Department of Gastrointestinal Surgery, The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Department of Gastrointestinal Surgery, The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Eur J Surg Oncol. 2020 Oct;46(10 Pt A):1932-1940. doi: 10.1016/j.ejso.2020.06.021. Epub 2020 Jun 27.
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 showed that splenic characteristics (p = 0.042), age (p < 0.001), tumor location (p = 0.002), and pTNM stage (p < 0.001) were independent risk factors for survival. The prognostic prediction model combined with splenic characteristics significantly improved the accuracy of prognosis, predicting one-and three-year survival rates.
Splenic features extracted from imaging technology can accurately predict the long-term survival of patients with gastric cancer. Splenic characteristic grouping can effectively improve the accuracy of survival prediction and gastric cancer prognosis.
放射组学允许从影像学数据中挖掘组织特征，从而实现非侵入性检查，可用于预测患者的预后。本研究探讨了使用影像学技术评估脾脏组织特征，以预测胃癌患者的预后。
回顾性收集胃癌患者的计算机断层扫描图像。随机将训练组患者的脾脏图像特征（用 pyradiomics 提取）进行分组。选择 P 值 < 0.1 的特征进行套索回归，构建生存风险模型。建立高低风险组模型。对患者进行单因素和多因素回归分析生存相关因素，并建立可视化预后预测模型。
脾脏特征预后模型在训练组和验证组中具有一致性（p < 0.001 和 p = 0.016）。两组脾脏特征不同，但除肿瘤-淋巴结-转移（pTNM）分期外，其他基本数据无统计学差异（p = 0.007）。生存风险因素的单因素和多因素分析显示，脾脏特征（p = 0.042）、年龄（p < 0.001）、肿瘤位置（p = 0.002）和 pTNM 分期（p < 0.001）是独立的生存危险因素。结合脾脏特征的预后预测模型显著提高了预后的准确性，预测了 1 年和 3 年的生存率。
从影像学技术中提取的脾脏特征可以准确预测胃癌患者的长期生存。脾脏特征分组可以有效提高生存预测和胃癌预后的准确性。
