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Abstract：
BACKGROUND: To explore the potential of different quantitative dynamic contrast-enhanced (qDCE)-MRI tracer kinetic (TK) models and qDCE parameters in discriminating benign from malignant soft tissue tumors (STTs).
METHODS: This research included 92 patients (41females, 51 males; age range 16-86 years, mean age 51.24 years) with STTs. The qDCE parameters (Ktrans, Kep, Ve, Vp, F, PS, MTT and E) for regions of interest of STTs were estimated by using the following TK models: Tofts (TOFTS), Extended Tofts (EXTOFTS), adiabatic tissue homogeneity (ATH), conventional compartmental (CC), and distributed parameter (DP). We established a comprehensive model combining the morphologic features, time-signal intensity curve shape, and optimal qDCE parameters. The capacities to identify benign and malignant STTs was evaluated using the area under the curve (AUC), degree of accuracy, and the analysis of the decision curve.
RESULTS: TOFTS-Ktrans, EXTOFTS-Ktrans, EXTOFTS-Vp, CC-Vp and DP-Vp demonstrated good diagnostic performance among the qDCE parameters. Compared with the other TK models, the DP model has a higher AUC and a greater level of accuracy. The comprehensive model (AUC, 0.936, 0.884-0.988) demonstrated superiority in discriminating benign and malignant STTs, outperforming the qDCE models (AUC, 0.899-0.915) and the traditional imaging model (AUC, 0.802, 0.712-0.891) alone.
CONCLUSIONS: Various TK models successfully distinguish benign from malignant STTs. The comprehensive model is a noninvasive approach incorporating morphological imaging aspects and qDCE parameters, and shows significant potential for further development.
METHODS: This research included 92 patients (41females, 51 males; age range 16-86 years, mean age 51.24 years) with STTs. The qDCE parameters (Ktrans, Kep, Ve, Vp, F, PS, MTT and E) for regions of interest of STTs were estimated by using the following TK models: Tofts (TOFTS), Extended Tofts (EXTOFTS), adiabatic tissue homogeneity (ATH), conventional compartmental (CC), and distributed parameter (DP). We established a comprehensive model combining the morphologic features, time-signal intensity curve shape, and optimal qDCE parameters. The capacities to identify benign and malignant STTs was evaluated using the area under the curve (AUC), degree of accuracy, and the analysis of the decision curve.
RESULTS: TOFTS-Ktrans, EXTOFTS-Ktrans, EXTOFTS-Vp, CC-Vp and DP-Vp demonstrated good diagnostic performance among the qDCE parameters. Compared with the other TK models, the DP model has a higher AUC and a greater level of accuracy. The comprehensive model (AUC, 0.936, 0.884-0.988) demonstrated superiority in discriminating benign and malignant STTs, outperforming the qDCE models (AUC, 0.899-0.915) and the traditional imaging model (AUC, 0.802, 0.712-0.891) alone.
CONCLUSIONS: Various TK models successfully distinguish benign from malignant STTs. The comprehensive model is a noninvasive approach incorporating morphological imaging aspects and qDCE parameters, and shows significant potential for further development.
摘要：
背景：探讨不同定量动态对比增强（qDCE）-MRI示踪动力学（TK）模型和qDCE参数在区分良性和恶性软组织肿瘤（STT）中的潜力。
方法：这项研究包括92例患者（41例女性，51名男性；年龄范围16-86岁，平均年龄51.24岁），有STT。qDCE参数(Ktrans，Kep,Ve,Vp,F,PS,通过使用以下TK模型估计STT的感兴趣区域的MTT和E）：Tofts（TOFTS），扩展字体(EXTOFTS)，绝热组织均匀性（ATH），常规隔室(CC)，和分布参数(DP)。我们建立了一个结合形态学特征的综合模型，时间-信号强度曲线形状，和最优qDCE参数。使用曲线下面积（AUC）评估鉴定良性和恶性STT的能力，准确度,和决策曲线的分析。
结果：TOFTS-Ktrans，EXTOFTS-Ktrans,EXTOFTS-Vp,CC-Vp和DP-Vp在qDCE参数中表现出良好的诊断性能。与其他传统知识模型相比，DP模型具有更高的AUC和更高的准确性。综合模型(AUC，0.936，0.884-0.988）在区分良性和恶性STT方面表现出优势，优于qDCE模型(AUC，0.899-0.915)和传统成像模型(AUC，0.802、0.712-0.891）单独使用。
结论：各种TK模型成功区分良性和恶性STT。综合模型是一种非侵入性方法，结合了形态学成像方面和qDCE参数，并显示出进一步发展的巨大潜力。
方法：这项研究包括92例患者（41例女性，51名男性；年龄范围16-86岁，平均年龄51.24岁），有STT。qDCE参数(Ktrans，Kep,Ve,Vp,F,PS,通过使用以下TK模型估计STT的感兴趣区域的MTT和E）：Tofts（TOFTS），扩展字体(EXTOFTS)，绝热组织均匀性（ATH），常规隔室(CC)，和分布参数(DP)。我们建立了一个结合形态学特征的综合模型，时间-信号强度曲线形状，和最优qDCE参数。使用曲线下面积（AUC）评估鉴定良性和恶性STT的能力，准确度,和决策曲线的分析。
结果：TOFTS-Ktrans，EXTOFTS-Ktrans,EXTOFTS-Vp,CC-Vp和DP-Vp在qDCE参数中表现出良好的诊断性能。与其他传统知识模型相比，DP模型具有更高的AUC和更高的准确性。综合模型(AUC，0.936，0.884-0.988）在区分良性和恶性STT方面表现出优势，优于qDCE模型(AUC，0.899-0.915)和传统成像模型(AUC，0.802、0.712-0.891）单独使用。
结论：各种TK模型成功区分良性和恶性STT。综合模型是一种非侵入性方法，结合了形态学成像方面和qDCE参数，并显示出进一步发展的巨大潜力。
