目的:使用来自不使用造影剂的双能CT(DECT)的定量数据,创建用于区分良性和恶性肝脏病变的预测模型(PM)。
方法:这项回顾性研究包括接受DECT的肝脏病变患者,包括非对比增强扫描。良性病变包括肝血管瘤,而恶性病变包括肝细胞癌,转移性肝癌,和肝内胆管细胞癌。将患者分为推导组和验证组。在派生组中,两个放射科医生使用单变量和多变量逻辑回归计算了十个多参数数据以生成PM。在验证组中,另外两名放射科医生测量了参数以评估PMs的诊断性能。
结果:该研究包括121名连续患者(平均年龄67.4±13.8岁,80男性),衍生组97(良性25例,恶性72例),验证组24例(良性7例,恶性17例)。过采样将良性病变样本增加到75,等于恶性组的PM。所有参数在单变量分析中都有统计学意义(所有p<0.05),导致在多变量分析中创建了五个PM。两个观察者的五个PM的曲线下面积如下:PM1(斜率K,血)=0.76,0.74;PM2(斜率K,脂肪)=0.55,0.51;PM3(有效Z差,血)=0.75,0.72;PM4(斜率K,血,脂肪)=0.82,0.78;和PM5(斜率K,有效-Z差,血)=0.90,0.87。PM5产生了最好的诊断性能。
结论:多参数非对比增强DECT是区分肝脏病变的一种非常有效的方法。
结论:使用非对比增强DECT对于区分良性和恶性肝脏病变非常有用。这种方法使医生能够计划更好的治疗策略,缓解与对比过敏相关的担忧,造影剂肾病,辐射暴露,以及过高的医疗费用。
结论:用非增强CT区分良性和恶性肝脏病变是可取的。这个模型,结合坡度K,有效Z,和血液定量,区分良性和恶性肝脏病变。非对比增强DECT有好处,特别是对碘过敏的患者,肾功能衰竭,或者哮喘.
OBJECTIVE: To create prediction models (PMs) for distinguishing between benign and malignant liver lesions using quantitative data from dual-energy CT (DECT) without contrast agents.
METHODS: This retrospective study included patients with liver lesions who underwent DECT, including non-contrast-enhanced scans. Benign lesions included hepatic hemangioma, whereas malignant lesions included hepatocellular carcinoma, metastatic liver cancer, and intrahepatic cholangiocellular carcinoma. Patients were divided into derivation and validation groups. In the derivation group, two radiologists calculated ten multiparametric data using univariate and multivariate logistic regression to generate PMs. In the validation group, two additional radiologists measured the parameters to assess the diagnostic performance of PMs.
RESULTS: The study included 121 consecutive patients (mean age 67.4 ± 13.8 years, 80 males), with 97 in the derivation group (25 benign and 72 malignant) and 24 in the validation group (7 benign and 17 malignant). Oversampling increased the benign lesion sample to 75, equalizing the malignant group for building PMs. All parameters were statistically significant in univariate analysis (all p < 0.05), leading to the creation of five PMs in multivariate analysis. The area under the curve for the five PMs of two observers was as follows: PM1 (slope K, blood) = 0.76, 0.74; PM2 (slope K, fat) = 0.55, 0.51; PM3 (effective-Z difference, blood) = 0.75, 0.72; PM4 (slope K, blood, fat) = 0.82, 0.78; and PM5 (slope K, effective-Z difference, blood) = 0.90, 0.87. PM5 yielded the best diagnostic performance.
CONCLUSIONS: Multiparametric non-contrast-enhanced DECT is a highly effective method for distinguishing between liver lesions.
CONCLUSIONS: The utilization of non-contrast-enhanced DECT is extremely useful for distinguishing between benign and malignant liver lesions. This approach enables physicians to plan better treatment strategies, alleviating concerns associated with contrast allergy, contrast-induced nephropathy, radiation exposure, and excessive medical expenses.
CONCLUSIONS: Distinguishing benign from malignant liver lesions with non-contrast-enhanced CT would be desirable. This model, incorporating slope K, effective Z, and blood quantification, distinguished benign from malignant liver lesions. Non-contrast-enhanced DECT has benefits, particularly in patients with an iodine allergy, renal failure, or asthma.