Prostate cancer (PCa) is the second most diagnosed cancer in men. Early diagnosis
and right management of PCa is critical to reducing deaths; the life expectancy is
the main factors to be considered in the management of PCa. Among patients who die
from PCa, the incidence of skeletal involvement appears to be >85%. Bone scan (BS)
is the most common method for monitoring bone metastases in patients with PCa. The
extent of bone metastasis was also associated with patient survival until now there
is no clinically useful technique for measuring bone tumors and includes this information
in the risk assessment. An alternative approach is to calculate a BS index (BSI) and
it has shown clinical significance as a prognostic imaging biomarker. Some computer-assisted
diagnosis (CAD) systems have been developed to measure BSI and are now available.
The aim of this study was to investigate automated BSI (aBSI) measurements as predictors'
survival in PCa. Retrospectively cohort studied fifty patients with PCa who had undergone
BS between January 2010 and December 2011 at our institution. All data collected was
updated up to August 2016. CAD system analyzing BS images to automatically compute
BSI measurements. Patients were stratified into three BSI categories BSI value 0,
BSI value ≤1 and BSI value >1. Kaplan–Meier estimates of the survival function and
the log-rank test were used to indicate a significant difference between groups stratified
in accordance with the BSI values. A total of 35 subjects deaths were registered,
with a median survival time 36 months after the follow-up BS of 5 years. Subjects
with low aBSI value had longer overall survival in comparison with the other subjects
(P = 0.004). aBSI measurements were shown to be a strong prognostic survival indicator
in PCa; survival is poor in high-BSI value.
Keywords
Artificial neural networks - bone metastases - bone scan - bone scan index - computer-assisted
diagnosis - prostate cancer - survival analysis
