Prediction of high infiltration levels in pituitary adenoma using MRI-based radiomics and machine learning

The current state of MRI-based radiomics in pituitary adenoma: promising but challenging

In the clinical diagnosis and treatment of pituitary adenomas, MRI plays a crucial role. However, traditional manual interpretations are plagued by inter-observer variability and limitations in recognizing details. Radiomics, based on MRI, facilitates quantitative analysis by extracting high-throughput data from images. This approach elucidates correlations between imaging features and pituitary tumor characteristics, thereby establishing imaging biomarkers. Recent studies have demonstrated the extensive application of radiomics in differential diagnosis, subtype identification, consistency evaluation, invasiveness assessment, and treatment response in pituitary adenomas. This review succinctly presents the general workflow of radiomics, reviews pertinent literature with a summary table, and provides a comparative analysis with traditional methods. We further elucidate the connections between radiological features and biological findings in the field of pituitary adenoma. While promising, the clinical application of radiomics still has a considerable distance to traverse, considering the issues with reproducibility of imaging features and the significant heterogeneity in pituitary adenoma patients.

Integrative clinical, hormonal, and molecular data associate with invasiveness in acromegaly: REMAH study

INTRODUCTION

Growth hormone (GH)-secreting pituitary tumors (GHomas) are the most common acromegaly cause. At diagnosis, most of them are macroadenomas, and up to 56% display cavernous sinus invasion. Biomarker assessment associated with tumor growth and invasion is important to optimize their management.

OBJECTIVES

The study aims to identify clinical/hormonal/molecular biomarkers associated with tumor size and invasiveness in GHomas and to analyze the influence of pre-treatment with somatostatin analogs (SSAs) or dopamine agonists (DAs) in key molecular biomarker expression.

METHODS

Clinical/analytical/radiological variables were evaluated in 192 patients from the REMAH study (ambispective multicenter post-surgery study of the Spanish Society of Endocrinology and Nutrition). The expression of somatostatin/ghrelin/dopamine system components and key pituitary/proliferation markers was evaluated in GHomas after the first surgery. Univariate/multivariate regression studies were performed to identify association between variables.

RESULTS

Eighty percent of patients harbor macroadenomas (63.8% with extrasellar growth). Associations between larger and more invasive GHomas with younger age, visual abnormalities, higher IGF1 levels, extrasellar/suprasellar growth, and/or cavernous sinus invasion were found. Higher GH1 and lower PRL/POMC/CGA/AVPR1B/DRD2T/DRD2L expression levels (P < .05) were associated with tumor invasiveness. Least Absolute Shrinkage and Selection Operator's penalized regression identified combinations of clinical and molecular features with areas under the curve between 0.67 and 0.82. Pre-operative therapy with DA or SSAs did not alter the expression of any of the markers analyzed except for DRD1/AVPR1B (up-regulated with DA) and FSHB/CRHR1 (down-regulated with SSAs).

CONCLUSIONS

A specific combination of clinical/analytical/molecular variables was found to be associated with tumor invasiveness and growth capacity in GHomas. Pre-treatment with first-line drugs for acromegaly did not significantly modify the expression of the most relevant biomarkers in our association model. These findings provide valuable insights for risk stratification and personalized management of GHomas.

Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review

PURPOSE

Pituitary adenoma surgery is a complex procedure due to critical adjacent neurovascular structures, variations in size and extensions of the lesions, and potential hormonal imbalances. The integration of artificial intelligence (AI) and machine learning (ML) has demonstrated considerable potential in assisting neurosurgeons in decision-making, optimizing surgical outcomes, and providing real-time feedback. This scoping review comprehensively summarizes the current status of AI/ML technologies in pituitary adenoma surgery, highlighting their strengths and limitations.

METHODS

PubMed, Embase, Web of Science, and Scopus were searched following the PRISMA-ScR guidelines. Studies discussing the use of AI/ML in pituitary adenoma surgery were included. Eligible studies were grouped to analyze the different outcomes of interest of current AI/ML technologies.

RESULTS

Among the 2438 identified articles, 44 studies met the inclusion criteria, with a total of seventeen different algorithms utilized across all studies. Studies were divided into two groups based on their input type: clinicopathological and imaging input. The four main outcome variables evaluated in the studies included: outcome (remission, recurrence or progression, gross-total resection, vision improvement, and hormonal recovery), complications (CSF leak, readmission, hyponatremia, and hypopituitarism), cost, and adenoma-related factors (aggressiveness, consistency, and Ki-67 labeling) prediction. Three studies focusing on workflow analysis and real-time navigation were discussed separately.

CONCLUSION

AI/ML modeling holds promise for improving pituitary adenoma surgery by enhancing preoperative planning and optimizing surgical strategies. However, addressing challenges such as algorithm selection, performance evaluation, data heterogeneity, and ethics is essential to establish robust and reliable ML models that can revolutionize neurosurgical practice and benefit patients.

Radiomic Analysis in Pituitary Tumors: Current Knowledge and Future Perspectives

Radiomic analysis has emerged as a valuable tool for extracting quantitative features from medical imaging data, providing in-depth insights into various contexts and diseases. By employing methods derived from advanced computational techniques, radiomics quantifies textural information through the evaluation of the spatial distribution of signal intensities and inter-voxel relationships. In recent years, these techniques have gained considerable attention also in the field of pituitary tumors, with promising results. Indeed, the extraction of radiomic features from pituitary magnetic resonance imaging (MRI) images has been shown to provide useful information on various relevant aspects of these diseases. Some of the key topics that have been explored in the existing literature include the association of radiomic parameters with histopathological and clinical data and their correlation with tumor invasiveness and aggressive behavior. Their prognostic value has also been evaluated, assessing their role in the prediction of post-surgical recurrence, response to medical treatments, and long-term outcomes. This review provides a comprehensive overview of the current knowledge and application of radiomics in pituitary tumors. It also examines the current limitations and future directions of radiomic analysis, highlighting the major challenges that need to be addressed before a consistent integration of these techniques into routine clinical practice.

Knowing when to discontinue Temozolomide therapy in responding aggressive pituitary tumors and carcinomas: a systematic review and Padua (Italy) case series

INTRODUCTION

Pituitary adenomas can show a tendency to grow, despite multimodal treatment. Temozolomide (TMZ) has been used in the last 15 years in patients with aggressive pituitary tumors. TMZ requires a careful balance of different expertise, especially for selection criteria.

AREAS COVERED

We conducted: (1) a systematic review of the published literature from 2006 to 2022, collecting only cases with a complete description of patient follow-up after TMZ discontinuation; (2) a description of all patients with aggressive pituitary adenoma or carcinoma treated in Padua (Italy).

EXPERT OPINION

There is considerable heterogeneity in the literature: TMZ cycles duration ranged from 3 to 47 months; the follow-up time after TMZ discontinuation ranged from 4 to 91 months (mean 24 months, median 18 months), at least a stable disease has been reported in 75% of patients after a mean 13 months (range 3-47 months, median 10 months). The Padua (Italy) cohort reflects the literature. Future directions to explore are to understand the pathophysiological mechanism of TMZ resistance escape, to develop predicting factors to TMZ treatment (especially through the delineation of the underlying transformation processes), and to further expand the therapeutic applications of TMZ (as neoadjuvant, combined with radiotherapy).