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It's important to have a standardized approach

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to interpreting lung cancer screening cts.

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Knowing what tools you have available

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to you can optimize your ability to detect early lung cancer

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and to characterize nodules.

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Importantly, these are thin sliced images,

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no greater than 2.5 millimeters.

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Optimally imaging should be done at one to 1.5 millimeters,

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which allows for the best characterization

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of nodules into their solid and part solid

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or even cavitary cystic components.

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As always, in chest imaging

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and most CT imaging, we view images in the axial sagal

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and coronal plane with some structure such

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as the spine best adapted to sagal imaging.

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We use MIPS to look for nodules as a way

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to increase the ability to detect small nodules,

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and studies have shown that using MIPS in addition

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to your basic axials,

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increases the detection of small nodules.

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And lastly, you can incorporate artificial intelligence

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analysis tools into your workflow.

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These can help you with nodule detection,

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characterizing the nodules you found measuring nodules,

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whether using diameters or volume,

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and to calculate the growth of a nodule,

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whether you're using diameter growth rate

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or volume doubling time.

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The practice parameter from the American College

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of Radiology and the Society

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of Thoracic Radiology outlines these criteria

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for slice thickness of being no more than 2.5 millimeters

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and the exam reconstruction slice thickness.

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It also discusses the concept of MIP imaging

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and its importance as well

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as the multiplanar reconstructions.

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So this is a good reference for you

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for the imaging technique

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and viewing methods used for lung cancer screening.

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This is my basic layout when I'm looking at our PAC system

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for a lung cancer screening CT in the top row,

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it brings up the MIP images next to the lung windows.

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I have my soft tissue windows

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and bone windows all in the axial plane.

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I always have the CT scout viewable, invisible

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as it sometimes I.

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Important way to detect findings that are

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outside the obvious central field of view.

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We have our coronal and sagittal mips.

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The latter, very good

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for looking at the sternum and the bone.

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We'll come to that in a later section

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on bone mineral density.

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And then I have an analysis tool up, which is a tool

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that we use to measure lung volumes and to detect emphysema.

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So this is our standard workflow.

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In addition, we run an artificial intelligence no detection

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tool on all of our chest cts,

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including lung cancer screening.

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The one that we use when it finds no nodules

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that are four millimeters

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or bigger, gives us a graphic like this

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that shows there were no nodules identified.

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Every time a nodule is identified,

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we can configure the system to show us the nodules

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and our system at our institution.

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We can figure to show us up to the six largest nodules

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that might be found on a lung cancer screening or chest ct.

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It provides us nodule diameters, volumes,

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And change over time.

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Then lastly, I have radiation dose up here.

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Lung cancer screening exams are low dose exams

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and it's important to look to make sure

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and verify that the radiation exposure is indeed low.

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As people undergoing lung cancer screening will have CT

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exams performed annually from as young as 50 years of age

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to as old as 80.

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If you identify a radiation dose that is out of the norm

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for a low dose protocol, it's important to work

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with your CT team to understand what might have happened in

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that case and to prevent it from happening in the future.

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I wanna address the topic of image noise.

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These are low dose cts.

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We do not expect the soft tissue windows to look as the

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as smooth as they do on a standard diagnostic chest ct.

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So you'll note all the graininess, the dots

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and the streaks throughout the soft tissues in the chest

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wall, as well as overlying the soft tissues.

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In the mediastinum, you'll see a lot of image noise,

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in particular when you get up

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to the images across the shoulders, as well as

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through the upper abdomen

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where you have more solid tissue image.

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Noise of course, is increased related to large body habitus,

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and so this is that individual with a lot of image noise,

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and you can see the surrounding soft tissues.

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You can set up your imaging protocols

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to increase the radiation exposure

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to get image quality within the lung parenchyma at an

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appropriate noise value, noting that you don't need

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as much radiation exposure to image the lung

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as you do the soft tissue or bones.

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Here's an example of an axial thin slice

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and the corresponding axial mep,

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and you'll notice the noise impacts the thin slices more

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than it does the axial mips,

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which are pulling together a slab of data

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so you have more signal relative to the amount of noise.

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That's a little bit about how I approach the viewing

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and the tools I have at my fingertips

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to evaluate lung cancer screening.

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cts.