Interactive Transcript
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So, pattern one is the ovarian endometrioma.
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And even though it's the one that we most
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commonly learn about, it can happen in as few
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as 30 percent of patients with endometriosis.
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But once you have one endometrioma,
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the risk of an endometrioma on the
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contralateral ovary is pretty high.
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It's about 30 to 50%.
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And we're aware of that
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classic ultrasound appearance.
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The circumscribed hypoechoic, low-level
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internal echoes, ground glass, et cetera,
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with posterior acoustic enhancement.
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Now, how about the MRI characteristics?
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Well, this is where having the T1 with
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and without fat sat is really important.
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So on the top image here, you can see
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this patient has bilateral endometriomas
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and they're really, really bright on
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T1-weighted images with fat saturation,
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which shows that this is hemorrhage.
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T1. On T2, we see something called shading.
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So on this image here, you can see that the
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endometrioma has pretty low to intermediate
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T2 signal and it's pretty diffuse.
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So why that happens is because the blood product
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within the ovary has a decrease in free water
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content, and they're kind of changing over
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time, but the free water content reduces, and
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then we've got blood products which settle.
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So sometimes you might see different layering
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of that T2 signal depending on the stage of the
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blood product breakdown, but the whole mass can
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also have T2 shading as we see in this case.
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And typically, hemorrhagic cysts
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do not have T2 shading because
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they tend to resolve on their own.
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So the blood product breaks down
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and then is resorbed by the body.
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Whereas in endometrioma, the blood
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product kind of stays within the lesion
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and then it tends to evolve over time.
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And that's why you get that T2 shading
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just based on the hemosiderin content.
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So typically, hemorrhagic cysts don't have this.
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It's not impossible for hemorrhagic cysts
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to have T2 shading, but it's not common.
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So on the T1 Fatsat and T2-weighted images,
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we want to look at the ovaries, obviously,
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but we also want to look at the extraovarian
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foci using that compartment-based model.
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And again, we'll go into that
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in a little bit more detail.
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