Intraoperative radiation therapy (IORT) for previously untreated malignant gliomas

BACKGROUND: Intraoperative radiation therapy (IORT) is one of the methods used to deliver a large single dose to the tumor tissue while reducing the exposure of normal surrounding tissue. However, the usefulness of intraoperative electron therapy for malignant gliomas has not been established. METHODS: During the period from 1987 to 1997, 32 patients with malignant gliomas were treated with IORT. The histological diagnoses were anaplastic astrocytoma in 11 patients and glioblastoma in 21 patients. Therapy consisted of surgical resection and intraoperative electron therapy using a dose of 12–15 Gy (median, 15 Gy). The patients later underwent postoperative external radiation therapy (EXRT) with a median total dose of 60 Gy. Each of the 32 patients treated with IORT was randomly matched with patients who had been treated with postoperative EXRT alone (control). Patients were matched according to histological grade, age, extent of tumor removal, and tumor location. RESULTS: In the anaplastic astrocytoma group, the one-, two- and five-year survival rates were 81%, 51% and 15%, respectively in the IORT patients and 54%, 43% and 21%, respectively in the control patients. In the glioblastoma group, one-, two- and five-year survival rates were 63%, 26% and 0%, respectively in the IORT patients and 70%, 18% and 6%, respectively in the control patients. There was no significant difference between survival rates in the IORT patients and control patients in either the anaplastic astrocytoma group or glioblastoma group. CONCLUSIONS: IORT dose not improve survival of patients with malignant gliomas compared to that of patients who have received EXRT alone

[(18)F]Fluoroethyltyrosine- positron emission tomography-guided radiotherapy for high-grade glioma

BACKGROUND: To compare morphological gross tumor volumes (GTVs), defined as pre- and postoperative gadolinium enhancement on T1-weighted magnetic resonance imaging to biological tumor volumes (BTVs), defined by the uptake of (18)F fluoroethyltyrosine (FET) for the radiotherapy planning of high-grade glioma, using a dedicated positron emission tomography (PET)-CT scanner equipped with three triangulation lasers for patient positioning. METHODS: Nineteen patients with malignant glioma were included into a prospective protocol using FET PET-CT for radiotherapy planning. To be eligible, patients had to present with residual disease after surgery. Planning was performed using the clinical target volume (CTV = GTV union or logical sum BTV) and planning target volume (PTV = CTV + 20 mm). First, the interrater reliability for BTV delineation was assessed among three observers. Second, the BTV and GTV were quantified and compared. Finally, the geometrical relationships between GTV and BTV were assessed. RESULTS: Interrater agreement for BTV delineation was excellent (intraclass correlation coefficient 0.9). Although, BTVs and GTVs were not significantly different (p = 0.9), CTVs (mean 57.8 +/- 30.4 cm(3)) were significantly larger than BTVs (mean 42.1 +/- 24.4 cm(3); p < 0.01) or GTVs (mean 38.7 +/- 25.7 cm(3); p < 0.01). In 13 (68%) and 6 (32%) of 19 patients, FET uptake extended >or= 10 and 20 mm from the margin of the gadolinium enhancement. CONCLUSION: Using FET, the interrater reliability had excellent agreement for BTV delineation. With FET PET-CT planning, the size and geometrical location of GTVs and BTVs differed in a majority of patients

Socially and biologically inspired computing for self-organizing communications networks

The design and development of future communications networks call for a careful examination of biological and social systems. New technological developments like self-driving cars, wireless sensor networks, drones swarm, Internet of Things, Big Data, and Blockchain are promoting an integration process that will bring together all those technologies in a large-scale heterogeneous network. Most of the challenges related to these new developments cannot be faced using traditional approaches, and require to explore novel paradigms for building computational mechanisms that allow us to deal with the emergent complexity of these new applications. In this article, we show that it is possible to use biologically and socially inspired computing for designing and implementing self-organizing communication systems. We argue that an abstract analysis of biological and social phenomena can be made to develop computational models that provide a suitable conceptual framework for building new networking technologies: biologically inspired computing for achieving efficient and scalable networking under uncertain environments; socially inspired computing for increasing the capacity of a system for solving problems through collective actions. We aim to enhance the state-of-the-art of these approaches and encourage other researchers to use these models in their future work

ICAR: endoscopic skull‐base surgery

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