Setiawan Hananiel, Ria Francesco, Abadi Ehsan, Marin Daniele, Molvin Lior, Samei Ehsan
Carl E. Ravin Advanced Imaging Labs, Center for Virtual Imaging Trials, Department of Radiology, and.
Department of Radiology, Duke University Health System, Durham, NC.
J Comput Assist Tomogr. 2025;49(2):239-246. doi: 10.1097/RCT.0000000000001677. Epub 2024 Nov 30.
Patient characteristics, iodine injection, and scanning parameters can impact the quality and consistency of contrast enhancement of hepatic parenchyma in CT imaging. Improving the consistency and adequacy of contrast enhancement can enhance diagnostic accuracy and reduce clinical practice variability, with added positive implications for safety and cost-effectiveness in the use of contrast medium. We developed a clinical tool that uses patient attributes (height, weight, sex, age) to predict hepatic enhancement and suggest alternative injection/scanning parameters to optimize the procedure.
The tool was based on a previously validated neural network prediction model that suggested adjustments for patients with predicted insufficient enhancement. We conducted a prospective clinical study in which we tested this tool in 24 patients aiming for a target portal-venous parenchyma CT number of 110 HU ± 10 HU.
Out of the 24 patients, 15 received adjustments to their iodine contrast injection parameters, resulting in median reductions of 8.8% in volume and 9.1% in injection rate. The scan delays were reduced by an average of 42.6%. We compared the results with the patients' previous scans and found that the tool improved consistency and reduced the number of underenhanced patients. The median enhancement remained relatively unchanged, but the number of underenhanced patients was reduced by half, and all previously overenhanced patients received enhancement reductions.
Our study showed that the proposed patient-informed clinical framework can predict optimal contrast enhancement and suggest empiric injection/scanning parameters to achieve consistent and sufficient contrast enhancement of hepatic parenchyma. The described GUI-based tool can prospectively inform clinical decision-making predicting optimal patient's hepatic parenchyma contrast enhancement. This reduces instances of nondiagnostic/insufficient enhancement in patients.
患者特征、碘剂注射及扫描参数会影响CT成像中肝实质对比增强的质量和一致性。提高对比增强的一致性和充分性可提高诊断准确性，减少临床实践中的变异性，对造影剂使用的安全性和成本效益也有积极影响。我们开发了一种临床工具，利用患者属性（身高、体重、性别、年龄）来预测肝脏增强情况，并建议替代的注射/扫描参数以优化检查过程。
该工具基于先前验证的神经网络预测模型，该模型为预测增强不足的患者建议调整方案。我们进行了一项前瞻性临床研究，对24例患者进行测试，目标是门静脉期肝实质CT值达到110 HU±10 HU。
24例患者中，15例的碘造影剂注射参数进行了调整，体积中位数减少了8.8%，注射速率中位数减少了9.1%。扫描延迟平均减少了42.6%。我们将结果与患者之前的扫描结果进行比较，发现该工具提高了一致性，减少了增强不足患者的数量。中位数增强相对保持不变，但增强不足患者的数量减少了一半，所有之前增强过度的患者增强程度都有所降低。
我们的研究表明，所提出的基于患者信息的临床框架可以预测最佳对比增强，并建议经验性的注射/扫描参数，以实现肝实质一致且充分的对比增强。所描述的基于图形用户界面的工具可以前瞻性地为临床决策提供信息，预测患者肝脏实质的最佳对比增强。这减少了患者中无法诊断/增强不足的情况。
