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Predicting meningioma grades and pathologic marker expression via deep learning.
Chen, Jiawei; Xue, Yanping; Ren, Leihao; Lv, Kun; Du, Peng; Cheng, Haixia; Sun, Shuchen; Hua, Lingyang; Xie, Qing; Wu, Ruiqi; Gong, Ye.
Afiliação
  • Chen J; Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China.
  • Xue Y; Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China.
  • Ren L; Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China.
  • Lv K; Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
  • Du P; Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
  • Cheng H; Department of Pathology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
  • Sun S; Department of Neurosurgery, Shanghai International Hospital, Shanghai, China.
  • Hua L; Department of Neurosurgery, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Xie Q; Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China.
  • Wu R; Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China. neurosurgeon.qing@gmail.com.
  • Gong Y; Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China. ruiqiwu@fudan.edu.cn.
Eur Radiol ; 2023 Oct 19.
Article em En | MEDLINE | ID: mdl-37853176
OBJECTIVES: To establish a deep learning (DL) model for predicting tumor grades and expression of pathologic markers of meningioma. METHODS: A total of 1192 meningioma patients from two centers who underwent surgical resection between September 2018 and December 2021 were retrospectively included. The pathological data and post-contrast T1-weight images for each patient were collected. The patients from institute I were subdivided into training, validation, and testing sets, while the patients from institute II served as the external testing cohort. The fine-tuned ResNet50 model based on transfer learning was adopted to classify WHO grade in the whole cohort and predict Ki-67 index, H3K27me3, and progesterone receptor (PR) status of grade 1 meningiomas. The predictive performance was evaluated by the accuracy and loss curve, confusion matrix, receiver operating characteristic curve (ROC), and area under curve (AUC). RESULTS: The DL prediction model for each label achieved high predictive performance in two cohorts. For WHO grade prediction, the area under the curve (AUC) was 0.966 (95%CI 0.957-0.975) in the internal testing set and 0.669 (95%CI 0.643-0.695) in the external validation cohort. The AUC in predicting Ki-67 index, H3K27me3, and PR status were 0.905 (95%CI 0.895-0.915), 0.773 (95%CI 0.760-0.786), and 0.771 (95%CI 0.750-0.792) in the internal testing set and 0.591 (95%CI 0.562-0.620), 0.658 (95%CI 0.648-0.668), and 0.703 (95%CI 0.674-0.732) in the external validation cohort, respectively. CONCLUSION: DL models can preoperatively predict meningioma grades and pathologic marker expression with favorable predictive performance. CLINICAL RELEVANCE STATEMENT: Our DL model could predict meningioma grades and expression of pathologic markers and identify high-risk patients with WHO grade 1 meningioma, which would suggest a more aggressive operative intervention preoperatively and a more frequent follow-up schedule postoperatively. KEY POINTS: WHO grades and some pathologic markers of meningioma were associated with therapeutic strategies and clinical outcomes. A deep learning-based approach was employed to develop a model for predicting meningioma grades and the expression of pathologic markers. Preoperative prediction of meningioma grades and the expression of pathologic markers was beneficial for clinical decision-making.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Eur Radiol Assunto da revista: RADIOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China País de publicação: Alemanha

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Eur Radiol Assunto da revista: RADIOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China País de publicação: Alemanha