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Radiomics Model of Dynamic Contrast-Enhanced MRI for Evaluating Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma.
Yang, Jiawen; Dong, Xue; Jin, Shengze; Wang, Sheng; Wang, Yanna; Zhang, Limin; Wei, Yuguo; Wu, Yitian; Wang, Lingxia; Zhu, Lingwei; Feng, Yuyi; Gan, Meifu; Hu, Hongjie; Ji, Wenbin.
Afiliación
  • Yang J; Department of Radiology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, Zhejiang 317000, China; Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China. Electronic address: 2397746531@qq.com.
  • Dong X; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China. Electronic address: 17866634962@163.com.
  • Jin S; Department of Radiology, Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 318000 Zhejiang, China. Electronic address: 1018595244@qq.com.
  • Wang S; Department of Radiology, Taizhou First People's Hospital, Wenzhou Medical College, Taizhou 318020 Zhejiang, China. Electronic address: 610084642@qq.com.
  • Wang Y; Department of Radiology, Taizhou Central Hospital,Wenzhou Medical University, Taizhou 318000 Zhejiang,China. Electronic address: 18815239258@163.com.
  • Zhang L; Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China. Electronic address: lmzjyhh@163.com.
  • Wei Y; Precision Health Institution, GE Healthcare, 310000 Xihu District, Hangzhou, China. Electronic address: weiyuguo6@outlook.com.
  • Wu Y; Department of Radiology, Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 318000 Zhejiang, China. Electronic address: wuyt1029@163.com.
  • Wang L; Department of Radiology, Taizhou Hospital, Zhejiang University, Taizhou 318000 Zhejiang, China. Electronic address: wanglingxia2022@163.com.
  • Zhu L; Department of Radiology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, Zhejiang 317000, China. Electronic address: zlww0113@163.com.
  • Feng Y; Department of Radiology, Taizhou Hospital of Zhejiang Province, Shaoxing University, Taizhou 318000 Zhejiang, China. Electronic address: fengyuyi0519@163.com.
  • Gan M; Department of Pathology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, Zhejiang 317000, China. Electronic address: ganmf@enzemed.com.
  • Hu H; Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310016 Hangzhou, People's Republic of China. Electronic address: hongjiehu@zju.edu.cn.
  • Ji W; Department of Radiology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, Zhejiang 317000, China; Key Laboratory of evidence-based Radiology of Taizhou, Linhai 317000, Zhejiang, China. Electronic address: wb.j@163.com.
Acad Radiol ; 2024 Jul 17.
Article en En | MEDLINE | ID: mdl-39025700
ABSTRACT
RATIONALE AND

OBJECTIVES:

To develop and validate a clinical-radiomics model of dynamic contrast-enhanced MRI (DCE-MRI) for the preoperative discrimination of Vessels encapsulating tumor clusters (VETC)- microvascular invasion (MVI) and prognosis of hepatocellular carcinoma (HCC). MATERIALS AND

METHODS:

219 HCC patients from Institution 1 were split into internal training and validation groups, with 101 patients from Institution 2 assigned to external validation. Histologically confirmed VETC-MVI pattern categorizing HCC into VM-HCC+ (VETC+/MVI+, VETC-/MVI+, VETC+/MVI-) and VM-HCC- (VETC-/MVI-). The regions of intratumor and peritumor were segmented manually in the arterial, portal-venous and delayed phase (AP, PP, and DP, respectively) of DCE-MRI. Six radiomics models (intratumor and peritumor in AP, PP, and DP of DCE-MRI) and one clinical model were developed for assessing VM-HCC. Establishing intra-tumoral and peri-tumoral models through combining intratumor and peritumor features. The best-performing radiomics model and the clinical model were then integrated to create a Combined model.

RESULTS:

In institution 1, pathological VM-HCC+ were confirmed in 88 patients (training set 61, validation set 27). In internal testing, the Combined model had an AUC of 0.85 (95% CI 0.76-0.93), which reached an AUC of 0.75 (95% CI 0.66-0.85) in external validation. The model's predictions were associated with early recurrence and progression-free survival in HCC patients.

CONCLUSIONS:

The clinical-radiomics model offers a non-invasive approach to discern VM-HCC and predict HCC patients' prognosis preoperatively, which could offer clinicians valuable insights during the decision-making phase.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Acad Radiol Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Acad Radiol Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article
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