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CT radiomics for prediction of microvascular invasion in hepatocellular carcinoma: A systematic review and meta-analysis
Zhou, Hai-ying; Cheng, Jin-mei; Chen, Tian-wu; Zhang, Xiao-ming; Ou, Jing; Cao, Jin-ming; Li, Hong-jun.
  • Zhou, Hai-ying; Affiliated Hospital of North Sichuan Medical College. Medical Imaging Key Laboratory of Sichuan Province. Department of Radiology. CN
  • Cheng, Jin-mei; Affiliated Hospital of North Sichuan Medical College. Medical Imaging Key Laboratory of Sichuan Province. Department of Radiology. CN
  • Chen, Tian-wu; Affiliated Hospital of North Sichuan Medical College. Medical Imaging Key Laboratory of Sichuan Province. Department of Radiology. CN
  • Zhang, Xiao-ming; Affiliated Hospital of North Sichuan Medical College. Medical Imaging Key Laboratory of Sichuan Province. Department of Radiology. CN
  • Ou, Jing; Affiliated Hospital of North Sichuan Medical College. Medical Imaging Key Laboratory of Sichuan Province. Department of Radiology. CN
  • Cao, Jin-ming; North Sichuan Medical College. Nanchong Central Hospital/Second School of Clinical Medicine. Department of Radiology. CN
  • Li, Hong-jun; Capital Medical University. Beijing YouAn Hospital. Department of Radiology. CN
Clinics ; 78: 100264, 2023. tab, graf
Artículo en Inglés | LILACS-Express | LILACS | ID: biblio-1506008
ABSTRACT
Abstract The power of computed tomography (CT) radiomics for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) demonstrated in current research is variable. This systematic review and meta-analysis aim to evaluate the value of CT radiomics for MVI prediction in HCC, and to investigate the methodologic quality in the workflow of radiomics research. Databases of PubMed, Embase, Web of Science, and Cochrane Library were systematically searched. The methodologic quality of included studies was assessed. Validation data from studies with Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement type 2a or above were extracted for meta-analysis. Eleven studies were included, among which nine were eligible for meta-analysis. Radiomics quality scores of the enrolled eleven studies varied from 6 to 17, accounting for 16.7%-47.2% of the total points, with an average score of 14. Pooled sensitivity, specificity, and Area Under the summary receiver operator Characteristic Curve (AUC) were 0.82 (95% CI 0.77-0.86), 0.79 (95% CI 0.75-0.83), and 0.87 (95% CI 0.84-0.91) for the predictive performance of CT radiomics, respectively. Meta-regression and subgroup analyses showed radiomics model based on 3D tumor segmentation, and deep learning model achieved superior performances compared to 2D segmentation and non-deep learning model, respectively (AUC 0.93 vs. 0.83, and 0.97 vs. 0.83, respectively). This study proves that CT radiomics could predict MVI in HCC. The heterogeneity of the included studies precludes a definition of the role of CT radiomics in predicting MVI, but methodology warrants uniformization in the radiology community regarding radiomics in HCC.


Texto completo: Disponible Índice: LILACS (Américas) Tipo de estudio: Estudio pronóstico / Factores de riesgo / Revisiones Sistemáticas Evaluadas Idioma: Inglés Revista: Clinics Asunto de la revista: Medicina Año: 2023 Tipo del documento: Artículo País de afiliación: China Institución/País de afiliación: Affiliated Hospital of North Sichuan Medical College/CN / Capital Medical University/CN

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Texto completo: Disponible Índice: LILACS (Américas) Tipo de estudio: Estudio pronóstico / Factores de riesgo / Revisiones Sistemáticas Evaluadas Idioma: Inglés Revista: Clinics Asunto de la revista: Medicina Año: 2023 Tipo del documento: Artículo País de afiliación: China Institución/País de afiliación: Affiliated Hospital of North Sichuan Medical College/CN / Capital Medical University/CN