Department of Radiology, Shengjing Hospital of China Medical University, No.36 Sanhao Street, Heping District, Shenyang, 100004, China.
Department of Radiology, The First Hospital of China Medical University, 155 North Nanjing Street, Heping District, Shenyang, 110001, China.
BMC Cancer. 2024 Aug 16;24(1):1020. doi: 10.1186/s12885-024-12793-7.
Vascular invasion (VI) is closely related to the metastasis, recurrence, prognosis, and treatment of gastric cancer. Currently, predicting VI preoperatively using traditional clinical examinations alone remains challenging. This study aims to explore the value of radiomics analysis based on preoperative enhanced CT images in predicting VI in gastric cancer.
We retrospectively analyzed 194 patients with gastric adenocarcinoma who underwent enhanced CT examination. Based on pathology analysis, patients were divided into the VI group (n = 43) and the non-VI group (n = 151). Radiomics features were extracted from arterial phase (AP) and portal venous phase (PP) CT images. The radiomics score (Rad-score) was then calculated. Prediction models based on image features, clinical factors, and a combination of both were constructed. The diagnostic efficiency and clinical usefulness of the models were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).
The combined prediction model included the Rad-score of AP, the Rad-score of PP, Ki-67, and Lauren classification. In the training group, the area under the curve (AUC) of the combined prediction model was 0.83 (95% CI 0.76-0.89), with a sensitivity of 64.52% and a specificity of 92.45%. In the validation group, the AUC was 0.80 (95% CI 0.67-0.89), with a sensitivity of 66.67% and a specificity of 88.89%. DCA indicated that the combined prediction model might have a greater net clinical benefit than the clinical model alone.
The integrated models, incorporating enhanced CT radiomics features, Ki-67, and clinical factors, demonstrate significant predictive capability for VI. Moreover, the radiomics model has the potential to optimize personalized clinical treatment selection and patient prognosis assessment.
血管侵犯（VI）与胃癌的转移、复发、预后和治疗密切相关。目前，仅通过传统临床检查预测胃癌术前 VI 仍然具有挑战性。本研究旨在探讨基于术前增强 CT 图像的放射组学分析预测胃癌 VI 的价值。
我们回顾性分析了 194 例接受增强 CT 检查的胃腺癌患者。根据病理分析，患者分为 VI 组（n=43）和非 VI 组（n=151）。从动脉期（AP）和门静脉期（PP）CT 图像中提取放射组学特征。然后计算放射组学评分（Rad-score）。基于图像特征、临床因素以及两者的组合构建预测模型。使用受试者工作特征（ROC）曲线和决策曲线分析（DCA）评估模型的诊断效率和临床实用性。
联合预测模型包括 AP 的 Rad-score、PP 的 Rad-score、Ki-67 和 Lauren 分类。在训练组中，联合预测模型的曲线下面积（AUC）为 0.83（95%CI 0.76-0.89），灵敏度为 64.52%，特异性为 92.45%。在验证组中，AUC 为 0.80（95%CI 0.67-0.89），灵敏度为 66.67%，特异性为 88.89%。DCA 表明，联合预测模型可能比单独的临床模型具有更大的净临床获益。
结合增强 CT 放射组学特征、Ki-67 和临床因素的综合模型对 VI 具有显著的预测能力。此外，放射组学模型有可能优化个性化临床治疗选择和患者预后评估。
