Classification Of Pituitary Tumors Research Articles

How to standardize the diagnostic approach to pituitary neuroendocrine tumors.

Pituitary tumors present heterogeneous biochemical, clinico-radiological, and histological features. Although histologically benign, a non-negligible number of cases present an unpredictable aggressive behavior with local invasiveness, partial/complete resistance to treatment and/or recurrence after surgery, and, rarely, metastasize, overall leading to a significant increase of morbidity, and, thus, requiring skilled multidisciplinary management in referral Centers. Histopathological diagnosis is essential to stratify cancer patient risk and uniform follow-up among Centers. Classification of pituitary neoplasia is continuously evolving in relation to the increased knowledge of mechanisms underlying adenohypophyseal cell tumorigenesis, and the attempts of combining clinico-radiological, biochemical, intraoperative, histological, and molecular elements, with the aim of identifying aggressive forms through. An integrated standardized histopathological report has been proposed in 2019 by the European Pituitary Pathology Group, based on the indications of the 2017 WHO classification of pituitary tumors. The last edition of the WHO Classification of Central Nervous System Tumors and of Endocrine and Neuroendocrine Tumors brought substantial novelties: 1) the replacement of the term "adenoma" with "Pituitary Neuroendocrine Tumor" (PitNET), and of "carcinoma" with "metastatic PitNET," and the consequent ICD-11 recoding from benign to malignant disease; and 2) the pivotal role of lineage restricted pituitary transcription factors for histological typing and subtyping. However, this approach does not reflect the spectrum of tumor phenotypes based on hormone secretion, nor include molecular features. Efforts of interdisciplinary groups of pituitary experts should be strongly encouraged to better understand factors involved in PitNETs evolution and, consequently, standardize diagnosis and reporting based on the most recent knowledges, essential to stratify cancer patient risk and uniform follow-up among centers.

Pituitary tumours without distinct lineage differentiation express stem cell marker SOX2

ContextThe recent WHO 2022 Classification of pituitary tumours identified a novel group of ‘plurihormonal tumours without distinct lineage differentiation (WDLD)’. By definition, these express multiple combinations of lineage commitment transcription factors, in a monomorphous population of cells.ObjectivesTo determine the expression of stem cell markers (SOX2, Nestin, CD133) within tumours WDLD, immature PIT-1 lineage and acidophil stem cell tumours, compared with committed cell lineage tumours.MethodsRetrospective evaluation of surgically resected pituitary tumours from St Vincent’s Hospital, Sydney. Patients were selected to cover a range of tumour types, based on transcription factor and hormone immunohistochemistry. Clinical data was collected from patient files. Radiology reports were reviewed for size and invasion. Samples were analysed by immunohistochemistry and RT-qPCR for SF-1, PIT-1, T-PIT, SOX2, Nestin and CD133. Stem cell markers were compared between tumours WDLD and those with classically “mature” types.ResultsOn immunohistochemistry, SOX2 was positive in a higher proportion of tumours WDLD compared with those meeting WHO lineage criteria, 7/10 v 10/42 (70 v 23.4%, p = 0.005). CD133 was positive in 2/10 tumours WDLD but 0/41 meeting lineage criteria, P = 0.003. On RT-qPCR, there was no significant difference in relative expression of stem cell markers (SOX2, CD133, Nestin) between tumours with and WDLD.ConclusionsOur study is the first to biologically characterise pituitary tumours WDLD. We demonstrate that these tumours exhibit a higher expression of the stem cell marker SOX2 compared with other lineage-differentiated tumours, suggesting possible involvement of stem cells in their development.

Pituitary adenoma classification: Tools to improve the current system.

The World Health Organization classification of pituitary tumors provides a framework for pathologists and researchers to classify pituitary adenomas. From the perspective of a practicing pathologist, this classification can be improved by pooling immunohistochemical data in a more standardized way, and by deliberately distinguishing features that assist in classification from those that do not. This article illustrates one general workflow to examine classification features consisting of immunohistochemical stains for anterior pituitary tumors, in order to promote debate and advance an evidence-based framework for classification.

Morphological Classification of Pituitary Tumors With Suprasellar Extension.

The objective of this study was to study the association among various morphological parameters and surgical outcomes in pituitary macroadenomas with suprasellar extension. MRI studies of 160 patients undergoing endoscopic transsphenoidal resection of pituitary macroadenomas with suprasellar extension were reviewed. In the coronal plane, tumors were classified into Type 1 (dome-shaped, no constriction at the level of diaphragma sellae) and Type 2 (dumbbell-shaped, with constriction at the level of diaphragma sellae). Based on the dome-to-neck ratio (D/Nr), Type 2 tumors were further classified as Type 2A (wide neck; D/Nr >1 and <1.3) and Type 2B (narrow neck; D/Nr ≥1.3). Surgical outcomes and complications were analyzed using a logistic regression model. Overall extent of resection (EOR) and presence of residual sellar-suprasellar tumor was separately assessed in all patients with available postoperative MRI (n = 149). There were 108 Type 1 tumors and 26 patients each in the Type 2A and Type 2B subgroups. Tumor subtype was significantly associated with tumor size (P < .001), intraoperative cerebrospinal fluid leak (P < .001), EOR (P < .001), postoperative suprasellar residual tumor (P < .001), and postoperative complications, including diabetes insipidus (P = .005) and visual worsening (P = .003). On multivariate analysis, after adjusting for confounders, Type 2B tumors were negatively associated with EOR (odds ratio [OR] 0.22; 95% CI 0.07-0.68; P = .008) and associated with the presence of postoperative suprasellar residual tumor (OR 18.08; 95% CI 5.20-62.89; P < .001), intraoperative cerebrospinal fluid leak (OR 5.33; 95% CI 1.89-14.99; P = .002), and postoperative diabetes insipidus (OR 4.89; 95% CI 1.67-14.35; P < .001). Preoperative tumor classification based on D/Nr is clinically and surgically relevant, and Type 2B macroadenomas are significantly associated with lower rates of gross total resection and higher rates of postoperative complications after endoscopic transsphenoidal resection.

CAD System Design for Pituitary Tumor Classification based on Transfer Learning Technique.

A brain tumor is an asymmetrical expansion by cells inevitably emulating amid them. Image processing is a vibrant research area where the handing out of the image in the medical field is an exceedingly tricky field. In this paper, an expert algorithm is suggested for the detection of pituitary brain tumors from MR images. The preprocessing techniques (smoothing, edge detection, filtering) and segmentation techniques (watershed) are applied to the online data set. The transfer learning technique is used as a classifier whose performance is measured in terms of classification accuracy. Resnet 50, Inception V3VGG16, and VGG19 models are used as classification algorithms. The proposed model is validated using different machine learning techniques considering hybrid features. 96% accuracy was obtained employing the Inception V3 model & 95% accuracy was attained using hybrid GLDS and GLCM features employing Support Vector Machine algorithm while 93% was attained using Probabilistic Neural Network and k Nearest Neighbor techniques. Computer-aided systems gave much faster and more accurate results than image processing techniques.1.0% accuracy improvement was observed while using Inception V3 over GLDS + GLCM + SVM and 2.1% accuracy improvement using GLDS + GLCM + SVM over GLDS + GLCM + kNN.

Role of Tumor Microenvironment in Pituitary Neuroendocrine Tumors: New Approaches in Classification, Diagnosis and Therapy.

Adenohypophysal pituitary tumors account for 10-15% of all intracranial tumors, and 25-55% display signs of invasiveness. Nevertheless, oncology still relies on histopathological examination to establish the diagnosis. Considering that the classification of pituitary tumors has changed significantly in recent years, we discuss the definition of aggressive and invasive tumors and the latest molecular criteria used for classifying these entities. The pituitary tumor microenvironment (TME) is essential for neoplastic development and progression. This review aims to reveal the impact of TME characteristics on stratifying these tumors in view of finding appropriate therapeutic approaches. The role of the pituitary tumor microenvironment and its main components, non-tumoral cells and soluble factors, has been addressed. The variable display of different immune cell types, tumor-associated fibroblasts, and folliculostellate cells is discussed in relation to tumor development and aggressiveness. The molecules secreted by both tumoral and non-tumoral cells, such as VEGF, FGF, EGF, IL6, TNFα, and immune checkpoint molecules, contribute to the crosstalk between the tumor and its microenvironment. They could be considered potential biomarkers for diagnosis and the invasiveness of these tumors, together with emerging non-coding RNA molecules. Therefore, assessing this complex network associated with pituitary neuroendocrine tumors could bring a new era in diagnosing and treating this pathology.

An advanced data fabric architecture leveraging homomorphic encryption and federated learning

Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple users or clients (hospitals/medical data centers) can collaborate in training a machine-learning model without exchanging raw data. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method’s effectiveness through a case study on pituitary tumor classification, achieving a significant accuracy of 83.31%. However, the primary focus of the study is using the data fabric architecture to securely store and analyze medical images while complying with HIPAA and GDPR regulations. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning.

WHO Histological Classification of Pituitary Tumors

The major changes in the upcoming 5th edition of the "2022 WHO Classification of Endocrine and Neuroendocrine Tumors" include:(1)evolution of the nomenclature: from pituitary adenoma to pituitary neuroendocrine tumour(PitNET),(2)detailed subtyping of a PitNET based on the tumor lineage, cell type, and related characteristics,(3)endorsement of the routine use of immunohistochemistry for pituitary transcription factors(PIT1, TPIT, SF1, GATA3, and ER-alpha),(4)introduction of some additional clinicopathologically distinct PitNET subtypes,(5)introduction of the term "metastatic PitNET" to replace the previous terminology "pituitary carcinoma," and(6)unifying posterior lobe tumors, the family of pituicyte tumors that invariably express TTF1, et al. Currently, no widely agreed grading or staging systems for PitNETs exist. Prognosis varies by tumor subtype and certain tumor subtypes are recognized as more aggressive(high-risk PitNETs)than others. Potentially aggressive PitNETs should be identified on an individual basis upon considering the tumor subtype, proliferative potential, and tumor invasion assessment.

Pituitary Neuroendocrine Tumor: Is It Benign or Malignant?

The World Health Organization (WHO) updated the classification of pituitary tumors in 2022. The new classification presents detailed histological subtyping of a pituitary neuroendocrine tumor (PitNET) based on the tumor cell lineage, cell type, and related characteristics. The immunohistochemistry for pituitary transcription factors (PIT1, TPIT, SF1, GATA3, and ERα) is routinely needed in this classification. The controversy regarding the change of behavior code of all PitNET/pituitary adenoma from "0" for benign tumors to "3" for primary malignant tumors is a topic of debate among experts, nowadays. Some authors represent that pituitary adenoma has a tendency for hemorrhage and necrosis and frequent invasion of the cavernous sinus. However, most small PitNET/pituitary adenoma do not need any treatment because of benign biologic behavior or less than 5% recurrence after gross total removal. Pituitary apoplexy is also benign nature but has a tendency of cranial nerve compression or panhypopituitarism. Most of cavernous invasion is compression of the cavernous sinus. Aggressive PitNET/pituitary adenoma with malignant biological behavior is less than 1%.

Real-life clinical impact of a five-tiered classification of pituitary tumors.

Usually benign, pituitary tumors (PT) can be invasive and aggressive with a propensity to progress and/or recur. Trouillas's clinicopathological classification attempts to predict the evolutionary risk of a PT. In this study, we assessed the prognostic value of this classification in an independent patient cohort and analyzed its impact on treatment strategies. In this study, 607 patients operated on between 2008 and 2018 for a PT were included. Grading was established based on invasion, proliferative activity (Ki-67, mitotic index) and p53 positivity. The therapeutic management following surgery was analyzed. Progression-free survival (PFS) of the graded tumors was estimated (Kaplan-Meier method and log-rank test) and a multivariate analysis was performed (Cox regression model). Grading identified non-invasive PT without (grade 1a: 303 cases) or with proliferative activity (grade 1b: 53 cases) and invasive PT without (grade 2a: 202 cases) or with proliferative activity (grade 2b: 49 cases). The mean follow-up was 47 ± 30 months (median: 38 months). Progression/recurrence occurred in 127 cases. Grades were significant and independent predictors of PFS (P < 0.001) with a 4.8-fold higher risk of progression/recurrence in grade 2b as compared to grade 1a. As second-line therapy, gamma knife or conventional radiotherapy controlled tumor growth in 91.6 and 100% of cases, respectively, irrespective of the grade. Proliferative tumors exposed the patient to a 9.5-fold higher risk of having ≥3 adjuvant therapeutic lines as compared to non-proliferative tumors. Grading of a PT according to Trouillas's classification predicts its risk of progression and should advocate for a personalized therapeutic approach in invasive and proliferative tumors. This is the first study to assess, on a cohort of 607 well-characterized patients, the real-life therapeutic impact of the five-tiered clinicopathological classification of pituitary tumors. First, we validate that pituitary tumor grades predict the evolutionary risk of the tumor, with a significant higher risk of progression/recurrence in invasive and/or proliferative tumors (mean follow-up: 47 ± 30 months, median: 38 months). Moreover, our study provides evidence that patients with proliferative tumors have a higher risk to be retreated after primary surgery and point toward the fact that radiotherapy can successfully control tumor growth in case of progression or recurrence. Our findings advocate for a personalized therapeutic approach in clinically aggressive pituitary tumors.

Development of a cost-effective diagnostic algorithm incorporating transcription factor immunohistochemistry in the evaluation of pituitary tumours

PurposeTo determine the utility of the 2022 WHO Classification of pituitary tumours in routine clinical practice and to develop an optimal diagnostic algorithm for evaluation of tumour type in a real-world setting.MethodsRetrospective evaluation of pituitary tumour immunohistochemistry (IHC), operatively managed at St Vincent’s Hospital Sydney, between 2019 and 2021. Routine IHC comprised evaluation of transcription factors [steroidogenic factor 1 (SF1), T-box transcription factor 19 (TPIT) and pituitary-specific positive transcription factor (PIT1)] and anterior pituitary hormones. Three tiered algorithms were tested, in which hormone IHC was performed selectively based on the initial transcription factor results. These were applied retrospectively and compared with current practice ‘gold standard’ comprising all transcription factor and hormone IHC. Diagnostic accuracy and cost were evaluated for each.ResultsThere were 113 tumours included in the analysis. All three algorithms resulted in 100% concordance with the ‘gold standard’ in the characterisation of tumour lineage. While all three were associated with relative cost reduction, Algorithm #3, which omitted hormone IHC in the setting of positive SF1 or TPIT and performed IHC for growth hormone, prolactin and thyroid stimulating hormone only in the setting of PIT1 positivity, was the most cost-efficient. Additionally, there were 12/113 tumours with no distinct cell lineage.ConclusionA diagnostic algorithm omitting hormone IHC except in cases of PIT1 positivity is an accurate and cost-effective approach to diagnose the type of pituitary tumour. A significant subgroup of pituitary tumours with no distinct cell lineage, frequently plurihormonal, remains difficult to classify with the new WHO criteria and requires further evaluation.

P11.40.A Clinicopathological Analysis of High risk Pituitary Adenomas According to the 2017 WHO Classification System for Pituitary Neuroendocrine Tumors (PitNET)

Abstract Background The 2017 WHO classification of pituitary tumors has revealed “high-risk pituitary neuroendocrine tumors (PitNETs)”, which were known to have high probability for recurrence. Tumor invasion was not included in the pathological grading and classification due to frequent lack of proper pathologic assessment. However, it mentioned tumor invasion as an important prognostic feature in identifying clinically aggressive adenomas. Material and Methods We performed a retrospective review of a prospectively collected dataset from January 2018 to March 2021. Patient’s clinical presentation, radiologic features, pathologic findings, and clinical outcome were gathered. Inter-group analysis was performed for high-risk versus low-risk tumors, and invasive versus non-invasive tumors. Results Among total 116 cases of PitNETs, high-risk and low-risk tumors were identified in 32 and 84 cases, respectively. The inter-group comparison showed no differences in clinical presentation, radiologic features, pathologic findings, and clinical outcomes.Invasive and non-invasive tumors were identified in 49 and 67 cases, respectively. The invasive group tumors were more symptomatic ( 29 (59.2%) vs. 30 (44.8%), p= 0.031), with larger tumor size over 40mm (9 (18.4%) vs. 1 (1.5%), p = 0.002), and more likely to have Knosp grade higher than 3. The gross-total resection was less achievable (7 (14.3%) vs 26 (38.8%), p = 0.007) However, Ki-67 index showed no significantly difference between the invasive group and non-invasive group (2.0 vs 2.0 , p= 0.556). Conclusion According to our study results, the pathologic diagnosis of a high-risk tumor does not necessarily seem to properly reflect the clinical aggressiveness. Tumor invasion, however, seems to better represent the aggressive tumors that requires proper and active treatment.

A Hybrid Deep Learning-Based Approach for Brain Tumor Classification

Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of people die due to deadly brain tumors. Therefore, accurate detection and classification are essential in the treatment of brain tumors. Numerous research techniques have been introduced for BT detection as well as classification based on traditional machine learning (ML) and deep learning (DL). The traditional ML classifiers require hand-crafted features, which is very time-consuming. On the contrary, DL is very robust in feature extraction and has recently been widely used for classification and detection purposes. Therefore, in this work, we propose a hybrid deep learning model called DeepTumorNet for three types of brain tumors (BTs)—glioma, meningioma, and pituitary tumor classification—by adopting a basic convolutional neural network (CNN) architecture. The GoogLeNet architecture of the CNN model was used as a base. While developing the hybrid DeepTumorNet approach, the last 5 layers of GoogLeNet were removed, and 15 new layers were added instead of these 5 layers. Furthermore, we also utilized a leaky ReLU activation function in the feature map to increase the expressiveness of the model. The proposed model was tested on a publicly available research dataset for evaluation purposes, and it obtained 99.67% accuracy, 99.6% precision, 100% recall, and a 99.66% F1-score. The proposed methodology obtained the highest accuracy compared with the state-of-the-art classification results obtained with Alex net, Resnet50, darknet53, Shufflenet, GoogLeNet, SqueezeNet, ResNet101, Exception Net, and MobileNetv2. The proposed model showed its superiority over the existing models for BT classification from the MRI images.

Overview of the 2022 WHO Classification of Pituitary Tumors.

This review summarizes the changes in the 5th Edition of the WHO Classification of Endocrine and Neuroendocrine Tumors that relate to the pituitary gland. The new classification clearly distinguishes anterior lobe (adenohypophyseal) from posterior lobe (neurohypophyseal) and hypothalamic tumors. Other tumors arising in the sellar region are also discussed. Anterior lobe tumors include (i) well-differentiated adenohypophyseal tumors that are now classified as pituitary neuroendocrine tumors (PitNETs; formerly known as pituitary adenomas), (ii) pituitary blastoma, and (iii) the two types of craniopharyngioma. The new WHO classification provides detailed histological subtyping of a PitNET based on the tumor cell lineage, cell type, and related characteristics. The routine use of immunohistochemistry for pituitary transcription factors (PIT1, TPIT, SF1, GATA3, and ERα) is endorsed in this classification. The major PIT1, TPIT, and SF1 lineage-defined PitNET types and subtypes feature distinct morphologic, molecular, and clinical differences. The "null cell" tumor, which is a diagnosis of exclusion, is reserved for PitNETs with no evidence of adenohypophyseal lineage differentiation. Unlike the 2017 WHO classification, mammosomatotroph and acidophil stem cell tumors represent distinct PIT1-lineage PitNETs. The diagnostic category of PIT1-positive plurihormonal tumor that was introduced in the 2017 WHO classification is replaced by two clinicopathologically distinct PitNETs: the immature PIT1-lineage tumor (formerly known assilent subtype 3 tumor) and the mature plurihormonal PIT1-lineage tumor. Rare unusual plurihormonal tumors feature multi-lineage differentiation. The importance of recognizing multiple synchronous PitNETs is emphasized to avoid misclassification. The term "metastatic PitNET" is advocated to replace the previous terminology "pituitary carcinoma" in order to avoid confusion with neuroendocrine carcinoma (a poorly differentiated epithelial neuroendocrine neoplasm). Subtypes of PitNETs that are associated with a high risk of adverse biology are emphasized within their cell lineage and cell type as well as based on clinical variables. Posterior lobe tumors, the family of pituicyte tumors, include the traditional pituicytoma, the oncocytic form (spindle cell oncocytoma), the granular cell form (granular cell tumor), and the ependymal type (sellar ependymoma). Although these historical terms are entrenched in the literature, they are nonspecific and confusing, such that oncocytic pituicytoma, granular cell pituicytoma, and ependymal pituicytoma are now proposed as more accurate. Tumors with hypothalamic neuronal differentiation are classified as gangliocytomas or neurocytomas based on large and small cell size, respectively. This classification sets the standard for a high degree of sophistication to allow individualized patient management approaches.

Pituitary blastoma, expanding the spectrum of newly recognized entities. A case report

Tumors of the pituitary gland and sellar region represent about 15% of all brain tumors, with pituitary adenoma being the commonest and pituitary carcinoma being very rare. Pituitary tumors in children are even rarer. Pituitary blastoma, a pediatric adenohypophysial tumor, is a new entity described in the 2017 WHO classification of pituitary tumors. This is a very rare tumor with only 21 cases reported so far. Hence, we are reporting this unusual case seen in a 7-month-old infant who presented with a large sellar/suprasellar mass with pressure symptoms of short duration. Typically, they present between 7-24 months of age. On histopathology, a cellular tumor was seen with primitive-looking round cells with scanty cytoplasm with few well-defined gland or rosette-like structures. The immunohistochemical stains showed diffuse strong staining for synaptophysin with a very high MIB-1 index. Other markers for common round cell tumors in this age group and hormonal markers of pituitary tumors were negative with INI-1 being intact. The initial cases described by Scheithauer presented with Cushing's disease and at least focally expressed adrenocorticotrophic hormone on immunohistochemistry. However, nonfunctioning tumors are also seen, albeit rarely. These are known to be associated with DICER 1 mutations and have a poor prognosis. Hence, morphologic recognition in the right clinical context and excluding other differential diagnoses in infants help make the correct diagnosis.

Epidemiology of common and uncommon adult pituitary tumors in the U.S. according to the 2017 World Health Organization classification.

To examine the contemporary epidemiology of adult pituitary tumors with a particular focus on uncommon tumor types, using the 2017 WHO Classification of pituitary tumors. Adult patients presenting with a pituitary or sellar tumor between 2004 and 2017 were identified from the U.S. National Cancer Database, with tumor type categorized according to the 2017 WHO classification. Descriptive epidemiological statistics were evaluated and reported for all pituitary tumor types and subtypes. 113,349 adults with pituitary tumors were identified, 53.0% of whom were female. The majority of pituitary tumors were pituitary adenomas (94.0%), followed by craniopharyngiomas (3.8%). Among pituitary adenomas, whereas 71.6% of microadenomas presented in females, only 46.7% of macroadenomas and 41.3% of giant adenomas did (p < 0.001). For craniopharyngiomas, 71.2% were adamantinomatous and 28.8% were papillary, with adamantinomatous tumors associated with Black non-Hispanic race/ethnicity (ORadj=2.44 vs. White non-Hispanic, 99.9%CI = 1.25-4.75, p < 0.001) in multivariable analysis.The remaining 0.7% (n = 676) of pathology-confirmed pituitary tumor types were composed of: 21% tumors of the posterior pituitary, 16% chordomas, 11% pituitary carcinomas (i.e. adenohypophyseal histology with metastasis; herein most frequently to bone), 10% meningiomas, 8% germ cell tumors, 7% hematolymphoid (largely DLBCLs), and 4% neuronal/paraneuronal (largely gangliogliomas). Pituitary carcinomas and posterior pituitary tumors demonstrated a male predilection (62.2% and 56.0%, respectively), whereas sellar meningiomas predominated in females (84.1%). Age, race/ethnicity, tumor size, and overall survival further varied across uncommon pituitary tumor types. Our findings provide a detailed contemporary dissection of the epidemiology of common and uncommon adult pituitary tumors in the context of WHO2017.

Transcription factor immunohistochemistry in the diagnosis of pituitary tumours.

The clinical utility and prognostic value of WHO 2017 lineage-based classification of pituitary tumours have not been assessed. This study aimed to (1) determine the clinical utility of transcription factor analysis for classification of pituitary tumours and (2) determine the prognostic value of improved lineage-based classification of pituitary tumours. This was a retrospective evaluation of patients who underwent surgical resection of pituitary tumours at St Vincent's Public and Private Hospitals, Sydney, Australia between 1990 and 2016. Included patients were at least 18 years of age and had complete histopathological data, forming the 'histological cohort'. Patients with at least 12 months of post-surgical follow-up were included in the subgroup 'clinical cohort'. The diagnostic efficacy of transcription factor immunohistochemistry in conjunction with hormone immunohistochemistry was compared with hormone immunohistochemistry alone. The prognostic value of identifying 'higher-risk' histological subtypes was assessed. There were 171 patient tumour samples analyzed in the histological cohort. Of these, there were 95 patients forming the clinical cohort. Subtype diagnosis was changed in 20/171 (12%) of tumours. Within the clinical cohort, there were 21/95 (22%) patients identified with higher-risk histological subtype tumours. These were associated with tumour invasiveness (P = 0.050), early recurrence (12-24 months, P = 0.013), shorter median time to recurrence (49 (IQR: 22.5-73.0) vs 15 (IQR: 12.0-25.0) months, P = 0.005) and reduced recurrence-free survival (P = 0.031). Application of transcription factor analysis, in addition to hormone immunohistochemistry, allows for refined pituitary tumour classification and may facilitate an improved approach to prognostication.

Transcription Factor Immunohistochemistry in the Diagnosis of Pituitary Tumors

Objective: The clinical utility and prognostic value of WHO 2017 lineage-based classification of pituitary tumors have not been assessed. This study aimed to (1) To determine the clinical utility of transcription factor analysis for classification of pituitary tumors and (2) To determine the prognostic value of improved lineage-based classification of pituitary tumors. Methods: This was a retrospective evaluation of patients who underwent surgical resection of pituitary tumors at a tertiary referral centre between 1990 and 2016. Included patients were at least 18 years of age and had complete histopathological data, forming the “histological cohort”. Patients with at least 12 months of post-surgical follow up were included in the subgroup “clinical cohort”. The diagnostic efficacy of transcription factor immunohistochemistry in conjunction with hormone immunohistochemistry was compared with hormone immunohistochemistry alone. The prognostic value of identifying “higher risk” histological subtypes was assessed. Results: There were 172 patient tumor samples analyzed in the histological cohort. Of these, there were 96 patients forming the clinical cohort. Subtype diagnosis was changed in 24/172 (14%) of tumors. Within the clinical cohort, there were 21/96 (22%) patients identified with higher risk histological subtype tumors. These were associated with tumor invasiveness (p=0.032), early recurrence (12-24 months, p=0.016), shorter median time to recurrence (38 [IQR 20-68.5] v 15 [IQR 12-27.25] months, p=0.02) and reduced recurrence-free survival (p=0.023). Conclusions: Application of transcription factor analysis, in addition to hormone IHC, is associated with improved diagnostic information.

Characterization of Gonadotroph Pituitary Adenomas Based on the Recent 2017 WHO Pituitary Tumor Classification

Abstract Introduction: More than 20% of pituitary adenomas are nonfunctional, the majority of which are of gonadotroph origin. Whereas previously, immunohistochemistry of pituitary hormones was used to classify adenoma subtypes, in 2017 the World Health Organization (WHO) reclassified pituitary adenomas using transcription factor expression in addition to immunohistochemistry. With this change, clinically nonfunctional gonadotroph adenomas can be distinguished among: (1) those staining for the transcription factor SF-1 and gonadotropins FSH and/or LH (FSH/LH+), (2) those that stain for SF-1 but not for FSH or LH (FSH/LH- SF1+), and (3) true null cell adenomas. It is unclear whether these three subgroups behave similarly clinically, or if they have distinct manifestations or outcomes. Our aim was to characterize these subgroups in regard to tumor size, recurrence and pituitary insufficiency. Methods: In a retrospective chart review, 71 patients from 2017-2020 who presented to the hospital for transsphenoidal resection of clinically nonfunctioning pituitary adenomas were reviewed. All patients with pituitary adenomas that stained positive for SF-1 and negative for T-PIT and PIT-1, and tumors that were negative for all three transcription factors were evaluated. Those lacking clinical data were excluded. Clinical characteristics examined include: demographics, tumor size, invasion of cavernous sinus, and hormone deficiencies. Results: Of the 71 pituitary tumors, 45% (n=32) stained positive for the beta subunit FSH and/or LH (FSH/LH+) and SF-1, 44% (n=31) stained for SF-1 with negative pituitary hormone stains (FSH/LH- SF1+), and 11% (n=8) were negative for all transcription factors and hormones (true null). All tumors were macroadenomas (&amp;gt;1 cm). While there were &amp;gt;50% males in the FSH/LH+ and FSH/LH- SF1+ groups, in the true null group only 25% of patients were male. Most patients were &amp;gt;50 years old in all 3 groups (81% FSH/LH+, 75% FSH/LH- SF1+, 88% true null). The prevalence of cavernous sinus involvement was 36% in both groups that stained for SF-1, but was 62% in the true null group. Both SF-1+ groups had similar tumor sizes and prevalence of panhypopituitarism (15-21%), but there were more episodes of recurrence since last known follow up in the FSH/LH- SF1+ group (20%), compared to FSH/LH+ tumors (7%). The true null group had ≥50% rates for both panhypopituitarism and recurrence. Conclusions: In this study, we highlighted the category of FSH/LH- SF1+ gonadotroph adenomas and compared these to FSH/LH+ and true null cell tumors. Based on clinical features, FSH/LH- SF1+ gonadotroph adenomas are similar to FSH/LH+ staining pituitary adenomas in regard to age, sex, size, and degree of cavernous sinus invasion, although there were more recurrences in the FSH/LH- SF1+ group. Though less common, our cohort suggests more aggressive tendencies in the true null group compared to SF-1 staining tumors.

Ki-67 labeling index and Knosp classification of pituitary adenomas

The classification of pituitary tumors is frequently updated to optimize guidance for clinical treatment based on current knowledge. To date, the World Health Organization conducts periodic expert review consensus meetings and publishes the results. These include recommendations for the behavior of more aggressive, high Ki-67 index (>3%)pituitary. A high Knosp grade (>3) is associated with a high risk of recurrence. We report an inverse relationship between the Ki-67 labeling index and Knosp grade for functional pituitary adenomas and nonfunctional pituitary adenoma (Spearman correlation coefficient in functional pituitary adenomas r= −0.59, p < .001 n = 75 and r = 0.367, p < .01 n = 168 in nonfunctional pituitary adenoma), and suggest that this results from early diagnosis and treatment before they become aggressive and recurrent. There were few articles analyzing the correlation of Ki-67 labeling index and Knosp classification of pituitary adenomas.Our study showed they were negatively correlated in functional pituitary adenomas and positive correlated in nonfunctional pituitary adenomas.