Morphometric analysis of nuclear symmetry in urothelial carcinoma for predicting tumor recurrence. Issue 11 (30th April 2021) - Klorin, Geula; Halachmi, Sarel; Nativ, Ofer; Massalha, Yamen; Stroller, Leah; Amit, Amnon; Sabo, Edmond
Abstract: Urothelial carcinoma is the ninth most common cancer in the world. Cytological analysis of the urine is used for screening, as well as for cases suspected for neoplasia of the urinary tract. However, the sensitivity of urine cytology examination is low. The golden standard for diagnosing bladder cancer relies upon cystoscopy followed by a biopsy, which is microscopically assessed by the pathologist. Treatment decisions are based on the histological grade and stage of the tumor. Posttreatment tumor recurrence is 50%. The purpose of this study is to predict recurrence of urothelial carcinoma using a novel morphometric method of nuclear symmetry analysis. This method may help tailor the appropriate treatment and may reduce the need of invasive surgical procedures in patients. Computerized morphometry was applied to develop multiple symmetry indices of the nuclei of the tumor cells as follows: each nucleus was physically divided along its digital axis in two segments that were separately analyzed for their shape, size, optical density, and texture. Subsequently, ratios were obtained by mathematically dividing between the morphometric values of the two nuclear segments where the denominator contained the largest value of the two. These ratios were named symmetry indices and were included as variables to predict the recurrence time of the tumors. The change in the symmetry indices (loss of symmetry) of the nuclear roundness, fractal dimension and margination were the only independent predictors of recurrence time. Computerized morphometry of nuclear symmetry indices may help to predict tumor recurrence in urothelial carcinomas. Abstract : Computerized morphometry was used to develop symmetry indices to predict recurrence time of urothelial tumors. Fractal dimension symmetry changes independently predicted recurrence time. This may allow clinicians to personalize and reduce urothelial carcinoma treatments.