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Prediction of lymphovascular invasion of gastric cancer based on contrast-enhanced computed tomography radiomics.
Zhen, Si-Yu; Wei, Yong; Song, Ran; Liu, Xiao-Huan; Li, Pei-Ru; Kong, Xiang-Yan; Wei, Han-Yu; Fan, Wen-Hua; Liang, Chang-Hua.
Afiliación
  • Zhen SY; Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China.
  • Wei Y; Henan Key Laboratory of Chronic Disease Prevention and Therapy & Intelligent Health Management, Xinxiang, China.
  • Song R; Xinxiang Key Laboratory for Esophageal Cancer Imaging Diagnosis and Artificial Intelligence, Xinxiang, China.
  • Liu XH; Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China.
  • Li PR; Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China.
  • Kong XY; Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China.
  • Wei HY; Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China.
  • Fan WH; Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China.
  • Liang CH; Department of Radiology, Xinxiang Medical University First Affiliated Hospital, Xinxiang, China.
Front Oncol ; 14: 1389278, 2024.
Article en En | MEDLINE | ID: mdl-39301548
ABSTRACT

Background:

Lymphovascular invasion (LVI) is a significant risk factor for lymph node metastasis in gastric cancer (GC) and is closely related to the prognosis and recurrence of GC. This study aimed to establish clinical models, radiomics models and combination models for the diagnosis of GC vascular invasion.

Methods:

This study enrolled 146 patients with GC proved by pathology and who underwent radical resection of GC. The patients were assigned to the training and validation cohorts. A total of 1,702 radiomic features were extracted from contrast-enhanced computed tomography images of GC. Logistic regression analyses were performed to establish a clinical model, a radiomics model and a combined model. The performance of the predictive models was measured by the receiver operating characteristic (ROC) curve.

Results:

In the training cohort, the age of LVI negative (-) patients and LVI positive (+) patients were 62.41 ± 8.41 and 63.76 ± 10.08 years, respectively, and there were more male (n = 63) than female (n = 19) patients in the LVI (+) group. Diameter and differentiation were the independent risk factors for determining LVI (-) and (+). A combined model was found to be relatively highly discriminative based on the area under the ROC curve for both the training (0.853, 95% CI 0.784-0.920, sensitivity 0.650 and specificity 0.907) and the validation cohorts (0.742, 95% CI 0.559-0.925, sensitivity 0.736 and specificity 0.700).

Conclusions:

The combined model had the highest diagnostic effectiveness, and the nomogram established by this model had good performance. It can provide a reliable prediction method for individual treatment of LVI in GC before surgery.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Oncol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Oncol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza