{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T11:39:06Z","timestamp":1773747546202,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T00:00:00Z","timestamp":1687737600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T00:00:00Z","timestamp":1687737600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"University of Bern"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Clin Neuroradiol"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objective:<\/jats:title>\n                <jats:p>To evaluate the influence of quantitative reports (QReports) on the radiological assessment of hippocampal sclerosis (HS) from MRI of patients with epilepsy in a\u00a0setting mimicking clinical reality.<\/jats:p>\n                <jats:p><jats:bold>Methods:<\/jats:bold> The study included 40 patients with epilepsy, among them 20 with structural abnormalities in the mesial temporal lobe (13 with HS). Six raters blinded to the diagnosis assessed the 3T\u202fMRI in two rounds, first using MRI only and later with both MRI <jats:italic>and<\/jats:italic> the QReport. Results were evaluated using inter-rater agreement (Fleiss\u2019 kappa <jats:inline-formula><jats:alternatives><jats:tex-math>$$k$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mi>k<\/mml:mi>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) and comparison with a\u00a0consensus of two radiological experts derived from clinical and imaging data, including 7T\u202fMRI.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results:<\/jats:title>\n                <jats:p>For the primary outcome, diagnosis of HS, the mean accuracy of the raters improved from 77.5% with MRI only to 86.3% with the additional QReport (effect size <jats:inline-formula><jats:alternatives><jats:tex-math>$$d=1.43$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mi>d<\/mml:mi>\n                      <mml:mo>=<\/mml:mo>\n                      <mml:mn>1.43<\/mml:mn>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>). Inter-rater agreement increased from <jats:inline-formula><jats:alternatives><jats:tex-math>$$k=0.56$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mi>k<\/mml:mi>\n                      <mml:mo>=<\/mml:mo>\n                      <mml:mn>0.56<\/mml:mn>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> to <jats:inline-formula><jats:alternatives><jats:tex-math>$$k=0.72$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mi>k<\/mml:mi>\n                      <mml:mo>=<\/mml:mo>\n                      <mml:mn>0.72<\/mml:mn>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. Five of the six raters reached higher accuracies, and all reported higher confidence when using the QReports.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion:<\/jats:title>\n                <jats:p>In this pre-use clinical evaluation study, we demonstrated clinical feasibility and usefulness as well as the potential impact of a\u00a0previously suggested imaging biomarker for radiological assessment of HS.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00062-023-01308-9","type":"journal-article","created":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T15:03:11Z","timestamp":1687791791000},"page":"1045-1053","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Clinical Evaluation of a\u00a0Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8441-1485","authenticated-orcid":false,"given":"Michael","family":"Rebsamen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baudouin Zongxin","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomas","family":"Klail","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sophie","family":"De Beukelaer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rike","family":"Barth","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beata","family":"Rezny-Kasprzak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Uzeyir","family":"Ahmadli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Serge","family":"Vulliemoz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Margitta","family":"Seeck","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaspar","family":"Schindler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roland","family":"Wiest","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piotr","family":"Radojewski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Rummel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"key":"1308_CR1","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1111\/nan.12150","volume":"40","author":"M Thom","year":"2014","unstructured":"M.\u00a0Thom, Hippocampal sclerosis in epilepsy: a\u00a0neuropathology review, Neuropathology and applied neurobiology 40 (2014) 520\u2013543. https:\/\/doi.org\/10.1111\/nan.12150.","journal-title":"Neuropathol Appl Neurobiol"},{"key":"1308_CR2","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1136\/jnnp.73.6.643","volume":"73","author":"J Von Oertzen","year":"2002","unstructured":"J.\u00a0Von\u00a0Oertzen, H.\u00a0Urbach, S.\u00a0Jungbluth, M.\u00a0Kurthen, M.\u00a0Reuber, G.\u00a0Fern\u00e1ndez, C.\u00a0Elger, Standard magnetic resonance imaging is inadequate for patients with refractory focal epilepsy, Journal of Neurology, Neurosurgery & Psychiatry 73 (2002) 643\u2013647. https:\/\/doi.org\/10.1136\/jnnp.73.6.643.","journal-title":"J Neurol Neurosurg Psychiatry"},{"key":"1308_CR3","doi-asserted-by":"publisher","first-page":"1977","DOI":"10.1111\/epi.12375","volume":"54","author":"J Wellmer","year":"2013","unstructured":"J.\u00a0Wellmer, C.\u00a0M. Quesada, L.\u00a0Rothe, C.\u00a0E. Elger, C.\u00a0G. Bien, H.\u00a0Urbach, Proposal for a\u00a0magnetic resonance imaging protocol for the detection of epileptogenic lesions at early outpatient stages, Epilepsia 54 (2013) 1977\u20131987. https:\/\/doi.org\/10.1111\/epi.12375.","journal-title":"Epilepsia"},{"key":"1308_CR4","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1111\/epi.15612","volume":"60","author":"A Bernasconi","year":"2019","unstructured":"A.\u00a0Bernasconi, F.\u00a0Cendes, W.\u00a0H. Theodore, R.\u00a0S. Gill, M.\u00a0J. Koepp, R.\u00a0E. Hogan, G.\u00a0D. Jackson, P.\u00a0Federico, A.\u00a0Labate, A.\u00a0E. Vaudano, et\u00a0al., Recommendations for the use of structural magnetic resonance imaging in the care of patients with epilepsy: a\u00a0consensus report from the international league against epilepsy neuroimaging task force, Epilepsia 60 (2019) 1054\u20131068. https:\/\/doi.org\/10.1111\/epi.15612.","journal-title":"Epilepsia"},{"key":"1308_CR5","doi-asserted-by":"publisher","first-page":"78.e1","DOI":"10.1016\/j.crad.2018.08.013","volume":"74","author":"B Kreilkamp","year":"2019","unstructured":"B.\u00a0Kreilkamp, K.\u00a0Das, U.\u00a0Wieshmann, S.\u00a0Biswas, A.\u00a0Marson, S.\u00a0Keller, Neuroradiological findings in patients with \u201cnon-lesional\u201d focal epilepsy revealed by research protocol, Clinical Radiology 74 (2019) 78\u2013e1. https:\/\/doi.org\/10.1016\/j.crad.2018.08.013.","journal-title":"Clin Radiol"},{"key":"1308_CR6","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1111\/epi.12220","volume":"54","author":"I Bl\u00fcmcke","year":"2013","unstructured":"I.\u00a0Bl\u00fcmcke, M.\u00a0Thom, E.\u00a0Aronica, D.\u00a0D. Armstrong, F.\u00a0Bartolomei, A.\u00a0Bernasconi, N.\u00a0Bernasconi, C.\u00a0G. Bien, F.\u00a0Cendes, R.\u00a0Coras, et\u00a0al., International consensus classification of hippocampal sclerosis in temporal lobe epilepsy: a\u00a0task force report from the ILAE Commission on Diagnostic Methods, Epilepsia 54 (2013) 1315\u20131329. https:\/\/doi.org\/10.1111\/epi.12220.","journal-title":"Epilepsia"},{"key":"1308_CR7","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.eplepsyres.2013.05.008","volume":"106","author":"LW Ver Hoef","year":"2013","unstructured":"L.\u00a0W. Ver\u00a0Hoef, F.\u00a0B. Williams, R.\u00a0E. Kennedy, J.\u00a0P. Szaflarski, R.\u00a0C. Knowlton, Predictive value of hippocampal internal architecture asymmetry in temporal lobe epilepsy, Epilepsy research 106 (2013) 155\u2013163. https:\/\/doi.org\/10.1016\/j.eplepsyres.2013.05.008.","journal-title":"Epilepsy Res"},{"key":"1308_CR8","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1016\/S1474-4422(15)00383-X","volume":"15","author":"JS Duncan","year":"2016","unstructured":"J.\u00a0S. Duncan, G.\u00a0P. Winston, M.\u00a0J. Koepp, S.\u00a0Ourselin, Brain imaging in the assessment for epilepsy surgery, The Lancet Neurology 15 (2016) 420\u2013433. 10.1016\/S1474-4422(15)00383-X.","journal-title":"Lancet Neurol"},{"key":"1308_CR9","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1097\/RLI.0000000000000256","volume":"51","author":"E Springer","year":"2016","unstructured":"E.\u00a0Springer, B.\u00a0Dymerska, P.\u00a0L. Cardoso, S.\u00a0D. Robinson, C.\u00a0Weisstanner, R.\u00a0Wiest, B.\u00a0Schmitt, S.\u00a0Trattnig, Comparison of routine brain imaging at 3 T and 7 T, Investigative radiology 51 (2016) 469. https:\/\/doi.org\/10.1097\/RLI.0000000000000256.","journal-title":"Invest Radiol"},{"key":"1308_CR10","doi-asserted-by":"publisher","first-page":"e0213642","DOI":"10.1371\/journal.pone.0213642","volume":"14","author":"RE Feldman","year":"2019","unstructured":"R.\u00a0E. Feldman, B.\u00a0N. Delman, P.\u00a0S. Pawha, H.\u00a0Dyvorne, J.\u00a0W. Rutland, J.\u00a0Yoo, M.\u00a0C. Fields, L.\u00a0V. Marcuse, P.\u00a0Balchandani, 7T MRI in epilepsy patients with previously normal clinical MRI exams compared against healthy controls, Plos one 14 (2019) e0213642. https:\/\/doi.org\/10.1371\/journal.pone.0213642.","journal-title":"PLoS ONE"},{"key":"1308_CR11","doi-asserted-by":"publisher","first-page":"102602","DOI":"10.1016\/j.nicl.2021.102602","volume":"30","author":"R van Lanen","year":"2021","unstructured":"R.\u00a0van Lanen, A.\u00a0Colon, C.\u00a0Wiggins, M.\u00a0Hoeberigs, G.\u00a0Hoogland, A.\u00a0Roebroeck, D.\u00a0Ivanov, B.\u00a0Poser, R.\u00a0Rouhl, P.\u00a0Hofman, et\u00a0al., Ultra-high field magnetic resonance imaging in human epilepsy: A\u00a0systematic review, NeuroImage: Clinical 30 (2021) 102602. https:\/\/doi.org\/10.1016\/j.nicl.2021.102602.","journal-title":"Neuroimage Clin"},{"key":"1308_CR12","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.seizure.2019.06.023","volume":"71","author":"Y Zhang","year":"2019","unstructured":"Y.\u00a0Zhang, Y.\u00a0Lv, H.\u00a0You, W.\u00a0Dou, B.\u00a0Hou, L.\u00a0Shi, Z.\u00a0Zuo, W.\u00a0Mao, F.\u00a0Feng, Study of the hippocampal internal architecture in temporal lobe epilepsy using 7 T and 3 T MRI, Seizure 71 (2019) 116\u2013123. https:\/\/doi.org\/10.1016\/j.seizure.2019.06.023.","journal-title":"Seizure"},{"key":"1308_CR13","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1212\/WNL.0000000000011413","volume":"96","author":"G Opheim","year":"2021","unstructured":"G.\u00a0Opheim, A.\u00a0van\u00a0der Kolk, K.\u00a0M. Bloch, A.\u00a0J. Colon, K.\u00a0A. Davis, T.\u00a0R. Henry, J.\u00a0F. Jansen, S.\u00a0E. Jones, J.\u00a0W. Pan, K.\u00a0R\u00f6ssler, et\u00a0al., 7T epilepsy task force consensus recommendations on the use of 7T MRI in clinical practice, Neurology 96 (2021) 327\u2013341. https:\/\/doi.org\/10.1212\/WNL.0000000000011413.","journal-title":"Neurology"},{"key":"1308_CR14","doi-asserted-by":"publisher","first-page":"621936","DOI":"10.3389\/fneur.2021.621936","volume":"12","author":"JE Park","year":"2021","unstructured":"J.\u00a0E. Park, E.-N. Cheong, D.\u00a0E. Jung, W.\u00a0H. Shim, J.\u00a0S. Lee, Utility of 7 tesla magnetic resonance imaging in patients with epilepsy: a\u00a0systematic review and meta-analysis, Frontiers in Neurology 12 (2021) 621936. https:\/\/doi.org\/10.3389\/fneur.2021.621936.","journal-title":"Front Neurol"},{"key":"1308_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eplepsyres.2022.106861","author":"J Sollee","year":"2022","unstructured":"J.\u00a0Sollee, L.\u00a0Tang, A.\u00a0B. Igiraneza, B.\u00a0Xiao, H.\u00a0X. Bai, L.\u00a0Yang, Artificial intelligence for medical image analysis in epilepsy, Epilepsy Research (2022) 106861. https:\/\/doi.org\/10.1016\/j.eplepsyres.2022.106861.","journal-title":"Epilepsy Res"},{"key":"1308_CR16","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1111\/epi.13843","volume":"58","author":"GP Winston","year":"2017","unstructured":"G.\u00a0P. Winston, S.\u00a0B. Vos, J.\u00a0L. Burdett, M.\u00a0J. Cardoso, S.\u00a0Ourselin, J.\u00a0S. Duncan, Automated T2 relaxometry of the hippocampus for temporal lobe epilepsy, Epilepsia 58 (2017) 1645\u20131652. https:\/\/doi.org\/10.1111\/epi.13843.","journal-title":"Epilepsia"},{"key":"1308_CR17","doi-asserted-by":"publisher","first-page":"2519","DOI":"10.1111\/epi.16392","volume":"60","author":"J Mo","year":"2019","unstructured":"J.\u00a0Mo, Z.\u00a0Liu, K.\u00a0Sun, Y.\u00a0Ma, W.\u00a0Hu, C.\u00a0Zhang, Y.\u00a0Wang, X.\u00a0Wang, C.\u00a0Liu, B.\u00a0Zhao, et\u00a0al., Automated detection of hippocampal sclerosis using clinically empirical and radiomics features, Epilepsia 60 (2019) 2519\u20132529. https:\/\/doi.org\/10.1111\/epi.16392.","journal-title":"Epilepsia"},{"key":"1308_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-76283-z","volume":"10","author":"YW Park","year":"2020","unstructured":"Y.\u00a0W. Park, Y.\u00a0S. Choi, S.\u00a0E. Kim, D.\u00a0Choi, K.\u00a0Han, H.\u00a0Kim, S.\u00a0S. Ahn, S.-A. Kim, H.\u00a0J. Kim, S.-K. Lee, et\u00a0al., Radiomics features of hippocampal regions in magnetic resonance imaging can differentiate medial temporal lobe epilepsy patients from healthy controls, Scientific reports 10 (2020) 1\u20138. https:\/\/doi.org\/10.1038\/s41598-020-76283-z.","journal-title":"Sci Rep"},{"key":"1308_CR19","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2022.812432","author":"M Rebsamen","year":"2022","unstructured":"M.\u00a0Rebsamen, P.\u00a0Radojewski, R.\u00a0McKinley, M.\u00a0Reyes, R.\u00a0Wiest, C.\u00a0Rummel, A\u00a0quantitative imaging biomarker supporting radiological assessment of hippocampal sclerosis derived from deep learning-based segmentation of T1w-MRI., Frontiers in neurology 13 (2022). https:\/\/doi.org\/10.3389\/fneur.2022.812432.","journal-title":"Front Neurol"},{"key":"1308_CR20","doi-asserted-by":"publisher","first-page":"591","DOI":"10.3174\/ajnr.A6454","volume":"41","author":"S Louis","year":"2020","unstructured":"S.\u00a0Louis, M.\u00a0Morita-Sherman, S.\u00a0Jones, D.\u00a0Vegh, W.\u00a0Bingaman, I.\u00a0Blumcke, N.\u00a0Obuchowski, F.\u00a0Cendes, L.\u00a0Jehi, Hippocampal sclerosis detection with NeuroQuant compared with neuroradiologists, American Journal of Neuroradiology 41 (2020) 591\u2013597. https:\/\/doi.org\/10.3174\/ajnr.A6454.","journal-title":"AJNR Am J Neuroradiol"},{"key":"1308_CR21","doi-asserted-by":"publisher","first-page":"3797","DOI":"10.1007\/s00330-021-07892-z","volume":"31","author":"KG van Leeuwen","year":"2021","unstructured":"K.\u00a0G. van Leeuwen, S.\u00a0Schalekamp, M.\u00a0J. Rutten, B.\u00a0van Ginneken, M.\u00a0de\u00a0Rooij, Artificial intelligence in radiology: 100 commercially available products and their scientific evidence, European Radiology 31 (2021) 3797\u20133804. https:\/\/doi.org\/10.1007\/s00330-021-07892-z.","journal-title":"Eur Radiol"},{"key":"1308_CR22","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1007\/s00234-021-02818-4","volume":"63","author":"HG Pemberton","year":"2021","unstructured":"H.\u00a0G. Pemberton, L.\u00a0A. Zaki, O.\u00a0Goodkin, R.\u00a0K. Das, R.\u00a0M. Steketee, F.\u00a0Barkhof, M.\u00a0W. Vernooij, Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis\u2014a\u00a0systematic review, Neuroradiology 63 (2021) 1773\u20131789. https:\/\/doi.org\/10.1007\/s00234-021-02818-4.","journal-title":"Neuroradiology"},{"key":"1308_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s00234-022-03074-w","author":"Z Mendelsohn","year":"2022","unstructured":"Z.\u00a0Mendelsohn, H.\u00a0G. Pemberton, J.\u00a0Gray, O.\u00a0Goodkin, F.\u00a0P. Carrasco, M.\u00a0Scheel, J.\u00a0Nawabi, F.\u00a0Barkhof, Commercial volumetric MRI reporting tools in multiple sclerosis: a\u00a0systematic review of the evidence, Neuroradiology (2022) 1\u201320. https:\/\/doi.org\/10.1007\/s00234-022-03074-w.","journal-title":"Neuroradiology"},{"key":"1308_CR24","doi-asserted-by":"publisher","first-page":"20190365","DOI":"10.1259\/bjr.20190365","volume":"92","author":"O Goodkin","year":"2019","unstructured":"O.\u00a0Goodkin, H.\u00a0Pemberton, S.\u00a0B. Vos, F.\u00a0Prados, C.\u00a0H. Sudre, J.\u00a0Moggridge, M.\u00a0J. Cardoso, S.\u00a0Ourselin, S.\u00a0Bisdas, M.\u00a0White, et\u00a0al., The quantitative neuroradiology initiative framework: application to dementia, The British journal of radiology 92 (2019) 20190365. https:\/\/doi.org\/10.1259\/bjr.20190365.","journal-title":"Br J Radiol"},{"key":"1308_CR25","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1111\/epi.16416","volume":"61","author":"SB Vos","year":"2020","unstructured":"S.\u00a0B. Vos, G.\u00a0P. Winston, O.\u00a0Goodkin, H.\u00a0G. Pemberton, F.\u00a0Barkhof, F.\u00a0Prados, M.\u00a0Galovic, M.\u00a0Koepp, S.\u00a0Ourselin, M.\u00a0J. Cardoso, et\u00a0al., Hippocampal profiling: localized magnetic resonance imaging volumetry and T2 relaxometry for hippocampal sclerosis, Epilepsia 61 (2020) 297\u2013309. https:\/\/doi.org\/10.1111\/epi.16416.","journal-title":"Epilepsia"},{"key":"1308_CR26","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/s00330-020-07075-2","volume":"31","author":"O Goodkin","year":"2021","unstructured":"O.\u00a0Goodkin, H.\u00a0G. Pemberton, S.\u00a0B. Vos, F.\u00a0Prados, R.\u00a0K. Das, J.\u00a0Moggridge, B.\u00a0De\u00a0Blasi, P.\u00a0Bartlett, E.\u00a0Williams, T.\u00a0Campion, et\u00a0al., Clinical evaluation of automated quantitative MRI reports for assessment of hippocampal sclerosis, European Radiology 31 (2021) 34\u201344. https:\/\/doi.org\/10.1007\/s00330-020-07075-2.","journal-title":"Eur Radiol"},{"key":"1308_CR27","doi-asserted-by":"publisher","first-page":"4804","DOI":"10.1002\/hbm.25159","volume":"41","author":"M Rebsamen","year":"2020","unstructured":"M.\u00a0Rebsamen, C.\u00a0Rummel, M.\u00a0Reyes, R.\u00a0Wiest, R.\u00a0McKinley, Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation, Human Brain Mapping 41 (2020) 4804\u20134814. https:\/\/doi.org\/10.1002\/hbm.25159.","journal-title":"Hum Brain Mapp"},{"key":"1308_CR28","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.26117","author":"M Rebsamen","year":"2022","unstructured":"M.\u00a0Rebsamen, R.\u00a0McKinley, P.\u00a0Radojewski, M.\u00a0Pistor, C.\u00a0Friedli, R.\u00a0Hoepner, A.\u00a0Salmen, A.\u00a0Chan, M.\u00a0Reyes, F.\u00a0Wagner, R.\u00a0Wiest, C.\u00a0Rummel, Reliable brain morphometry from contrast-enhanced T1w-MRI in patients with multiple sclerosis, Human Brain Mapping (2022). https:\/\/doi.org\/10.1002\/hbm.26117.","journal-title":"Hum Brain Mapp"},{"key":"1308_CR29","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1111\/epi.13471","volume":"57","author":"MP Malter","year":"2016","unstructured":"M.\u00a0P. Malter, G.\u00a0Widman, N.\u00a0Galldiks, W.\u00a0Stoecker, C.\u00a0Helmstaedter, C.\u00a0E. Elger, J.\u00a0Wagner, Suspected new-onset autoimmune temporal lobe epilepsy with amygdala enlargement, Epilepsia 57 (2016) 1485\u20131494. https:\/\/doi.org\/10.1111\/epi.13471.","journal-title":"Epilepsia"},{"key":"1308_CR30","doi-asserted-by":"publisher","first-page":"fcac225","DOI":"10.1093\/braincomms\/fcac225","volume":"4","author":"A Ballerini","year":"2022","unstructured":"A.\u00a0Ballerini, M.\u00a0Tondelli, F.\u00a0Talami, M.\u00a0A. Molinari, E.\u00a0Micalizzi, G.\u00a0Giovannini, G.\u00a0Turchi, M.\u00a0Malagoli, M.\u00a0Genovese, S.\u00a0Meletti, et\u00a0al., Amygdala subnuclear volumes in temporal lobe epilepsy with hippocampal sclerosis and in non-lesional patients, Brain communications 4 (2022) fcac225. https:\/\/doi.org\/10.1093\/braincomms\/fcac225.","journal-title":"Brain Commun"},{"key":"1308_CR31","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.1016\/S1474-4422(14)70156-5","volume":"13","author":"P Ryvlin","year":"2014","unstructured":"P.\u00a0Ryvlin, J.\u00a0H. Cross, S.\u00a0Rheims, Epilepsy surgery in children and adults, The Lancet Neurology 13 (2014) 1114\u20131126. 10.1016\/S1474-4422(14)70156-5.","journal-title":"Lancet Neurol"},{"key":"1308_CR32","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.1480624","volume-title":"effsize: efficient effect size computation","author":"M Torchiano","year":"2020","unstructured":"M.\u00a0Torchiano, effsize: Efficient Effect Size Computation, 2020. https:\/\/CRAN.R-project.org\/package=effsize. https:\/\/doi.org\/10.5281\/zenodo.1480624, R package version 0.8.1."},{"key":"1308_CR33","doi-asserted-by":"publisher","DOI":"10.4324\/9780203771587","volume-title":"Statistical power analysis for the behavioral sciences","author":"J Cohen","year":"2013","unstructured":"J.\u00a0Cohen, Statistical power analysis for the behavioral sciences, Routledge, 2013. https:\/\/doi.org\/10.4324\/9780203771587."},{"key":"1308_CR34","doi-asserted-by":"publisher","first-page":"26","DOI":"10.22237\/jmasm\/1257035100","volume":"8","author":"SS Sawilowsky","year":"2009","unstructured":"S.\u00a0S. Sawilowsky, New effect size rules of thumb, Journal of modern applied statistical methods 8 (2009) 26. https:\/\/doi.org\/10.22237\/jmasm\/1257035100.","journal-title":"J Mod Appl Stat Methods"},{"key":"1308_CR35","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1037\/h0031619","volume":"76","author":"JL Fleiss","year":"1971","unstructured":"J.\u00a0L. Fleiss, Measuring nominal scale agreement among many raters., Psychological bulletin 76 (1971) 378. https:\/\/doi.org\/10.1037\/h0031619.","journal-title":"Psychol Bull"},{"key":"1308_CR36","volume-title":"irr: various coefficients of Interrater reliability and agreement","author":"M Gamer","year":"2019","unstructured":"M.\u00a0Gamer, J.\u00a0Lemon, I.\u00a0F.\u00a0P. Singh, irr: Various Coefficients of Interrater Reliability and Agreement, 2019. https:\/\/CRAN.R-project.org\/package=irr, R package version 0.84.1. Accessed 09.12.2022"},{"key":"1308_CR37","volume-title":"R: a\u00a0language and environment for statistical computing","author":"R Core Team","year":"2022","unstructured":"R Core Team, R: A\u00a0Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2022. https:\/\/www.R-project.org\/."},{"key":"1308_CR38","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/978-3-319-24277-4_9","volume-title":"ggplot2: elegant graphics for data analysis","author":"H Wickham","year":"2016","unstructured":"H.\u00a0Wickham, ggplot2: Elegant Graphics for Data Analysis, Springer, 2016, pp.\u00a0189\u2013201. https:\/\/doi.org\/10.1007\/978-3-319-24277-4_9."},{"key":"1308_CR39","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"B.\u00a0Fischl, FreeSurfer, Neuroimage 62 (2012) 774\u2013781. https:\/\/doi.org\/10.1016\/j.neuroimage.2012.01.021.","journal-title":"Neuroimage"},{"key":"1308_CR40","doi-asserted-by":"publisher","first-page":"e190043","DOI":"10.1148\/ryai.2020190043","volume":"2","author":"M Reyes","year":"2020","unstructured":"M.\u00a0Reyes, R.\u00a0Meier, S.\u00a0Pereira, C.\u00a0A. Silva, F.-M. Dahlweid, H.\u00a0v. Tengg-Kobligk, R.\u00a0M. Summers, R.\u00a0Wiest, On the interpretability of artificial intelligence in radiology: challenges and opportunities, Radiology: artificial intelligence 2 (2020) e190043. https:\/\/doi.org\/10.1148\/ryai.2020190043.","journal-title":"Radiol Artif Intell"},{"key":"1308_CR41","doi-asserted-by":"publisher","first-page":"e1583","DOI":"10.1212\/WNL.0000000000012699","volume":"97","author":"B Caldairou","year":"2021","unstructured":"B.\u00a0Caldairou, N.\u00a0A. Foit, C.\u00a0Mutti, F.\u00a0Fadaie, R.\u00a0Gill, H.\u00a0M. Lee, T.\u00a0Demerath, H.\u00a0Urbach, A.\u00a0Schulze-Bonhage, A.\u00a0Bernasconi, et\u00a0al., MRI-based machine learning prediction framework to lateralize hippocampal sclerosis in patients with temporal lobe epilepsy, Neurology 97 (2021) e1583\u2013e1593. https:\/\/doi.org\/10.1212\/WNL.0000000000012699.","journal-title":"Neurology"},{"key":"1308_CR42","doi-asserted-by":"publisher","first-page":"102765","DOI":"10.1016\/j.nicl.2021.102765","volume":"31","author":"E Gleichgerrcht","year":"2021","unstructured":"E.\u00a0Gleichgerrcht, B.\u00a0C. Munsell, S.\u00a0Alhusaini, M.\u00a0K. Alvim, N.\u00a0Bargall\u00f3, B.\u00a0Bender, A.\u00a0Bernasconi, N.\u00a0Bernasconi, B.\u00a0Bernhardt, K.\u00a0Blackmon, et\u00a0al., Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A\u00a0worldwide ENIGMA-Epilepsy study, NeuroImage: Clinical 31 (2021) 102765. https:\/\/doi.org\/10.1016\/j.nicl.2021.102765.","journal-title":"Neuroimage Clin"},{"key":"1308_CR43","doi-asserted-by":"publisher","first-page":"106815","DOI":"10.1016\/j.eplepsyres.2021.106815","volume":"178","author":"Y Ito","year":"2021","unstructured":"Y.\u00a0Ito, M.\u00a0Fukuda, H.\u00a0Matsuzawa, H.\u00a0Masuda, Y.\u00a0Kobayashi, N.\u00a0Hasegawa, H.\u00a0Kitaura, A.\u00a0Kakita, Y.\u00a0Fujii, Deep learning-based diagnosis of temporal lobe epilepsy associated with hippocampal sclerosis: An MRI study, Epilepsy Research 178 (2021) 106815. https:\/\/doi.org\/10.1016\/j.eplepsyres.2021.106815.","journal-title":"Epilepsy Res"},{"key":"1308_CR44","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1007\/978-3-030-87589-3_27","volume-title":"International Workshop on Machine Learning in Medical Imaging","author":"Z Cao","year":"2021","unstructured":"Z.\u00a0Cao, F.\u00a0Shi, Q.\u00a0Xu, G.\u00a0Liu, T.\u00a0Sun, X.\u00a0Xing, Y.\u00a0He, G.\u00a0Lu, Z.\u00a0Zhang, D.\u00a0Shen, Diagnosis of hippocampal sclerosis from clinical routine head MR images using structure-constrained super-resolution network, in: International Workshop on Machine Learning in Medical Imaging, Springer, 2021, pp. 258\u2013266. https:\/\/doi.org\/10.1007\/978-3-030-87589-3_27."},{"key":"1308_CR45","doi-asserted-by":"publisher","first-page":"613967","DOI":"10.3389\/fneur.2021.613967","volume":"12","author":"JP Princich","year":"2021","unstructured":"J.\u00a0P. Princich, P.\u00a0A. Donnelly-Kehoe, A.\u00a0Deleglise, M.\u00a0N. Vallejo-Azar, G.\u00a0O. Pascariello, P.\u00a0Seoane, J.\u00a0G. Veron Do\u00a0Santos, S.\u00a0Collavini, A.\u00a0H. Nasimbera, S.\u00a0Kochen, Diagnostic performance of MRI volumetry in epilepsy patients with hippocampal sclerosis supported through a\u00a0random forest automatic classification algorithm, Frontiers in Neurology 12 (2021) 613967. https:\/\/doi.org\/10.3389\/fneur.2021.613967.","journal-title":"Front Neurol"},{"key":"1308_CR46","doi-asserted-by":"publisher","DOI":"10.1002\/epi4.12655","author":"D Kim","year":"2022","unstructured":"D.\u00a0Kim, J.\u00a0Lee, J.\u00a0Moon, T.\u00a0Moon, Interpretable deep learning-based hippocampal sclerosis classification, Epilepsia Open (2022). https:\/\/doi.org\/10.1002\/epi4.12655.","journal-title":"Epilepsia Open"},{"key":"1308_CR47","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.jocn.2017.10.012","volume":"47","author":"E Kogias","year":"2018","unstructured":"E.\u00a0Kogias, D.-M. Altenm\u00fcller, J.-H. Klingler, B.\u00a0Schmeiser, H.\u00a0Urbach, S.\u00a0Doostkam, Histopathology of 3 Tesla MRI-negative temporal lobe epilepsies, Journal of Clinical Neuroscience 47 (2018) 273\u2013277. https:\/\/doi.org\/10.1016\/j.jocn.2017.10.012.","journal-title":"J Clin Neurosci"},{"key":"1308_CR48","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.28105","author":"A Arani","year":"2022","unstructured":"A.\u00a0Arani, C.\u00a0G. Schwarz, H.\u00a0J. Wiste, S.\u00a0D. Weigand, P.\u00a0M. Cogswell, M.\u00a0C. Murphy, J.\u00a0D. Trzasko, J.\u00a0L. Gunter, M.\u00a0L. Senjem, K.\u00a0P. McGee, et\u00a0al., Left\u2013right intensity asymmetries vary depending on scanner model for FLAIR and T1 weighted MRI images, Journal of Magnetic Resonance Imaging (2022). https:\/\/doi.org\/10.1002\/jmri.28105.","journal-title":"J Magn Reson Imaging"},{"key":"1308_CR49","doi-asserted-by":"publisher","first-page":"3786","DOI":"10.1007\/s00330-020-07684-x","volume":"31","author":"P Omoumi","year":"2021","unstructured":"P.\u00a0Omoumi, A.\u00a0Ducarouge, A.\u00a0Tournier, H.\u00a0Harvey, C.\u00a0E. Kahn, F.\u00a0Louvet-de Verch\u00e8re, D.\u00a0Pinto Dos\u00a0Santos, T.\u00a0Kober, J.\u00a0Richiardi, To buy or not to buy\u2014evaluating commercial AI solutions in radiology (the ECLAIR guidelines), European radiology 31 (2021) 3786\u20133796. https:\/\/doi.org\/10.1007\/s00330-020-07684-x.","journal-title":"Eur Radiol"}],"container-title":["Clinical Neuroradiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00062-023-01308-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00062-023-01308-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00062-023-01308-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T21:44:43Z","timestamp":1700171083000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00062-023-01308-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,26]]},"references-count":49,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["1308"],"URL":"https:\/\/doi.org\/10.1007\/s00062-023-01308-9","relation":{},"ISSN":["1869-1439","1869-1447"],"issn-type":[{"value":"1869-1439","type":"print"},{"value":"1869-1447","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,26]]},"assertion":[{"value":"1 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"M.R., C.R. and R.W. are authors of the non-commercial and freely available <i>DL+DiReCT<\/i> tool. The remaining authors have no relevant conflicts of interest to disclose.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study was carried out in accordance with the recommendations of Kantonale Ethikkommission Bern with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Kantonale Ethikkommission Bern (protocols 2017-00697 and 2020-02902).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Statement"}},{"value":"DL+DiReCT, the morphometry tool for segmentation and extraction of the radiomics measures is publicly available, including trained models for non-enhanced and contrast-enhanced T1-weighted MRI ().","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data Availability Statement"}}]}}