Performance of Hybrid Radial Basis Function network: Adaptive Fuzzy K-Means versus Moving k-Means Clustering as centre positioning algorithms on cervical cell precancerous stage classification
Deaths from uterine cervical cancer have been dramatically reduced since the development in the 1940s of the Papanicolaou smear (also called the Pap smear or test) for detection of precursor lesions. Along with the vaccines for smallpox, polio, and other viruses, this screening test has been hailed as one of the most successful public health measures and is the only cancer screening test proved to reduce mortality (1). Since its first availability for patient care, the categorization of the Pap smear as a clinical laboratory test, with no allowance for difficult and time-consuming microscopy, contributed to the disparity between actual cost and reimbursement by insurance companies. Even now, the U.S. Government’s Health Care Financing Administration continues to price the Pap smear at the same low reimbursement level as highly automated tests, despite expert testimony against such equivalence. As a result of this pricing, many laboratories are being forced to discontinue performing Pap tests; therefore, more and more Pap smears are being sent to larger reference laboratories for analysis. A number of deaths from cervical cancer following misreadings of smears (2,3) has led to a near epidemic of legal actions based on perceived errors in Pap test diagnoses (4–7). Publicity given to these tragedies presents an impression that errors in diagnoses should be a rare event, which has reinforced a public misconception that the cervical cancer screening test is errorfree. Thus, decision-makers at pathology laboratories face unrealistic expectations from a litigation-prone public, along with other critical economic questions, but they have no certain solution to their dilemma (8). Understandably, many cytopathologists are questioning the wisdom of continuing to interpret Pap smears. Automation is one frequently proposed answer to the question of how to save this endangered screening test. To date, several automated systems have been designed to prevent the most common causes of errors encountered during processing and interpretation of cellular samples. This commentary will describe the current status of the new technologies and will examine their potential as cost-effective solutions. Readers should be aware that much of the available performance data cited here are results from manufacturersponsored trials.