Pituitary adenoma classification

Pituitary adenoma classification

They are classified based on size or cell of origin. Pituitary adenoma can be described as microadenomamacroadenoma, and giant tumors based on size. Microadenoma is tumors less than 10 mm, while macroadenoma includes tumors larger than 10mm. Giant pituitary adenomas are more than 40 mm. There are functional pituitary adenomas in which the cell type that composes them causes increased secretion of one or multiple hormones of the anterior pituitary. Alternatively, there are Non-Functioning Pituitary Adenomas that do not secrete hormones, but they can compress the surrounding areas of the anterior pituitary leading to hormonal deficiencies 1).

see The 2017 World Health Organization classification of tumors of the pituitary gland.

In the fourth edition of the World Health Organization classification of endocrine tumors, are two critical changes to the classification for pituitary adenomas.

One is that the term “atypical adenoma,” which was characterized based on highly proliferative properties to predict adenomas that carry a poor prognosis, was completely eliminated due to the lack of definitive evidence. The other change is the introduction of more precise cell lineage-based classification of pituitary adenoma that is defined based on lineage-specific transcription factors and hormones produced. Accordingly, null cell adenomas have been re-defined as those that show completely negative immunostaining either for hormones or for adenohypophyseal transcription factors 2).

Somatotroph adenoma.

Lactotroph adenoma.

Tyrotroph adenoma.

Corticotroph adenoma.

Gonadotroph adenoma.

Null cell adenoma

Plurihormonal pituitary adenoma and double adenomas.


The classification is based upon the size, invasion of adjacent structures, sporadic or familial cases, biochemical activity, clinical manifestations, morphological characteristics, response to treatment, and recurrence 3).

Current classification systems for PAs are based primarily on secretory characteristics of the tumor but are also classified on the basis of phenotypical characteristics, including tumor size, degree of invasiveness (e.g., Knosp grade), and immunohistological findings 4).

The anterior WHO classification system for PAs was refined to include designations for benign adenoma, atypical adenoma, and pituitary carcinoma on the basis of p53 immunoreactivity, MIB-1 indexmitotic activity, and the absence/presence of metastases 5) 6).

These tumor types can be microadenomas or macroadenomas and can either be functional or non-functional.

By Size

Pituitary microadenoma

Pituitary macroadenoma

Giant pituitary adenoma

Volume can be calculated using MRI-guided volumetrics and an ellipsoid approximation (TV × AP × CC/2) transverse (TV), antero-posterior (AP) and cranio-caudal (CC).

By Function

Functioning pituitary adenoma

Nonfunctioning pituitary adenoma

Pituitary adenomas with gangliocytic component are rare tumors of the sellar region that are composed of pituitary adenoma cells and a ganglion cell component. Their histogenesis and hence nosology is not yet resolved because of the small number of cases reported and lack of large series in the literature 7).

Invasive pituitary adenomas and pituitary carcinomas are clinically indistinguishable until the identification of metastases.

Consistency

Although most authors differentiate easily aspirated (soft) tumors from those that are not (fibrous, might require prior fragmentation), there is no universally accepted PA consistency classification. Fibrous PA tends to be hypointense on T2WI and has lower apparent diffusion coefficient (ADC) values. Fibrous tumors seemed to present higher invasion into neighboring structures, including the cavernous sinus. Several articles suggest that dopamine agonists could increase PA consistency and that prior surgery and radiotherapy also make PA more fibrous. The anatomopathological studies identify collagen as being mainly responsible for fibrous consistency of adenomas.

Conclusions: Preoperative knowledge of PA consistency affords the neurosurgeon substantial benefit, which clearly appears to be relevant to surgical planning, risks, and surgery outcomes. It could also encourage the centralization of these high complexity tumors in reference centers. Further studies may be enhanced by applying standard consistency classification of the PA and analyzing a more extensive and prospective series of fibrous PA. 8).

Knosp Grade.

Hardy’s Classification of Pituitary Adenomas.


1)

Russ S, Shafiq I. Pituitary Adenoma. 2020 Feb 4. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2020 Jan-. Available from http://www.ncbi.nlm.nih.gov/books/NBK554451/ PubMed PMID: 32119338.
2)

Inoshita N, Nishioka H. The 2017 WHO classification of pituitary adenoma: overview and comments. Brain Tumor Pathol. 2018 Apr;35(2):51-56. doi: 10.1007/s10014-018-0314-3. Epub 2018 Apr 23. Review. PubMed PMID: 29687298.
3)

Syro LV, Rotondo F, Ramirez A, Di Ieva A, Sav MA, Restrepo LM, Serna CA, Kovacs K. Progress in the Diagnosis and Classification of Pituitary Adenomas. Front Endocrinol (Lausanne). 2015 Jun 12;6:97. doi: 10.3389/fendo.2015.00097. eCollection 2015. Review. PubMed PMID: 26124750; PubMed Central PMCID: PMC4464221.
4)

Knosp E, Steiner E, Kitz K, Matula C: Pituitary adenomas with invasion of the cavernous sinus space: a magnetic resonance imaging classification compared with surgical findings. Neurosurgery 33:610–618, 1993
5)

Barnes L, Eveson JW, Reichart P, David Sidransky: World Health Organization Classification of Tumours: Pathology and Genetics of Head and Neck Tumours Lyon, IARC Press, 2005
6)

Zada G, Woodmansee WW, Ramkissoon S, Amadio J, Nose V, Laws ER Jr: Atypical pituitary adenomas: incidence, clinical characteristics, and implications. J Neurosurg 114:336–344, 2011
7)

Balci S, Saglam A, Oruckaptan H, Erbas T, Soylemezoglu F. Pituitary adenoma with gangliocytic component: report of 5 cases with focus on immunoprofile of gangliocytic component. Pituitary. 2014 Jan 16. [Epub ahead of print] PubMed PMID: 24430434.
8)

Acitores Cancela A, Rodríguez Berrocal V, Pian H, Martínez San Millán JS, Díez JJ, Iglesias P. Clinical relevance of tumor consistency in pituitary adenoma. Hormones (Athens). 2021 Jun 19. doi: 10.1007/s42000-021-00302-5. Epub ahead of print. PMID: 34148222.

Diffuse midline glioma H3 K27M-mutant MRI

Diffuse midline glioma H3 K27M-mutant MRI

T1: decreased intensity

T2: heterogeneously increased

T1 C+ (Gd): usually minimal (can enhance post-radiotherapy)

DWI/ADC: usually normal, occasionally mildly restricted

Extensive spread is relatively frequent, both craniocaudally to involve the cerebral hemispheres and spinal cord, as well as leptomeningeal spread 1)

A study included 66 cases (40 in men, 26 in women) of H3 K27M-mutant glioma in adult patients. Tumors were found in the following sites: thalamus (n = 38), brainstem (n = 6), brainstem with cerebellar or thalamic involvement (n = 4), whole-brain (n = 8), corpus callosum (n = 3), hypothalamus (n = 1), hemispheres (n = 2), and spinal cord (n = 4). All pure brainstem lesions were located posteriorly, and all corpus callosal lesions were in the genu. Most spinal tumors were long-segment lesions. Hemispheric lesions mimicked gliomatosis cerebri in presentation, with the addition of traditional midline structure involvement. Most tumors were solid with relatively uniform signals on plain MRI. Of the 61 cases with contrast-enhanced MR images, 36 (59%) showed partial to no enhancement, whereas 25 (41%) showed diffuse or irregular peripheral enhancement. Hemorrhage and edema were rare. Most lesions were solid and showed mild diffusion restriction on diffusion-weighted imaging. Tumor dissemination to the leptomeninges (n = 8) and subependymal layer (n = 3) was observed.

Qiu et al. described the MRI features of diffuse midline glioma with H3 K27M mutation in the largest study done to date in adult patients. Tumors were found in both midline and nonmidline structures, with the thalamus being the most common site. Although adult H3 K27M-mutant gliomas demonstrated highly variable presentations in this cohort of patients, the authors were able to observe shared characteristics within each location 2).


The radiographic features of diffuse midline gliomas with histone H3 K27M mutation were highly variable, ranging from expansile masses without enhancement or necrosis with large areas of surrounding infiltrative growth to peripherally enhancing masses with central necrosis with the significant mass effect but little surrounding T2/FLAIR hyperintensity. When we compared diffuse midline gliomas on the basis of the presence or absence of histone H3 K27M mutation, there was no significant correlation between enhancement or border characteristics, infiltrative appearance, or presence of edema 3)


Zhuo et al. from the Beijing Tiantan Hospital aimed to predict H3K27M mutation status by Amide proton transfer imaging (APTw) and radiomic features.

Methods: Eighty-one BSG patients with APTw imaging at 3T MR and known H3K27M status were retrospectively studied. APTw values (mean, median, and max) and radiomic features within manually delineated 3D tumor masks were extracted. Comparison of APTw measures between H3K27M-mutant and wildtype groups was conducted by two-sample Student’s T/Mann-Whitney U test and receiver operating characteristic curve (ROC) analysis. H3K27M-mutant prediction using APTw-derived radiomics was conducted using a machine learning algorithm (support vector machine) in randomly selected train (n = 64) and test (n = 17) sets. Sensitivity analysis with additional random splits of train and test sets, 2D tumor masks, and other classifiers were conducted. Finally, a prospective cohort including 29 BSG patients was acquired for validation of the radiomics algorithm.

Results: BSG patients with H3K27M-mutant were younger and had higher max APTw values than those with wildtype. APTw-derived radiomic measures reflecting tumor heterogeneity could predict H3K27M mutation status with an accuracy of 0.88, the sensitivity of 0.92, and specificity of 0.80 in the test set. Sensitivity analysis confirmed the predictive ability (accuracy range: 0.71-0.94). In the independent prospective validation cohort, the algorithm reached an accuracy of 0.86, the sensitivity of 0.88, and specificity of 0.85 for predicting H3K27M-mutation status.

Conclusion: BSG patients with H3K27M-mutant had higher max APTw values than those with wildtype. APTw-derived radiomics could accurately predict an H3K27M-mutant status in BSG patients 4).


Piccardo et al., from Genoa, retrospectively analyzed 22 pediatric patients with DMG histologically proved and molecularly classified as H3K27M-mutant (12 subjects) and wild-type (10 subjects) who underwent DWIProton magnetic resonance spectroscopic imaging, and ASL performed within 2 weeks of 18F-FDOPA PET. DWI-derived relative minimum apparent diffusion coefficient (rADC min), 1H-MRS data choline/N-acetylaspartate (Cho/NAA), choline/creatine (Cho/Cr), and presence of lactate and relative ASL-derived cerebral blood flow max (rCBF max) were compared with 18F-DOPA uptake Tumor/Normal tissue (T/N) and Tumor/Striatum (T/S) ratios, and correlated with histological and molecular features of DMG. Statistics included Pearson’s chi-squared test and Mann-Whitney U tests, Spearman’s rank correlation and receiver operating characteristic (ROC) analysis.

The highest degrees of correlation among different techniques were found between T/S, rADC min and Cho/NAA ratio (p < 0.01), and between rCBF max and rADC min (p < 0.01). Significant differences between histologically classified low- and high-grade DMG, independently of H3K27M-mutation, were found among all imaging techniques (p ≤ 0.02). Significant differences in terms of rCBF max, rADC min, Cho/NAA and 18F-DOPA uptake were also found between molecularly classified mutant and wild-type DMG (p ≤ 0.02), even though wild-type DMG included low-grade astrocytomas, not present among mutant DMG. When comparing only histologically defined high-grade mutant and wild-type DMG, only the 18F-DOPA PET data T/S demonstrated statistically significant differences independently of histology (p < 0.003). ROC analysis demonstrated that T/S ratio was the best parameter for differentiating mutant from wild-type DMG (AUC 0.94, p < 0.001).

Advanced MRI and 18F-DOPA PET characteristics of DMG depend on histological features; however, 18F-DOPA PET-T/S was the only parameter able to discriminate H3K27M-mutant from wild-type DMG independently of histology 5).


1)

Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 2007 Aug;114(2):97-109. doi: 10.1007/s00401-007-0243-4. Epub 2007 Jul 6. Erratum in: Acta Neuropathol. 2007 Nov;114(5):547. PMID: 17618441; PMCID: PMC1929165.
2)

Qiu T, Chanchotisatien A, Qin Z, Wu J, Du Z, Zhang X, Gong F, Yao Z, Chu S. Imaging characteristics of adult H3 K27M-mutant gliomas. J Neurosurg. 2019 Nov 15:1-9. doi: 10.3171/2019.9.JNS191920. Epub ahead of print. PMID: 31731269.
3)

Aboian MS, Solomon DA, Felton E, Mabray MC, Villanueva-Meyer JE, Mueller S, Cha S. Imaging Characteristics of Pediatric Diffuse Midline Gliomas with Histone H3 K27M Mutation. AJNR Am J Neuroradiol. 2017 Apr;38(4):795-800. doi: 10.3174/ajnr.A5076. Epub 2017 Feb 9. PMID: 28183840; PMCID: PMC5394943.
4)

Zhuo Z, Qu L, Zhang P, Duan Y, Cheng D, Xu X, Sun T, Ding J, Xie C, Liu X, Haller S, Barkhof F, Zhang L, Liu Y. Prediction of H3K27M-mutant brainstem glioma by amide proton transfer-weighted imaging and its derived radiomics. Eur J Nucl Med Mol Imaging. 2021 Jun 16. doi: 10.1007/s00259-021-05455-4. Epub ahead of print. PMID: 34131804.
5)

Piccardo A, Tortora D, Mascelli S, Severino M, Piatelli G, Consales A, Pescetto M, Biassoni V, Schiavello E, Massollo M, Verrico A, Milanaccio C, Garrè ML, Rossi A, Morana G. Advanced MR imaging and (18)F-DOPA PET characteristics of H3K27M-mutant and wild-type pediatric diffuse midline gliomas. Eur J Nucl Med Mol Imaging. 2019 Apr 27. doi: 10.1007/s00259-019-04333-4. [Epub ahead of print] PubMed PMID: 31030232.

Intraoperative direct electrocortical stimulation for glioma surgery

see also Awake surgery for glioma.

see also Resting-state functional magnetic resonance for glioma surgery.


Stimulation-induced seizures (SISs) are rare but serious events during electrocortical stimulation (ECS) mapping. SISs are most common when mapping the frontal lobe. Greater stimulation current is not associated with the identification of more cortical functional sites during glioma surgery 1).


Glioma surgery represents a significant advance with respect to improving resection rates using new surgical techniques, including intraoperative functional mappingmonitoring, and imaging. Functional mapping under awake craniotomy can be used to detect individual eloquent tissues of speech and/or motor functions in order to prevent unexpected deficits and promote extensive resection. In addition, monitoring the patient’s neurological findings during resection is also very useful for maximizing the removal rate and minimizing deficits by alarming that the touched area is close to eloquent regions and fibers. Assessing several types of evoked potentials, including motor evoked potentials (MEPs), sensory evoked potentials (SEPs), and visual evoked potentials (VEPs), is also helpful for performing surgical monitoring in patients under general anesthesia (GA) 2).


The greater extent of resection (EOR) of low-grade gliomas is associated with improved survival. Proximity to eloquent cortical regions often limits resectability and elevates the risk of surgery-related deficits. Therefore, functional localization of eloquent cortex or subcortical fiber tracts can enhance the EOR and functional outcomeImaging techniques such as functional MRI and diffusion tensor imaging fiber tracking, and neurophysiological methods like navigated transcranial magnetic stimulation and magnetoencephalography, make it possible to identify eloquent areas prior to resective surgery and to tailor indication and surgical approach but also to assess the surgical risk. Intraoperative monitoring with direct cortical stimulation and subcortical stimulation enables surgeons to preserve essential functional tissue during surgery. Through tailored, pre-and intraoperative mapping and monitoring the EOR can be maximized, with reduced rates of surgery-related deficits 3).


As the most accurate and reliable method of brain functional area positioning, Intraoperative direct electrocortical stimulation is able to determine in real-time the parts of the brain necessary for such functions as movementsensationlanguage, and even memory. A meta-analysis suggested that it could also improve the degree of resection of glioma while reducing the incidence of permanent neurological dysfunction 4).


Findings suggest that surgeons using Intraoperative direct electrocortical stimulation and awake craniotomy during their resections of high-grade glioma in eloquent areas experienced better surgical outcomes: a significantly longer overall postoperative survival, a lower rate of postoperative complications, and a higher percentage of GTR 5).


Resting-state functional magnetic resonance imaging likely reflects similar neural information as detected with intraoperative direct electrocortical stimulation (DES), but in its current form does not reach the spatial resolution of DES. 6).


1)

Muster RH, Young JS, Woo PYM, Morshed RA, Warrier G, Kakaizada S, Molinaro AM, Berger MS, Hervey-Jumper SL. The Relationship Between Stimulation Current and Functional Site Localization During Brain Mapping. Neurosurgery. 2021 May 13;88(6):1043-1050. doi: 10.1093/neuros/nyaa364. PMID: 33289525; PMCID: PMC8117445.
2)

Saito T, Muragaki Y, Maruyama T, Tamura M, Nitta M, Okada Y. Intraoperative Functional Mapping and Monitoring during Glioma Surgery. Neurol Med Chir (Tokyo). 2015;55 Suppl 1:1-13. PMID: 26236798.
3)

Ottenhausen M, Krieg SM, Meyer B, Ringel F. Functional preoperative and intraoperative mapping and monitoring: increasing safety and efficacy in glioma surgery. Neurosurg Focus. 2015 Jan;38(1):E3. doi: 10.3171/2014.10.FOCUS14611. PMID: 25552283.
4)

De Witt Hamer PC, Robles SG, Zwinderman AH, Duffau H, Berger MS. Impact of intraoperative stimulation brain mapping on glioma surgery outcome: a meta-analysis. J Clin Oncol. 2012;30:2559–2565. doi: 10.1200/JCO.2011.38.4818.
5)

Gerritsen JKW, Arends L, Klimek M, Dirven CMF, Vincent AJE. Impact of intraoperative stimulation mapping on high-grade glioma surgery outcome: a meta-analysis. Acta Neurochir (Wien). 2019 Jan;161(1):99-107. doi: 10.1007/s00701-018-3732-4. Epub 2018 Nov 21. PMID: 30465276; PMCID: PMC6331492.
6)

van Lieshout J, Debaene W, Rapp M, Noordmans HJ, Rutten GJ. fMRI Resting-State Connectivity between Language and Nonlanguage Areas as Defined by Intraoperative Electrocortical Stimulation in Low-Grade Glioma Patients. J Neurol Surg A Cent Eur Neurosurg. 2021 Feb 22. doi: 10.1055/s-0040-1721757. Epub ahead of print. PMID: 33618418.
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