Use of radiomics to extract splenic features to predict prognosis of patients with gastric cancer.

European Journal of Surgical Oncology : the Journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Xiang WangXian Shen

Abstract

Radiomics allows for mining of imaging data to examine tissue characteristics non-invasively, which can be used to predict the prognosis of a patient. This study explored the use of imaging techniques to evaluate splenic tissue characteristics to predict the prognosis of patients with gastric cancer. Computed tomography images from patients with gastric cancer were collected retrospectively. Splenic image characteristics, extracted with pyradiomics, of patients in the training group were randomly divided. Characteristics with a P value < 0.1 were selected for lasso regression to construct a survival risk model. Models for high-and low-risk groups were established. Patients were divided into the high- and low-risk groups for univariate and multivariate regression analysis of survival-related factors, and a visual prognostic prediction model was established. The splenic characteristic prognostic model was consistent in the training and verification groups (p < 0.001 and p = 0.016, respectively). The two groups that displayed different splenic characteristics showed no statistical difference in other basic data except the tumour-node-metastasis (pTNM) stage (p = 0.007). Univariate and multivariate analysis of survival risk factors...Continue Reading

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Citations

Sep 30, 2020·Movement Disorders : Official Journal of the Movement Disorder Society·Linliu PengHong Jiang
May 10, 2021·Diagnostic and Interventional Imaging·Maxime BaratPhilippe Soyer

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