Predicting Survival in Patients With Brain Metastases Treated With Radiosurgery Using Artificial Neural Networks

Oermann, Eric K.; Kress, M.; Collins, Brian T.; Collins, Sean P.; Morris, David E.; Ahalt, Stanley C.; Ewend, Matthew G.

doi:10.1227/neu.0b013e31828ea04b

Cited by 35 publications

(27 citation statements)

References 22 publications

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“…ANNs have been used to accurately predict survival in patients with brain metastases treated with radiosurgery 2. They have been shown to predict the occurrence of symptomatic cerebral vasospasm in patients with aneurysmal subarachnoid haemorrhage 33.…”

Section: Resultsmentioning

confidence: 99%

“…ANNs are well suited to address solvable problems or dilemmas, such as prediction, clinical diagnosis determination, pattern recognition and image analysis and interpretation. The use of ANNs for clinical decision-making support systems began in the late 1980s1 2; however, there has been little use of this method in neurosurgery 1 2. The history and theory of ANNs has been reported in detail elsewhere 1 3 4…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural networks in neurosurgery

Azimi

Mohammadi

Benzel

et al. 2014

Journal of Neurology, Neurosurgery & Psychiatry

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Artificial neural networks (ANNs) effectively analyze non-linear data sets. The aimed was A review of the relevant published articles that focused on the application of ANNs as a tool for assisting clinical decision-making in neurosurgery. A literature review of all full publications in English biomedical journals (1993-2013) was undertaken. The strategy included a combination of key words 'artificial neural networks', 'prognostic', 'brain', 'tumor tracking', 'head', 'tumor', 'spine', 'classification' and 'back pain' in the title and abstract of the manuscripts using the PubMed search engine. The major findings are summarized, with a focus on the application of ANNs for diagnostic and prognostic purposes. Finally, the future of ANNs in neurosurgery is explored. A total of 1093 citations were identified and screened. In all, 57 citations were found to be relevant. Of these, 50 articles were eligible for inclusion in this review. The synthesis of the data showed several applications of ANN in neurosurgery, including: (1) diagnosis and assessment of disease progression in low back pain, brain tumours and primary epilepsy; (2) enhancing clinically relevant information extraction from radiographic images, intracranial pressure processing, low back pain and real-time tumour tracking; (3) outcome prediction in epilepsy, brain metastases, lumbar spinal stenosis, lumbar disc herniation, childhood hydrocephalus, trauma mortality, and the occurrence of symptomatic cerebral vasospasm in patients with aneurysmal subarachnoid haemorrhage; (4) the use in the biomechanical assessments of spinal disease. ANNs can be effectively employed for diagnosis, prognosis and outcome prediction in neurosurgery.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial neural networks in neurosurgery

Azimi

Mohammadi

Benzel

et al. 2014

Journal of Neurology, Neurosurgery & Psychiatry

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“…8,11 An ANN as a prediction technique has been in use for more than 20 years in clinical medicine for clini cal diagnosis; prognosis and survival analyses; clinical outcome; and the medical domains of oncology, critical care, and cardiovascular medicine. 3,10 Artificial neural networks have also been successfully used in neurosur gery disorders such as prediction of death due to trauma, surgical decision making for patients who have suffered a traumatic brain injury, 3 surgical satisfaction in spine disorders, 1 and predicting survival in patients with brain metastases. 10 For neurosurgery disorder applications, due to the limited number of treatment options available, these pre diction models can potentially improve diagnostic accu racy, treatment decisions, and efficiency.…”

Section: Ann Theorymentioning

confidence: 99%

“…3,10 Artificial neural networks have also been successfully used in neurosur gery disorders such as prediction of death due to trauma, surgical decision making for patients who have suffered a traumatic brain injury, 3 surgical satisfaction in spine disorders, 1 and predicting survival in patients with brain metastases. 10 For neurosurgery disorder applications, due to the limited number of treatment options available, these pre diction models can potentially improve diagnostic accu racy, treatment decisions, and efficiency. Relationships between prognostic factors and a successful ETV surgery outcome in children with hydrocephalus have not been previously investigated using the ANN model.…”

Section: Ann Theorymentioning

confidence: 99%

Predicting endoscopic third ventriculostomy success in childhood hydrocephalus: an artificial neural network analysis

Azimi¹,

Mohammadi²

2014

PED

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Object Artificial neural networks (ANNs) can be used as a measure for the clinical decision-making process. The aim of this study was to develop an ANN model to predict endoscopic third ventriculostomy (ETV) success at 6 months and to compare the findings with those obtained using traditional predictive measures in childhood hydrocephalus. Methods The ANN, ETV Success Score (ETVSS), CURE Children's Hospital of Uganda (CCHU) ETV (CCHU ETV) Success Score, and logistic regression models were applied to predict outcomes. The cause of hydrocephalus, patient age, whether choroid plexus cauterization (CPC) was performed, previous shunt surgery, sex, type of hydrocephalus, and body weight were considered as input variables for an established ANN model. Data from hydrocephalic children who underwent ETV were applied, and the computer program that analyzes the data was trained to predict successful ETV by using several input variables. Successful ETV outcome was defined as the absence of ETV failure within 6 months of follow-up. Then, sensitivity analysis was performed for the established ANN model to identify the most important variables that predict outcome. The area under a receiver operating characteristic curve, accuracy rate of the prediction, and Hosmer-Lemeshow statistics were measured to test different prediction models. Results Data for 168 patients (80 males and 88 females; mean age 1.4 ± 2.6 years) were analyzed. Data from patients were divided into 3 groups: a training group (n = 84), a testing group (n = 42), and a validation group (n = 42). The successful ETV outcome rate, defined as the absence of ETV failure within 6 months of follow-up, was 47%. Etiology, age, CPC status, type of hydrocephalus, and previous shunt placement were the most important variables that were indicated by the ANN analysis. Compared with the ETVSS, CCHU ETV Success Score, and the logistic regression models, the ANN model showed better results, with an accuracy rate of 95.1%, a Hosmer-Lemeshow statistic of 41.2, and an area under the curve of 0.87. Conclusions The findings show that ANNs can predict ETV success at 6 months with a high level of accuracy in childhood hydrocephalus. The authors' results will need to be confirmed with further prospective studies.

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“…Some of the most commonly used methods include artificial neural networks (ANNs), decision tree, support vector machine (SVM) and logistic regression (LR). These methods are used in the different clinical fields such as, prediction of surgery outcome in disc herniation using artificial neural network and logistic regression, 1,5 prediction of surgery outcome in spinal canal stenosis using artificial neural network and logistic regression, 2 decision making and prediction of mortality in the patients with head injury by comparison of ANN and LR, [6][7][8][9] classification of fatty and cirrhosis liver using texture analysis of Computed tomography (CT) images and probabilistic neural network, back propagation neural network and linear vector quantization, 10 prediction of survival in brain metastases using an ensemble of 5 ANNs, single ANN and LR, 11 prediction and classification of low back pain by ANNs and LR, 12,13 identifying prognostic factors in patients with primary pontine hemorrhage by LR, 14 using binary LR analysis to predict effect of gamma-knife radiosurgery in patients with cerebral arteriovenous malformations, 15 identification of patients with acute coronary syndrome using neural networks and multiple LR, 16 prediction of the recurrence-proneness for cervical cancer through the SVM, C5.0 and extreme learning machine, 17 applying multivariate LR analyses for measurement intraventricular and intraparenchymal intracranial pressure monitoring in brain injury, 18 using SVMs, k-nearest neighbors and ensemble AdaBoost classifier for diagnosis of Parkinson disease by kinematic features and pressure features of handwriting, 19 using Naive Bayesian classifier for diagnosis of Alzheimer disease as one of the types of the dementia, 20 classification of pediatric posterior fossa tumors using neural networks, 21 evaluation of spinal loads and muscle forces using ANN. 22 ANNs are powerful analyzers that discover the complex and non-linear relationships between the data set.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery

Babaee

Soleimani

Zali

et al. 2017

Int Clin Neurosci J

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Background: Low back pain is a common problem in many people. Neurosurgeons recommend posterior spinal fusion (PSF) surgery as one of the therapeutic strategies to the patients with low back pain. Due to the high risk of this type of surgery and the critical importance of making the right decision, accurate prediction of the surgical outcome is one of the main concerns for the neurosurgeons. Methods: In this study, 12 types of multi-layer perceptron (MLP) networks and 66 radial basis function (RBF) networks as the types of artificial neural network methods and a logistic regression (LR) model created and compared to predict the satisfaction with PSF surgery as one of the most well-known spinal surgeries. Results:The most important clinical and radiologic features as twenty-seven factors for 480 patients (150 males, 330 females; mean age 52.32 ± 8.39 years) were considered as the model inputs that included: age, sex, type of disorder, duration of symptoms, job, walking distance without pain (WDP), walking distance without sensory (WDS) disorders, visual analog scale (VAS) scores, Japanese Orthopaedic Association (JOA) score, diabetes, smoking, knee pain (KP), pelvic pain (PP), osteoporosis, spinal deformity and etc. The indexes such as receiver operating characteristicarea under curve (ROC-AUC), positive predictive value, negative predictive value and accuracy calculated to determine the best model. Postsurgical satisfaction was 77.5% at 6 months follow-up. The patients divided into the training, testing, and validation data sets.

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Predicting Survival in Patients With Brain Metastases Treated With Radiosurgery Using Artificial Neural Networks

Cited by 35 publications

References 22 publications

Artificial neural networks in neurosurgery

Artificial neural networks in neurosurgery

Predicting endoscopic third ventriculostomy success in childhood hydrocephalus: an artificial neural network analysis

A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery

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