skip to main content
Idioma:
Tipo de recurso Mostra resultados com: Mostra resultados com: Índice

Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma

Peng, Jie ; Lu, Fangyang ; Huang, Jinhua ; Zhang, Jing ; Gong, Wuxing ; Hu, Yong ; Wang, Jun

Frontiers in oncology, 2022-10, Vol.12, p.853254-853254 [Periódico revisado por pares]

Frontiers Media S.A

Texto completo disponível

Citações Citado por
  • Título:
    Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma
  • Autor: Peng, Jie ; Lu, Fangyang ; Huang, Jinhua ; Zhang, Jing ; Gong, Wuxing ; Hu, Yong ; Wang, Jun
  • Assuntos: epatocellular carcinoma ; Oncology ; progression-free survival ; pyradiomics ; TACE ; therapy response
  • É parte de: Frontiers in oncology, 2022-10, Vol.12, p.853254-853254
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    This article was submitted to Cancer Imaging and Image-directed Interventions, a section of the journal Frontiers in Oncology
    These authors have contributed equally to this work
    Reviewed by: Massimo Galia, University of Palermo, Italy; Kunal Bharat Gala, Tata Memorial Hospital, India
    Edited by: Chandan Guha, Albert Einstein College of Medicine, United States
  • Descrição: We aimed to develop and validate a pyradiomics model for preoperative prediction of initial treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC). To this end, computed tomography (CT) images were acquired from multi-centers. Numerous pyradiomics features were extracted and machine learning approach was used to build a model for predicting initial response of TACE treatment. The predictive accuracy, overall survival (OS), and progression-free survival (PFS) were analyzed. Gene Set Enrichment Analysis (GSEA) was further used to explore signaling pathways in The Cancer Genome Atlas (TCGA)-HCC cohort. Overall, 24 of the 1,209 pyradiomic features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm. The pyradiomics signature showed high predictive accuracy across the discovery set (AUC: 0.917, 95% confidence interval [CI]: 86.93-96.39), validation set 1 (AUC: 0.902, 95% CI: 84.81-95.59), and validation set 2 (AUC: 0.911; 95% CI: 83.26-98.98). Based on the classification of pyradiomics model, we found that a group with high values base on pyramidomics score showed good PFS and OS (both P <0.001) and was negatively correlated with glycolysis pathway. The proposed pyradiomics signature could accurately predict initial treatment response and prognosis, which may be helpful for clinicians to better screen patients who are likely to benefit from TACE.
  • Editor: Frontiers Media S.A
  • Idioma: Inglês
Voltar para lista de resultados
Ir para página anteriorAnterior Resultado  6 AvançarIr para próxima página

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.