Ota Takashi, Onishi Hiromitsu, Fukui Hideyuki, Tsuboyama Takahiro, Nakamoto Atsushi, Honda Toru, Matsumoto Shohei, Tatsumi Mitsuaki, Tomiyama Noriyuki
Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.
Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Osaka, Japan.
Eur Radiol. 2025 Mar;35(3):1361-1377. doi: 10.1007/s00330-024-11024-8. Epub 2024 Aug 26.
To create prediction models (PMs) for distinguishing between benign and malignant liver lesions using quantitative data from dual-energy CT (DECT) without contrast agents.
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.
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.
Multiparametric non-contrast-enhanced DECT is a highly effective method for distinguishing between liver lesions.
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.
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.
利用双能CT（DECT）的定量数据在无造影剂的情况下创建区分肝脏良性和恶性病变的预测模型（PMs）。
这项回顾性研究纳入了接受DECT检查（包括非增强扫描）的肝脏病变患者。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。患者被分为推导组和验证组。在推导组中，两名放射科医生使用单变量和多变量逻辑回归计算十个多参数数据以生成PMs。在验证组中，另外两名放射科医生测量参数以评估PMs的诊断性能。
该研究纳入了121例连续患者（平均年龄67.4±13.8岁，男性80例），其中推导组97例（25例良性和72例恶性），验证组24例（7例良性和17例恶性）。过采样将良性病变样本增加到75例，使恶性组在构建PMs时达到平衡。所有参数在单变量分析中均具有统计学意义（所有p<0.05），在多变量分析中生成了五个PMs。两名观察者的五个PMs的曲线下面积如下：PM1（斜率K，血液）=0.76，0.74；PM2（斜率K，脂肪）=0.55，0.51；PM3（有效Z差值，血液）=0.75，0.72；PM4（斜率K，血液，脂肪）=0.82，0.78；PM5（斜率K，有效Z差值，血液）=0.90，0.87。PM5具有最佳的诊断性能。
多参数非增强DECT是区分肝脏病变的高效方法。
非增强DECT的应用对于区分肝脏良性和恶性病变极为有用。这种方法使医生能够制定更好的治疗策略，减轻与造影剂过敏、造影剂肾病、辐射暴露和医疗费用过高相关的担忧。
使用非增强CT区分肝脏良性和恶性病变是可取的。该模型结合斜率K、有效Z和血液定量，区分了肝脏良性和恶性病变。非增强DECT有诸多益处，尤其对于碘过敏、肾衰竭或哮喘患者。
