Relationships of the internodal distance of biological tissue with its sound velocity and attenuation at high frequency in doublet mechanics

In view of the discrete characteristics of biological tissue, doublet mechanics has demonstrated its advantages in the mathematic description of tissue in terms of high frequency （〉 10 MHz） ultrasound. In this paper, we take human breast biopsies as an example to study the influence of the internodal distance, a microscope parameter in biological tissue in doublet mechanics, on the sound velocity and attenuation by numerical simulation. The internodal distance causes the sound velocity and attenuation in biological tissue to change with the increase of frequency. The magnitude of such a change in pathological tissue is distinctly different from that in normal tissue, which can be used to differentiate pathological tissue from normal tissue and can depict the diseased tissue structure by obtaining the sound and attenuation distribution in the sample at high ultrasound frequency. A comparison of sensitivity between the doublet model and conventional continuum model is made, indicating that this is a new method of characterizing ultrasound tissue and diagnosing diseases.

Prostate cancer prediction forest algorithm that takes using the random into account transrectal ultrasound findings, age, and serum levels of prostate-specific antigen

The aim of this study is to evaluate the ability of the random forest algorithm that combines data on transrectal ultrasound findings, age, and serum levels of prostate-specific antigen to predict prostate carcinoma. Clinico-demographic data were analyzed for 941 patients with prostate diseases treated at our hospital, including age, serum prostate-specific antigen levels, transrectal ultrasound findings, and pathology diagnosis based on ultrasound-guided needle biopsy of the prostate. These data were compared between patients with and without prostate cancer using the Chi-square test, and then entered into the random forest model to predict diagnosis. Patients with and without prostate cancer differed significantly in age and serum prostate-specific antigen levels （P 〈 0.001）, as well as in all transrectal ultrasound characteristics （P 〈 0.05） except uneven echo （P = 0.609）. The random forest model based on age, prostate-specific antigen and ultrasound predicted prostate cancer with an accuracy of 83.10%, sensitivity of 65.64%, and specificity of 93.83%. Positive predictive value was 86.72%, and negative predictive value was 81.64%. By integrating age, prostate-specific antigen levels and transrectal ultrasound findings, the random forest algorithm shows better diagnostic performance for prostate cancer than either diagnostic indicator on its own. This algorithm may help improve diagnosis of the disease by identifying patients at high risk for biopsy.