A dynamic-static combination model based on radiomics features for prostate cancer using multiparametric MRI. (1st July 2023)

• Record Type: Journal Article
• Authors: Li, Shuqin; Zheng, Tingting; Fan, Zhou; Qu, Hui; Wang, Jianfeng; Bi, Jianbin; Lv, Qingjie; Zhang, Gejun; Cui, Xiaoyu; Zhao, Yue
• Is Part Of: Physics in medicine & biology. Volume 68:Number 1(2023)
• Publication Date: 2023-07-01
• Subjects: radiomics -- dynamic radiomics -- prostate cancer -- machine learning -- magnetic resonance imaging -- diffusion-weighted imaging; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aca954
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Objective . To propose a new dynamic multiparametric magnetic resonance imaging (mpMRI) radiomics method for the detection of prostate cancer (PCa), and establish a combined model using dynamic and static radiomics features based on this method. Approach . A total of 166 patients (82 PCa patients and 84 non-PCa patients) were enrolled in the study, and 31 872 mpMRI images were performed in a radiomics workflow. The whole prostate segmentation and traditional static radiomics features extraction were performed on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI, b value of 10, 50, 100, 150, 200, 400, 600, 800, 1000, 1500 s mm −2 respectively), apparent diffusion coefficient (ADC), and T2-weighted imaging (T2WI) sequences respectively. Through the building of each b -value DWI model and the analysis of the static key radiomics features, three types of dynamic features called standard discrete (SD), parameter ( P ) and relative change rate (RCR) were constructed. And the b-value parameters used to construct dynamic features were divided into three groups ('Df_', 'Db_' and 'Da_'): the front part (10–200 s mm −2 ), the back part (400–1500 s mm −2 ), and all (10–1500 s mm −2 ) of the b -values set, respectively. Afterwards, the dynamic mpMRI model and combined model construction were constructed, and the PCa discrimination performance of each model was evaluated. Main results. The models based on dynamic features showed good potential for PCa identification. Where, the results of Db_SD, Da_P and Db_P models were encouraging (test cohort AUCs: 90.78%, 87.60%, 86.3%), which was better than the commonly used ADC model (AUC of ADC was 75.48%). Among the combined models, the models using static features of T2WI and dynamic features performed the best. The AUC of Db_SD + T2WI, Db_P + T2WI and Db_RCR + T2WI model was 92.90%, 91.29% and 81.46%. Significance. The dynamic-static combination model based on dynamic mpMRI radiomics method has a good effect on the identification of PCa. This method has broad application prospects in PCa individual diagnosis management.