Predictive value of multiparametric MRI for prostate cancer pathology and tumor staging
10.13491/j.issn.1004-714X.2021.05.022
- VernacularTitle:多参数MRI成像技术用于前列腺癌病理和分期的预测价值研究
- Author:
Shangpeng YAO
1
;
Wanji ZOU
1
;
Shuai WANG
1
Author Information
1. Imaging Department of Jingxian Hospital, Jingxian 242500 China.
- Publication Type:DiagnosisandTreatment/OriginalArticles
- Keywords:
Prostate Cancer;
Multiparametric MRI;
Clinical Imaging;
Predictive Value
- From:
Chinese Journal of Radiological Health
2021;30(5):628-631
- CountryChina
- Language:Chinese
-
Abstract:
Objective To evaluate the value of multiparametric MRI for prediction of prostate cancer pathology and tumor staging. Methods A total of 80 patients with prostate cancer admitted during the period from 2016 to 2021 were enrolled as the study subjects, and all patients underwent multiparametric MRI. The pathological examination of radical prostatectomy specimens was used as a gold standard to evaluate the predictive value of multiparametric MRI for significant prostate cancaer, extracapsular extension, index lesion location and lymph node metastasis. Results Multiparametric MRI showed a sensitive of 96%, specificity of 60%, positive predictive value of 97.3% and negative predictive value of 50% for prediction of significant prostate cancer, and the concordance between multiparametric MRI and pathology of radical prostatectomy specimens was 75% for prediction of index lesions. In addition, multiparametric MRI showed a 40% sensitivity, 91.4% specificity, 85.7% positive predictive value and 54.2% negative predictive value for identification of extracapsular extension and a 16.7% accuracy for detection of lymph node metastasis. Conclusion Multiparametric MRI is highly sensitive for predicting significant prostate cancer with a high positive predictive value and is highly accurate for identification of index lesions of prostate cancer, which is of great value to propose prostate cancer treatment schemes and evaluate the clinical prognosis among prostate cancer patients.
