Minimum Variance Algorithm-Based Correlation Analysis between Body Mass Index and the Malignant Degree of Prostate Cancer Mediated under Ultrasound Images

Abstract

This study was to explore the correlation between the malignant degree of prostate cancer (PCa) and body mass index (BMI) mediated by ultrasound images under multioperator algorithm (MOA) based on minimum variance (MV) algorithm. MOA was established by optimizing the smoothing technique and diagonal loading algorithms of MV, and its quality and processing speed of ultrasound images were compared with other algorithms. Ninety two patients were selected as the subjects investigated, who had transrectal prostate biopsy mediated by ultrasound to be diagnosed as PCa in the hospital. Based on Gleason score and prostate specific antigen (PSA) value, all patients were divided into a high-risk PCa group (a high-risk group) and a non-high-risk PCa group (a non-high-risk group). The proportion of obese patients in the two groups was compared. The logistic regression analysis method was applied to analyze related factors of PCa development, and Pearson correlation was for analyzing the correlation between Gleason score and BMI of patients. The results showed that the number of patients in the high-risk group was greater than that of the non-high-risk group (P < 0.05), while the proportion of nonobese patients in the non-high-risk group was markedly higher than that of the higher-risk group (P < 0.01). Both PSA and BMI were obviously correlated with the development of high-risk PCa (P < 0.05), and there was an extreme positive correlation between BMI and Gleason score (r = 0.661 and P = 0.007). It indicated that MOA was established based on conventional MV, which could increase the ultrasonic image quality and calculation speed. Besides, BMI was a risk factor of high-risk PCa and was positively correlated with malignant degree of PCa, which provided a referable evidence for clinical evaluation of malignant degree of PCa.