Preoperative and postoperative prediction of long-term meningioma outcomes.

Efstathios D Gennatas; Ashley Wu; Steve E Braunstein; Olivier Morin; William C Chen; Stephen T Magill; Chetna Gopinath; Javier E Villaneueva-Meyer; Arie Perry; Michael W McDermott; Timothy D Solberg; Gilmer Valdes; David R Raleigh

doi:10.1371/JOURNAL.PONE.0204161

Preoperative and postoperative prediction of long-term meningioma outcomes.

Efstathios D Gennatas
, Ashley Wu
, Steve E Braunstein
, Olivier Morin
, William C Chen
, Stephen T Magill
, Chetna Gopinath
, Javier E Villaneueva-Meyer
, Arie Perry
, Michael W McDermott
, Timothy D Solberg
, Gilmer Valdes
, David R Raleigh

Miami Neuroscience Institute

Research output: Contribution to journal › Article › peer-review

Original language	American English
Journal	Plos One
Volume	13
DOIs	https://doi.org/10.1371/JOURNAL.PONE.0204161
State	Published - Sep 20 2018

Disciplines

Radiology
Medicine and Health Sciences

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@article{7936449a6af94dd58c3c06ac4874f030,

title = "Preoperative and postoperative prediction of long-term meningioma outcomes.",

author = "Gennatas, \{Efstathios D\} and Ashley Wu and Braunstein, \{Steve E\} and Olivier Morin and Chen, \{William C\} and Magill, \{Stephen T\} and Chetna Gopinath and Villaneueva-Meyer, \{Javier E\} and Arie Perry and McDermott, \{Michael W\} and Solberg, \{Timothy D\} and Gilmer Valdes and Raleigh, \{David R\}",

note = "Background Meningiomas are stratified according to tumor grade and extent of resection, often in isolation of other clinical variables. Here, we use machine learning (ML) to integrate demographic, clinical, radiographic and pathologic data to develop predictive models for meningioma outcomes.",

year = "2018",

month = sep,

day = "20",

doi = "10.1371/JOURNAL.PONE.0204161",

language = "American English",

volume = "13",

journal = "Plos One",

}

TY - JOUR

T1 - Preoperative and postoperative prediction of long-term meningioma outcomes.

AU - Gennatas, Efstathios D

AU - Wu, Ashley

AU - Braunstein, Steve E

AU - Morin, Olivier

AU - Chen, William C

AU - Magill, Stephen T

AU - Gopinath, Chetna

AU - Villaneueva-Meyer, Javier E

AU - Perry, Arie

AU - McDermott, Michael W

AU - Solberg, Timothy D

AU - Valdes, Gilmer

AU - Raleigh, David R

N1 - Background Meningiomas are stratified according to tumor grade and extent of resection, often in isolation of other clinical variables. Here, we use machine learning (ML) to integrate demographic, clinical, radiographic and pathologic data to develop predictive models for meningioma outcomes.

PY - 2018/9/20

Y1 - 2018/9/20

UR - https://dx.plos.org/10.1371/journal.pone.0204161

U2 - 10.1371/JOURNAL.PONE.0204161

DO - 10.1371/JOURNAL.PONE.0204161

M3 - Article

VL - 13

JO - Plos One

JF - Plos One

ER -

Preoperative and postoperative prediction of long-term meningioma outcomes.

Disciplines

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