Clinical study of multifactorial diagnosis in prostate biopsy

Abstract

AbstractPurposeTo study the feasibility of using an artificial intelligence (AI) algorithm for the diagnosis of clinically significant prostate cancer (csPCa) on multiparametric MRI (mpMRI) in combination with conventional clinical information.MethodsA retrospective study cohort with 505 patients was collected, with complete information on age (≤60, 60–80, and >80 years), PSA (≤4, 4–10, and >10 ng/dL), and pathology results. The patients with ISUP group >2 were classified as csPCa, and the patients with ISUP = 1 or no evidence of prostate cancer were classified as non‐csPCa. The diagnosis of mpMRI was made by experienced radiologists following the prostate imaging reporting and data system (PIRADS ≤ 2, PIRADS = 3, and PIRADS > 3). The mpMRI images were processed by a homemade AI algorithm, and the AI results were obtained as positive or negative for csPCa. Two logistic regression models were fitted, with pathological findings as the dependent variable, that is, a conventional model and an AI model. The conventional model used age, PSA, and PIRADS as the independent variables. The AI model took the AI result and the abovementioned clinical information as the independent variables. The predicted probability of the patients from the conventional model and the AI model were used to test the prediction efficacy of the models. The DeLong test was performed to compare differences in the area under the receiver operating characteristic (ROC) area under the curve (AUC) between the conventional model and the AI model.ResultsIn total, 505 patients were included in the study; 280 were diagnosed with csPCa, and 225 were non‐csPCa. The median age was 72.0 (67.0, 76.0) years, with a median PSA value of 13.0 (7.46, 27.5) ng/dL. Statically significant differences were found in age, PSA, PIRADS score and AI results between the csPCa and non‐csPCa groups (all p < 0.001). In the multivariable regression models, all the variables were independently associated with csPCa. The conventional model (R2 = 0.361) and the AI model (R2 = 0.474) were compared with analysis of variance (ANOVA) and showed statistically significant differences (χ2 = 63.695, p < 0.001). The AUC of the ROC curve for the conventional model was 0.782 (95% confidence interval [CI]: 0.742–0.823), which was less than the AUC of the AI model with statistical significance (0.849 [95% CI: 0.815–0.883], p < 0.001).ConclusionIn combination with routine clinical information, such as age, PSA, and PIRADS category, adding information from the AI algorithm based on mpMRI could improve the diagnosis of csPCa.