncan7,11, Jorge Cardoso17. 1. Centre for Medical Image Computing (CMIC), University College London, London, UK. o.goodkin@ucl.ac.uk. 2. Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK. o.goodkin@ucl.ac.uk. 3. Centre for Medical Image Computing (CMIC), University College London, London, UK. 4. Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK. 5. Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK. 6. Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands. 7. Epilepsy Society MRI Unit, Chalfont St Peter, UK. 8. Universitat Oberta de Catalunya, Barcelona, Spain. 9. Clinical, Educational and Health Psychology, University College London, London, UK. 10. Department of Medical Physics and Bioengineering, University College London, London, UK. 11. Department of Clinical and Experimental Epilepsy, University College London, London, UK. 12. Wellcome Trust Centre for Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK. 13. Department of Biomedical Imaging and Image Guided Therapy, Medical University of Vienna, Vienna, Austria. 14. NMR Research Unit, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK. 15. Radiology Department, Hospital Clínic de Barcelona and Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi I Sunyer (IDIBAPS), Barcelona, Spain. 16. Digital Services, University College London Hospital, London, UK. 17. School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. 18. Department of Medicine, Division of Neurology, Queen's University, Kingston, Ontario, Canada.
Abstract
OBJECTIVES: Hippocampal sclerosis (HS) is a common cause of temporal lobe epilepsy. Neuroradiological practice relies on visual assessment, but quantification of HS imaging biomarkers-hippocampal volume loss and T2 elevation-could improve detection. We tested whether quantitative measures, contextualised with normative data, improve rater accuracy and confidence. METHODS: Quantitative reports (QReports) were generated for 43 individuals with epilepsy (mean age ± SD 40.0 ± 14.8 years, 22 men; 15 histologically unilateral HS; 5 bilateral; 23 MR-negative). Normative data was generated from 111 healthy individuals (age 40.0 ± 12.8 years, 52 men). Nine raters with different experience (neuroradiologists, trainees, and image analysts) assessed subjects' imaging with and without QReports. Raters assigned imaging normal, right, left, or bilateral HS. Confidence was rated on a 5-point scale. RESULTS: Correct designation (normal/abnormal) was high and showed further trend-level improvement with QReports, from 87.5 to 92.5% (p = 0.07, effect size d = 0.69). Largest magnitude improvement (84.5 to 93.8%) was for image analysts (d = 0.87). For bilateral HS, QReports significantly improved overall accuracy, from 74.4 to 91.1% (p = 0.042, d = 0.7). Agreement with the correct diagnosis (kappa) tended to increase from 0.74 ('fair') to 0.86 ('excellent') with the report (p = 0.06, d = 0.81). Confidence increased when correctly assessing scans with the QReport (p < 0.001, η2p = 0.945). CONCLUSIONS: QReports of HS imaging biomarkers can improve rater accuracy and confidence, particularly in challenging bilateral cases. Improvements were seen across all raters, with large effect sizes, greatest for image analysts. These findings may have positive implications for clinical radiology services and justify further validation in larger groups. KEY POINTS: • Quantification of imaging biomarkers for hippocampal sclerosis-volume loss and raised T2 signal-could improve clinical radiological detection in challenging cases. • Quantitative reports for individual patients, contextualised with normative reference data, improved diagnostic accuracy and confidence in a group of nine raters, in particular for bilateral HS cases. • We present a pre-use clinical validation of an automated imaging assessment tool to assist clinical radiology reporting of hippocampal sclerosis, which improves detection accuracy.
OBJECTIVES: Hippocampal sclerosis (HS) is a common cause of temporal lobe epilepsy. Neuroradiological practice relies on visual assessment, but quantification of HS imaging biomarkers-hippocampal volume loss and T2 elevation-could improve detection. We tested whether quantitative measures, contextualised with normative data, improve rater accuracy and confidence. METHODS: Quantitative reports (QReports) were generated for 43 individuals with epilepsy (mean age ± SD 40.0 ± 14.8 years, 22 men; 15 histologically unilateral HS; 5 bilateral; 23 MR-negative). Normative data was generated from 111 healthy individuals (age 40.0 ± 12.8 years, 52 men). Nine raters with different experience (neuroradiologists, trainees, and image analysts) assessed subjects' imaging with and without QReports. Raters assigned imaging normal, right, left, or bilateral HS. Confidence was rated on a 5-point scale. RESULTS: Correct designation (normal/abnormal) was high and showed further trend-level improvement with QReports, from 87.5 to 92.5% (p = 0.07, effect size d = 0.69). Largest magnitude improvement (84.5 to 93.8%) was for image analysts (d = 0.87). For bilateral HS, QReports significantly improved overall accuracy, from 74.4 to 91.1% (p = 0.042, d = 0.7). Agreement with the correct diagnosis (kappa) tended to increase from 0.74 ('fair') to 0.86 ('excellent') with the report (p = 0.06, d = 0.81). Confidence increased when correctly assessing scans with the QReport (p < 0.001, η2p = 0.945). CONCLUSIONS: QReports of HS imaging biomarkers can improve rater accuracy and confidence, particularly in challenging bilateral cases. Improvements were seen across all raters, with large effect sizes, greatest for image analysts. These findings may have positive implications for clinical radiology services and justify further validation in larger groups. KEY POINTS: • Quantification of imaging biomarkers for hippocampal sclerosis-volume loss and raised T2 signal-could improve clinical radiological detection in challenging cases. • Quantitative reports for individual patients, contextualised with normative reference data, improved diagnostic accuracy and confidence in a group of nine raters, in particular for bilateral HS cases. • We present a pre-use clinical validation of an automated imaging assessment tool to assist clinical radiology reporting of hippocampal sclerosis, which improves detection accuracy.
Entities:
Keywords:
Biomarkers; Epilepsy; Hippocampus; Magnetic resonance imaging; Temporal lobe
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