ISSN 1004-4140
CN 11-3017/P
ZHANG Subo, XU Yongjun, SUN Yi, WAN Jinxin, ZHAO Yan, LIU Jingfang, WANG Rui. Study on the Value of Predicting Prostate Cancer in the Grey Zone of PSA by Use of PI-RADS v2.1 Combined with PSA-related Indicators[J]. CT Theory and Applications, 2021, 30(5): 567-574. DOI: 10.15953/j.1004-4140.2021.30.05.04
Citation: ZHANG Subo, XU Yongjun, SUN Yi, WAN Jinxin, ZHAO Yan, LIU Jingfang, WANG Rui. Study on the Value of Predicting Prostate Cancer in the Grey Zone of PSA by Use of PI-RADS v2.1 Combined with PSA-related Indicators[J]. CT Theory and Applications, 2021, 30(5): 567-574. DOI: 10.15953/j.1004-4140.2021.30.05.04

Study on the Value of Predicting Prostate Cancer in the Grey Zone of PSA by Use of PI-RADS v2.1 Combined with PSA-related Indicators

  • Objective: We intend to investigate the diagnostic value of establishing the Logistic regression model based on Prostate Imaging Report and Data System (PI-RADS v2.1) combined with prostate specific antigen (PSA) which is applied in PSA gray area(4~10ng/mL) prostate cancer. Materials and Methods: We retrospectively analyzed the pathologically-certified clinical data of 49 cases with prostate cancer (PCa) and 118 non-cancer cases who underwent prostate biopsy, the data covered age, tPSA, fPSA, PI-RADS v2.1 evaluation, PSAD and fPSA/tPSA. We performed logistic regression analysis on the indicators with statistical difference between the groups, and obtained ascertained independent PCa predictors, furthermore we respectively established regression prediction model by combination with PI-RADS v2.1 evaluation. The diagnostic efficacy of each model was evaluated by the operating characteristic curve (ROC) of subjects. Results: (1) There was no significant statistical difference in age, tPSA and fPSA between the PCa and non-cancer patients. But evaluation of PI-RADS v2.1, fPSA/tPSA, and PSAD showed significant statistical differences. (2) Logistic regression analysis indicated that PI-RADS v2.1evaluation, PSAD, and fPSA/tPSA are independent predictors of PCa; we established prediction models A and B as follows;Model A: Logit (P)=-10.82+2.32×PI-RADS v2.1+11.89×PSAD; Model B: Logit (P)=-6.13+2.19×PI-RADS v2.1-12.02×fPSA/tPSA. The area under the ROC curve was respectively 0.918 and 0.893, both were higher than the PI-RADS v2.1 evaluation independently applied, and we found that the differences were statistically significant. The sensitivity of model A was 0.843 and the specificity was 0.829, which showed better diagnostic efficacy compared with the sensitivity and specificity we got when the PI-RADS v2.1 evaluation was independently used (sensitivity 0.767 and specificity 0.801).Conclusion: The Logistic model established by combing PI-RADS v2.1 evaluation with PSA-related indicators showed better diagnostic efficacy in PSA grey area prostate cancer than PI-RADS v2.1 applied independently, in this way unnecessary needle biopsy can be avoided, and would play a significant instructive role in optimizing clinical treatment strategies.
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