Modified Mixture of Experts for the Diagnosis of Perfusion Magnetic Resonance Imaging Measures in Locally Rectal Cancer Patients.
10.4258/hir.2013.19.2.130
- Author:
Sungmin MYOUNG
1
Author Information
1. Department of Medical Information and Administration, College of Health Science, Jungwon University, Goesan, Korea. smmyoung@jwu.ac.kr
- Publication Type:Original Article
- Keywords:
Mixture of Experts;
Classification;
Magnetic Resonance Imaging;
Rectal Neoplasms;
Medical Decision Making
- MeSH:
Chemoradiotherapy;
Cohort Studies;
Decision Making;
Humans;
Joints;
Magnetic Resonance Angiography;
Magnetic Resonance Imaging;
Perfusion;
Pliability;
Rectal Neoplasms
- From:Healthcare Informatics Research
2013;19(2):130-136
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: This study demonstrates the feasibility of using a modified mixture of experts (ME) model with repeated measured tumoural Ktrans value to perform an automatic diagnosis of responder based on perfusion magnetic resonance imaging (MRI) of rectal cancer. METHODS: The data used in this study was obtained from 39 patients with primary rectal carcinoma who were scheduled for preoperative chemoradiotherapy. The modified ME model is a joint modeling of the ME model via the linear mixed effect model. First, we considered two local experts and a gating network, and the modified expert network as a liner mixed effect model. Afterward, the finding estimates were obtained via the expectation-maximization algorithm. All computation was performed by R-2.15.2. RESULTS: We found that two experts have different patterns. The feature of expert 1 (n = 10) had a higher baseline value and a lower slope than expert 2 (n = 29). A comparison of the estimated experts and responder/non-responder groups according to T-downstaging criteria showed that expert 1 had a more effect treatment responder than expert 2. CONCLUSIONS: A novel feature of this study is that it is an extension of classical ME models in case of repeatedly measured data. The proposed model has the advantages of flexibility and adaptability for identifying distinct subgroups with various time patterns, and it can be applied to biomedical data which is measured repeatedly, such as time-course microarray data or cohort data. This method can assist physicians as important diagnostic decision making mechanism.
