Non-invasive quantitative visualization of multi-parametric MRI habitat imaging for predicting prostate cancer risk degree
10.3760/cma.j.cn112149-20240624-00348
- VernacularTitle:多参数MRI生境成像无创定量可视化预测前列腺癌危险度的研究
- Author:
Lei YUAN
1
;
Jingliang ZHANG
;
Lina MA
;
Ye HAN
;
Guorui HOU
;
Weijun QIN
;
Jing ZHANG
;
Yi HUAN
;
Jing REN
Author Information
1. 空军军医大学西京医院放射科，西安 710032
- Publication Type:Journal Article
- Keywords:
Prostate neoplasms;
Magnetic resonance imaging;
Habitat imaging;
Risk degree
- From:
Chinese Journal of Radiology
2025;59(4):393-400
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the value of non-invasive habitat imaging (HI) multi-parametric MRI (mpMRI) in predicting the risk of prostate cancer (PCa).Methods:In this cross-sectional study, 220 patients with PCa confirmed by radical prostatectomy (RP) who underwent multi-parametric MRI (mpMRI) scanning at Xijing Hospital, Air Force Military Medical University from January 2018 to May 2024 were retrospectively collected. Patients were divided into a training set (154 cases) and a test set (66 cases) by simple random sampling in a 7∶3 ratio. Based on mpMRI imaging, the apparent diffusion coefficient (ADC), perfusion fraction (f), and mean kurtosis (MK) of each voxel were integrated. The K-means clustering algorithm was used to divide the PCa target lesions into habitat subregions, generate habitat maps, and calculate the proportion of each habitat subregion in the entire lesion. According to the 2019 International Society of Urological Pathology (ISUP) guidelines, patients were categorized into a low-risk group (ISUP≤2, 65 cases) and a high-risk group (ISUP≥3, 155 cases). The RP specimens were matched with the habitat map to identify corresponding habitat subregions, and the ISUP grade of each subregion was individually evaluated to calculate the detection rate of high-risk PCa patients. The logistic regression analysis was applied to identify the independent risk factors associated with PCa risk, and the HI-clinical imaging model and clinical imaging model were constructed. The efficacy of the models was assessed using receiver operating characteristic curve.Results:Based on the optimal cluster number, the habitat was divided into three subregions. Habitat 1 had lower ADC and f values and higher MK values, while habitat 2 had the opposite characteristics, and habitat 3 was intermediate. The proportion of habitat 1 in the high-risk group was 28.8%, in the low-risk group was 8.9%. In the training set, the comparison of habitat subregions with pathological results showed that the detection rate of high-risk lesions was 66.9% (103/154) in habitat 1, 25.3% (39/154) in habitat 2, and 47.4% (73/154) in habitat 3. The logistic regression analysis indicated that the proportion of habitat 1 ( OR=3.03, 95% CI 1.77-5.18, P<0.001), prostate-specific antigen ( OR=1.66, 95% CI 1.04-2.66, P=0.034), and the prostate imaging reporting and data system score ( OR=1.65, 95% CI 1.00-2.70, P=0.048) as independent risk factors for high-risk PCa. In the training set, the area under the curve (AUC) for predicting PCa risk was 0.854 (95% CI 0.789-0.920) for the HI-clinical imaging model and 0.779 (95% CI 0.701-0.856) for the clinical imaging model. In the test set, the AUC values were 0.809 (95% CI 0.693-0.895) and 0.738 (95% CI 0.619-0.856), respectively. Conclusion:HI based on mpMRI can effectively predict the risk of PCa.
