Types Of Pituitary Tumors Research Articles

7120 Pituitary Prolactinoma Transforming To Invasive Aggressive Adenoma After 10 Years of Acting As Benign Adenoma

Abstract Disclosure: S.N. Polis: None. S. Nakhle: None. N.J. Vernetti: None. Prolactinomas are the most common type of secretory pituitary tumors. Symptoms occur due to hyperprolactinemia and compressive symptoms with larger tumors. The majority of the prolactinomas are benign adenomas, with up to 30-40% of them being invasive. Malignant prolactinomas are rare, counting only 0.2% of pituitary tumors with an average latency period of 4.7 years. We present a 70-year-old woman who was found to have asymptomatic mild hyperprolactinemia in 2009 with pituitary MRI that showed a 3 mm left pituitary adenoma. Treatment with cabergoline started three years later due to an increase in prolactin above 300 ng/ml. Annual pituitary MRI showed a stable mass. After four years of treatment, pituitary MRI showed an increase in pituitary adenoma size to 8 mm and prolactin level remained elevated above 300 ng/ml. The patient complained of facial and acral numbness and emotional outbursts, so cabergoline was changed to bromocriptine. A few months later, a decision was made to stop treatment as she was asymptomatic and prolactin levels remained stable with a stable adenoma on imaging. 18 months later, prolactin increased to 544 ng/ml and pituitary MRI showed an increase adenoma size to 14mm, contacting the chiasm, and cabergoline was restarted. The prolactin levels continued to climb after 5 months of treatment despite titrating the dose to 1 mg daily. The patient started to develop vision impairment and visual field testing confirmed a new central vision defect. The patient was eventually referred to neurosurgery and went for transsphenoidal surgery. Prolactin level dropped to 50 ng/ml post operatively. The pathology report showed Ki 67 10-20%. Cabergoline was restarted and the patient was enrolled in a clinical trial for temozolomide for 18 months. During that time, prolactin levels remained stable so treatment was discontinued. A year later, cabergoline was restarted as prolactin levels increased to 219 ng/ml and pituitary MRI showed an 8 mm sellar mass with optic chiasm compression. For the next year, multiple medical treatments including high dose cabergoline (1 mg daily) and temozolomide were attempted, which were intolerable due to side effects. Prolactin continued to climb (> 1700 ng/ml). Four years later, the patient went for a second transsphenoidal surgery. Surgical pathology revealed Ki67 20%. Prolactin levels dropped to 242 ng/ml, but a month later, went up again to over 600 ng/ml. Cabergoline was restarted with a plan for an open craniotomy and radiotherapy. Conclusion: We present this interesting case of pituitary prolactinoma that behaved as benign adenoma for over 10 years prior to invasion and showing aggressive behavior requiring multiple surgeries. This unusual presentation is worrisome for malignancy and prompting consideration for closer and longer follow up for prolactinomas, especially the ones resistant to medical treatment. Presentation: 6/2/2024

Approaches based on natural products and miRNAs in pituitary adenomas: unveiling therapeutic intervention.

Pituitary adenomas (PAs) are tumors originating in the pituitary gland, a small gland located at the base of the brain. They are the most common type of pituitary tumor, affecting approximately 1 in 10 people over their lifetime. Common symptoms include headaches, vision problems, hormonal imbalances, and weight changes. Treatment options depend on the type and size of the adenoma and may consist of medication, surgery, radiation therapy, or a combination. PAs are typically benign and slow-growing, but they can cause significant health issues if left untreated. Proper diagnosis and management by an experienced multidisciplinary team is important for achieving the best outcomes. Natural compounds like celastrol, curcumin, quercetin, apigenin, resveratrol, epigallocatechin gallate (EGCG), and genistein have shown the ability to inhibit cell growth, promote cell death, and suppress hormone activity in pituitary tumor cells, suggesting their potential as alternative or complementary treatments for PAs. MicroRNAs (miRNAs) are a kind of tiny RNA molecules that do not code for proteins and have a vital function in controlling gene expression. These 21-23 nucleotide-long molecules regulate gene expression by binding to complementary sequences in mRNA molecules, leading to mRNA degradation. miRNAs participate in a wide range of biological activities, including apoptosis, metastasis, differentiation, and proliferation. The research indicates that miRNAs play a crucial role in the pathogenesis, therapeutic approaches, diagnosis, and prognosis of PAs. This review article will provide a comprehensive analysis of the current understanding of the efficacy of naturally derived anti-cancer agents in the treatment of PAs. Furthermore, the study provides a comprehensive assessment of the miRNAs in PAs, their role in the development of PAs, and their potential application in the treatment of the condition.

Gender Differences in Patients with Prolactinoma: Single-center Ukrainian Experience.

Prolactinomas are the most common type of pituitary gland tumors that secrete overly prolactin. They account for approximately 60% of all hormone-secreting hypophysis tumors. This study aims to analyze gender differences in patients with prolactinomas who were operated on transsphenoidal surgery and conduct a single-center retrospective analysis of patient data. This study evaluated the medical records of 109 patients (61 females and 48 males) from 2009 to 2019 at Feofaniya Clinical Hospital of the State Administration of Affairs in Kyiv, Ukraine. The primary criterion for including patients was a Serum Prolactin (PRL) level of over 100 ng/ml and the presence of a pituitary adenoma (PA) as observed on MRI. Additionally, the histological examination needed to confirm the presence of Prolactin-Secreting Pituitary Adenomas (PSPAs) without plurihormonal activity through both microscopy and immunohistochemical (IHC) staining. Significant differences in preoperative PRL levels were not observed. However, males had significantly larger tumor sizes and prevalence of macroadenomas. In male patients, the preoperative PLR levels showed a weak negative correlation with age (r=-0.304, p < 0.036) and a positive correlation with tumor size (r=0.555, p < 0.001) and cavernous sinus invasion (r=0.339, p < 0.018). In females, preoperative PRL was significantly associated only with tumor size and Knosp grade. Prolactin-Secreting Pituitary Adenomas (PSPAs) are more common in women than men and are characterized by larger and more invasive tumors with high PRL levels at diagnosis. The PRL level and tumor size before surgery can predict early biochemical remission in both males and females with an accuracy of 58.3% and 68.8%, respectively.

Pituitary Crooke cell neuroendocrine tumor of adrenocorticotropic hormone differentiation-specific transcription factor lineage: a clinicopathological analysis of six cases

Objective: To investigate the clinicopathological features of Crooke cell tumor of adrenocorticotropic hormone differentiation specific transcription factor (TPIT, also known as transcription factor 19, TBX19) lineage neuroendocrine tumors. Methods: Six cases of Crooke cell tumor diagnosed at the First Affiliated Hospital of University of Science and Technology of China, Hefei, China from October 2019 to October 2023 were collected. The clinical and pathological features of these cases were analyzed. Results: Among the six cases, one was male and five were female, with ages ranging from 26 to 75 years, and an average age of 44 years. All tumors occurred within the sella turcica. Clinical presentations included visual impairment in two cases, menstrual disorders in one case, Cushing's syndrome in one case, headache in one case, and one asymptomatic case discovered during a physical examination. Preoperative serum analyses revealed elevated levels of cortisol and adrenocorticotropic hormones in two cases, elevated cortisol in two cases, elevated adrenocorticotropic hormone in one case, and one case with a mild increase in prolactin due to the pituitary stalk effect. Magnetic resonance imaging revealed uneven enhancement of masses with maximum diameters ranging from 1.7 to 3.2 cm, all identified as macroadenomas. Microscopically, tumor cells exhibited irregular polygonal shapes, solid sheets, or pseudo-papillary arrangements around blood vessels. The cell nuclei were eccentric or centrally located, varying in size, with abundant cytoplasm. Some tumor cells showed perinuclear halo. Immunohistochemistry demonstrated diffuse strong positivity for TPIT in five cases, focal weak positivity for TPIT in one case, diffuse strong positivity for adrenocorticotropic hormone in all cases, and faint staining around the nuclei in a few cells. CK8/18 showed a strong positive ring pattern in more than 50% of tumor cells, focal weak positive expression of p53, and the Ki-67 positive index ranged 1%-5%. Periodic acid-Schiff staining revealed positive cytoplasm and negative perinuclear areas. Conclusions: Crooke cell tumor is a rare type of pituitary neuroendocrine tumors. Its pathological characteristics include a distinctive perinuclear clear zone and immunohistochemical markers, such as CK8/18 exhibiting a ring or halo pattern. This entity represents a high-risk subtype among pituitary neuroendocrine tumors, displaying a high risk of invasion and a propensity for recurrence. Accurate diagnosis is crucial for the postoperative follow-up and multimodal treatment planning.

Establishment of a risk prediction model for olfactory disorders in patients with transnasal pituitary tumors by machine learning

To construct a prediction model of olfactory dysfunction after transnasal sellar pituitary tumor resection based on machine learning algorithms. A cross-sectional study was conducted. From January to December 2022, 158 patients underwent transnasal sellar pituitary tumor resection in three tertiary hospitals in Sichuan Province were selected as the research objects. The olfactory status was evaluated one week after surgery. They were randomly divided into a training set and a test set according to the ratio of 8:2. The training set was used to construct the prediction model, and the test set was used to evaluate the effect of the model. Based on different machine learning algorithms, BP neural network, logistic regression, decision tree, support vector machine, random forest, LightGBM, XGBoost, and AdaBoost were established to construct olfactory dysfunction risk prediction models. The accuracy, precision, recall, F1 score, and area under the ROC curve (AUC) were used to evaluate the model's prediction performance, the optimal prediction model algorithm was selected, and the model was verified in the test set of patients. Of the 158 patients, 116 (73.42%) had postoperative olfactory dysfunction. After missing value processing and feature screening, an essential order of influencing factors of olfactory dysfunction was obtained. Among them, the duration of operation, gender, type of pituitary tumor, pituitary tumor apoplexy, nasal adhesion, age, cerebrospinal fluid leakage, blood scar formation, and smoking history became the risk factors of olfactory dysfunction, which were the key indicators of the construction of the model. Among them, the random forest model had the highest AUC of 0.846, and the accuracy, precision, recall, and F1 score were 0.750, 0.870, 0.947, and 0.833, respectively. Compared with the BP neural network, logistic regression, decision tree, support vector machine, LightGBM, XGBoost, and AdaBoost, the random forest model has more advantages in predicting olfactory dysfunction in patients after transnasal sellar pituitary tumor resection, which is helpful for early identification and intervention of high-risk clinical population, and has good clinical application prospects.

Lysine Methyltransferase 5A Promotes the Progression of Growth Hormone Pituitary Neuroendocrine Tumors through the Wnt/β-Catenin Signaling Pathway

Introduction: Growth hormone (GH) secreting pituitary adenoma is considered one of the most harmful types of Pituitary Neuroendocrine Tumors (PitNETs). Our previous research has found that high expression of Lysine methyltransferase 5A (KMT5A) is closely related to the proliferation of PitNETs. The aim of this study was to investigate the role and molecular mechanism of KMT5A in the progression of GH PitNETs. Methods: Immunohistochemistry, qRT-PCR, and Western blot (WB) were used to assess the expression levels of KMT5A in human normal pituitary and GH PitNETs, as well as in rat normal pituitary and GH3 cells. Additionally, we utilized RNA interference technology and treatment with a selective KMT5A inhibitor to decrease the expression of KMT5A in GH3 cells. CCK-8, EdU, flow cytometry (FCM), clone formation, and WB assay were further employed to evaluate the impact of KMT5A on the proliferation of GH3 cells in vitro. A xenograft model was established to evaluate the role of KMT5A in GH PitNETs progression in vivo. Results: KMT5A was highly expressed in GH PitNETs and GH3 cells. Moreover, the reduction of KMT5A expression led to inhibited growth of GH PitNETs and increased apoptosis of tumor cells, as indicated by the findings from CCK-8, EdU, clone formation, and FCM assays. Additionally, WB analysis identified the Wnt/β-catenin signaling pathway as a potential mechanism through which KMT5A promotes GH PitNETs progression. Conclusion: Our research suggests that KMT5A may facilitate the progression of GH PitNETs via the Wnt/β-catenin signaling pathway. Therefore, KMT5A may serve as a potential therapeutic target and molecular biomarker for GH PitNETs.

Surgical approaches in pituitary neuroendocrine tumors

Introduction. Pituitary neuroendocrine tumors account for 3.9-7.4/100.000 of central nervous system tumors in the Western world. They are particularly noteworthy, comprising 10-15% of all cases, with a higher prevalence in the 75-79 age group. In the Republic of Moldova, these tumors account for 34% of cases in postmortem examinations while remain an actual theme of discussion in the ENDO WHO congress and are regarded as a factor, which may influence the quality of life (QOL). Material and methods. We have critically revised 66 literary sources, which were selected using the PubMed library after introducing the keywords “pituitary adenoma surgical approach”. Results. The main surgical approaches were the transsphenoidal (transnasal, sublabial and endonasal) and transcranial (subfrontal unilateral/bilateral, fronto-lateral, fronto-temporal and median basilar) while the additional surgical approaches were designed for complicated and unusual pituitary neuroendocrine tumors and included combined versions, multiple surgeries or extended approaches. Numerous factors were influential for the selection of a surgical approach concerning the pituitary neuroendocrine tumors. They are not sensible for a type of pituitary neuroendocrine tumor according to the WHO classification while the size of a tumor may dictate its surgical approach. Conclusion. Each surgical intervention requires a personalized approach and the critical thinking of the surgical team but most of them can be systematically considered before confronting the tumor in an intraoperatory environment because most of the preoperatory investigations are proven unreliable. There is no established superior surgical approach for each surgical intervention.

Clinicopathological Analysis of Densely and Sparsely Granulated Somatotroph Tumors of Pituitary

ObjectiveSomatotroph tumors are the second most common type of pituitary neuroendocrine tumors (PitNETs) that can be further classified into two subtypes, namely densely granulated somatotroph tumors (DGSTs) and sparsely granulated somatotroph tumors (SGSTs). The aim of this study is to investigate the clinical significance of the two subtypes by conducting a retrospective analysis. MethodsFrom the database of the Ningbo Clinical Pathology Diagnosis Center, we collected cases diagnosed with pituitary somatotroph tumors. We then compiled pertinent clinical and radiological data and proceeded with histopathological examination involving HE staining and immunohistochemical staining. Subsequent tumor analysis comparing the two subtypes used either the Chi-square test or Fisher's exact test. ResultsIn this study, we analyzed a total of forty cases of somatotroph tumors, with 18 cases classified as DGSTs and 22 cases as SGSTs. The male-to-female ratio was 5:4 for DGSTs and 4:7 for SGSTs. The mean age was 52.83 years for DGSTs and 47.18 years for SGSTs. Statistically significant differences were observed between the DGSTs and SGSTs groups concerning invasiveness (P=0.0267) and postoperative remission (P=0.007). Both DGSTs and SGSTs cells exhibited co-expression of PIT1, GH, and CAM5.2, although the patterns of CAM5.2 expression differed between the two subtypes. ConclusionsThe efficacy of CAM5.2 staining in distinguishing between DGSTs and SGSTs was demonstrated. SGSTs, with their increased invasiveness and lower remission rate, emerge as a high-risk subtype. The histological subtype of somatotroph tumors plays a crucial role in guiding treatment decisions and prognostic evaluation in affected patients.

Plurihormonal PIT1-lineage pituitary neuroendocrine tumors: a clinicopathological study

Objective: To investigate the clinicopathological characteristics of plurihormonal PIT1-lineage pituitary neuroendocrine tumors. Methods: Forty-eight plurihormonal PIT1-lineage tumors were collected between January 2018 and April 2022 from the pathological database of Sanbo Brain Hospital, Capital Medical University. The related clinical and imaging data were retrieved. H&E, immunohistochemical and special stains were performed. Results: Out of the 48 plurihormonal PIT1-lineage tumors included, 13 cases were mature PIT1-lineage tumors and 35 cases were immature PIT1-lineage tumors. There were some obvious clinicopathological differences between the two groups. Clinically, the mature plurihormonal PIT1-lineage tumor mostly had endocrine symptoms due to increased hormone production, while a small number of immature PIT1-lineage tumors had endocrine symptoms accompanied by low-level increased serum pituitary hormone; patients with the immature PIT1-lineage tumors were younger than the mature PIT1-lineage tumors; the immature PIT1-lineage tumors were larger in size and more likely invasive in imaging. Histopathologically, the mature PIT1-lineage tumors were composed of large eosinophilic cells with high proportion of growth hormone expression, while the immature PIT1-lineage tumors consisted of chromophobe cells with a relatively higher expression of prolactin; the mature PIT1-lineage tumors had consistently diffuse cytoplasmic positive staining for keratin, while the immature PIT1-lineage tumors had various expression for keratin; the immature PIT1-lineage tumors showed more mitotic figures and higher Ki-67 proliferation index; in addition, 25.0% (12/48) of PIT1-positive plurihormonal tumors showed abnormal positive staining for gonadotropin hormones. There was no significant difference in the progression-free survival between the two groups (P=0.648) by Kaplan-Meier analysis. Conclusions: Plurihormonal PIT1-lineage tumor belongs to a rare type of PIT1-lineage pituitary neuroendocrine tumors, most of which are of immature lineage. Clinically increased symptoms owing to pituitary hormone secretion, histopathologically increased number of eosinophilic tumor cells with high proportion of growth hormone expression, diffusely cytoplasmic keratin staining and low proliferative activity can help differentiate the mature plurihormonal PIT1-lineage tumors from the immature PIT1-lineage tumors. The immature PIT1-lineage tumors have more complicated clinicopathological characteristics.

FRI309 Functional Gonadotrophin Secreting Adenoma With Elevated Testosterone—A Rare Entity

Abstract Disclosure: S. Bulchandani: None. S. Syal: None. E. Sloan: None. S.T. Sharma: None. Introduction: Gonadotroph adenomas are a common type of pituitary neuroendocrine tumor (PitNET). However, most do not lead to elevated sex hormone levels1. Presenting symptoms include headache, visual field deficits, and hypopituitarism.2-3. Gonadotropin-secreting PitNET leading to high circulating testosterone levels are extremely rare. We report a case of a male patient with a gonadotroph PitNET causing hypersecretion of testosterone. Case Report: 53-year-old male with history of hypertension presented for evaluation of dystonia. He also reported fatigue and an episode of diplopia. He had normal libido and sexual function, no mood fluctuations. MRI of the spine performed for evaluation of dystonia revealed a 2.8 cm sellar mass. MRI Pituitary showed a 31 mm in height by 24 mm in width by 19 mm in depth sellar mass suggestive of a pituitary adenoma with compression of the optic chiasm and extension into the left cavernous sinus. Laboratory evaluation revealed an elevated follicle stimulating hormone level (FSH) of 73.6 mIU/ml (1.6-8.0), luteinizing hormone (LH) 13.3mIU/ml (1.5-9.3), elevated total testosterone of 2176 ng/dl (250-1100) and Free testosterone 367.7 pg/mL (35-155), Sex hormone binding globulin of 68 nmol/L, Alpha subunit of 3ng/mL, prolactin 23 ng/ml (4-15.2), no increase with dilution, TSH 2.24 iIU/ml, free thyroxine level 0.9 ng/dL, and AM Cortisol 6.4 mcg/dl. A Cosyntropin stimulation test revealed adrenal insufficiency and treatment with hydrocortisone was initiated. Ophthalmology evaluation revealed left &amp;gt;right asymmetric bitemporal hemianopia. He underwent transsphenoidal resection of the pituitary tumor. Post-operative laboratory evaluation revealed testosterone level of 43.65ng/dL and FSH of 6.6mIU/ml. Pathology showed a gonadotroph PitNet with diffuse nuclear positivity for SF1 and patchy cytoplasmic positivity for FSH with Ki-67 index of 2-3%. Discussion and Conclusion: Most gonadotropinomas are nonfunctional2 and clinically present with hypopituitarism and often low or normal sex hormone levels. We present a rare case of gonadotroph PitNet with elevated testosterone levels not associated with a recognizable clinical syndrome of sex hormone excess. The standard therapy for gonadotropin-secreting macroadenomas is trans-sphenoidal surgery.2-3 Early diagnosis is essential for timely and appropriate management. Presentation: Friday, June 16, 2023

THU084 Corticotroph Tumor Type Influences Clinical Presentation In Patients With Nonfunctioning Pituitary Neuroendocrine Tumors

Abstract Disclosure: N. Al-Shamkhi: None. B. Edén Engström: None. O. Gudjonsson: None. J. Wikström: None. O. Casar-Borota: None. E. Rask: None. Introduction: According to the current World Health Organization classification of endocrine tumors the classification of non-functioning pituitary neuroendocrine tumors (NF-PitNET) is based on pituitary cell lineages, defined by immunohistochemical expression of anterior pituitary hormones and pituitary specific transcription factors. The second most common NF-PitNET type, after silent gonadotroph tumors, are silent corticotroph tumors that are regarded as a potentially more aggressive type. If there is indication for treatment, the first-line treatment is transsphenoidal surgery. Some, but not all studies have shown a higher degree of preoperative pituitary failure or more frequent postoperative pituitary failure in patients with silent corticotroph tumors compared to the other NF-PitNET. Objectives: We compared patients with silent corticotroph and silent gonadotroph tumors, and explored whether tumor type and radiological findings were associated with pre- or postoperative pituitary failure. Design and method: One hundred patients who underwent surgery for NF-PitNET at the same tertiary center were included. Data regarding age at surgery, sex, pre- and postoperative pituitary function and tumor type, were collected after chart review. Preoperative MRI examinations were revised regarding tumor volume and Knosp classification. Results: The majority, 72% (72/100) were classified as silent gonadotroph tumors followed by 18% (18/100) silent corticotroph tumors. Patients with silent corticotroph tumors were younger at surgery, P=0.003. There were no differences regarding sex, preoperative tumor volume, invasive growth on MRI, defined as Knosp grade &amp;gt; 3, pituitary failure or prolactin elevation between the two tumor types. Binary logistic regression showed that having a silent corticotroph tumor, P=0.04, odds ratio 5.7 (CI: 1.06-30.71), higher age at surgery, P=0.003, odds ratio 1.07 (CI: 1.02-1.13), and a larger preoperative tumor volume, P=0.01, odds ratio 1.18 (CI: 1.04-1.33), were all factors associated with an increased likelihood of having postoperative pituitary failure. Sex, invasive growth and preoperative prolactin elevation were not significant predictors of postoperative pituitary failure in the binary logistic regression. Conclusion: The results indicate that silent corticotroph tumors influence the clinical presentation of NF-PitNET. Patients with silent corticotroph tumors were significantly younger at surgery, and demonstrated an increased likelihood of postoperative pituitary failure. Presentation: Thursday, June 15, 2023

Efficient simultaneous segmentation and classification of brain tumors from MRI scans using deep learning

Brain tumors can be difficult to diagnose, as they may have similar radiographic characteristics, and a thorough examination may take a considerable amount of time. To address these challenges, we propose an intelligent system for the automatic extraction and identification of brain tumors from 2D CE MRI images. Our approach comprises two stages. In the first stage, we use an encoder-decoder based U-net with residual network as the backbone to detect different types of brain tumors, including glioma, meningioma, and pituitary tumors. Our method achieved an accuracy of 99.60%, a sensitivity of 90.20%, a specificity of 99.80%, a dice similarity coefficient of 90.11%, and a precision of 90.50% for tumor extraction. In the second stage, we employ a YOLO2 (you only look once) based transfer learning approach to classify the extracted tumors, achieving a classification accuracy of 97%. Our proposed approach outperforms state-of-the-art methods found in the literature. The results demonstrate the potential of our method to aid in the diagnosis and treatment of brain tumors.

A Deep Convolutional Neural Network Framework for Enhancing Brain Tumor Diagnosis on MRI Scans

Brain tumors are a critical public health concern, often resulting in limited life expectancy for patients. Accurate diagnosis of brain tumors is crucial to develop effective treatment strategies and improve patients' quality of life. Computer-aided diagnosis (CAD) systems that accurately classify tumor images have been challenging to develop. Deep convolutional neural network (DCNN) models have shown significant potential for tumor detection, and outperform traditional deep neural network models. In this study, a novel framework based on two pre-trained deep convolutional architectures (VGG16 and EfficientNetB0) is proposed for classifying different types of brain tumors, including meningioma, glioma, and pituitary tumors. Features are extracted from MR images using each architecture and merged before feeding them into machine learning algorithms for tumor classification. The proposed approach achieves a training accuracy of 98% and a test accuracy of 99% on the brain-tumor-classification-mri dataset available on Kaggle and btc_navoneel. The model shows promise to improve the accuracy and generalizability of medical image classification for better clinical decision support, ultimately leading to improved patient outcomes.

Imaging of pituitary tumors: an update with the 5th WHO Classifications-part 1. Pituitary neuroendocrine tumor (PitNET)/pituitary adenoma.

The pituitary gland is the body's master gland of the endocrine glands. Although it is a small organ, many types of tumors can develop within it. The recently revised fifth edition of the World Health Organization (WHO) classifications (2021 World Health Organization Classification of Central Nervous System Tumors and 2022 World Health Organization Classification of Endocrine and Neuroendocrine Tumors) revealed significant changes to the classification of pituitary adenomas, the most common type of pituitary gland tumor. This change categorized pituitary adenomas as neuroendocrine tumors and proposed the name to be revised to pituitary neuroendocrine tumor (PitNET). The International Classification of Diseases for Oncology behavior code for this tumor was previously "0" for benign tumor. In contrast, the fifth edition WHO classification has changed this code to "3" for primary malignant tumors as same to neuroendocrine tumor in other organs. Because the WHO classification made an important and significant change in the fundamental concept of the disease, in this paper, we will discuss the imaging diagnosis (magnetic resonance imaging, computed tomography, and positron emission tomography) of PitNET/pituitary adenoma in detail, considering these revisions as per the latest version of the WHO classification.

MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques

BackgroundDetecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through definitive brain surgery. Computational intelligence-oriented techniques can help physicians identify and classify brain tumors. Herein, we proposed two deep learning methods and several machine learning approaches for diagnosing three types of tumor, i.e., glioma, meningioma, and pituitary gland tumors, as well as healthy brains without tumors, using magnetic resonance brain images to enable physicians to detect with high accuracy tumors in early stages.Materials and MethodsA dataset containing 3264 Magnetic Resonance Imaging (MRI) brain images comprising images of glioma, meningioma, pituitary gland tumors, and healthy brains were used in this study. First, preprocessing and augmentation algorithms were applied to MRI brain images. Next, we developed a new 2D Convolutional Neural Network (CNN) and a convolutional auto-encoder network, both of which were already trained by our assigned hyperparameters. Then 2D CNN includes several convolution layers; all layers in this hierarchical network have a 2*2 kernel function. This network consists of eight convolutional and four pooling layers, and after all convolution layers, batch-normalization layers were applied. The modified auto-encoder network includes a convolutional auto-encoder network and a convolutional network for classification that uses the last output encoder layer of the first part. Furthermore, six machine-learning techniques that were applied to classify brain tumors were also compared in this study.ResultsThe training accuracy of the proposed 2D CNN and that of the proposed auto-encoder network were found to be 96.47% and 95.63%, respectively. The average recall values for the 2D CNN and auto-encoder networks were 95% and 94%, respectively. The areas under the ROC curve for both networks were 0.99 or 1. Among applied machine learning methods, Multilayer Perceptron (MLP) (28%) and K-Nearest Neighbors (KNN) (86%) achieved the lowest and highest accuracy rates, respectively. Statistical tests showed a significant difference between the means of the two methods developed in this study and several machine learning methods (p-value < 0.05).ConclusionThe present study shows that the proposed 2D CNN has optimal accuracy in classifying brain tumors. Comparing the performance of various CNNs and machine learning methods in diagnosing three types of brain tumors revealed that the 2D CNN achieved exemplary performance and optimal execution time without latency. This proposed network is less complex than the auto-encoder network and can be employed by radiologists and physicians in clinical systems for brain tumor detection.

Attention and working memory in patients with prolactinomas: a case–control study

Prolactinomas (prolactin-secreting adenomas) are the most common type of hormone-secreting pituitary tumor. Mounting evidence indicates that excess prolactin impairs cognitive function, but specific assessments of attention in patients with prolactinomas are lacking. Case–control study gathered 54 participants—27 patients with prolactinoma and 27 healthy controls. Neuropsychological assessment included a comprehensive set of diagnostic methods for the evaluation of attention and working memory. Patients showed slower information processing, expressed as a longer working time on the d2 Test of Attention and Color Trails Test (CTT-2), and lower attention-switching shown in the CTT-2 and in two subtests of the Tests of Everyday Attention (Visual Elevator), and Telephone Search While Counting. Working memory disturbances were observed in Digit Span and Symbol Span tests. A level of prolactin correlated negatively with scores in some of the neuropsychological tests measuring attentional switching (Visual Elevator), spatial screening and working memory (CTT-2), spatial working memory (Symbol Span) and auditory-verbal working memory (Digit Span backwards). There were no significant correlations between cognitive performance and tumor size. In conclusion, patients with prolactinoma suffer from impaired cognitive functions, including attention and working memory. Comprehensive neuropsychological assessment should be a permanent element of the diagnostics of this group of patients.

Integrated proteogenomic characterization across major histological types of pituitary neuroendocrine tumors

Pituitary neuroendocrine tumor (PitNET) is one of the most common intracranial tumors. Due to its extensive tumor heterogeneity and the lack of high-quality tissues for biomarker discovery, the causative molecular mechanisms are far from being fully defined. Therefore, more studies are needed to improve the current clinicopathological classification system, and advanced treatment strategies such as targeted therapy and immunotherapy are yet to be explored. Here, we performed the largest integrative genomics, transcriptomics, proteomics, and phosphoproteomics analysis reported to date for a cohort of 200 PitNET patients. Genomics data indicate that GNAS copy number gain can serve as a reliable diagnostic marker for hyperproliferation of the PIT1 lineage. Proteomics-based classification of PitNETs identified 7 clusters, among which, tumors overexpressing epithelial-mesenchymal transition (EMT) markers clustered into a more invasive subgroup. Further analysis identified potential therapeutic targets, including CDK6, TWIST1, EGFR, and VEGFR2, for different clusters. Immune subtyping to explore the potential for application of immunotherapy in PitNET identified an association between alterations in the JAK1-STAT1-PDL1 axis and immune exhaustion, and between changes in the JAK3-STAT6-FOS/JUN axis and immune infiltration. These identified molecular markers and alternations in various clusters/subtypes were further confirmed in an independent cohort of 750 PitNET patients. This proteogenomic analysis across traditional histological boundaries improves our current understanding of PitNET pathophysiology and suggests novel therapeutic targets and strategies.

Prolactinoma - which patients react favorably to cabergoline medication?

Objective. Prolactinoma, as a common endocrine disorder and the most frequent type of pituitary tumor, acts primarily as a suppressor on the gonadal functions. It is generally successfully treated with dopamine agonists; however, treatment resistance still remains in an unneglectable ratio. In this study, we aimed to identify factors, which may play a role in the treatment response. Methods. Seventy-six patients with prolactinoma, who have been routinely followed between 2018 and 2022 in Istanbul Research and Educational Hospital Endocrinology Outpatient Clinic, were included into the study. Initial prolactin level, adenoma size, baseline weight, body mass index (BMI), glucose, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglyceride levels were obtained from the patient's medical records. The patients were divided into two groups: treatment respondent and non-respondent (refractory) ones, according to treatment response in the duration as suggested by the guidelines. The treatment respondent and non-respondent groups were compared according to the initial and the 3rd month prolactin levels, adenoma size, weight, BMI, and metabolic values. Results. The initial tumor diameter was 15.27±10.62 mm in the refractory and 7.42±4.42 mm in the treatment respondent groups (p=0.01). The refractory group had higher prolactin baseline level 269.96±275.78 µg/l vs. 124.55±67.35 µg/l of the respondent group (p=0.01). The refractory group had higher the 3rd month prolactin level 50.97±52.55 µg/l vs. 29.70±27.31 µg/l of the respondent group (p=0.04). The refractory group had higher frequency of cystic/hemorrhagic adenoma (47.6%, n=11/21) (p=0.01), baseline pituitary failure (33.3%, n=7/21) (p=0.01), and baseline cavernous sinus invasion (25.8, n=5/21) (p=0.01). The treatment respondent group had lower initial body weight (69.54±17.51 kg vs. 83.29±16.21 kg) (p<0.01), and lower BMI (25.98±5.47 kg/m2 vs. 27.69±6.42 kg/m2) (p=0.02). Conclusions. In this study, initial tumor size, male gender, weight, BMI, the 3rd month prolactin level, initial pituitary deficiency, and cystic/hemorrhagic component in pituitary imaging in patients with prolactinoma were associated with a lower treatment response.

Abstract #1180135: Prolactinoma associated with Primary Hyperaldosteronism: ¿Is there a connection between the two entities?

Prolactinomas are the most common type of pituitary tumor. Patients presents with hyperprolactinemia associated with symptoms and signs of hypogonadism and galactorrhea. The primary hyperaldosteronism (PA) is an overlooked cause of hypertension, characterized by autonomous secretion of aldosterone. The coexistence of these two syndromes has been infrequently reported in the literature. We present a case of coexisting prolactinoma-PA.

Incidence and Prognostic Factors of Patients with Benign Pituitary Tumors Based on the Surveillance, Epidemiology, and End Results (SEER) Database

To study the incidence and prognostic factors of patients with benign pituitary tumors based on the Surveillance, Epidemiology, and End Results (SEER) database. This was a retrospective cohort study. Patients with benign pituitary tumors reported in the Surveillance, Epidemiology, and End Results database from 2004 to 2016, who presented completed demographic and clinical data, were included in our study. The age-adjusted incidence rate was calculated and stratified by year at diagnosis, age, gender, and the pathological type of benign pituitary tumor. We used Kaplan-Meier curves and multivariable Cox regressions to determine the factors associated with overall survival. A total of 29,967 patients were included in the study, of whom 26,691 (89.07%) survived and 3276 (10.93%) died. The age-adjusted incidence rate increased from 3.15 per 100,000 person-years in 2004 to 4.66 per 100,000 person-years in 2011 (annual percent change= 5.51, P < 0.001), and the subsequent growth trend from 2011 to 2016 was not statistically significant (annual percent change= 0.26, P= 0.711). Most patients were female, aged 60-79 years, and pituitary adenomas accounted for the main proportion of the incidence of benign pituitary tumors. Surgery was associated with the overall survival on the multivariable Cox regression model (hazard ratio= 0.677, 95% confidential interval: 0.629-0.727) and Kaplan-Meier curves, especially in pituitary adenoma. Radiation was not associated with the overall survival of benign pituitary tumor. The overall incidence of benign pituitary tumors was low but showed an increasing trend. Surgery may be beneficial to the prognosis. It should be noted that benign pituitary tumors may not require excessive treatment, such as radiation.