Literature DB >> 21940556

Receiver operating characteristic analysis.

Sukru Mehmet Erturk.   

Abstract

Mesh：

Year:  2011        PMID： 21940556     DOI： 10.2214/AJR.11.6484

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN： 0361-803X            Impact factor:   3.959


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  4 in total

1.  Development and assessment of machine learning algorithms for predicting remission after transsphenoidal surgery among patients with acromegaly.

Authors:  Yanghua Fan; Yansheng Li; Yichao Li; Shanshan Feng; Xinjie Bao; Ming Feng; Renzhi Wang
Journal:  Endocrine       Date:  2019-10-30       Impact factor: 3.633

2.  Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma.

Authors:  Bing Xiao; Yanghua Fan; Zhe Zhang; Zilong Tan; Huan Yang; Wei Tu; Lei Wu; Xiaoli Shen; Hua Guo; Zhen Wu; Xingen Zhu
Journal:  Front Oncol       Date:  2021-04-15       Impact factor: 6.244

3.  Machine Learning-Based Radiomics Predicts Radiotherapeutic Response in Patients With Acromegaly.

Authors:  Yanghua Fan; Shenzhong Jiang; Min Hua; Shanshan Feng; Ming Feng; Renzhi Wang
Journal:  Front Endocrinol (Lausanne)       Date:  2019-08-27       Impact factor: 5.555

4.  Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up.

Authors:  Congxin Dai; Yanghua Fan; Yichao Li; Xinjie Bao; Yansheng Li; Mingliang Su; Yong Yao; Kan Deng; Bing Xing; Feng Feng; Ming Feng; Renzhi Wang
Journal:  Front Endocrinol (Lausanne)       Date:  2020-09-16       Impact factor: 5.555
  4 in total

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