Building a diagnosis and prediction model for prostate cancer based on multimodal data / 中国医师杂志

ABSTRACT

Objective:To explore the diagnostic value of clinical, multi-parameter magnetic resonance imaging (MP-MRI) combined with transrectal ultrasound elasticity data for prostate cancer.Methods:A retrospective analysis was conducted on patient data from November 2021 to March 2023 when transrectal prostate two-dimensional ultrasound, real-time strain elastography of the prostate, MP-MRI examination of the prostate, and prostate biopsy were performed simultaneously at the Meizhou People′s Hospital. We collected patient age, height, weight, free serum prostate specific antigen (fPSA), total prostate specific antigen (tPSA), fPSA/tPSA, MRI prostate imaging report and data system (PI-RADS) scores, and ultrasound elasticity values. Four predictive models for prostate cancer diagnosis were constructed using multivariate logistic regression for comparison, and the optimal model was selected to construct a column chart. The diagnostic performance of different models was evaluated using receiver operating characteristic (ROC) curves, and the diagnostic performance of column charts was evaluated using calibration curves.Results:This study included a total of 117 patients with 117 prostate lesions, 47 benign prostate lesions, and 70 prostate cancer lesions. There were statistically significant differences in age, fPSA, tPSA, fPSA/tPSA, PI-RADS scores, and ultrasound elasticity values between benign and malignant lesions patients (all P<0.01). The area under the curve (AUC) of the clinical model (age+ tPSA+ fPSA+ fPSA/tPSA), MRI model (PI-RADS score), ultrasound elastic model, and clinical+ MRI+ ultrasound elastic combined model for diagnosing prostate cancer were 0.86, 0.86, 0.92, and 0.98, respectively. Conclusions:Compared with a single diagnostic model, the combination of age, tPSA, fPSA/tPSA, PI-RADS scores, and ultrasound elasticity value model can improve the diagnostic rate of prostate cancer.