Classification Between Different Malignancy Levels of Prostate Cancer Using Ultrasound RF Time Series

Prostate cancer is one of the most common cancers diagnosed in North American men, exceeding lung cancer. The objective of this work is to evaluate the performance of RF time series on differentiating between low grade and high grade of prostate cancer. The detection of cancer is important in the diagnosis stages of prostate cancer. However, given the high life expectancy nowadays, an invasive surgery should be avoided as long as it is not necessary. Detection of the grade of the cancer or differentiation between low and high grade of cancer can help physician for this purpose. It can prevent the patient from undergoing an invasive surgery if the cancer is not high grade, which can also benefit the patient economically. Given the proven ability of RF time series in differentiating between cancerous and normal prostate tissue, here we have used this tool on characterizing multiple grades of cancer in prostate. A database of ex vivo ultrasound images of prostate tissue has been collected from 25 patients who underwent prostatectomy. Spectral based features of RF time series have been used to highlight the high grade of prostate cancer in areas as small as 1 mm x 1 mm. Gleason Score (GS) system has been used as a prognostic factor in determining the aggressiveness of prostate cancer. Cancerous areas with GS of 7 and above with primary Gleason Grade of 4 or 5 have been considered to be high grade. Using a Support Vector Machine (SVM), a classification accuracy of 81 % and an area under ROC curve of 0.88 has been achieved on a leave-one-patient out cross validation criteria. The promising results confirm the ability of RF time series on classification between multiple malignancy levels of prostate cancer.