Preoperative prediction of granulation pattern subtypes in GH‐secreting pituitary adenomas

Abstract

Objective The aim of this study was to establish a preoperative prediction method for sparsely granulated (SG) growth hormone (GH)‐secreting pituitary adenoma, an aggressive tumour subtype with high recurrence risk, in acromegaly patients. Methods Eighty‐three patients with GH‐secreting pituitary adenomas were included in this study. GH measurements, cytokeratin immunostaining and electron microscopy were performed to detect granulation patterns. Preoperative factors, including general, radiological and endocrinological features and acute octreotide suppression test outcomes, were compared between SG and densely granulated (DG) groups. The predictive capabilities of these features were analysed using a receiver operating characteristic (ROC) curve, and the most predictive features were combined to establish a grading scale. Results Thirty‐nine of the 83 patients had SG GH‐secreting pituitary adenomas; 44 had DG tumours. SG tumours tended to occur in younger patients and have larger diameters and volumes, higher Knosp grades, lower GH indexes and normalized insulin‐like growth factor‐1 (IGF‐1) level, and a lower ∆GH% after octreotide treatment. The tumour size, Knosp grade, GH index and ∆GH% after octreotide treatment had good predictive performance, with area under the curve (AUC) values ranging from 0.70 to 0.80. Combining four parameters, including diameter, Knosp grade, GH index and ∆GH% after octreotide treatment, we established a grading scale for predicting SG GH‐secreting pituitary adenomas with an AUC of 0.84 and relatively high sensitivity and specificity. Conclusions We propose a predictive method for distinguishing SG and DG GH‐secreting pituitary adenomas preoperatively. This method will help physicians identify candidates for presurgical medical treatment and neurosurgeons determine radical surgical strategies for high‐risk tumours.

Pathologic Characteristics of Somatotroph Pituitary Tumors—An Observational Single-Center Study

“…This makes the comparison of our results with others complicated. In our cohort, SG tumors occurred more frequently in younger patients compared to DG tumors, which complies with the previous literature [6,[21][22][23][24]. The cited authors showed that diagnosis or operation age in patients with SG tumors ranged from 36 to 44 years, wherein the majority of them were indicated as being over 40 years and below 44 years.…”

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

“…On the one hand, this value represents a quantification of the difficulty of total resection during transsphenoidal surgery. Patients with residual tumors have a greater risk of progression postoperatively according to previous studies ( 5 , 15 – 18 ). The relapse rate was higher after 10 years of follow-up than after only 5 years of follow-up in patients with a residual tumor.…”

“…With the development of artificial intelligence and data science, many studies have used radiomics methods or machine learning to predict the recurrence of brain tumors (18,21,22). However, models built by those methods lack universality between different study centers due to the heterogeneity of data collection, which is the key to the accuracy of model building.…”

The evolution in pituitary tumour classification: a clinical perspective

“…Sparsely granulated tumour cells have characteristic juxtanuclear fibrous bodies, in over 70% of cells, with a histological dot-like appearance ( Mete & Lopes 2017 ). Sparsely granulated somatotroph tumours have been associated with larger size, invasiveness, aryl hydrocarbon receptor-interacting protein (AIP) mutations, lower SSTR2 expression and poor response to somatostatin receptor ligands ( Brzana et al 2013 , Heng et al 2021 , Swanson et al 2021 ). Clinically silent somatotroph tumours are more frequently sparsely granulated ( Chinezu et al 2017 ).…”