Application of histogram analysis in the differential diagnosis of renal solid lesions

Renal cell carcinoma (RCC) is the most common type of kidney cancer in adults, accounting for approximately 85% of kidney malignancies. Early diagnosis and accurate characterization are critical for disease prognosis, treatment planning, and the prevention of unnecessary interventions. In this context, radiological imaging techniques remain essential tools for tumor detection, staging, and follow-up. However, advanced methods such as histogram analysis, which provides more detailed and objective analysis of imaging data, are gaining attention for their ability to quantitatively assess tumor heterogeneity. In this retrospective study, a total of 304 renal masses were evaluated, with 33.88% being benign and 66.12% malignant. The most common benign lesion was angiomyolipoma (AML) (19.74%), followed by oncocytoma (10.20%) and fat-poor AML (3.95%). Among the malignant group, clear cell RCC (32.89%) was the most frequently encountered tumor type, while chromophobe RCC (15.79%), urothelial carcinoma (9.87%), and papillary cell RCC (7.57%) were detected at lower rates. All cases included in the study had computed tomography (CT) images; however, patients without pathological diagnosis or suitable images were excluded. In most AML cases, typical radiological features in CT and magnetic resonance ımaging (MRI) were accepted diagnostically instead of histopathological diagnosis. In the analysis, non-contrast CT images were not considered, and only the most prominent slices obtained in the axial plane during the portal venous phase containing the solid component of the mass were evaluated. Areas of necrosis, cysts, calcifications, and large vascular structures were excluded from the measurement area. For lesions containing fat, measurements were taken from the fat-free regions whenever possible. Histogram analyses were performed using MATLAB 2009b software, and the mean, median, skewness, kurtosis, and uniformity parameters were calculated for each lesion. The sizes of renal masses ranged from 3 to 10 cm. According to the analysis results, these histogram parameters showed significant statistical differences between benign and malignant renal masses. In the Receiver Operating Characteristic (ROC) curve analysis, particularly the mean, skewness, and uniformity parameters provided moderate sensitivity and specificity in distinguishing benign and malignant lesions. The findings suggest that histogram analysis could be a useful method for the non-invasive, objective, and reproducible evaluation of renal masses. This method may contribute to accurate diagnosis, the determination of appropriate treatment and follow-up strategies, potentially preventing unnecessary surgical interventions and reducing patient morbidity and mortality. It could also help reduce the economic burden on the healthcare system. While this study provides important insights into the integration of histogram analysis into routine clinical practice, it is anticipated that future advanced studies, supported by technologies such as machine learning with larger patient cohorts, may enhance the specificity and sensitivity of this method. Keywords: Renal cell carcinoma, Angiomyolipoma, Histogram analysis, Computed tomography, Tumor heterogeneity, Quantitative imaging, Radiomic evaluation