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Titolo:
PREDICTION OF POSTERIOR-FOSSA TUMOR TYPE IN CHILDREN BY MEANS OF MAGNETIC-RESONANCE IMAGE PROPERTIES, SPECTROSCOPY, AND NEURAL NETWORKS
Autore:
ARLE JE; MORRISS C; WANG ZYJ; ZIMMERMAN RA; PHILLIPS PG; SUTTON LN;
Indirizzi:
CHILDRENS HOSP PHILADELPHIA,DEPT NEUROSURG,34TH ST & CIVIC CTR BLVD PHILADELPHIA PA 19104 CHILDRENS HOSP PHILADELPHIA,DEPT NEUROSURG PHILADELPHIA PA 19104 CHILDRENS HOSP PHILADELPHIA,DEPT NEURORADIOL PHILADELPHIA PA 00000 CHILDRENS HOSP PHILADELPHIA,DEPT NEUROONCOL PHILADELPHIA PA 00000 HOSP UNIV PENN,DEPT NEUROSURG PHILADELPHIA PA 00000
Titolo Testata:
Journal of neurosurgery
fascicolo: 5, volume: 86, anno: 1997,
pagine: 755 - 761
SICI:
0022-3085(1997)86:5<755:POPTTI>2.0.ZU;2-E
Fonte:
ISI
Lingua:
ENG
Soggetto:
HEPATOCELLULAR-CARCINOMA; STEREOTAXIC BIOPSY; CLASSIFICATION; TOMOGRAPHY; DIAGNOSIS; CANCER;
Keywords:
NEURAL NETWORK; PEDIATRIC BRAIN TUMOR; POSTERIOR FOSSA TUMOR; MAGNETIC RESONANCE SPECTROSCOPY; MAGNETIC RESONANCE IMAGING; CHILDREN;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
33
Recensione:
Indirizzi per estratti:
Citazione:
J.E. Arle et al., "PREDICTION OF POSTERIOR-FOSSA TUMOR TYPE IN CHILDREN BY MEANS OF MAGNETIC-RESONANCE IMAGE PROPERTIES, SPECTROSCOPY, AND NEURAL NETWORKS", Journal of neurosurgery, 86(5), 1997, pp. 755-761

Abstract

Recent studies have explored characteristics of brain tumors by meansof magnetic resonance spectroscopy (MRS) to increase diagnostic accuracy and improve understanding of tumor biology. In this study, a computer-based neural network was developed to combine MRS data (ratios of N-acetyl-aspartate, choline, and creatine) with 10 characteristics of tumor tissue obtained from magnetic resonance (MR) studies, as well astumor size and the patient's age and sex, in hopes of further improving diagnostic accuracy. Data were obtained in 33 children presenting with posterior fossa tumors. The cases were analyzed by a neuroradiologist, who then predicted the tumor type from among three categories (primitive neuroectodermal tumor, astrocytoma, or ependymoma/other) basedonly on the data obtained via MR imaging. These predictions were compared with those made by neural networks that had analyzed different combinations of the data. The neuroradiologist correctly predicted the tumor type in 73% of the cases, whereas four neural networks using different datasets as inputs were 58 to 95% correct. The neural network that used only the three spectroscopy ratios had the least predictive ability. With the addition of data including MR imaging characteristics,age, sex, and tumor size, the network's accuracy improved to 72%, consistent with the predictions of the neuroradiologist who was using thesame information. Use of only the analog data (leaving out information obtained from MR imaging), resulted in 88% accuracy. A network that used all of the data was able to identify 95% of the tumors correctly. It is concluded that a neural network provided with imaging data, spectroscopic data, and a limited amount of clinical information can predict pediatric posterior fossa tumor type with remarkable accuracy.

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Documento generato il 05/12/20 alle ore 12:38:49