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Pulmonary Nodule Characterization, Including Computer Analysis and Quantitative Features

Bartholmai, Brian J. MD; Koo, Chi Wan MD; Johnson, Geoffrey B. MD, PhD; White, Darin B. MD; Raghunath, Sushravya M. PhD; Rajagopalan, Srinivasan PhD; Moynagh, Michael R. MB, BCh; Lindell, Rebecca M. MD; Hartman, Thomas E. MD

doi: 10.1097/RTI.0000000000000137
Symposium Review Articles

Pulmonary nodules are commonly detected in computed tomography (CT) chest screening of a high-risk population. The specific visual or quantitative features on CT or other modalities can be used to characterize the likelihood that a nodule is benign or malignant. Visual features on CT such as size, attenuation, location, morphology, edge characteristics, and other distinctive “signs” can be highly suggestive of a specific diagnosis and, in general, be used to determine the probability that a specific nodule is benign or malignant. Change in size, attenuation, and morphology on serial follow-up CT, or features on other modalities such as nuclear medicine studies or MRI, can also contribute to the characterization of lung nodules. Imaging analytics can objectively and reproducibly quantify nodule features on CT, nuclear medicine, and magnetic resonance imaging. Some quantitative techniques show great promise in helping to differentiate benign from malignant lesions or to stratify the risk of aggressive versus indolent neoplasm. In this article, we (1) summarize the visual characteristics, descriptors, and signs that may be helpful in management of nodules identified on screening CT, (2) discuss current quantitative and multimodality techniques that aid in the differentiation of nodules, and (3) highlight the power, pitfalls, and limitations of these various techniques.

Supplemental Digital Content is available in the text.

*Department of Radiology, Division of Thoracic Radiology

Departments of Immunology

Biomedical Engineering and Physiology, Mayo Clinic, Rochester, MN

Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Website,

Dr Bartholmai, Dr Raghunath and Dr Rajagopalan have intellectual property stake and royalties <$5000 annually for CANARY software listed in the text. The Mayo Clinic has licensed CANARY software to Imbio LLC. The remaining authors declare no conflicts of interest.

Reprints: Brian J. Bartholmai, MD, Department of Radiology, Division of Thoracic Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 (e-mail:

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