[Sellar tumors]

Endoscopic Endonasal Management of Metastatic Lesions of the Anterior Skull Base: Case Series and Literature Review

BACKGROUND

The anterior skull base is an uncommon site for brain metastases. A limited number of cases have been reported where a region of the skull base is accessed through an expanded/extended transsphenoidal route. In addition, reports of overall and progression-free survival rates are lacking.

OBJECTIVE

To report progression-free and overall survival rates for skull base metastases treated with aggressive endoscopic endonasal surgery and radiation therapy.

METHODS

We retrospectively review a prospective database of patients at New York Presbyterian Hospital undergoing expanded endoscopic endonasal surgery from 2004 to 2014. From this database, we identified all patients whose final pathology revealed a metastatic lesion in the midline skull base.

RESULTS

Seven hundred forty-nine patients underwent endonasal endoscopic surgery. Final pathology in 12 patients (1.6%) revealed metastasis to the sellar or parasellar region. Tumors were located in the sella, suprasellar cistern, clivus, ethmoid sinuses, sphenoid sinus, cavernous sinus, and craniovertebral junction. Breast and lung cancers were the most common primary diagnoses. Gross total resection was achieved in 41.6% of patients. There were no postoperative cerebrospinal fluid leaks. Adjuvant radiation therapy was used in 92% of cases. Median follow-up was 14 months (range, 1-55). Median progression-free and overall survival were 18 and 16 months, respectively.

CONCLUSIONS

The range of anterior skull base metastatic lesions that can be managed endonasally has increased with the advent of extended endonasal endoscopic surgical approaches. Favorable results can be obtained with this surgical approach as part of the overall management paradigm for patients with skull base metastases.

Segmentation of pituitary adenoma: a graph-based method vs. a balloon inflation method

Among all abnormal growths inside the skull, the percentage of tumors in sellar region is approximately 10-15%, and the pituitary adenoma is the most common sellar lesion. A time-consuming process that can be shortened by using adequate algorithms is the manual segmentation of pituitary adenomas. In this contribution, two methods for pituitary adenoma segmentation in the human brain are presented and compared using magnetic resonance imaging (MRI) patient data from the clinical routine: Method A is a graph-based method that sets up a directed and weighted graph and performs a min-cut for optimal segmentation results: Method B is a balloon inflation method that uses balloon inflation forces to detect the pituitary adenoma boundaries. The ground truth of the pituitary adenoma boundaries - for the evaluation of the methods - are manually extracted by neurosurgeons. Comparison is done using the Dice Similarity Coefficient (DSC), a measure for spatial overlap of different segmentation results. The average DSC for all data sets is 77.5±4.5% for the graph-based method and 75.9±7.2% for the balloon inflation method showing no significant difference. The overall segmentation time of the implemented approaches was less than 4s - compared with a manual segmentation that took, on the average, 3.9±0.5min.

Pituitary adenoma volumetry with 3D Slicer

In this study, we present pituitary adenoma volumetry using the free and open source medical image computing platform for biomedical research: (3D) Slicer. Volumetric changes in cerebral pathologies like pituitary adenomas are a critical factor in treatment decisions by physicians and in general the volume is acquired manually. Therefore, manual slice-by-slice segmentations in magnetic resonance imaging (MRI) data, which have been obtained at regular intervals, are performed. In contrast to this manual time consuming slice-by-slice segmentation process Slicer is an alternative which can be significantly faster and less user intensive. In this contribution, we compare pure manual segmentations of ten pituitary adenomas with semi-automatic segmentations under Slicer. Thus, physicians drew the boundaries completely manually on a slice-by-slice basis and performed a Slicer-enhanced segmentation using the competitive region-growing based module of Slicer named GrowCut. Results showed that the time and user effort required for GrowCut-based segmentations were on average about thirty percent less than the pure manual segmentations. Furthermore, we calculated the Dice Similarity Coefficient (DSC) between the manual and the Slicer-based segmentations to proof that the two are comparable yielding an average DSC of 81.97±3.39%.

Square-cut: a segmentation algorithm on the basis of a rectangle shape

We present a rectangle-based segmentation algorithm that sets up a graph and performs a graph cut to separate an object from the background. However, graph-based algorithms distribute the graph's nodes uniformly and equidistantly on the image. Then, a smoothness term is added to force the cut to prefer a particular shape. This strategy does not allow the cut to prefer a certain structure, especially when areas of the object are indistinguishable from the background. We solve this problem by referring to a rectangle shape of the object when sampling the graph nodes, i.e., the nodes are distributed non-uniformly and non-equidistantly on the image. This strategy can be useful, when areas of the object are indistinguishable from the background. For evaluation, we focus on vertebrae images from Magnetic Resonance Imaging (MRI) datasets to support the time consuming manual slice-by-slice segmentation performed by physicians. The ground truth of the vertebrae boundaries were manually extracted by two clinical experts (neurological surgeons) with several years of experience in spine surgery and afterwards compared with the automatic segmentation results of the proposed scheme yielding an average Dice Similarity Coefficient (DSC) of 90.97±2.2%.

A medical software system for volumetric analysis of cerebral pathologies in magnetic resonance imaging (MRI) data

In this contribution, a medical software system for volumetric analysis of different cerebral pathologies in magnetic resonance imaging (MRI) data is presented. The software system is based on a semi-automatic segmentation algorithm and helps to overcome the time-consuming process of volume determination during monitoring of a patient. After imaging, the parameter settings-including a seed point-are set up in the system and an automatic segmentation is performed by a novel graph-based approach. Manually reviewing the result leads to reseeding, adding seed points or an automatic surface mesh generation. The mesh is saved for monitoring the patient and for comparisons with follow-up scans. Based on the mesh, the system performs a voxelization and volume calculation, which leads to diagnosis and therefore further treatment decisions. The overall system has been tested with different cerebral pathologies-glioblastoma multiforme, pituitary adenomas and cerebral aneurysms- and evaluated against manual expert segmentations using the Dice Similarity Coefficient (DSC). Additionally, intra-physician segmentations have been performed to provide a quality measure for the presented system.