“…Further, volumetric assessment in clinical routine is time-consuming, an automated segmentation is therefore warranted. The automated evaluation may further be transferred for the purpose of planning stereotactic radiation therapy and surgery [ 5 , 13 , 38 , 41 , 42 ]. Fig.…”
Objectives Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine MRI data including images from diverse referring institutions to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. Methods We included 56 of 136 consecutive preoperative MRI datasets [T1/T2-weighted, T1-weighted contrast-enhanced (T1CE), FLAIR] of meningiomas that were treated surgically at the University Hospital Cologne and graded histologically as tumour grade I (n = 38) or grade II (n = 18). The DLM was trained on an independent dataset of 249 glioma cases and segmented different tumour classes as defined in the brain tumour image segmentation benchmark (BRATS benchmark). The DLM was based on the DeepMedic architecture. Results were compared to manual segmentations by two radiologists in a consensus reading in FLAIR and T1CE. T1-weighted contrast-enhanced MRI
“…Further, volumetric assessment in clinical routine is time-consuming, an automated segmentation is therefore warranted. The automated evaluation may further be transferred for the purpose of planning stereotactic radiation therapy and surgery [ 5 , 13 , 38 , 41 , 42 ]. Fig.…”
Objectives Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine MRI data including images from diverse referring institutions to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. Methods We included 56 of 136 consecutive preoperative MRI datasets [T1/T2-weighted, T1-weighted contrast-enhanced (T1CE), FLAIR] of meningiomas that were treated surgically at the University Hospital Cologne and graded histologically as tumour grade I (n = 38) or grade II (n = 18). The DLM was trained on an independent dataset of 249 glioma cases and segmented different tumour classes as defined in the brain tumour image segmentation benchmark (BRATS benchmark). The DLM was based on the DeepMedic architecture. Results were compared to manual segmentations by two radiologists in a consensus reading in FLAIR and T1CE. T1-weighted contrast-enhanced MRI
“…The Simpson grade system was not used because it was developed prior to the widespread use of MRI scans and operating microscope, and some authors feel it may not have a significant impact on outcome as previously thought. 1,13,20,31,39 Moreover, Simpson grading of skull base meningiomas can be very difficult to measure given the proximity of the foramina carrying the ICA and optic and olfactory nerves, which are never drilled out in their entirety. To avoid over-or undergrading the Simpson grade, we preferred to present the EOR for these tumors as a more accurate measure.…”
OBJECTIVEAnterior skull base meningiomas are benign lesions that cause neurological symptoms through mass effect on adjacent neurovascular structures. While traditional transcranial approaches have proven to be effective at removing these tumors, minimally invasive approaches that involve using an endoscope offer the possibility of reducing brain and nerve retraction, minimizing incision size, and speeding patient recovery; however, appropriate case selection and results in large series are lacking.METHODSThe authors developed an algorithm for selecting a supraorbital keyhole minicraniotomy (SKM) for olfactory groove meningiomas or an expanded endoscopic endonasal approach (EEA) for tuberculum sella (TS) or planum sphenoidale (PS) meningiomas based on the presence or absence of olfaction and the anatomical extent of the tumor. Where neither approach is appropriate, a standard transcranial approach is utilized. The authors describe rates of gross-total resection (GTR), olfactory outcomes, and visual outcomes, as well as complications, for 7 subgroups of patients. Exceptions to the algorithm are also discussed.RESULTSThe series of 57 patients harbored 57 anterior skull base meningiomas; the mean tumor volume was 14.7 ± 15.4 cm3 (range 2.2–66.1 cm3), and the mean follow-up duration was 42.2 ± 37.1 months (range 2–144 months). Of 19 patients with olfactory groove meningiomas, 10 had preserved olfaction and underwent SKM, and preservation of olfaction in was seen in 60%. Of 9 patients who presented without olfaction, 8 had cribriform plate invasion and underwent combined SKM and EEA (n = 3), bifrontal craniotomy (n = 3), or EEA (n = 2), and one patient without both olfaction and cribriform plate invasion underwent SKM. GTR was achieved in 94.7%. Of 38 TS/PS meningiomas, 36 of the lesions were treated according to the algorithm. Of these 36 meningiomas, 30 were treated by EEA and 6 by craniotomy. GTR was achieved in 97.2%, with no visual deterioration and one CSF leak that resolved by placement of a lumbar drain. Two patients with tumors that, based on the algorithm, were not amenable to an EEA underwent EEA nonetheless: one had GTR and the other had a residual tumor that was followed and removed via craniotomy 9 years later.CONCLUSIONSUtilizing a simple algorithm aimed at preserving olfaction and vision and based on maximizing use of minimally invasive approaches and selective use of transcranial approaches, the authors found that excellent outcomes can be achieved for anterior skull base meningiomas.
“…Tumor pathology and grade were determined by a senior neuropathologist in all cases according to the 2007 World Health Organization (WHO) grading system. 22 Patients were assigned a preoperative Karnofsky Performance Scale (KPS) by the chart reviewer based on preoperative symptoms. A motor deficit was defined as any decrease in strength.…”
Object Meningiomas occur in various intracranial locations. Each location is associated with a unique set of surgical nuances and risk profiles. The incidence and risk factors that predispose patients to certain deficits based on tumor locations are unclear. This study aimed to determine which preoperative factors increase the risk of patients having new deficits after surgery based on tumor location for patients undergoing intracranial meningioma surgery.
Methods Adult patients who underwent primary, nonbiopsy resection of a meningioma at a tertiary care institution between 2007 and 2015 were retrospectively reviewed. Stepwise multivariate logistic regression analyses were used to identify associations with postoperative deficits based on tumor location.
Results Postoperatively, from the 761 included patients, there were 39 motor deficits (5.1%), 23 vision deficits (3.0%), 19 language deficits (2.5%), 27 seizures (3.5%), and 26 cognitive deficits (3.4%). The factors independently associated with any postoperative deficits were preoperative radiation (hazard ratio [HR] [95% confidence interval, CI] 3.000 [1.346–6.338], p = 0.008), cerebellopontine angle tumors (HR [95% CI] 2.126 [1.094–3.947], p = 0.03), Simpson grade 4 resections (HR [95% CI] 2.000 [1.271–3.127], p = 0.003), preoperative motor deficits (HR [95% CI] 1.738 [1.005–2.923], p = 0.048), preoperative cognitive deficits (HR [95% CI] 2.033 [1.144–3.504], p = 0.02), and perioperative pulmonary embolisms (HR [95% CI] 11.741 [2.803–59.314], p = 0.0009).
Conclusion Consideration of the factors associated with postoperative deficits in this study may help guide treatment strategies for patients with meningiomas.
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