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Assessment of Primary Solid Renal Mass using Texture Analysis of CT Images of Kidney by Active Contour Method: A Novel Method

机译:主动轮廓法通过肾脏CT图像纹理分析评估原发性固体肾脏肿块

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The overall incidence of the renal masses is on the rise. With better imaging modalities more of these masses are picked up earlier. Most of the times, the diagnosis is confirmed after radical nephrectomy. More often there is an inherent tendency to offer overtreatment in cases of benign renal masses. Renal biopsy to discriminate benign from malignant masses can be very useful in such instances but are more invasive. Grey Level Co-Occurence Matrix (GLCM) is recognized as the most representative radiological parameter to define the heterogeneity of solid renal masses.Aim: To identify certain radiological parameters that might help us to differentiate the benign from malignant renal masses, obviating the need for a biopsy.Materials and Methods: This was a prospective study done over three years from June 2014 to May 2017. A total of 188 patients were included. These patients were equally divided into two broad groups of 94 patients each: Group 1 was patients with renal mass, of which 67 were malignant and 27 were benign. The group 2 was the control group. We used the active contour method to delineate the renal mass and study the features in them. Data analysis for each feature was individually calculated with the help of Sigma Stats 4.0 software and one-way ANOVA analysis.Results: Six CT parameters showed significant data that helped the clinician to differentiate the benign from the malignant renal masses. From the study it was evident that the parameters namely, entropy, energy, sum average, sum variance, inertia and low gray level emphasis were found to be statistically significant which helps the clinician to differentiate the benign from the malignant renal masses.Conclusion: Our data shows that GLCM parameters are crucial tool for the determination of the solid mass composition of tumour. This obviates the need for an invasive procedure like Ultrasound or a CT guided biopsy of the mass.
机译:肾脏肿块的总发病率正在上升。通过更好的成像方式,可以更早地拾取更多的这些肿块。在大多数情况下,根治性肾切除术后可以确诊。在良性肾脏肿块的情况下,内在趋势往往会提供过度治疗。在这种情况下,肾脏活检可区分良性与恶性肿块,可能非常有用,但更具侵入性。灰度共生矩阵(GLCM)被认为是定义实体肾肿块异质性的最具代表性的放射学参数。目标:确定某些放射学参数可能有助于我们区分良性和恶性肾病,从而避免材料和方法:这是一项从2014年6月至2017年5月的三年中进行的前瞻性研究。其中包括188名患者。将这些患者平均分为两组,每组94名患者:第1组为肾脏肿块患者,其中67例为恶性,27例为良性。第2组是对照组。我们使用主动轮廓法来描绘肾脏肿块并研究其中的特征。借助于Stats 4.0软件和单向ANOVA分析,可以分别计算每个特征的数据分析。结果:六个CT参数显示出重要的数据,可帮助临床医生将良性与恶性肾肿块区分开。从研究中可以明显看出,熵,能量,总和,总方差,惯性和低灰度级强调这些参数具有统计学意义,这有助于临床医生将良性与恶性肾脏肿块区分开。结论:我们的数据表明,GLCM参数是确定肿瘤固体成分的关键工具。这消除了对诸如超声或CT引导的肿物活检等侵入性手术的需要。

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