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Magnetic resonance imaging (\n                      <jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content>\n                      ) postprocessing methods may provide essential help for detection of\n                      <jats:styled-content style=\"fixed-case\">FCD<\/jats:styled-content>\n                      . In this study, we utilized surface\u2010based\n                      <jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content>\n                      morphometry and machine learning for automated lesion detection in a mixed cohort of patients with\n                      <jats:styled-content style=\"fixed-case\">FCD<\/jats:styled-content>\n                      type\n                      <jats:styled-content style=\"fixed-case\">II<\/jats:styled-content>\n                      from 3 different epilepsy centers.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Sixty\u2010one patients with pharmacoresistant epilepsy and histologically proven\n                      <jats:styled-content style=\"fixed-case\">FCD<\/jats:styled-content>\n                      type\n                      <jats:styled-content style=\"fixed-case\">II<\/jats:styled-content>\n                      were included in the study. The patients had been evaluated at 3 different epilepsy centers using 3 different\n                      <jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content>\n                      scanners. T1\u2010volumetric sequence was used for postprocessing. A normal database was constructed with 120 healthy controls. We also included 35 healthy test controls and 15 disease test controls with histologically confirmed hippocampal sclerosis to assess specificity. Features were calculated and incorporated into a nonlinear neural network classifier, which was trained to identify lesional cluster. We optimized the threshold of the output probability map from the classifier by performing receiver operating characteristic (ROC) analyses. Success of detection was defined by overlap between the final cluster and the manual labeling. Performance was evaluated using\n                      <jats:italic>k<\/jats:italic>\n                      \u2010fold cross\u2010validation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The threshold of 0.9 showed optimal sensitivity of 73.7% and specificity of 90.0%. The area under the curve for the\n                      <jats:styled-content style=\"fixed-case\">ROC<\/jats:styled-content>\n                      analysis was 0.75, which suggests a discriminative classifier. Sensitivity and specificity were not significantly different for patients from different centers, suggesting robustness of performance. Correct detection rate was significantly lower in patients with initially normal\n                      <jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content>\n                      than patients with unequivocally positive\n                      <jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content>\n                      . Subgroup analysis showed the size of the training group and normal control database impacted classifier performance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance<\/jats:title>\n                    <jats:p>\n                      Automated surface\u2010based\n                      <jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content>\n                      morphometry equipped with machine learning showed robust performance across cohorts from different centers and scanners. The proposed method may be a valuable tool to improve\n                      <jats:styled-content style=\"fixed-case\">FCD<\/jats:styled-content>\n                      detection in presurgical evaluation for patients with pharmacoresistant epilepsy.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/epi.14064","type":"journal-article","created":{"date-parts":[[2018,4,10]],"date-time":"2018-04-10T23:01:07Z","timestamp":1523401267000},"page":"982-992","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":124,"title":["Automated detection of focal cortical dysplasia type\n                    <scp>II<\/scp>\n                    with surface\u2010based magnetic resonance imaging postprocessing and machine learning"],"prefix":"10.1111","volume":"59","author":[{"given":"Bo","family":"Jin","sequence":"first","affiliation":[{"name":"Department of Neurology School of Medicine Epilepsy Center Second Affiliated Hospital Zhejiang University Hangzhou China"},{"name":"Epilepsy Center Cleveland Clinic Cleveland OH USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Balu","family":"Krishnan","sequence":"additional","affiliation":[{"name":"Epilepsy Center Cleveland Clinic Cleveland OH USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie","family":"Adler","sequence":"additional","affiliation":[{"name":"Developmental Neurosciences UCL Great Ormond Street Institute of Child Health University College London London UK"},{"name":"Great Ormond Street Hospital for Children London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konrad","family":"Wagstyl","sequence":"additional","affiliation":[{"name":"Developmental Neurosciences UCL Great Ormond Street Institute of Child Health University College London London UK"},{"name":"Brain Mapping Unit Institute of Psychiatry University of Cambridge Cambridge 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