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Proceedings Paper

Investigation of sex hormones on the early diagnosis of schizophrenia
Author(s): Peilun Song; Yaping Wang; Xiujuan Geng; Xueqin Song
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Paper Abstract

Schizophrenia is a severe psychiatric disorder showing cognitive deficits among the early symptoms. Previous studies have reported that sex hormones are related to the cognitive dysfunction in schizophrenia. In this paper, we explore sex hormones as the potential biomarkers in differentiating first-episode schizophrenia patients from healthy controls, and investigate the most discriminative sex-hormone related biomarkers for the diagnosis of schizophrenia at the same time. Specifically, six sex hormones, which are follicle-stimulating hormone, luteinizing hormone, prolactin, estradiol, progesterone and testosterone, were examined as the basic features. Based on these sex hormones, the sex-hormone ratios and log-transformed sex-hormone ratios were computed for feature enhancement. T-test was further used for feature selection. In order to avoid the possible bias on sex hormones caused by female physiological factors, the classification experiment was performed on male participants using random forest. The effectiveness of different feature combination strategies was further studied. Among these strategies, the scheme fusing sex hormones, sex-hormone ratios and log-transformed sex-hormone ratios produced the best classification performance with an accuracy of 80.67%, which is comparable with the results obtained by complex neuroimaging data. Finally, the most discriminative features were further discussed according to the t-test results. Results show that the luteinizing hormone, prolactin, sex-hormone ratios and log-transformed sex-hormone ratios are the most significant features. This work supports the use of sex hormones as potential biomarkers for the diagnosis of schizophrenia. Given the convenient detection of these sex hormones in the routine examination, it could be a more straightforward and cost-saving way for the early diagnosis of schizophrenia patients.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113144E (16 March 2020); doi: 10.1117/12.2550325
Show Author Affiliations
Peilun Song, Zhengzhou Univ. (China)
Yaping Wang, Zhengzhou Univ. (China)
Xiujuan Geng, The Chinese Univ. of Hong Kong (Hong Kong, China)
Xueqin Song, Zhengzhou Univ. (China)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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