 Introduction/Background A pulmonary nodule is defined as a well or poorly defined rounded opacity measuring <3 cm in diameter [1-5]. Nodules are classified as solid, part-solid, and ground-glass on CT, based on their attenuation, allowing for a more accurate assessment of malignancy risk. Ground-glass nodules are areas of increased attenuation through which underlying structures such as vessels remain visible [3]. Incidental pulmonary nodules are common, with reported frequencies ranging from 5.6% to 51% on CT and 0.1% to 7% on chest radiographs [5-7]. While it is estimated that 70% to 97% of incidental pulmonary nodules are benign [8], most are indeterminate for malignancy when first encountered making their management challenging. Additional methods that can enhance the detection and characterization of lung nodules include computer-aided detection systems [18-20], pulmonary vessel subtraction [21,22], deep convolutional neural networks [23], and other artificial intelligence algorithms [24]. While some practices use these methods, a detailed discussion falls outside of the scope of this document. aUniversity of Wisconsin School of Medicine and Public Health, Madison, Wisconsin. bPanel Chair, Duke University, Durham, North Carolina. cStanford University Medical Center, Stanford, California; The Society of Thoracic Surgeons. dMayo Clinic, Rochester, Minnesota; Commission on Nuclear Medicine and Molecular Imaging. eEmory University Hospital, Atlanta, Georgia. fNew York University Langone Health, New York, New York; IF Committee. gHampton VA Medical Center, Hampton, Virginia. hUniversity of Michigan Health System, Ann Arbor, Michigan. iVanderbilt University Medical Center, Nashville, Tennessee; American College of Chest Physicians. jUniversity of Arizona College of Medicine, Phoenix, Arizona. kMedical University of South Carolina, Charleston, South Carolina; IF Committee. lMallinckrodt Institute of Radiology, Saint Louis, Missouri.
Incidentally Detected Indeterminate Pulmonary Nodule PCAs.  Introduction/Background A pulmonary nodule is defined as a well or poorly defined rounded opacity measuring <3 cm in diameter [1-5]. Nodules are classified as solid, part-solid, and ground-glass on CT, based on their attenuation, allowing for a more accurate assessment of malignancy risk. Ground-glass nodules are areas of increased attenuation through which underlying structures such as vessels remain visible [3]. Incidental pulmonary nodules are common, with reported frequencies ranging from 5.6% to 51% on CT and 0.1% to 7% on chest radiographs [5-7]. While it is estimated that 70% to 97% of incidental pulmonary nodules are benign [8], most are indeterminate for malignancy when first encountered making their management challenging. Additional methods that can enhance the detection and characterization of lung nodules include computer-aided detection systems [18-20], pulmonary vessel subtraction [21,22], deep convolutional neural networks [23], and other artificial intelligence algorithms [24]. While some practices use these methods, a detailed discussion falls outside of the scope of this document. aUniversity of Wisconsin School of Medicine and Public Health, Madison, Wisconsin. bPanel Chair, Duke University, Durham, North Carolina. cStanford University Medical Center, Stanford, California; The Society of Thoracic Surgeons. dMayo Clinic, Rochester, Minnesota; Commission on Nuclear Medicine and Molecular Imaging. eEmory University Hospital, Atlanta, Georgia. fNew York University Langone Health, New York, New York; IF Committee. gHampton VA Medical Center, Hampton, Virginia. hUniversity of Michigan Health System, Ann Arbor, Michigan. iVanderbilt University Medical Center, Nashville, Tennessee; American College of Chest Physicians. jUniversity of Arizona College of Medicine, Phoenix, Arizona. kMedical University of South Carolina, Charleston, South Carolina; IF Committee. lMallinckrodt Institute of Radiology, Saint Louis, Missouri.