Browsing by Subject "meningioma"

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    Management of Intracranial Meningiomas Using Keyhole Techniques
    (Cureus, Inc., 2016-04-27) Burks, Joshua D.; Conner, Andrew K.; Bonney, Phillip A.; Archer, Jacob B.; Christensen, Blake; Smith, Jacqueline; Safavi-Abbasi, Sam; Sughrue, Michael; Department of Neurological Surgery, IU School of Medicine
    BACKGROUND: Keyhole craniotomies are increasingly being used for lesions of the skull base. Here we review our recent experience with these approaches for resection of intracranial meningiomas. METHODS: Clinical and operative data were gathered on all patients treated with keyhole approaches by the senior author from January 2012 to June 2013. Thirty-one meningiomas were resected in 27 patients, including 9 supratentorial, 5 anterior fossa, 7 middle fossa, 6 posterior fossa, and 4 complex skull base tumors. Twenty-nine tumors were WHO Grade I, and 2 were Grade II. RESULTS: The mean operative time was 8 hours, 22 minutes (range, 2:55-16:14) for skull-base tumors, and 4 hours, 27 minutes (range, 1:45-7:13) for supratentorial tumors. Simpson Resection grades were as follows: Grade I = 8, II = 8, III = 1, IV = 15, V = 0. The median postoperative hospital stay was 4 days (range, 1-20 days). In the 9 patients presenting with some degree of visual loss, 7 saw improvement or complete resolution. In the 6 patients presenting with cranial nerve palsies, 4 experienced improvement or resolution of the deficit postoperatively. Four patients experienced new neurologic deficits, all of which were improved or resolved at the time of the last follow-up. Technical aspects and surgical nuances of these approaches for management of intracranial meningiomas are discussed. CONCLUSIONS: With careful preoperative evaluation, keyhole approaches can be utilized singly or in combination to manage meningiomas in a wide variety of locations with satisfactory results.
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    Mitotic cell detection in H&E stained meningioma histopathology slides
    (2019-12) Cheng, Huiwen; Tsechpenakis, Gavriil; Tuceryan, Mihran; Jiang, Yu zheng
    Meningioma represent more than one-third of all primary central nervous system (CNS) tumors, and it can be classified into three grades according to WHO (World Health Organization) in terms of clinical aggressiveness and risk of recurrence. A key component of meningioma grades is the mitotic count, which is defined as quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at 10 consecutive high-power fields (HPF) on a glass slide under a microscope, which is an extremely laborious and time-consuming process. The goal of this thesis is to investigate the use of computerized methods to automate the detection of mitotic nuclei with limited labeled data. We built computational methods to detect and quantify the histological features of mitotic cells on a whole slides image which mimic the exact process of pathologist workflow. Since we do not have enough training data from meningioma slide, we learned the mitotic cell features through public available breast cancer datasets, and predicted on meingioma slide for accuracy. We use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Hand crafted features are inspired by the domain knowledge, while the data-driven VGG16 models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. Our work on detection of mitotic cells shows 100% recall , 9% precision and 0.17 F1 score. The detection using VGG16 performs with 71% recall, 73% precision, and 0.77 F1 score. Finally, this research of automated image analysis could drastically increase diagnostic efficiency and reduce inter-observer variability and errors in pathology diagnosis, which would allow fewer pathologists to serve more patients while maintaining diagnostic accuracy and precision. And all these methodologies will increasingly transform practice of pathology, allowing it to mature toward a quantitative science.
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    Proton therapy for atypical meningiomas
    (Springer, 2015-05) McDonald, Mark W.; Plankenhorn, David A.; McMullen, Kevin P.; Henderson, Mark A.; Dropcho, Edward J.; Shah, Mitesh V.; Cohen-Gadol, Aaron A.; Department of Radiation Oncology, IU School of Medicine
    We report clinical outcomes of proton therapy in patients with World Health Organization grade 2 (atypical) meningiomas. Between 2005 and 2013, 22 patients with atypical meningiomas were treated to a median dose of 63 Gy (RBE) using proton therapy, as an adjuvant therapy after surgery (n = 12) or for recurrence or progression of residual tumor (n = 10). Six patients had presumed radiation-induced meningiomas, but none had received prior radiotherapy for their meningioma. The median follow-up time after radiation was 39 months (range 7–104) and all patients remain alive at last follow-up. The 5-year estimate of local control was 71.1 % (95 % CI 49.3–92.9 %). The 5-year estimate of local control was 87.5 % following a radiation dose >60 Gy (RBE), compared to 50.0 % for ≤60 Gy (RBE) (p = 0.038). The 5-year estimate of neuraxis dissemination was 5 % (95 % CI 0–14.6 %) and 6.2 % (95 % CI 0–18.2 %) for metastases outside of the central nervous system. Radiation necrosis was observed in one patient with a history of prior cranial irradiation. Fractionated proton therapy was associated with favorable tumor control rates for grade 2 meningiomas. Prospective studies are needed to define the optimal radiation dose for high-grade meningiomas.