PMID- 37877009
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231026
IS  - 1662-4548 (Print)
IS  - 1662-453X (Electronic)
IS  - 1662-453X (Linking)
VI  - 17
DP  - 2023
TI  - Development and validation of radiomics models for the prediction of diagnosis of 
      classic trigeminal neuralgia.
PG  - 1188590
LID - 10.3389/fnins.2023.1188590 [doi]
LID - 1188590
AB  - The study aims to develop a magnetic resonance imaging (MRI)-based radiomics 
      model for the diagnosis of classic trigeminal neuralgia (cTN). This study 
      involved 350 patients with cTN and 100 control participants. MRI data were 
      collected retrospectively for all the enrolled subjects. The symptomatic side 
      trigeminal nerve regions of patients and both sides of the trigeminal nerve 
      regions of control participants were manually labeled on MRI images. Radiomics 
      features of the areas labeled were extracted. Principle component analysis (PCA) 
      and least absolute shrinkage and selection operator (LASSO) regression were 
      utilized as the preliminary feature reduction methods to decrease the high 
      dimensionality of radiomics features. Machine learning methods were established, 
      including LASSO logistic regression, support vector machine (SVM), and Adaboost 
      methods, evaluating each model's diagnostic abilities using 10-fold 
      cross-validation. All the models showed excellent diagnostic ability in 
      predicting trigeminal neuralgia. A prospective study was conducted, 20 cTN 
      patients and 20 control subjects were enrolled to validate the clinical utility 
      of all models. Results showed that the radiomics models based on MRI can predict 
      trigeminal neuralgia with high accuracy, which could be used as a diagnostic tool 
      for this disorder.
CI  - Copyright (c) 2023 Wang, Ma, Wu, Xie, Lun and Sun.
FAU - Wang, Fuxu
AU  - Wang F
AD  - Department of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, 
      China.
FAU - Ma, Anbang
AU  - Ma A
AD  - Shanghai Xunshi Technology Co., Ltd., Shanghai, China.
FAU - Wu, Zeyu
AU  - Wu Z
AD  - Department of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, 
      China.
FAU - Xie, Mingchen
AU  - Xie M
AD  - Department of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, 
      China.
FAU - Lun, Peng
AU  - Lun P
AD  - Department of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, 
      China.
FAU - Sun, Peng
AU  - Sun P
AD  - Department of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, 
      China.
LA  - eng
PT  - Journal Article
DEP - 20231009
PL  - Switzerland
TA  - Front Neurosci
JT  - Frontiers in neuroscience
JID - 101478481
PMC - PMC10591183
OTO - NOTNLM
OT  - MRI
OT  - diagnosis
OT  - machine learning
OT  - radiology
OT  - trigeminal neuralgia
COIS- AM is employed by Shanghai Xunshi Technology Co., Ltd. He is a data engineer and 
      has directly participated in data analysis of this study. The remaining authors 
      declare that the research was conducted in the absence of any commercial or 
      financial relationships that could be construed as a potential conflict of 
      interest.
EDAT- 2023/10/25 06:42
MHDA- 2023/10/25 06:43
PMCR- 2023/01/01
CRDT- 2023/10/25 04:02
PHST- 2023/03/17 00:00 [received]
PHST- 2023/09/18 00:00 [accepted]
PHST- 2023/10/25 06:43 [medline]
PHST- 2023/10/25 06:42 [pubmed]
PHST- 2023/10/25 04:02 [entrez]
PHST- 2023/01/01 00:00 [pmc-release]
AID - 10.3389/fnins.2023.1188590 [doi]
PST - epublish
SO  - Front Neurosci. 2023 Oct 9;17:1188590. doi: 10.3389/fnins.2023.1188590. 
      eCollection 2023.