Treatment Outcome of Gamma Knife Radiosurgery for Petroclival Meningiomas: Retrospective Analysis of a Single Institution Experience

OBJECTIVEPetroclival meningiomas (PCMs) can cause devastating clinical symptoms due to mass effect on cranial nerves (CNs); thus, patients harboring these tumors need treatment. Many neurosurgeons advocate for microsurgery because removal of the tumor can provide relief or result in symptom disappearance. Gamma Knife radiosurgery (GKRS) is often an alternative for surgery because it can cause tumor shrinkage with improvement of symptoms. This study evaluates qualitative volumetric changes of PCM after primary GKRS and its impact on clinical symptoms.METHODSThe authors performed a retrospective study of patients with PCM who underwent primary GKRS between 2003 and 2015 at the Gamma Knife Center of the Elisabeth-Tweesteden Hospital in Tilburg, the Netherlands. This study yields 53 patients. In this study the authors concentrate on qualitative volumetric tumor changes, local tumor control rate, and the effect of the treatment on trigeminal neuralgia (TN).RESULTSLocal tumor control was 98% at 5 years and 93% at 7 years (Kaplan-Meier estimates). More than 90% of the tumors showed regression in volume during the first 5 years. The mean volumetric tumor decrease was 21.2%, 27.1%, and 31% at 1, 3, and 6 years of follow-up, respectively. Improvement in TN was achieved in 61%, 67%, and 70% of the cases at 1, 2, and 3 years of follow-up, respectively. This was associated with a mean volumetric tumor decrease of 25% at the 1-year follow-up to 32% at the 3-year follow-up.CONCLUSIONSGKRS for PCMs yields a high tumor control rate with a low incidence of neurological deficits. Many patients with TN due to PCM experienced improvement in TN after radiosurgery. GKRS achieves significant volumetric tumor decrease in the first years of follow-up and thereafter.

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The Treatment Outcome of Elderly Patients with Idiopathic Trigeminal Neuralgia : Micro-Vascular Decompression versus Gamma Knife Radiosurgery

Journal of Korean Neurosurgical Society ◽

10.3340/jkns.2008.44.4.199 ◽

2008 ◽

Vol 44 (4) ◽

pp. 199 ◽

Cited By ~ 24

Author(s):

In Ho Oh ◽

Seok Keun Choi ◽

Bong Jin Park ◽

Tae Sung Kim ◽

Bong Arm Rhee ◽

...

Keyword(s):

Treatment Outcome ◽

Trigeminal Neuralgia ◽

Elderly Patients ◽

Gamma Knife ◽

Gamma Knife Radiosurgery ◽

Idiopathic Trigeminal Neuralgia ◽

Vascular Decompression

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Treatment Outcome and Prognostic Factors for Patients with Bone‐Only Metastases of Breast Cancer: A Single‐Institution Retrospective Analysis

The Oncologist ◽

10.1634/theoncologist.2010-0350 ◽

2011 ◽

Vol 16 (2) ◽

pp. 155-164 ◽

Cited By ~ 39

Author(s):

Naoki Niikura ◽

Jun Liu ◽

Naoki Hayashi ◽

Shana L. Palla ◽

Yutaka Tokuda ◽

...

Keyword(s):

Breast Cancer ◽

Treatment Outcome ◽

Prognostic Factors ◽

Retrospective Analysis ◽

Single Institution

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Gamma Knife radiosurgery for intracranial meningiomas: Do we need to treat the dural tail? A single-center retrospective analysis and an overview of the literature

Surgical Neurology International ◽

10.4103/2152-7806.140192 ◽

2014 ◽

Vol 5 (9) ◽

pp. 391 ◽

Cited By ~ 7

Author(s):

VincentJ Bulthuis ◽

PatrickE. J. Hanssens ◽

SuanTe Lie ◽

JacobusJ van Overbeeke

Keyword(s):

Retrospective Analysis ◽

Gamma Knife ◽

Gamma Knife Radiosurgery ◽

Single Center ◽

Intracranial Meningiomas ◽

Dural Tail

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Predicting the Local Response of Metastatic Brain Tumor to Gamma Knife Radiosurgery by Radiomics With a Machine Learning Method

Frontiers in Oncology ◽

10.3389/fonc.2020.569461 ◽

2021 ◽

Vol 10 ◽

Author(s):

Daisuke Kawahara ◽

Xueyan Tang ◽

Chung K. Lee ◽

Yasushi Nagata ◽

Yoichi Watanabe

Keyword(s):

Machine Learning ◽

Treatment Outcome ◽

Prediction Model ◽

Gamma Knife ◽

Evaluation Method ◽

Gamma Knife Radiosurgery ◽

Training Data ◽

Local Response ◽

Visual Evaluation ◽

Group I

PurposeThe current study proposed a model to predict the response of brain metastases (BMs) treated by Gamma knife radiosurgery (GKRS) using a machine learning (ML) method with radiomics features. The model can be used as a decision tool by clinicians for the most desirable treatment outcome.Methods and MaterialUsing MR image data taken by a FLASH (3D fast, low-angle shot) scanning protocol with gadolinium (Gd) contrast-enhanced T1-weighting, the local response (LR) of 157 metastatic brain tumors was categorized into two groups (Group I: responder and Group II: non-responder). We performed a radiomics analysis of those tumors, resulting in more than 700 features. To build a machine learning model, first, we used the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of radiomics features to the minimum number of features useful for the prediction. Then, a prediction model was constructed by using a neural network (NN) classifier with 10 hidden layers and rectified linear unit activation. The training model was evaluated with five-fold cross-validation. For the final evaluation, the NN model was applied to a set of data not used for model creation. The accuracy and sensitivity and the area under the receiver operating characteristic curve (AUC) of the prediction model of LR were analyzed. The performance of the ML model was compared with a visual evaluation method, for which the LR of tumors was predicted by examining the image enhancement pattern of the tumor on MR images.ResultsBy the LASSO analysis of the training data, we found seven radiomics features useful for the classification. The accuracy and sensitivity of the visual evaluation method were 44 and 54%. On the other hand, the accuracy and sensitivity of the proposed NN model were 78 and 87%, and the AUC was 0.87.ConclusionsThe proposed NN model using the radiomics features can help physicians to gain a more realistic expectation of the treatment outcome than the traditional method.

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