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The tumor consistency was determined on surgical findings.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>T1\u2010weighted imaging (T1WI), contrast\u2010enhanced T1WI (T1CE), and T2\u2010weighted imaging (T2WI) using spin\u2010echo sequences with a 3.0\u2010T scanner.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>An automated three\u2010dimensional (3D) segmentation was performed to generate the volume of interest (VOI) on T2WI, then T1WI\/T1CE were coregistered to T2WI. A total of 388 radiomic features were extracted on each VOI of mpMRI. The top\u2010discriminative features were identified using the minimum\u2010redundancy maximum\u2010relevance method and 0.632+ bootstrapping. The radiomics models based on each sequence and their combinations were established via the random forest (RF) and support vector machine (SVM), and independently evaluated for their ability in distinguishing PMA consistency.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Mann\u2013Whitney <jats:italic>U<\/jats:italic>\u2010test and Chi\u2010square test were used for comparison analysis. The area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), and relative standard deviation (RSD) were calculated to evaluate each model's performance. ACC with <jats:italic>P<\/jats:italic>\u2010value&lt;0.05 was considered statistically significant.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Eleven mpMRI\u2010based features exhibited statistically significant differences between soft and hard PMA in the training cohort. The radiomics model built on combined T1WI\/T1CE\/T2WI demonstrated the best performance among all the radiomics models with an AUC of 0.90 (95% confidence interval [CI]: 0.87\u20130.92), ACC of 0.87 (CI: 0.84\u20130.89), SEN of 0.83 (CI: 0.81\u20130.85), and SPE of 0.87 (CI: 0.85\u20130.99) in the test cohort.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>Radiomic features based on mpMRI have good performance in the presurgical evaluation of PMA consistency.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>3<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 2<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.27930","type":"journal-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T10:46:24Z","timestamp":1632307584000},"page":"1491-1503","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Radiomic Features on Multiparametric <scp>MRI<\/scp> for Preoperative Evaluation of Pituitary Macroadenomas Consistency: Preliminary Findings"],"prefix":"10.1002","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2962-4066","authenticated-orcid":false,"given":"Tao","family":"Wan","sequence":"first","affiliation":[{"name":"School of Biological Science and Medical Engineering Beihang University  Beijing China"},{"name":"Beijing Advanced Innovation Center for Biomedical Engineering Beihang University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunxue","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Beijing Tiantan Hospital Capital Medical University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Meng","sequence":"additional","affiliation":[{"name":"Department of Radiology, Beijing Tiantan Hospital Capital Medical University  Beijing 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