Adamantinomatous craniopharyngioma diagnosis [Neurosurgery Wiki]

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====== Adamantinomatous craniopharyngioma diagnosis ======

===== Latest Pubmed Related Articles =====

{{rss>https://pubmed.ncbi.nlm.nih.gov/rss/search/1zy4NRF93Nh0RuSqq7M74phTJTp929cxKFpp-643t6gWFLjgcR/?limit=15&utm_campaign=pubmed-2&fc=20230424175227}}
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see also [[Craniopharyngioma diagnosis]].
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Diagnosis of [[adamantinomatous craniopharyngioma]] (ACP) is predominantly determined through invasive pathological examination of a neurosurgical biopsy specimen.

▷ ESSENTIAL ◁

[[Tumor]] in the [[sellar region]]

[[Squamous non-keratinizing epithelium]], [[benign]]

AND

[[stellate reticulum]] and/or wet [[keratin]]

▷ DESIRABLE ◁

▶ Nuclear immunoreactivity for [[β-catenin]]

▶ [[Mutation]] in [[CTNNB1]]

▶ Absence of [[BRAF]] [[p.V600E mutation]]
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[[Adamantinomatous craniopharyngioma]]s typically have a lobulated contour as a result of usually being multiple cystic [[lesion]]s. Solid components are present, but often form a relatively minor part of the mass and enhance vividly on both [[CT]] and [[MRI]]. Overall, [[calcification]] is very common, but this is only true of the adamantinomatous subtype (~90% are calcified).

These tumors have a predilection to being large, extending superiorly into the third ventricle, encasing vessels, and even adhering to adjacent structures.

===== Computed tomography =====

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[[Contrast enhancement]], [[cyst]] formation, and [[calcification]] are the three characteristic features of craniopharyngiomas on [[computed tomography]]. More than 90% of [[suprasellar]] [[craniopharyngioma]]s exhibit at least two of these three features, thus providing easy radiologic detection.
Imaging mnemonic: “90% rule” 90% of [[adamantinomatous craniopharyngioma]]s exhibit at least 2 of the following “C” features: cyst formation, prominent calcifications.
((Johnson LN, Hepler RS, Yee RD, Frazee JG, Simons KB. Magnetic resonance imaging of craniopharyngioma. Am J Ophthalmol. 1986 Aug 15;102(2):242-4. doi: 10.1016/0002-9394(86)90152-2. PMID: 3740186.))

===== Magnetic resonance imaging =====
 
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==== Cysts ====

[[T1]]: iso- to hyperintense to the brain (due to high [[protein]] content "motor oil cysts")

[[T2]]: variable but ~80% are mostly or partly T2 [[hyperintense]]

==== Solid component ====

T1 C+ (Gd): vivid enhancement

T2: variable or mixed 

==== Calcification ====

Difficult to appreciate on conventional imaging

Susceptible sequences may better demonstrate calcification

==== MR angiography ====
May show displacement of the A1 segment of the [[anterior cerebral artery]] (ACA)
==== MR spectroscopy ====
[[Cyst]] contents may show a broad [[lipid]] spectrum, with an otherwise flat baseline.

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Clinical experts can distinguish ACP from Magnetic Resonance Imaging (MRI) with an accuracy of 86%, and 9% of ACP cases are diagnosed this way. Classification using deep learning (DL) provides a solution to support a non-invasive diagnosis of ACP through neuroimaging, but it is still limited in implementation, a major reason being the lack of predictive uncertainty representation. We trained and tested a DL classifier on preoperative MRI from 86 suprasellar tumor patients across multiple institutions. We then applied a Bayesian DL approach to calibrate our previously published ACP classifier, extending beyond point-estimate predictions to predictive distributions. Our original classifier outperforms random forest and XGBoost models in classifying ACP. The calibrated classifier underperformed our previously published results, indicating that the original model was overfitting. The mean values of the predictive distributions were not informative regarding model uncertainty. However, the variance of predictive distributions was indicative of predictive uncertainty. We developed an algorithm to incorporate predicted values and the associated uncertainty to create a classification abstention mechanism. Our model accuracy improved from 80.8% to 95.5%, with a 34.2% abstention rate. We demonstrated that calibration of DL models can be used to estimate predictive uncertainty, which may enable the clinical translation of artificial intelligence to support the non-invasive diagnosis of brain tumors in the future
((Prince EW, Ghosh D, Görg C, Hankinson TC. Uncertainty-Aware Deep Learning Classification of Adamantinomatous Craniopharyngioma from Preoperative MRI. Diagnostics (Basel). 2023 Mar 16;13(6):1132. doi: 10.3390/diagnostics13061132. PMID: 36980440; PMCID: PMC10047069.)).