Radiomics for Detection of Pancreas Adenocarcinoma on CT Scans: Impact of Biliary Stents.
Radiomics for Detection of Pancreas Adenocarcinoma on CT Scans: Impact of Biliary Stents.
复制标题
CT 扫描检测胰腺腺癌的放射组学：胆管支架的影响。
DOI：
10.1148/rycan.210081
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Goenka,AjitH
中科院分区：
文献类型：
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作者：
Suman,Garima;Patra,Anurima;Mukherjee,Sovanlal;Korffiatis,Panagiotis;Goenka,AjitH
编辑：医学分割十项全能（MSD）数据集有420个不同胰腺肿瘤患者的腹部CT扫描。它分为两组：训练组（n= 281 CT扫描）和测试组（n= 139 CT扫描）。MSD团队只为培训组提供了细分(1)。在他们于2021年7月出版的《放射学：影像学癌症》（Radiology: Imaging Cancer）上精心设计的研究(2)中，Chen博士及其同事使用MSD团队提供的182例胰腺导管腺癌（PDAC）含CT扫描的分段，以建立广义放射组学模型，并对其局部模型进行外部验证。在基于贴片和患者的分析中，使用这些外部CT扫描将模型在外部MSD测试集上的灵敏度分别提高了近20%和10%。使用公共数据集来增加内部数据集和评估放射组学的普遍性是值得称赞的。然而，谨慎是有必要的，因为一些公共数据集的质量差距以前曾导致人工智能实验中的无意疏忽（1,3）。我们认为，MSD的PDAC CT扫描的某些特征与陈博士及其同事的研究密切相关。正如我们最近所记录的(1)，74例（约40%）来自MSD训练组的PDAC CT扫描显示胆道支架。诸如支架之类的设备是机器学习模型的一个偏差来源，因为模型学习将此类设备的存在与潜在诊断联系起来，这会导致无意中高估模型的性能（1,4）。其次，这种支架导致条纹伪影，模糊了PDAC（一种具有高度浸润性形态的肿瘤）的边缘，增加了肿瘤分割的可变性。最后，这样的装置会导致强度和纹理特征的不希望的、不可预测的和不可还原的变化(5)。这些变异影响了放射组学的可重复性和稳健性，这至少在一定程度上导致了该领域存在的临床翻译差距。考虑到这些因素，我们想知道台湾当地数据集中的支架CT扫描数量，以及作者采用的方法来解决当地和MSD数据集中胆道支架的混淆效应。这些资料将为今后关于这一专题的研究提供大量资料。
Editor: The Medical Segmentation Decathlon (MSD) data set has 420 abdomen CT scans of patients with different pancreatic tumors. It comes in two groups: a training group (n= 281 CT scans) and a testing group (n= 139 CT scans). The MSD team has provided segmentations only for the training group (1). In their carefully designed study (2) in the July 2021 issue of Radiology: Imaging Cancer, Dr Chen and colleagues used these MSD team-provided segmentations of 182 pancreatic ductal adenocarcinoma (PDAC)–containing CT scans for development of a generalized radiomics model and for external validation of their local model. Use of these external CT scans increased their model’s sensitivity on the external MSD test set by nearly 20% and 10% in patch-and patient-based analyses, respectively.The use of public data sets to augment internal data sets and to evaluate generalizability of radiomics is laudable. However, caution is warranted because quality gaps in some public data sets have previously resulted in inadvertent oversights in artificial intelligence experiments (1, 3). We believe that certain features of the CT scans with PDAC from the MSD are germane to the study by Dr Chen and colleagues. As we have recently documented (1), 74 (approximately 40%) of these CT scans with PDAC from the MSD training group have biliary stents. Devices such as stents are a source of bias for machine learning models because a model learns to associate the presence of such devices with the underlying diagnosis, which leads to inadvertent overestimation of the model’s performance (1, 4). Second, such stents result in streak artifacts that obscure margins of PDAC, a tumor with highly infiltrative morphology, and increase the variability in tumor segmentation. Finally, such devices result in undesirable, unpredictable, and nonreducible variations in intensity and texture features (5). Such variations impact the reproducibility and robustness of radiomics, which has at least partly contributed to the clinical translation gap that exists in this domain. In view of these considerations, we wonder about the number of CT scans with stents in their local Taiwanese data set and the process that the authors adopted to address the confounding effect of biliary stents in both the local and the MSD data sets. Such information would be highly informative for future studies on this topic.