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Artificial neural networks predict the need for permanent cerebrospinal fluid diversion following posterior fossa tumor resection.
Bray, David P; Saad, Hassan; Douglas, James Miller; Grogan, Dayton; Dawoud, Reem A; Chow, Jocelyn; Deibert, Christopher; Pradilla, Gustavo; Nduom, Edjah K; Olson, Jeffrey J; Alawieh, Ali M; Hoang, Kimberly B.
Afiliación
  • Bray DP; Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA.
  • Saad H; Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA.
  • Douglas JM; Emory School of Medicine, Atlanta, Georgia, USA.
  • Grogan D; Medical College of Georgia-Augusta University, Augusta, Georgia, USA.
  • Dawoud RA; Emory School of Medicine, Atlanta, Georgia, USA.
  • Chow J; College of Arts and Sciences, Emory University, Atlanta, Georgia, USA.
  • Deibert C; Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA.
  • Pradilla G; Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA.
  • Nduom EK; Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA.
  • Olson JJ; Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA.
  • Alawieh AM; Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA.
  • Hoang KB; Department of Neurosurgery, Emory University School of Medicine, Atlanta, Georgia, USA.
Neurooncol Adv ; 4(1): vdac145, 2022.
Article en En | MEDLINE | ID: mdl-36299798
Background: Resection of posterior fossa tumors (PFTs) can result in hydrocephalus that requires permanent cerebrospinal fluid (CSF) diversion. Our goal was to prospectively validate a machine-learning model to predict postoperative hydrocephalus after PFT surgery requiring permanent CSF diversion. Methods: We collected preoperative and postoperative variables on 518 patients that underwent PFT surgery at our center in a retrospective fashion to train several statistical classifiers to predict the need for permanent CSF diversion as a binary class. A total of 62 classifiers relevant to our data structure were surveyed, including regression models, decision trees, Bayesian models, and multilayer perceptron artificial neural networks (ANN). Models were trained using the (N = 518) retrospective data using 10-fold cross-validation to obtain accuracy metrics. Given the low incidence of our positive outcome (12%), we used the positive predictive value along with the area under the receiver operating characteristic curve (AUC) to compare models. The best performing model was then prospectively validated on a set of 90 patients. Results: Twelve percent of patients required permanent CSF diversion after PFT surgery. Of the trained models, 8 classifiers had an AUC greater than 0.5 on prospective testing. ANNs demonstrated the highest AUC of 0.902 with a positive predictive value of 83.3%. Despite comparable AUC, the remaining classifiers had a true positive rate below 35% (compared to ANN, P < .0001). The negative predictive value of the ANN model was 98.8%. Conclusions: ANN-based models can reliably predict the need for ventriculoperitoneal shunt after PFT surgery.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Neurooncol Adv Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Neurooncol Adv Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido