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Identifying IDH-mutant and 1p/19q noncodeleted astrocytomas from nonenhancing gliomas: Manual recognition followed by artificial intelligence recognition.
He, Lei; Zhang, Hong; Li, Tianshi; Yang, Jianing; Zhou, Yanpeng; Wang, Jiaxiang; Saidaer, Tuerhong; Bai, Xiaoyan; Liu, Xing; Wang, Yinyan; Wang, Lei.
Afiliación
  • He L; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Zhang H; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Li T; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Yang J; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Zhou Y; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Wang J; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Saidaer T; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Bai X; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Liu X; Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Wang Y; Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Wang L; Beijing Neurosurgical Institute, Capital Medical University, Beijing, People's Republic of China.
Neurooncol Adv ; 6(1): vdae013, 2024.
Article en En | MEDLINE | ID: mdl-38405203
ABSTRACT

Background:

The T2-FLAIR mismatch sign (T2FM) has nearly 100% specificity for predicting IDH-mutant and 1p/19q noncodeleted astrocytomas (astrocytomas). However, only 18.2%-56.0% of astrocytomas demonstrate a positive T2FM. Methods must be considered for distinguishing astrocytomas from negative T2FM gliomas. In this study, positive T2FM gliomas were manually distinguished from nonenhancing gliomas, and then a support vector machine (SVM) classification model was used to distinguish astrocytomas from negative T2FM gliomas.

Methods:

Nonenhancing gliomas (regardless of pathological type or grade) diagnosed between January 2022 and October 2022 (N = 300) and November 2022 and March 2023 (N = 196) will comprise the training and validation sets, respectively. Our method for distinguishing astrocytomas from nonenhancing gliomas was examined and validated using the training set and validation set.

Results:

The specificity of T2FM for predicting astrocytomas was 100% in both the training and validation sets, while the sensitivity was 42.75% and 67.22%, respectively. Using a classification model of SVM based on radiomics features, among negative T2FM gliomas, the accuracy was above 85% when the prediction score was greater than 0.70 in identifying astrocytomas and above 95% when the prediction score was less than 0.30 in identifying nonastrocytomas.

Conclusions:

Manual screening of positive T2FM gliomas, followed by the SVM classification model to differentiate astrocytomas from negative T2FM gliomas, may be a more effective method for identifying astrocytomas in nonenhancing gliomas.
Palabras clave

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Neurooncol Adv Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Neurooncol Adv Año: 2024 Tipo del documento: Article