Applying dynamic contrast-enhanced MRI tracer kinetic models to differentiate benign and malignant soft tissue tumors.

IF 3.5 2区 医学 Q2 ONCOLOGY Cancer Imaging Pub Date : 2024-05-21 DOI:10.1186/s40644-024-00710-x
Aixin Gao, Hexiang Wang, Xiuyun Zhang, Tongyu Wang, Liuyang Chen, Jingwei Hao, Ruizhi Zhou, Zhitao Yang, Bin Yue, Dapeng Hao
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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.

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应用动态对比增强磁共振成像示踪剂动力学模型区分良性和恶性软组织肿瘤。
背景:目的:探讨不同的定量动态对比增强(qDCE)-MRI示踪剂动力学(TK)模型和qDCE参数在区分良性和恶性软组织肿瘤(STTs)方面的潜力:本研究纳入了 92 名 STTs 患者(41 名女性,51 名男性;年龄范围为 16-86 岁,平均年龄为 51.24 岁)。采用以下 TK 模型估算 STT 相关区域的 qDCE 参数(Ktrans、Kep、Ve、Vp、F、PS、MTT 和 E):Tofts (TOFTS)、Extended Tofts (EXTOFTS)、绝热组织均匀性 (ATH)、传统分区 (CC) 和分布参数 (DP)。我们结合形态特征、时间信号强度曲线形状和最佳 qDCE 参数建立了一个综合模型。我们使用曲线下面积(AUC)、准确度和决策曲线分析评估了识别良性和恶性 STT 的能力: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 TK 模型相比,DP 模型的 AUC 更大,准确度更高。综合模型(AUC,0.936,0.884-0.988)在区分良性和恶性 STT 方面表现优异,优于 qDCE 模型(AUC,0.899-0.915)和单独的传统成像模型(AUC,0.802,0.712-0.891):结论:各种 TK 模型都能成功区分良性和恶性 STT。综合模型是一种结合形态学成像和 qDCE 参数的无创方法,具有进一步发展的巨大潜力。
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来源期刊
Cancer Imaging
Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.
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