Nuclear Morphometry is a superior Prognostic Predictor in comparison to Histological grading in Renal cell Carcinoma: A Retrospective Clinico-pathological study

Abstract

AbstractBackgroundRenal cell carcinoma (RCC) comprises of a spectrum of clinico-pathologically distinct entities thereby making it difficult to accurately predict the clinical outcome. Though many predictive factors have been described in literature, tumor stage and nuclear grade have been established to consistently correlate with the tumor behaviour. However, tumors in the same stage have shown to behave differently. Similarly subjectivity and lack of reproducibility in nuclear grade mandates use of more objective parameters such as digital nuclear morphometry which could provide consistent and more reliable results in predicting prognosis. The study was conducted with the main objective of comparing the histological grade and the nuclear morphometric variables in RCC for predicting the clinical outcome.Material and methodsA total of 219 cases of renal tumors in adults were retrieved retrospectively from the archives of pathology department in Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow and their clinical, gross and microscopic features were noted. Nuclear grading was done in 181 cases of clear cell and papillary RCC of which computer-assisted morphometry for various nuclear parameters was done in 100 cases where a follow-up data of at least 3 years was available. Nuclear grade and morphometric parameters were correlated statistically with the clinical outcome of the patients.ResultsHistological nuclear grade did not show statistically significant correlation with progression free survival (PFS). Higher values of mean nuclear area, mean nuclear circumference, mean nuclear major diameter and mean nuclear minor diameter were significant predictors of PFS with a strong inverse correlation.ConclusionNuclear morphometry is a more reliable predictor of clinical outcome in patients of RCC when compared to histological grade and should be included in predictive model with other clinical and pathological parameters to accurately determine tumor behaviour.