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• Clinical Article •
To analyze the value of radiomics based on different diffusion model parameter maps in the early diagnosis of clinically significant prostate cancer by magnetic resonance imaging
DU Bing  QI Xuan  YANG Hongkai  QI Dong  HE Yongsheng 

Cite this article as: DU B, QI X, YANG H K, et al. To analyze the value of radiomics based on different diffusion model parameter maps in the early diagnosis of clinically significant prostate cancer by magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2024, 15(2): 83-89. DOI:10.12015/issn.1674-8034.2024.02.012.


[Abstract] Objective To explore the predictive value of radiomics analysis basedon magnetic resonance single-index and diffusion kurtosis model functional parameter maps for clinically significant prostate cancer (csPCa).Materials and Methods A retrospective analysis was conducted on 238 prostate patients who visited Ma'anshan People's Hospital from April 2022 to July 2023. They were confirmed by ultrasound-guided puncture or surgical pathology, including 96 csPCa patients and 142 non-csPCa patients. The age of the patients 56-84 (62.34±7.62) years old. The Clinical data within and between the groups were compared. All patients underwent magnetic resonance multi-parameter scanning, after post-processing, the apparent diffusion coefficient (ADC) pseudo-color plots were generated, and the mean kurtosis (MK) and mean diffusivty (MD) pseudo-color plots in the diffusion kurtosis model were obtained. After image preprocessing, the image features of eachfunctional parameter map are extracted. There are a total of 1 056 radiomics features. The maximum correlation minimum redundancy (MRMR) algorithm and least absolute shrinkage and selection operator (LASSO) are used to eliminateredundancy, perform feature dimensionality reduction, and retain high-quality labels for the data of ADC, MD, and MK models. For relevant features, 10-foldcross-validation was applied to obtain a feature subset, and 238 patients were randomly divided into groups in a ratio of 7∶3. Finally, the ADC model screened out 5 omics features, and the MD model screened out 6 omics features. The MK model screened out 6 omics features, established alogistic regression model, calculated the threshold, accuracy, sensitivity, and specificity of the clinical models, radiology, and clinical-radiology models, and drew the receiver operating characteristic (ROC) curve. Calculate the area under the curve (AUC) and 95% confidence interval (CI), use the DeLong test to combine each model in pairs, compare whether the AUC values between the two groups are statistically significant, and further use decision curve analysis (DCA) to evaluate model performance.Results The AUC, specificity and sensitivity of the clinical model in the training set were 0.840 (95% CI: 0.778-0.901), 78.7% and 76.8%, and in the test set were 0.675 (95% CI: 0.539-0.812), 79.0% and 59.2%, respectively. The AUC, specificity and sensitivity of the ADC model in the training set were 0.927 (95% CI: 0.890-0.964), 81.9%, 86.9%, and in the test set were 0.909 (95% CI: 0.835-0.983), 90.6%, 84.1%, respectively; the AUC, specificity and sensitivity of the MD model in the trainingset were 0.934 (95% CI: 0.899-0.969), 85.1%, 84.0%, and in the test set were 0.960 (95% CI: 0.910-1.000), 93.0%, 85.1%, respectively; the AUC, specificity and sensitivity of the MK model in the training set were 0.935 (95% CI: 0.900-0.971), 90.4%, 84.0%, and in the test set were 0.856 (95% CI: 0.770-0.941), 81.3%, 66.6%, respectively. The AUC, specificity and sensitivity of the clinical-radiology model in the training set were 0.946 (95% CI: 0.912-0.980), 88.2% and 89.8%, and in the test set were 0.963 (95% CI: 0.925-1.000), 93.0% and 85.1%, respectively. DeLong test results showed that there was no significant difference between the radiology model and the clinical-radiology combined model (P>0.05). There was a significant difference in AUC value between the clinical model and the other two models (Z=2.836, P=0.004), and there was no significant difference between the other two groups of models (P>0.05). The decision curve shows that the threshold probability of each model is in the range of 0.1-1.0, which has a net benefit for clinical practice. Different models have a positive effect on the diagnosis of csPCa. The clinical-radiology model having the highest diagnostic performance.Conclusions The radiomics analysis technology of MRI mono-exponential and diffusion kurtosis model functional parameter map is an effective method for the detection of csPCa. The clinical-radiology combined model has high diagnostic value for csPCa, which can provide relevant technical support for early clinical diagnosis and treatment.
[Keywords] prostate cancer；magnetic resonance imaging；radiomics；diffusion weighted imaging；diagnostic efficacy

DU Bing1, 2   QI Xuan1   YANG Hongkai1   QI Dong1, 3   HE Yongsheng1*  

1 Department of Radiology, Ma'anshan People's Hospital, Ma'anshan 243000, China

2 Wannan Medical College, Wuhu 241002, China

3 Anhui Medical University, Hefei 230032, China

Corresponding author: HE Y S, E-mail: heyongsheng881@163.com

Conflicts of interest   None.

Received  2023-10-07
Accepted  2024-02-02
DOI: 10.12015/issn.1674-8034.2024.02.012
Cite this article as: DU B, QI X, YANG H K, et al. To analyze the value of radiomics based on different diffusion model parameter maps in the early diagnosis of clinically significant prostate cancer by magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2024, 15(2): 83-89. DOI:10.12015/issn.1674-8034.2024.02.012.

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