{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T12:13:53Z","timestamp":1744200833737},"reference-count":25,"publisher":"American Scientific Publishers","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["j med imaging hlth inform"],"published-print":{"date-parts":[[2021,3,1]]},"abstract":"In order to better assist clinicians in the diagnosis and treatment of osteosarcoma, and our understanding of the advantages and disadvantages of different examination techniques of DR, CT and MRI, this article collects the pathological diagnosis of the PACS Medical system of Liaocheng\n People\u2019s Hospital 2015 to 2018. 130 patients with osteosarcoma and imaging examination data, retrospectively analyzed the diagnostic and clinical staging information contained in DR, CT and MRI examination data of osteosarcoma patients, and detected the diagnosis and clinical staging\n information of osteosarcoma by comparing various examination methods ability to obtain the optimal imaging examination method that can provide complete diagnosis and clinical staging information of osteosarcoma, and is recommended as a routine clinical examination program. Finally, we conclude\n that: DR, CT, and MRI examinations have no significant differences in the detection capacity of osteosarcoma bone destruction, periosteal response, and periosteal triangle; MRI has less ability to detect tumor bone than DR and CT; DR has soft tissue. The ability to detect masses is inferior\n to CT and MRI. Among the single DR, CT or MRI examinations, CT examinations have the best comprehensive detection ability for all imaging diagnostic signs of osteosarcoma. MR examination has the best comprehensive display of clinical staging information such as infiltration range and infiltration\n boundary of osteosarcoma. Combined DR, CT, and MRI examinations are optimized imaging procedures that provide complete osteosarcoma diagnosis and clinical staging information.<\/jats:p>","DOI":"10.1166\/jmihi.2021.3421","type":"journal-article","created":{"date-parts":[[2020,12,24]],"date-time":"2020-12-24T04:52:02Z","timestamp":1608785522000},"page":"871-877","source":"Crossref","is-referenced-by-count":4,"title":["Imaging Method for Osteosarcoma Diagnosis and Clinical Staging Information Optimization"],"prefix":"10.1166","volume":"11","author":[{"given":"Huijuan","family":"Jia","sequence":"first","affiliation":[{"name":"Computerized Tomography Room, Liaocheng People\u2019s Hospital, Shandong Liaocheng, 252000, China"}]},{"given":"Xiuqin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Magnetic Resonance Imaging Room, Liaocheng People\u2019s Hospital, Shandong Liaocheng, 252000, China"}]},{"given":"Lei","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, Liaocheng People\u2019s Hospital, Shandong Liaocheng, 252000, China"}]},{"given":"Xiansheng","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, Liaocheng People\u2019s Hospital, Shandong Liaocheng, 252000, China"}]}],"member":"17","reference":[{"issue":"(10)","key":"r1","doi-asserted-by":"crossref","first-page":"pp.1559","DOI":"10.1038\/s41591-018-0177-5","article-title":"Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning.","volume":"24","author":"Coudray","year":"2018","journal-title":"Nature Medicine"},{"issue":"(11)","key":"r2","article-title":"Qualitative and semi-quantitative 18-F FDG PET\/CT for osteosarcoma: A review.","volume":"5","author":"Nardo","year":"2017","journal-title":"Current Radiology Reports"},{"issue":"(10)","key":"r3","doi-asserted-by":"crossref","first-page":"pp.1712","DOI":"10.1007\/s00259-017-3698-0","article-title":"The role of 18 F-FDG PET\/CT 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