Estimation of disease severity in the NHS cervical screening programme. Part I: artificial cut-off points and semi-quantitative solutions

Abstract

Objective: Current cytology and histology classifications are based on ordered categories and have a strong emphasis on providing information that decides a woman’s management rather than the best estimate of disease severity. This two-part paper explores the use of a quantitative approach to both cytology and histology disease severity measurements. Methods: In Part I the problem of artificial cut-off points is discussed and a simple semi-quantitative solution to the problem is proposed. This closely relates to the revised British Society for Clinical Cytology (BSCC) terminology. The estimates of disease severity are designed as extensions of the existing methods, with an emphasis on probability rather than certainty, as a more natural way of approaching the problem. Borderline changes are treated as categorical variables, but koilocytosis, mild, moderate and severe dyskaryosis, and ?invasive as quasi-continuous and the disease severity estimated as a grade number (GN) with any value between 0-4 and the margin of error as a calculated grade range (CGR). Results: As an example, if the reader is unsure between moderate dyskaryosis (HSIL favouring CIN2) and mild dyskaryosis (LSIL favouring CIN1) they can register this uncertainty as a probability, such as 60%/40% moderate/mild. With 2 and 1 as the mid-points of the grade numbers for moderate and mild dyskaryosis the GN value is ((60 x 2) + (40 x 1))/100 = 1.6. The CGR is 1.5 - 0.4 to 1.5 + 0.6 = 1.1 to 2.1. The GN (CGR) estimate of disease severity is therefore 1.6 (1.1-2.1). In a similar manner the disease severity from all slides showing koilocytosis or dyskaryosis can be estimated as a number between 0 and 4 with an associated error. Histology can be treated in a similar way. Conclusions: This semi-quantitative approach provides a framework more suitable for research and audit of disease severity estimates. It avoids the paradox inherent in the current systems using artificial cut-points to produce categories whereby increasing agreement can only be achieved by losing information.