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Detection, risk stratification and therapy monitoring of hepatocellular carcinoma – a deep learning approach based on iodine maps derived from dual energy computed tomography

肝细胞癌的检测、风险分层和治疗监测——基于双能计算机断层扫描碘图的深度学习方法

基本信息

批准号：
426969820
负责人：
Dr. Simon Lennartz
金额：
--
依托单位：
Institut für Diagnostische und Interventionelle Radiologie
依托单位国家：
德国
项目类别：
Research Fellowships
财政年份：
2019
资助国家：
德国
起止时间：
2018-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/426969820?language=en
关键词：
Detection risk stratification therapy monitoring

项目摘要

Primary liver cancer is the sixth most common cancer type worldwide and the fourth major cause of cancer deaths, while hepatocellular carcinoma (HCC) represents its most common form. For diagnosis of HCC based on dynamic, contrast-enhanced computed tomography (CT) or magnetic resonance imaging, there are defined diagnostic criteria (LI-RADS) which, inter alia, are based on tumor perfusion. These perfusion patterns can be determined more accurately in dual-energy CT-derived iodine maps as compared to conventional CT as they allow for precise quantification of iodinated contrast media. Previous studies have shown that these iodine maps can be used to improve diagnosis of HCC, particularly in diagnostically challenging cases (e.g. very small HCC lesions). Moreover, it was shown that they can be beneficial for the response assessment of patients who underwent locoregional tumor therapy (e.g. microwave ablation, radiofrequency ablation, transarterial chemoembolization). On the other hand, advanced machine learning methods continuously gain importance in almost every field of radiology. One of these methods, the so-called deep learning, has been shown to be beneficial for imaging of HCC as well. In this project, quantitative iodine maps derived by several different dual-energy CT scanners will be transferred to deep learning models to assess whether the combination of these two emerging technologies is beneficial in terms of detection and differentiation of HCC and its risk stratification and therapy monitoring in patients who underwent locoregional tumor therapy.

原发性肝癌是全球第六大常见癌症类型，也是癌症死亡的第四大主要原因，而肝细胞癌（HCC）是其最常见的形式。对于基于动态对比增强计算机断层扫描（CT）或磁共振成像的HCC诊断，存在定义的诊断标准（LI-RADS），其阿利亚基于肿瘤灌注。与常规CT相比，双能量CT衍生碘标测图可以更准确地确定这些灌注模式，因为它们允许碘化造影剂的精确定量。先前的研究表明，这些碘图可用于改善HCC的诊断，特别是在诊断具有挑战性的病例中（例如非常小的HCC病变）。此外，研究表明，它们有助于对接受局部肿瘤治疗（例如微波消融、射频消融、经动脉化疗栓塞）的患者进行反应评估。另一方面，先进的机器学习方法在放射学的几乎每个领域都越来越重要。其中一种方法，即所谓的深度学习，已被证明对HCC的成像也是有益的。在该项目中，由几种不同的双能CT扫描仪得出的定量碘图将被转移到深度学习模型中，以评估这两种新兴技术的组合是否有利于检测和区分HCC及其在接受局部肿瘤治疗的患者中的风险分层和治疗监测。