Clinical Analysis of Patients with Small Cell Prostate Carcinoma

Abstract

The aim of this work was to investigate the prognostic value of the small cell prostate carcinoma. The data of patients with prostate carcinoma were retrospectively analyzed in this study, including the immunohistochemical methods, PSA level, Gleason score and the patients survival time. The tumor specimens were stained by neuron specific enolase(NSE), synaptophysin (SYN), chromogranin A (CgA) and NED (Neuroendocrine differentiation). Small cell carcinoma of prostate was detected in 5 cases by biopsy or autopsy. Metabasis was found in 4 patients when the small cell prostate carcinoma was diagnosed. One patient died post-diagnosis of 1 month, one patient died in 6 months, while the other 3 patients died in 3 years. The 5 year survival rate of patients with the small cell prostate carcinoma was significantly lower than that of the other prostate carcinoma patients. The expression of NED has positive correlation to Gleason score. NED was related to the prostate carcinoma patients' surviva1. The results indicated that the combination detection of NED markers and Gleason score could provide useful information of prognosis for the patients with small cell prostate carcinoma. The progression to small cell prostate carcinoma indicated a poor prognosis and slight changes in the serum prostate-specific antigen level. Early diagnosis and early treatment might improve the prognosis of the patients diagnosed as small cell prostate carcinoma.

Modeling Depression Data: Feed Forward Neural Network vs. Radial Basis Function Neural Network

“…The usage of Artificial Intelligence in Medicine is a relatively new concept and highly relevant to the modern requirements of diagnosis [1][2][3][4][5][6][7]. In cases such as depression diagnosis where the uncertainty plays a major role, an intelligent algorithm can provide high levels of accuracy than the traditional methods of diagnosis.…”