Narrative review of radiomics for classifying pulmonary nodules and potential impact on lung cancer screening

M. Stephens
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Abstract

Lung cancer screening has proven to be a useful tool for identifying early stage lung cancers, however, the overall accuracy can sometimes lead to false positive and negatives that have potential adverse effects on patient outcomes. Advancement in computational methods have allowed for quantification of pulmonary nodule imaging features, referred to as radiomics, which have the potential to increase lung cancer screening accuracy and improve patient management. The initial part of this review covers common radiomic features and the challenges in deriving them. The second part of this review systematically evaluates literature relating to radiomics and lung cancer finding articles in areas that might have the potential to change management in lung cancer screening. Pertinent literature included initial nodule classification as benign or malignant, classifying subsolid nodules as invasive or noninvasive, and prediction of tumor recurrence after surgical resection. The reviewed articles evaluating use of radiomics are mostly limited due to small sample sizes and lack of a validation cohort. These studies show potential for radiomic features to improve pulmonary nodule classification and change the way patients are managed, however, comparison between studies is limited due to variabilities in the way these features are derived. To make these features useful will require further research and standardization of the workflows that derive these features.
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肺结节分类的放射组学叙述性综述及其对癌症筛查的潜在影响
癌症筛查已被证明是识别早期肺癌的有用工具,然而,总体准确性有时会导致假阳性和假阴性,这对患者的预后有潜在的不利影响。计算方法的进步使肺结节成像特征(称为放射组学)得以量化,这有可能提高癌症筛查的准确性并改善患者管理。这篇综述的第一部分涵盖了常见的放射学特征和推导这些特征的挑战。这篇综述的第二部分系统地评估了与放射组学和癌症相关的文献,发现了可能改变癌症筛查管理的领域的文章。相关文献包括最初将结节分为良性或恶性,将皮下结节分为侵袭性或非侵袭性,以及预测手术切除后肿瘤复发。由于样本量小和缺乏验证队列,评估放射组学使用的综述文章大多受到限制。这些研究表明,放射学特征有可能改善肺结节的分类并改变患者的管理方式,然而,由于这些特征的来源方式不同,研究之间的比较有限。要使这些功能发挥作用,需要对衍生这些功能的工作流进行进一步的研究和标准化。
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