Quantitative Assessment of Lung Nodule Size, Shape, and Malignant Potential Using Both Reactive and Limited-Memory Lung CT AI

CT Assessment of Lung Nodules—CT Versus Projection Radiography (PR)

This chapter will describe the importance of detecting and assessing the risk of lung cancer in a lung nodule by lung CT AI. The diameter of the pulmonary nodule was the first widely used research and clinical quantitative CT (QCT) metric derived from chest CT scans and predated the use of clinical QCT metrics of diffuse lung disease by several decades. This chapter introduces the topic of the solitary pulmonary nodule and the importance of assessing these nodules using multiple QCT metrics that help in determining the malignant potential of a lung nodule.

A lung nodule is a spherical structure of soft tissue density, approximately 40 HU, replacing the normal lung tissue with a diameter between 3 and 30 mm ( Fig. 4.1 ). The important features of a lung nodule include size, shape, density, contour, texture, and also assessing tissue features of the lung tissue adjacent to the lung nodule ( Box 4.1 ). It is very common to detect a nodule in screening populations that are at high risk for lung cancer. At least one nodule is detected in up to 51% of these patients, and greater than 95% of these nodules are benign. For this reason, there has been much research into determining what specific CT features of a lung nodule determine if it is benign or malignant. The size of the solitary pulmonary nodule is a simple feature that is a very strong predictor of the risk that the etiology of the nodule is due to lung cancer.

Fig. 4.1, Graphical descriptions of a spherical soft tissue lung nodule. The formulas for calculating the area and volume using the radius of the lung nodule are also shown. ( A) 2D view of ideal spherical lung nodule; Area = π × R 2 . ( B) 3D view of ideal spherical lung nodule; Volume = 4/3 × π × R 3 .

Box 4.1
Important Imaging Features in Lung Nodules

  • Shape

  • Size

  • Density in HU

  • Contour

  • Texture

  • Adjacent lung parenchymal density and texture

The detection and assessment of pulmonary nodules were an early strength of x-ray CT due to the true 3D assessment of the lung anatomy and increased contrast resolution compared to 2D x-ray projection radiographic imaging. The chest CT scan provides a 3D visualization of the lungs compared to the 2D representation that projection imaging generates, such as digital chest radiography. When a frontal and lateral 2D digital projection image of the chest is obtained on a person with suspected lung disease, the visual interpretation relies on the expert imaging physician being able to generate a 3D image of the lung anatomy in their brain, and this has its limitations. The chest CT is a more capable imaging method in the detection, localization, and assessment of nodule characteristics compared to projection radiography. This is due to chest CT’s accurate 3D depiction of the lung anatomy, high tissue contrast, compared to projection radiography, and the quantitative assessment of nodule features that are determined by the CT voxel size and HU value assigned to each voxel.

There are multiple clinical situations where one or more lung nodules may be detected on chest CT scans. The etiology of the lung nodule is most commonly due to prior infection, current infection, lung cancer, or metastatic cancer. There are multiple clinical scenarios, each with important considerations in the assessment of lung nodule(s). We will focus on two of these clinical scenarios: incidental lung nodules detected on noncontrast chest CT scans, and lung nodules detected on noncontrast low-dose chest CT scans as part of an approved ACR lung cancer screening program for high-risk smokers.

CT Protocol to Assess Lung Nodules

Nodule detection and the assessment of nodule size, shape, contour, density, and texture are important because lung cancer commonly presents as a solitary lung nodule, and the size and other CT features of the nodule can help in determining if the nodule is cancer or due to a benign tumor or infection. The best treatment approach is to detect a cancerous lung nodule when it is small and have it removed before it has spread to other structures within and/or outside of the thorax. Accurately detecting and following the size and other important features of a lung nodule require adherence to recommended CT scanning protocols, CT scanner quality assurance, and routine use of CT scanner lung nodule phantoms or test objects. A CT slice thickness of 2 mm or less is required to assess important features of the lung nodule. The size of a lung nodule increases on expiration and decreases on inspiration, so consistent inspiratory effort is necessary for the initial assessment of lung nodules and to accurately follow the size of the lung nodule over time. It has been reported that the mean change in nodule volume increases 23% between inspiration and expiration. The size, density, contour, and textural pattern of lung nodules are affected by slice thickness and reconstruction kernel. A neutral reconstruction kernel and narrow slice thickness represent the most accurate way of assessing lung nodule size, density, contour, and texture. A larger slice thickness (>2 mm) will reduce the size of the nodule due to partial volume effects at the surface of the nodule. The smaller the nodule, the greater this effect. The smooth kernel and large slice thickness (>2 mm) will average out important density, contour, and textural features. A sharp kernel will introduce artifacts within the nodule that will affect the accuracy of density and texture measurements. There are well-established quantitative criteria for the assessment of lung nodule size, growth, density, contour, and morphology.

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