DataSheet_4_A Multiparametric Method Based on Clinical and CT-Based Radiomics to Predict the Expression of p53 and VEGF in Patients With Spinal Giant Cell Tumor of Bone.pdf

PurposeThis project aimed to assess the significance of vascular endothelial growth factor (VEGF) and p53 for predicting progression-free survival (PFS) in patients with spinal giant cell tumor of bone (GCTB) and to construct models for predicting these two biomarkers based on clinical and computer tomography (CT) radiomics to identify high-risk patients for improving treatment.Material and MethodsA retrospective study was performed from April 2009 to January 2019. A total of 80 patients with spinal GCTB who underwent surgery in our institution were identified. VEGF and p53 expression and clinical and general imaging information were collected. Multivariate Cox regression models were used to verify the prognostic factors. The radiomics features were extracted from the regions of interest (ROIs) in preoperative CT, and then important features were selected by the SVM to build classification models, evaluated by 10-fold crossvalidation. The clinical variables were processed using the same method to build a conventional model for comparison.ResultsThe immunohistochemistry of 80 patients was obtained: 49 with high-VEGF and 31 with low-VEGF, 68 with wild-type p53, and 12 with mutant p53. p53 and VEGF were independent prognostic factors affecting PFS found in multivariate Cox regression analysis. For VEGF, the Spinal Instability Neoplastic Score (SINS) was greater in the high than low groups, p

本研究旨在评估血管内皮生长因子（VEGF）和p53在预测脊柱骨巨细胞瘤（GCTB）患者无病生存期（PFS）中的重要性，并基于临床及计算机断层扫描（CT）影像组学构建预测这两个生物标志物的模型，以识别高风险患者，从而优化治疗方案。研究方法：自2009年4月至2019年1月，对80例在我院接受手术治疗的脊柱GCTB患者进行了回顾性研究。收集了VEGF和p53的表达以及临床和一般影像信息。采用多因素Cox回归模型验证预后因素。从术前CT的兴趣区域（ROIs）中提取影像组学特征，然后通过支持向量机（SVM）选择重要特征构建分类模型，并通过10折交叉验证进行评估。采用相同的方法处理临床变量，以构建传统模型进行比较。结果：80例患者的免疫组化结果如下：49例高VEGF表达，31例低VEGF表达，68例野生型p53，12例突变型p53。多因素Cox回归分析发现，p53和VEGF是独立影响PFS的预后因素。对于VEGF，高VEGF组的脊柱不稳新肿瘤评分（SINS）高于低VEGF组，p