Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non–Small Cell Lung Cancer on FDG PET Images
Noam Tau, A. B. Stundžia, Kazuhiro Yasufuku, Douglas Hussey, Ur Metser
Abstract
This study showed that using a CNN to analyze segmented PET images of patients with previously untreated NSCLC can yield moderately high accuracy for designation of N category, although this may be insufficient to preclude invasive lymph node sampling. The sensitivity of the CNN in predicting distant metastases is fairly poor, although specificity is moderately high.
Topics & Concepts
MedicineConvolutional neural networkLung cancerRadiologyPositron emission tomographyLungPathologyNuclear medicineOncologyArtificial intelligenceInternal medicineComputer scienceRadiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and TreatmentMedical Imaging Techniques and Applications