{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T21:53:05Z","timestamp":1793829185637,"version":"4.6.1"},"reference-count":30,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T00:00:00Z","timestamp":1668988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019M3E5D1A02068142"],"award-info":[{"award-number":["2019M3E5D1A02068142"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Background and Objectives: This study investigated the usefulness of deep neural network (DNN) models based on 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and blood inflammatory markers to assess the therapeutic response in pyogenic vertebral osteomyelitis (PVO). Materials and Methods: This was a retrospective study with prospectively collected data. Seventy-four patients diagnosed with PVO underwent clinical assessment for therapeutic responses based on clinical features during antibiotic therapy. The decisions of the clinical assessment were confirmed as \u2018Cured\u2019 or \u2018Non-cured\u2019. FDG-PETs were conducted concomitantly regardless of the decision at each clinical assessment. We developed DNN models depending on the use of attributes, including C-reactive protein (CRP), erythrocyte sedimentation ratio (ESR), and maximum standardized FDG uptake values of PVO lesions (SUVmax), and we compared their performances to predict PVO remission. Results: The 126 decisions (80 \u2018Cured\u2019 and 46 \u2018Non-cured\u2019 patients) were randomly assigned with training and test sets (7:3). We trained DNN models using a training set and evaluated their performances for a test set. DNN model 1 had an accuracy of 76.3% and an area under the receiver operating characteristic curve (AUC) of 0.768 [95% confidence interval, 0.625\u20130.910] using CRP and ESR, and these values were 79% and 0.804 [0.674\u20130.933] for DNN model 2 using ESR and SUVmax, 86.8% and 0.851 [0.726\u20130.976] for DNN model 3 using CRP and SUVmax, and 89.5% and 0.902 [0.804\u20130.999] for DNN model 4 using ESR, CRP, and SUVmax, respectively. Conclusions: The DNN models using SUVmax showed better performances when predicting the remission of PVO compared to CRP and ESR. The best performance was obtained in the DNN model using all attributes, including CRP, ESR, and SUVmax, which may be helpful for predicting the accurate remission of PVO.<\/jats:p>","DOI":"10.3390\/medicina58111693","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T03:02:16Z","timestamp":1669086136000},"page":"1693","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Assessment of Therapeutic Responses Using a Deep Neural Network Based on 18F-FDG PET and Blood Inflammatory Markers in Pyogenic Vertebral Osteomyelitis"],"prefix":"10.3390","volume":"58","author":[{"given":"Hyunkwang","family":"Shin","sequence":"first","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eunjung","family":"Kong","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, Yeungnam University College of Medicine, Daegu 42415, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongwoo","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Yeungnam University College of Medicine, Daegu 42415, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gyu Sang","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1293-2724","authenticated-orcid":false,"given":"Ikchan","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Yeungnam University College of Medicine, Daegu 42415, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1136\/ard.56.12.709","article-title":"Pyogenic, tuberculous, and brucellar vertebral osteomyelitis: A descriptive and comparative study of 219 cases","volume":"56","author":"Colmenero","year":"1997","journal-title":"Ann. 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