{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T18:18:23Z","timestamp":1787681903222,"version":"build-2784847793"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T00:00:00Z","timestamp":1710547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T00:00:00Z","timestamp":1710547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Jpn J Radiol"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objectives<\/jats:title>\n                <jats:p>To investigate the usefulness of machine learning (ML) models using pretreatment <jats:sup>18<\/jats:sup>F-FDG-PET-based radiomic features for predicting adverse clinical events (ACEs) in patients with cardiac sarcoidosis (CS).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Materials and methods<\/jats:title>\n                <jats:p>This retrospective study included 47 patients with CS who underwent <jats:sup>18<\/jats:sup>F-FDG-PET\/CT scan before treatment. The lesions were assigned to the training (<jats:italic>n<\/jats:italic>\u2009=\u200938) and testing (<jats:italic>n<\/jats:italic>\u2009=\u20099) cohorts. In total, 49 <jats:sup>18<\/jats:sup>F-FDG-PET-based radiomic features and the visibility of right ventricle <jats:sup>18<\/jats:sup>F-FDG uptake were used to predict ACEs using seven different ML algorithms (namely, decision tree, random forest [RF], neural network, k-nearest neighbors, Na\u00efve Bayes, logistic regression, and support vector machine [SVM]) with tenfold cross-validation and the synthetic minority over-sampling technique. The ML models were constructed using the top four features ranked by the decrease in Gini impurity. The AUCs and accuracies were used to compare predictive performances.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Patients who developed ACEs presented with a significantly higher surface area and gray level run length matrix run length non-uniformity (GLRLM_RLNU), and lower neighborhood gray-tone difference matrix_coarseness and sphericity than those without ACEs (each, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). In the training cohort, all seven ML algorithms had a good classification performance with AUC values of\u2009&gt;\u20090.80 (range: 0.841\u20130.944). In the testing cohort, the RF algorithm had the highest AUC and accuracy (88.9% [8\/9]) with a similar classification performance between training and testing cohorts (AUC: 0.945 vs 0.889). GLRLM_RLNU was the most important feature of the modeling process of this RF algorithm.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>ML analyses using <jats:sup>18<\/jats:sup>F-FDG-PET-based radiomic features may be useful for predicting ACEs in patients with CS.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11604-024-01546-y","type":"journal-article","created":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T03:01:27Z","timestamp":1710558087000},"page":"744-752","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis"],"prefix":"10.1007","volume":"42","author":[{"given":"Masatoyo","family":"Nakajo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daisuke","family":"Hirahara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Megumi","family":"Jinguji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoko","family":"Ojima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mitsuho","family":"Hirahara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atsushi","family":"Tani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koji","family":"Takumi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiyohisa","family":"Kamimura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mitsuru","family":"Ohishi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takashi","family":"Yoshiura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,16]]},"reference":[{"key":"1546_CR1","first-page":"50","volume":"6","author":"E Hulten","year":"2016","unstructured":"Hulten E, Aslam S, Osborne M, et al. 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