Recent Developments in Tissue-Type Imaging (TTI) for Planning and Monitoring Treatment of Prostate Cancer

Abstract

Because current methods of imaging prostate cancer are inadequate, biopsies cannot be effectively guided and treatment cannot be effectively planned and targeted. Therefore, our research is aimed at ultrasonically characterizing cancerous prostate tissue so that we can image it more effectively and thereby provide improved means of detecting, treating and monitoring prostate cancer. We base our characterization methods on spectrum analysis of radiofrequency (rf) echo signals combined with clinical variables such as prostate-specific antigen (PSA). Tissue typing using these parameters is performed by artificial neural networks. We employed and evaluated different approaches to data partitioning into training, validation, and test sets and different neural network configuration options. In this manner, we sought to determine what neural network configuration is optimal for these data and also to assess possible bias that might exist due to correlations among different data entries among the data for a given patient. The classification efficacy of each neural network configuration and data-partitioning method was measured using relative-operating-characteristic (ROC) methods. Neural network classification based on spectral parameters combined with clinical data generally produced ROC-curve areas of 0.80 compared to curve areas of 0.64 for conventional transrectal ultrasound imaging combined with clinical data. We then used the optimal neural network configuration to generate lookup tables that translate local spectral parameter values and global clinical-variable values into pixel values in tissue-type images (TTIs). TTIs continue to show cancerous regions successfully, and may prove to be particularly useful clinically in combination with other ultrasonic and nonultrasonic methods, e.g., magnetic-resonance spectroscopy.

Quantitative Ultrasound Characterization of Responses to Radiotherapy in Cancer Mouse Models

“…QUS estimates describe the statistical properties of tissue structures or cell samples from a well-defined region of interest (ROI). QUS methods have been used to diagnose prostate cancer, ocular tumors, liver and cardiac abnormalities (15 -18); differentiate benign fibroadenomas from mammary carcinomas and sarcomas (19); and have provided good diagnostic accuracy in prostate cancer detection and lesion localization (20). QUS methods, specifically spectral parameters, can be used to detect structural alterations induced by ultrasonically induced hyperthermia in tumor xenografts (21), discern between different types of ocular tumors (22,23), and classify these depending on their lethality potential (16,24).…”

Improved visualization of high-intensity focused ultrasound lesions

“…This general methodology has been applied by our research group for characterization of ocular tumors (Coleman et al 2004;Liu et al 2004;Silverman et al 2003) and prostate cancer (Feleppa et al 2002;Feleppa et al 2004).…”

Clinical and imaging tools in the early diagnosis of prostate cancer, a review

“…In patients with elevated PSA or an abnormal digital rectal examination (DRE), however, it can be used for initial morphologic assessment of the prostate and seminal vesicles, to measure the prostatic volume (for calculation of PSA density), or for biopsy guidance (1,4,18,19). In an effort to improve the diagnostic accuracy of transrectal ultrasound, a number of ancillary techniques have echo-signal spectrum before it is converted into an image (19,21,22).…”