Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Recommendations for Measuring Pulmonary Nodules at CT: A Statement from the Fleischner Society
335
Zitationen
6
Autoren
2017
Jahr
Abstract
These recommendations for measuring pulmonary nodules at computed tomography (CT) are a statement from the Fleischner Society and, as such, incorporate the opinions of a multidisciplinary international group of thoracic radiologists, pulmonologists, surgeons, pathologists, and other specialists. The recommendations address nodule size measurements at CT, which is a topic of importance, given that all available guidelines for nodule management are essentially based on nodule size or changes thereof. The recommendations are organized according to practical questions that commonly arise when nodules are measured in routine clinical practice and are, together with their answers, summarized in a table. The recommendations include technical requirements for accurate nodule measurement, directions on how to accurately measure the size of nodules at the workstation, and directions on how to report nodule size and changes in size. The recommendations are designed to provide practical advice based on the available evidence from the literature; however, areas of uncertainty are also discussed, and topics needing future research are highlighted. <sup>©</sup> RSNA, 2017 Online supplemental material is available for this article.
Ähnliche Arbeiten
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
2011 · 10.849 Zit.
Pembrolizumab versus Chemotherapy for PD-L1–Positive Non–Small-Cell Lung Cancer
2016 · 9.970 Zit.
Gefitinib or Carboplatin–Paclitaxel in Pulmonary Adenocarcinoma
2009 · 8.215 Zit.
Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial
2015 · 6.489 Zit.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
2016 · 5.736 Zit.