A radiomics approach based on MR imaging for classification of deficiency and excess syndrome of traditional Chinese medicine in prostate cancer

Yongsheng Zhang; Huan Yang; Zhiping Li; Chen Gao; Yin Chen; Yasheng Huang; Xianjie Yue; Chang Shu; Yuguo Wei; Feng Cui; Maosheng Xu

doi:10.1016/j.heliyon.2023.e23242

A radiomics approach based on MR imaging for classification of deficiency and excess syndrome of traditional Chinese medicine in prostate cancer

Heliyon. 2023 Dec 3;9(12):e23242. doi: 10.1016/j.heliyon.2023.e23242. eCollection 2023 Dec.

Authors

Yongsheng Zhang¹, Huan Yang², Zhiping Li¹, Chen Gao³, Yin Chen⁴, Yasheng Huang⁴, Xianjie Yue¹, Chang Shu⁵, Yuguo Wei⁶, Feng Cui¹, Maosheng Xu³

Affiliations

¹ Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China.
² Department of Acupuncture and Moxibustion, Community Health Service of Xiaohehushu District, Hangzhou, 310005, China.
³ Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, 310006, China.
⁴ Department of Urology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China.
⁵ Department of Pathology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China.
⁶ Advanced Analytics, Global Medical Service, GE Healthcare, Hangzhou, 310007, China.

Abstract

Objective: To explore the potential imaging biomarkers for predicting Traditional Chinese medicine (TCM) deficiency and excess syndrome in prostate cancer (PCa) patients by radiomics approach based on MR imaging.

Methods: A total of 121 PCa patients from 2 centers were divided into 1 training cohort with 84 PCa patients and 1 validation cohort with 37 PCa patients. The PCa patients were divided into deficiency and excess syndrome group according to TCM syndrome differentiation. Radiomic features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging and apparent diffusion coefficient images originated from diffusion-weighted imaging. A radiomic signature was constructed after reduction of dimension in training group by the minimum redundancy maximum relevance and the least absolute shrinkage and selection operator. The performance of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve.

Results: The radiomic scores of PCa with TCM excess syndrome group were statistically higher than those of PCa with TCM deficiency syndrome group among T2WI, diffusion-weighted imaging and apparent diffusion coefficient imaging models. The area under ROC curves for T2WI, diffusion-weighted imaging and apparent diffusion coefficient imaging models were 0.824, 0.824, 0.847 in the training cohort and 0.759, 0.750, 0.809 in the validation cohort, respectively. The apparent diffusion coefficient imaging model had the best discrimination in separating patients with TCM excess syndrome and deficiency syndrome, and its accuracy was 0.788, 0.778 in the training and validation cohort, respectively. The calibration curve demonstrated that there was a high consistency between the prediction of radiomic scores and the actual classification of TCM's deficiency and excess syndrome in PCa.

Conclusion: The radiomic signature based on MR imaging can be performed as a non-invasive, potential approach to discriminate TCM deficiency syndrome from excess syndrome in PCa, in which apparent diffusion coefficient imaging model has the best diagnostic efficiency.

Keywords: Magnetic resonance imaging; Prostate cancer; Radiomic; TCM syndrome.