Prediction of the Recurrence of Non-Functioning Pituitary Adenomas Using Preoperative Supra-Intra Sellar Volume and Tumor-Carotid Distance

Longitudinal multiomics analysis of aggressive pituitary neuroendocrine tumors: comparing primary and recurrent tumors from the same patient, reveals genomic stability and heterogeneous transcriptomic profiles with alterations in metabolic pathways

Pituitary neuroendocrine tumors (PitNET) represent the vast majority of sellar masses. Some behave aggressively, growing rapidly and invading surrounding tissues, with high rates of recurrence and resistance to therapy. Our aim was to establish patterns of genomic, transcriptomic and methylomic evolution throughout time in primary and recurrent tumors from the same patient. Therefore, we performed transcriptome- and exome-sequencing and methylome microarrays of aggressive, primary, and recurrent PitNET from the same patient. Primary and recurrent tumors showed a similar exome profile, potentially indicating a stable genome over time. In contrast, the transcriptome of primary and recurrent PitNET was dissimilar. Gonadotroph, silent corticotroph, as well as metastatic corticotroph and a somatotroph PitNET expressed genes related to fatty acid biosynthesis and metabolism, phosphatidylinositol signaling, glycerophospholipid and phospholipase D signaling, respectively. Diacylglycerol kinase gamma (DGKG), a key enzyme in glycerophospholipid metabolism and phosphatidylinositol signaling pathways, was differentially expressed between primary and recurrent PitNET. These alterations did not seem to be regulated by DNA methylation, but rather by several transcription factors. Molecular docking showed that dasatinib, a small molecule tyrosine kinase inhibitor used in the treatment of chronic lymphocytic and acute lymphoblastic leukemia, could target DGKG. Dasatinib induced apoptosis and decreased proliferation in GH3 cells. Our data indicate that pituitary tumorigenesis could be driven by transcriptomically heterogeneous clones, and we describe alternative pharmacological therapies for aggressive and recurrent PitNET.

Natural history of post-operative non-functioning pituitary adenomas - a single centre cohort analysis

PURPOSE

To study behaviour of endonasally operated non-functioning pituitary adenomas (NFPA) and propose a cost-effective stratified follow-up regimen.

METHODS

A single centre retrospective cohort analysis from June 2009 till December 2019. All endonasally operated pituitary adenomas were identified with sub-analysis of the NFPA's. Patients of all age groups with radiological follow-up more than 30 months were included. Patients with any kind of cranial intervention performed < within 30 months of surgery were excluded. The post-operative MRI for this cohort was evaluated until either any intervention was performed or until the last follow-up. The maximal tumour diameter in any plane (mm) was measured from the MRI scans. The annual growth rate and the statistical relationship between age, sex, IHC, Ki-67, resection %, residual tumour was calculated.

RESULTS

Out of 610 pituitary adenomas identified in the dataset, 116 patients met the inclusion criteria. Follow-up period ranged from 30 to 142 months (mean 78.5 months). A strong relationship existed between predicting tumour progression with first post-operative residue size (p = .001). A statistically significant relationship was found to be present between tumour growth and a residue of less than 10 mm diameter and 11-20 mm in diameter (Log rank p value .0216). On average, each patient with a residue < 5mm had MRI scans costing 976 £.

CONCLUSION

Based on statistical analysis and internal validation of the growth rate of the residue, we have proposed MRI follow-up scans. These recommendations have the potential to save more than 300 £per patient towards MRI costs and can lay down a marker for defining time interval of serial scans for post-operative NFPA's.

Current and Future Advances in Surgical Therapy for Pituitary Adenoma

The vital physiological role of the pituitary gland, alongside its proximity to critical neurovascular structures, means that pituitary adenomas can cause significant morbidity or mortality. While enormous advancements have been made in the surgical care of pituitary adenomas, numerous challenges remain, such as treatment failure and recurrence. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (eg, endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient's journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, surgical abilities will be augmented by the future operative armamentarium, including advanced optical devices, smart instruments, and surgical robotics. Intraoperative support to surgical team members will benefit from a data science approach, utilizing machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, neural networks leveraging multimodal datasets will allow early detection of individuals at risk of complications and assist in the prediction of treatment failure, thus supporting patient-specific discharge and monitoring protocols. While these advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of the translation of such technologies, ensuring systematic assessment of risk and benefit prior to clinical implementation. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future.

Evaluating the expression pattern of the opioid receptor in pituitary neuroendocrine tumors (PitNET) and the role of morphine and naloxone in the regulation of pituitary cell line growth and apoptosis

PURPOSE

The expression pattern of the opioid receptor (MOR) in pituitary neuroendocrine tumors (PitNET) and the possible effect of morphine and naloxone on GH3 cell growth and apoptosis were evaluated.

METHODS

The 114 pituitary tissues including non-functioning, GH-producing and ACTH-producing PitNET and healthy cadaver pituitary tissues were included. The expression level of the MOR gene and protein was assessed using real-time PCR and Western blot. The association with patient demographic characteristics was assessed. Morphine and naloxone were applied to assess their possible pharmacological role in GH3 pituitary adenoma cell death. The cytotoxic effect, the apoptosis rate, the cell cycle distribution, the content of reactive oxygen species and the caspase 3 activity were measured.

RESULTS

MOR gene levels increased significantly in pituitary neuroendocrine tumors (PitNET) compared to the healthy pituitary samples. The increased level of MOR gene expression was prominent in invasive functional and non-functional pituitary tumors. A consistent expression pattern was demonstrated for MOR protein levels in PitNET samples. A dose- and time-dependent reduction in the rate of GH3 pituitary cells was observed after morphine treatment with an IC50 of 483 µM after 24 h of incubation. Morphine induced early apoptosis, accumulation of cells in sub-G1 phase, increase in cellular ROS levels and caspase-3 activity. The observed effects of morphine were reversed after MOR blockade using 10 and 25 µM naloxone.

CONCLUSION

The possible contributing role of the MOR in pituitary tumor cell growth and the putative pharmaceutical effect of morphine in pituitary neuroendocrine tumor cell death (PitNET) is illustrated.

The Value of Intraoperative Magnetic Resonance Imaging in Endoscopic Endonasal Resection of Pituitary Adenoma

Background  Intraoperative magnetic resonance images (iMRIs) have been variably adopted by some centers to help increase the rate of gross total resection (GTR) of pituitary adenomas. In this comparative study, we report our institution's experience with using iMRIs for endoscopic endonasal approach (EEA) pituitary adenoma resection to better elucidate its role and potential value for pituitary surgery. Methods  All adult patients who underwent EEA for a pituitary adenoma from January 2013 to September 2021 were retrospectively reviewed. GTR was defined as no residual tumor or recurrence on postoperative imaging within 6 months. Univariate analysis followed by multivariate analysis was performed with GTR as the categorical endpoint. To measure the independent effect of iMRI on GTR, propensity score matching was then performed. Results  A total of 351 pituitary adenoma patients who underwent EEA were identified. The mean age was 51.2 (range: 18-90) years and 196 (55.8%) patients were female. iMRI was utilized in 87 (24.8%) cases. The overall rate of GTR was 69.2%. On multivariate analysis, low Knosp grade, low tumor volume, and the use of iMRI were predictive of GTR. There was no difference in the need for desmopressin or hydrocortisone at 90 days postoperatively. Conclusion  At our institution, we report a significant absolute increase in GTR rates of 16.4% for patients undergoing an iMRI. Among iMRI patients who did not have GTR, the majority of residuals were intentionally left behind after being deemed too risky to pursue. Overall, this study suggests the high value that iMRI adds to endoscopic pituitary adenoma surgery.

Deep Learning for Prediction of Progression and Recurrence in Nonfunctioning Pituitary Macroadenomas: Combination of Clinical and MRI Features

Objectives

A subset of non-functioning pituitary macroadenomas (NFMAs) may exhibit early progression/recurrence (P/R) after tumor resection. The purpose of this study was to apply deep learning (DL) algorithms for prediction of P/R in NFMAs.

Methods

From June 2009 to December 2019, 78 patients diagnosed with pathologically confirmed NFMAs, and who had undergone complete preoperative MRI and postoperative MRI follow-up for more than one year, were included. DL classifiers including multi-layer perceptron (MLP) and convolutional neural network (CNN) were used to build predictive models. Categorical and continuous clinical data were fed into the MLP model, and images of preoperative MRI (T2WI and contrast enhanced T1WI) were analyzed by the CNN model. MLP, CNN and multimodal CNN-MLP architectures were performed to predict P/R in NFMAs.

Results

Forty-two (42/78, 53.8%) patients exhibited P/R after surgery. The median follow-up time was 42 months, and the median time to P/R was 25 months. As compared with CNN using MRI (accuracy 83%, precision 87%, and AUC 0.84) or MLP using clinical data (accuracy 73%, precision 73%, and AUC 0.73) alone, the multimodal CNN-MLP model using both clinical and MRI features showed the best performance for prediction of P/R in NFMAs, with accuracy 83%, precision 90%, and AUC 0.85.

Conclusions

DL architecture incorporating clinical and MRI features performs well to predict P/R in NFMAs. Pending more studies to support the findings, the results of this study may provide valuable information for NFMAs treatment planning.