Guglielmo Priscilla, Marturano Francesca, Bettinelli Andrea, Sepulcri Matteo, Pasello Giulia, Gregianin Michele, Paiusco Marta, Evangelista Laura
Nuclear Medicine Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy.
Medical Physics Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy.
Diagnostics (Basel). 2023 Jun 23;13(13):2153. doi: 10.3390/diagnostics13132153.
Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement serves as a crucial prognostic factor for tailoring treatment approaches. Invasive methods, such as mediastinoscopy and endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), are employed for preoperative LN staging. Among the preoperative non-invasive diagnostic methods, computed tomography (CT) and, recently, positron emission tomography (PET)/CT with fluorine-18-fludeoxyglucose ([F]FDG) are routinely recommended by several guidelines; however, they can both miss pathologically proven LN metastases, with an incidence up to 26% for patients staged with [F]FDG PET/CT. These undetected metastases, known as occult LN metastases (OLMs), are usually cases of micro-metastasis or small LN metastasis (shortest radius below 10 mm). Hence, it is crucial to find novel approaches to increase their discovery rate. Radiomics is an emerging field that seeks to uncover and quantify the concealed information present in biomedical images by utilising machine or deep learning approaches. The extracted features can be integrated into predictive models, as numerous reports have emphasised their usefulness in the staging of lung cancer. However, there is a paucity of studies examining the detection of OLMs using quantitative features derived from images. Hence, the objective of this review was to investigate the potential application of PET- and/or CT-derived quantitative radiomic features for the identification of OLMs.
肺癌是全球第二常见的恶性肿瘤，淋巴结（LN）受累是制定治疗方案的关键预后因素。侵入性方法，如纵隔镜检查和支气管内超声引导下经支气管针吸活检（EBUS-TBNA），用于术前LN分期。在术前非侵入性诊断方法中，计算机断层扫描（CT）以及最近的氟-18氟脱氧葡萄糖（[F]FDG）正电子发射断层扫描（PET）/CT被多项指南常规推荐；然而，它们都可能遗漏经病理证实的LN转移，[F]FDG PET/CT分期的患者中，这一发生率高达26%。这些未被检测到的转移，即隐匿性LN转移（OLM），通常是微转移或小LN转移（最短半径小于10 mm）的病例。因此，找到新的方法来提高它们的发现率至关重要。放射组学是一个新兴领域，旨在通过利用机器学习或深度学习方法揭示和量化生物医学图像中隐藏的信息。提取的特征可以整合到预测模型中，因为许多报告都强调了它们在肺癌分期中的有用性。然而，利用图像衍生的定量特征检测OLM的研究很少。因此,本综述的目的是研究PET和/或CT衍生的定量放射组学特征在识别OLM中的潜在应用。
