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Updated: Apr 14, 2026

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors
Published on: March 29, 2019
Role of morphometry and proliferative parameters in grading of urothelial neoplasms
Monika Sangwan1, Sunita Singh1, Santosh Kumar1
1Pt.B.D. Sharma Institute of Medical Sciences, Rohtak, Haryana, India.
Introduction:
Mean nuclear area of 10 nuclei (MNA-10), mitotic activity index (MAI) and Ki-67 are highly reproducible and can be routinely used as adjuncts to histopathological grading in classifying tumors. Assays of these biomarkers are non-invasive, rapid, easy to perform, more objective and accurate, with high sensitivity and specificity, and correlate well with tumor grade.
Material And Methods:
This study was conducted at the Department of Pathology PGIMS, Rohtak on 50 cases, of which 25 cases were high-grade, 15 low-grade, 6 Papillary Urothelial Neoplasm of Low Malignant Potentialand 4 reactive lesions as per the 2004 ISUP/WHO classification. MNA-10, MAI and Ki-67 immunoquantitation were performed on stained sections.
Results:
The age of the patients varied from 35 to 87 years. Male: female ratio was 3.5:1. The mean MNA-10 (μm(2)) for High Grade Malignant Potential was 104.52 ±25.64 μm(2), which was significantly higher than in PUNLMP (47.64 ±10.23) and LMP (51.57 ±15.66). MAI (/10 HPF) showed an increasing trend from reactive lesions to HMP, with a mean of (3 ±1.16)/10 HPF to (21.36 ±5.31)/10 HPF respectively. Ki-67 labelling index, a proliferative marker, revealed increasing trend lowest with reactive lesions (10 ±2.83%) and highest in high grade tumors (65.96 ±14.44). Spearman's correlation showed maximum correlation between MAI and Ki-67 and the increasing grade of tumor.
Conclusions:
MNA-10 in combination with Ki-67 and MAI was found to be stronger than MNA-10 alone. MAI has high reproducibility in differentiating low and high grade, with simple assessment in paraffin embedded sections allowing adequate histopathological analysis and visualization of proliferating cells simultaneously. This multivariate grading model should be applied in routine grading to overcome interobserver variability and to increase reproducibility of grading.
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