Application of Lightweight Deep Learning Model-Based Shear Wave Elastic Imaging with Abdominal Probe to Diagnose Prostate Cancer: A Biomedical Approach

Abstract

We aimed to investigate the diagnostic value of lightweight convolutional neural network (CNN) model abdominal probe shear wave elastic imaging (SWE) in the perineal diagnosis and guided puncture biopsy of prostate cancer (PCa), and to provide reference for the clinical diagnosis of PCa. 100 PCa patients were assigned to group I (malignant) and group II (benign), with 50 cases in each. Ultrasonic elastic imaging based on lightweight convolutional neural network denoising model was adopted for detection. In both systolic and diastolic blood pressure (SBP/DBP), there was not a significant intergroup difference (P > 0.05). The levels of prostate specific antigen (PSA) and its free variant (fPSA) in group II were markedly lower (P < 0.05). Patients in group II had obviously more cystic components and fewer solid components. Patients with hyperechogenicity was more in group II. Patients had clearly fewer irregular margins and outward margin spread in group II. Patients without focal hyperechogenicity and punctate hyperechogenicity was more in group II, and the number of calcifications in group II was less. Patients with type 0 and type I was more and patients with type IIa and type IIb was less in group II. The Emean level of patients in group II was clearly higher, and the Emax level and Esd level of patients in group II were clearly lower. The SI level of patients was clearly lower in group II TTP was higher in group II (P < 0.05). Multivariate logistic regression analysis of abdominal probe SWE for transperineal diagnosis of PCa and guided puncture biopsy showed that internal echoes had the greatest OR and were associated with the occurrence of PCa. Ultrasonic elastic imaging index based on the lightweight convolutional neural network denoising model can be used for the benign and malignant diagnosis of PCa patients.