The morphological changes advocating for peritoneal carcinomatosis are inconsistent and may be visible only in later stages of the disease. However, malignant ascites represents an early sign, and this fluid exhibits specific histological characteristics. This study aimed to quantify the fluid properties on computed tomography (CT) images of intraperitoneal effusions through texture analysis and evaluate its utility in differentiating benign and malignant collections. Fifty-two patients with histologically proven benign (n=29) and malignant (n=23) intraperitoneal effusions who underwent CT examinations were retrospectively included. Texture analysis of the fluid component was performed on the non-enhanced phase of each examination using dedicated software. Fisher and the probability of classification error and average correlation coefficients were used to select two sets of ten texture features, whose ability to distinguish between the two types of collections were tested using a k-nearest-neighbor classifier. Also, each of the selected feature's diagnostic power was assessed using univariate and receiver operating characteristics analysis with the calculation of the area under the curve. The k-nearest-neighbor classifier was able to distinguish between the two entities with 71.15% accuracy, 73.91% sensitivity, and 68.97% specificity. The highest-ranked texture parameter was Inverse Difference Moment (p=0.0023; area under the curve=0.748), based on which malignant collections could be diagnosed with 95.65% sensitivity and 44.83% specificity. Although successful, the texture assessment of benign and malignant collections most likely does not reflect the cytological differences between the two groups.

中文翻译：

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计算机断层扫描在腹腔积液诊断中的作用：质地分析的作用。

主张腹膜癌病的形态学变化是不一致的，可能仅在疾病晚期可见。然而，恶性腹水是一种早期征兆，这种液体表现出特定的组织学特征。本研究旨在通过纹理分析量化腹腔积液的计算机断层扫描 (CT) 图像上的流体特性，并评估其在区分良恶性集合中的效用。回顾性纳入了 52 名经组织学证实为良性 (n=29) 和恶性 (n=23) 腹腔积液且接受 CT 检查的患者。使用专用软件对每次检查的非增强相进行流体成分的质地分析。Fisher 和分类错误概率和平均相关系数用于选择两组 10 个纹理特征，使用 k-最近邻分类器测试其区分两种类型集合的能力。此外，使用单变量和接收器操作特征分析并计算曲线下面积来评估每个选定特征的诊断能力。k-最近邻分类器能够以 71.15% 的准确度、73.91% 的灵敏度和 68.97% 的特异性区分两个实体。排名最高的纹理参数是逆差异矩（p=0.0023；曲线下面积=0.748），基于该参数可以以 95.65% 的灵敏度和 44.83% 的特异性诊断恶性集合。虽然成功，