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Radiomics Analysis of Diffusion Kurtosis Imaging: Distinguishing Between Glioblastoma and Single Brain Metastasis.
Gao, Eryuan; Wang, Peipei; Bai, Jie; Ma, Xiaoyue; Gao, Yufei; Qi, Jinbo; Zhao, Kai; Zhang, Huiting; Yan, Xu; Yang, Guang; Zhao, Guohua; Cheng, Jingliang.
Affiliation
  • Gao E; Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China (E.G., P.W., J.B., X.M., J.Q., K.Z., G.Z., J.C.); Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, Henan, China (E.G., P
  • Wang P; Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China (E.G., P.W., J.B., X.M., J.Q., K.Z., G.Z., J.C.); Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, Henan, China (E.G., P
  • Bai J; Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China (E.G., P.W., J.B., X.M., J.Q., K.Z., G.Z., J.C.); Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, Henan, China (E.G., P
  • Ma X; Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China (E.G., P.W., J.B., X.M., J.Q., K.Z., G.Z., J.C.); Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, Henan, China (E.G., P
  • Gao Y; School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, Henan, China (Y.G.).
  • Qi J; Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China (E.G., P.W., J.B., X.M., J.Q., K.Z., G.Z., J.C.); Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, Henan, China (E.G., P
  • Zhao K; Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China (E.G., P.W., J.B., X.M., J.Q., K.Z., G.Z., J.C.); Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, Henan, China (E.G., P
  • Zhang H; MR Scientific Marketing, Siemens Healthineers China, Shanghai, China (H.Z., X.Y.).
  • Yan X; MR Scientific Marketing, Siemens Healthineers China, Shanghai, China (H.Z., X.Y.).
  • Yang G; Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China (G.Y.).
  • Zhao G; Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China (E.G., P.W., J.B., X.M., J.Q., K.Z., G.Z., J.C.); Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, Henan, China (E.G., P
  • Cheng J; Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China (E.G., P.W., J.B., X.M., J.Q., K.Z., G.Z., J.C.); Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, Henan, China (E.G., P
Acad Radiol ; 31(3): 1036-1043, 2024 Mar.
Article in En | MEDLINE | ID: mdl-37690885
RATIONALE AND OBJECTIVES: This study aimed to assess the value of diffusion kurtosis imaging (DKI)-based radiomics models in differentiating glioblastoma (GB) from single brain metastasis (SBM) and compare their diagnostic performance with that of routine magnetic resonance imaging (MRI) models. MATERIALS AND METHODS: A total of 110 patients who underwent DKI and were pathologically diagnosed with GB (n = 58) or SBM (n = 52) were enrolled in this study. Radiomics features were extracted from the manually delineated region of interest of the lesion. A training set for model development was constructed from the images of 88 random patients, and 22 patients were reserved for independent validation. Seven single-DKI-parametric models and a multi-DKI-parametric model were constructed using six classifiers, whereas four single-routine-sequence models (based on T2 weighted imaging, apparent diffusion coefficient, T2-dark-fluid, and contrast-enhanced T1 magnetization prepared rapid gradient echo) and a multisequence routine MRI model were constructed for comparison. Receiver operating characteristic curve analysis was conducted to assess the diagnostic performance. The areas under the curve (AUCs) of different models were compared using the DeLong test. RESULTS: The AUCs of the single-DKI-parametric models ranged from 0.800 to 0.933 (mean kurtosis [MK] model). The multi-DKI-parametric model had a slightly higher AUC (0.958) than the MK model; however, the difference was not statistically significant (P = 0.688). In comparison, the AUCs of the routine MRI models ranged from 0.633 to 0.733 (multisequence routine MRI model). The AUC of the multi-DKI-parametric model was significantly higher than that of the multisequence routine MRI model (P = 0.042). CONCLUSION: The multi-DKI-parametric radiomics model exhibited better performance than that of the single-DKI-parametric models and routine MRI models in distinguishing GB from SBM.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Brain Neoplasms / Glioblastoma Type of study: Prognostic_studies Limits: Humans Language: En Journal: Acad Radiol Journal subject: RADIOLOGIA Year: 2024 Document type: Article Country of publication:
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Brain Neoplasms / Glioblastoma Type of study: Prognostic_studies Limits: Humans Language: En Journal: Acad Radiol Journal subject: RADIOLOGIA Year: 2024 Document type: Article Country of publication: