Management of large aggressive nonfunctional pituitary tumors: experimental medical options when surgery and radiation fail

A Preoperative MRI-Based Radiomics-Clinicopathological Classifier to Predict the Recurrence of Pituitary Macroadenoma Within 5 Years

Objective: To investigate the ability of a MRI-based radiomics-clinicopathological model to predict pituitary macroadenoma (PMA) recurrence within 5 years.

Materials and Methods: We recruited 74 recurrent and 94 non-recurrent subjects, following first surgery with 5-year follow-up data. Univariate and multivariate analyses were conducted to identify independent clinicopathological risk factors. Two independent and blinded neuroradiologists used 3D-Slicer software to manually delineate whole tumors using preoperative axial contrast-enhanced T1WI (CE-T1WI) images. 3D-Slicer was then used to extract radiomics features from segmented tumors. Dimensionality reduction was carried out by the least absolute shrinkage and selection operator (LASSO). Two multilayer perceptron (MLP) models were established, including independent clinicopathological risk factors (Model 1) and a combination of screened radiomics features and independent clinicopathological markers (Model 2). The predictive performance of these models was evaluated by receiver operator characteristic (ROC) curve analysis.

Results: In total, 1,130 features were identified, and 4 of these were selected by LASSO. In the test set, the area under the curve (AUC) of Model 2 was superior to Model 1 {0.783, [95% confidence interval (CI): 0.718—.860] vs. 0.739, (95% CI: 0.665–0.818)}. Model 2 also yielded the higher accuracy (0.808 vs. 0.692), sensitivity (0.826 vs. 0.652), and specificity (0.793 vs. 0.724) than Model 1.

Conclusions: The integrated classifier was superior to a clinical classifier and may facilitate the prediction of individualized prognosis and therapy.

A Preoperative MRI-Based Radiomics-Clinicopathological Classifier to Predict the Recurrence of Pituitary Macroadenoma Within 5 Years

Objective: To investigate the ability of a MRI-based radiomics-clinicopathological model to predict pituitary macroadenoma (PMA) recurrence within 5 years. Materials and Methods: We recruited 74 recurrent and 94 non-recurrent subjects, following first surgery with 5-year follow-up data. Univariate and multivariate analyses were conducted to identify independent clinicopathological risk factors. Two independent and blinded neuroradiologists used 3D-Slicer software to manually delineate whole tumors using preoperative axial contrast-enhanced T1WI (CE-T1WI) images. 3D-Slicer was then used to extract radiomics features from segmented tumors. Dimensionality reduction was carried out by the least absolute shrinkage and selection operator (LASSO). Two multilayer perceptron (MLP) models were established, including independent clinicopathological risk factors (Model 1) and a combination of screened radiomics features and independent clinicopathological markers (Model 2). The predictive performance of these models was evaluated by receiver operator characteristic (ROC) curve analysis. Results: In total, 1,130 features were identified, and 4 of these were selected by LASSO. In the test set, the area under the curve (AUC) of Model 2 was superior to Model 1 {0.783, [95% confidence interval (CI): 0.718-.860] vs. 0.739, (95% CI: 0.665-0.818)}. Model 2 also yielded the higher accuracy (0.808 vs. 0.692), sensitivity (0.826 vs. 0.652), and specificity (0.793 vs. 0.724) than Model 1. Conclusions: The integrated classifier was superior to a clinical classifier and may facilitate the prediction of individualized prognosis and therapy.

Use of Artemis Neuro Evacuation Device in Resection of Pituitary Adenoma: Initial Technical Note

BACKGROUND

Endoscopic transsphenoidal resection has become the primary approach for resection of pituitary macroadenomas. However, challenges such as narrow sinuses, dense tumor consistency, and invasion of surrounding structures exist.

METHODS

We report a case in which the Artemis Neuro Evacuation device was used to endonasally remove a pituitary macroadenoma with suprasellar and parasellar extensions. The methodology and technical aspects of this device's novel use are illustrated.

RESULTS

The long and narrow wand of the Artemis system allowed the operators to navigate through patient's narrow sinuses. Despite a dense tumor that did not come easily into a suction, the entire tumor debulking procedure lasted <10 minutes with the preservation of an intact arachnoid membrane using the Artemis. No perioperative complication was encountered, and the patient's neurologic deficits resolved.

CONCLUSIONS

To the authors' knowledge, this represents the first reported case of the Artemis system's utility in the treatment of pituitary tumor beyond cranial hematoma and hemorrhage.

Null cell adenomas of the pituitary gland: an institutional review of their clinical imaging and behavioral characteristics

The aim of the study was to establish if the null cell adenoma (NCA) forms a distinct subgroup with unique clinicopathological characteristics within the nonfunctioning pituitary adenoma group particularly in relation to the silent gonadotroph adenomas (SGAs). We identified 31 patients with the pathological diagnosis of NCA verified by routine histology and immunohistochemistry with distinct differentiation from SGAs by an established negative testing for SF-1 at the Toronto Western Hospital between December 2004 and August 2010. We reviewed their demographic data, clinical features, magnetic resonance imaging, and the histologic variables: MIB-1, FGFR4, and P27. We compared these to 63 SGAs identified within the same period. All the NCAs were macroadenomas with diameter ranging from 15-57 mm and tumor volumes between 1.95-53.5 mm(3). Preoperative cavernous sinus tumor growth was able to predict the presence of a residual after surgery (p = 0.023). Furthermore, preoperative cavernous sinus extension (p = 0.002) and negative P27 expression (p = 0.035) were able to independently predict the subsequent growth of the postoperative tumor residual. Comparing the NCA to SGA, we found that MIB-1 was higher in NCA (mean ± SD = 3.43 ± 2.76 %) compared to SGAs (mean ± SD = 2.49 ± 1.41 %) (p = 0.044). The preoperative and postoperative tumor volume doubling times (TVDTs) displayed a negative correlation in the SGA (r = -0.855, p = 0.002) while in the NCA, a positive correlation was evident (r = 0.718, p = 0.029). Our study suggests that the NCAs are a distinct group with differing behavioral characteristics from the SGAs. It also appears that the finding of cavernous sinus extension on preoperative imaging and a negative P27 expression on immunohistochemistry in NCAs may be valuable tools in predicting residual tumor growth which may impact on postoperative care.