A Euristic Approach to the Volume Reconstruction of Arteriovenous Malformations from Biplane Angiography

Foroni, Roberto; Giri, Mariagrazia; Gerosa, Marco Aurélio; Nicolato, A.; Piovan, E.; Zampieri, P.; Pasqualin, A.; Bortolazzi, E.; Pasoli, A.; Marchini, Giorgio; Babighian, Silvia; Bricolo, A.

doi:10.1159/000098773

Cited by 5 publications

(2 citation statements)

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“…Dosage plans were developed using commercially available software (KULA; Elekta) from February 1993 to February 1998, and then Leksell Gamma Plan (Elekta Instruments) after February 1998. Up to February 1998, the calculation of AVM volume, the optimisation of the dose/volume ratio on the day of treatment, and the measurement of the AVM remnant volume at FU MRI were performed using a sophisticated computerised 3D reconstruction plan implemented ªin locoº, utilising the marching cubes algorithm running on parallel board I 860 and MATROX MG3DU [48,49]. After acquisition of the ªGamma Planº multimedial programme for dose planning (Elekta Instruments) in March 1998, the volume of the angioma was determined using this software, which makes it possible to integrate 2D angiographic images with 3D images from MRI and angio-MRI.…”

Section: Methodsmentioning

confidence: 99%

Gamma Knife Radiosurgery in the Management of Arteriovenous Malformations of the Basal Ganglia Region of the Brain

Nicolato

Foroni

Crocco

et al. 2002

Minim Invasive Neurosurg

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In our experience, the different clinical, epidemiological and anatomico-functional characteristics of BGAVMs and cortical AVMs do not seem to influence the radiosurgical outcome, since complete obliteration and permanent neurological sequelae rates were very similar in these two subgroups of patients. GK may be considered the first choice treatment modality for BGAVMs with < 10 cc volume, since it offers a very high cure rate, very low permanent morbidity, and zero mortality. Although haemorrhagic onset is much more frequent in BGAVMs than in cortical AVMs, the difference between the bleeding/rebleeding rates in the two subgroups of patients during the latency period is not statistically significant, and seems to be limited to the first year after GK.

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Section: Methodsmentioning

confidence: 99%

Gamma Knife Radiosurgery in the Management of Arteriovenous Malformations of the Basal Ganglia Region of the Brain

Nicolato

Foroni

Crocco

et al. 2002

Minim Invasive Neurosurg

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“…Portions of this paper were presented as oral communications at the 68th Congress of the Italian Society of Neurosurgery, Rome, September [16][17][18]2019, and the 19th Congress of the European Association of Neurosurgical Societies, Dublin, September 28, 2019.…”

Section: Disclosuresmentioning

confidence: 99%

Machine learning–based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming?

Zoli

Staartjes

Guaraldi

et al. 2020

Neurosurgical Focus

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OBJECTIVEMachine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing disease (CD).METHODSAll consecutive patients in our center who underwent surgery for CD through the endoscopic endonasal approach were retrospectively reviewed. Study endpoints were gross-tumor removal (GTR), postsurgical remission, and long-term control of disease. Several demographic, radiological, and histological factors were assessed as potential predictors. For ML-based modeling, data were randomly divided into 2 sets with an 80% to 20% ratio for bootstrapped training and testing, respectively. Several algorithms were tested and tuned for the area under the curve (AUC).RESULTSThe study included 151 patients. GTR was achieved in 137 patients (91%), and postsurgical hypersecretion remission was achieved in 133 patients (88%). At last follow-up, 116 patients (77%) were still in remission after surgery and in 21 patients (14%), CD was controlled with complementary treatment (overall, of 131 cases, 87% were under control at follow-up). At internal validation, the endpoints were predicted with AUCs of 0.81–1.00, accuracy of 81%–100%, and Brier scores of 0.035–0.151. Tumor size and invasiveness and histological confirmation of adrenocorticotropic hormone (ACTH)–secreting cells were the main predictors for the 3 endpoints of interest.CONCLUSIONSML algorithms were used to train and internally validate robust models for all the endpoints, giving accurate outcome predictions in CD cases. This analytical method seems promising for potentially improving future patient care and counseling; however, careful clinical interpretation of the results remains necessary before any clinical adoption of ML. Moreover, further studies and increased sample sizes are definitely required before the widespread adoption of ML to the study of CD.

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An introduction and overview of machine learning in neurosurgical care

et al. 2017

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ML has started to find applications aimed at improving neurosurgical care by increasing the efficiency and precision of perioperative decision-making. A thorough validation of specific ML models is essential before implementation in clinical neurosurgical care. To bridge the gap between research and clinical care, practical and ethical issues should be considered parallel to the development of these techniques.

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A Euristic Approach to the Volume Reconstruction of Arteriovenous Malformations from Biplane Angiography

Cited by 5 publications

References 0 publications

Gamma Knife Radiosurgery in the Management of Arteriovenous Malformations of the Basal Ganglia Region of the Brain

Gamma Knife Radiosurgery in the Management of Arteriovenous Malformations of the Basal Ganglia Region of the Brain

Machine learning–based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming?

An introduction and overview of machine learning in neurosurgical care

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