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It is often tricky to differentiate cystic pituitary adenoma from Rathke cleft cyst with visual inspection because of similar MRI presentations between them. We aimed to design an MR-based radiomics model for improving differential diagnosis between them. Methods. Conventional diagnostic MRI data (T1-,T2-, and postcontrast T1-weighted MR images) were obtained from 215 pathologically confirmed patients (105 cases with cystic pituitary adenoma and the other 110 cases with Rathke cleft cyst) and were divided into training (<jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mi>n<\/a:mi>\n                        <a:mo>=<\/a:mo>\n                        <a:mn>172<\/a:mn>\n                     <\/a:math>\n                  <\/jats:inline-formula>) and test sets (<jats:inline-formula>\n                     <c:math xmlns:c=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                        <c:mi>n<\/c:mi>\n                        <c:mo>=<\/c:mo>\n                        <c:mn>43<\/c:mn>\n                     <\/c:math>\n                  <\/jats:inline-formula>). MRI radiomics features were extracted from the imaging data, and semantic imaging features (<jats:inline-formula>\n                     <e:math xmlns:e=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\">\n                        <e:mi>n<\/e:mi>\n                        <e:mo>=<\/e:mo>\n                        <e:mn>15<\/e:mn>\n                     <\/e:math>\n                  <\/jats:inline-formula>) were visually estimated by two radiologists. Four classifiers were used to construct radiomics models through 5-fold crossvalidation after feature selection with least absolute shrinkage and selection operator. An integrated model by combining radiomics and semantic features was further constructed. The diagnostic performance was validated in the test set. Receiver operating characteristic curve was used to evaluate and compare the performance of the models at the background of diagnostic performance by radiologist. Results. In test set, the combined radiomics and semantic model using ANN classifier obtained the best classification performance with an AUC of 0.848 (95% CI: 0.750-0.946), accuracy of 76.7% (95% CI: 64.1-89.4%), sensitivity of 73.9% (95% CI: 56.0-91.9%), and specificity of 80.0% (95% CI: 62.5-97.5%) and performed better than multiparametric model (<jats:inline-formula>\n                     <g:math xmlns:g=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\">\n                        <g:mtext>AUC<\/g:mtext>\n                        <g:mo>=<\/g:mo>\n                        <g:mn>0.792<\/g:mn>\n                     <\/g:math>\n                  <\/jats:inline-formula>, 95% CI: 0.674-0.910) or semantic model (<jats:inline-formula>\n                     <i:math xmlns:i=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\">\n                        <i:mtext>AUC<\/i:mtext>\n                        <i:mo>=<\/i:mo>\n                        <i:mn>0.823<\/i:mn>\n                     <\/i:math>\n                  <\/jats:inline-formula>, 95% CI: 0.705-0.941). The two radiologists had an accuracy of 69.8% and 74.4%, respectively, sensitivity of 69.6% and 73.9%, and specificity of 70.0% and 75.0%. Conclusions. The MR-based radiomics model had technical feasibility and good diagnostic performance in the differential diagnosis between cystic pituitary adenoma and Rathke cleft cyst.<\/jats:p>","DOI":"10.1155\/2021\/6438861","type":"journal-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T21:05:16Z","timestamp":1628715916000},"page":"1-12","source":"Crossref","is-referenced-by-count":14,"title":["MR-Based Radiomics for Differential Diagnosis between Cystic Pituitary Adenoma and Rathke Cleft Cyst"],"prefix":"10.1155","volume":"2021","author":[{"given":"Yanping","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sixuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, 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