A new computational and biomolecular tool, dubbed CompCyst, that can identify precancerous pancreatic cysts, has been developed by researchers from Johns Hopkins Kimmel Cancer Center. Reliably identifying cancer-causing cysts from those that are not cancer-causing is possible by using the technology.
More than 50 percent of the patients, who go for surgeries for removal of cysts, go through these procedures unnecessarily according to the demonstration given the team’s work, which proved that the probability of these cysts causing cancer is very low. Unfortunately, such surgeries only end up putting the patients under additional risks and worse of all, the rate of elimination of cancerous cysts.
Every year, 800,000 have pancreatic cysts, though out of these a small fraction progress to cancer. Notably, identifying whether or not a cyst is cancerous is tough as there are limitations in respect with clinical and imaging tests available currently. CompCyst is an answer to the problem of almost everyone having to go through long- term and surgical cyst removal just to be sure.
Based on Boolean Set Logic, CompCyst is a classification schemethat utilizes information from molecular tests and imaging data to identify whether a pancreatic cyst may lead to cancer. The system produces data which is compared to histopathology, the gold-standard to identify pancreatic cysts and an invasive method that is not regularly used in clinical practice.
From over 800 different pancreatic cysts’ molecular information was evaluated by the researchers in this study along clinical and imaging data into an algorithm known as MOCA: Multivariate Organization of Combinatorial Alterations. The results show that over physicians classifying whether cysts were cancerous, CompCyst performed better.
20+ years of diverse and extensive experience in higher education including teaching, research, and university and community service in overseas universities and colleges.
Associate Editor, and publications in international refereed journals and presented most of them in international conferences in the fields of Applied Multivariate Statistics, Mortality, Social Science, Economics.