目的:构建评估弥漫性胶质瘤患者预后的最佳预后模型。
方法:回顾性收集266例经病理证实的弥漫性神经胶质瘤患者(训练队列:验证队列=7:3)的术前磁共振成像和临床资料。构建了基于影像组学特征的影像组学预后模型(R-model)。还构建了基于临床因素的预后模型(C模型)和融合模型(F模型)。基于三个模型的最优模型,列线图是构造的。最后,根据列线图构建“弥漫性胶质瘤预后计算器”。
结果:R-的c指数,C-,验证队列中的F模型分别为0.742,0.796和0.814.在验证队列中,R-的1年AUC,C-,和F模型分别为0.749、0.806和0.836;3年AUC分别为0.896、0.966和0.963。在训练组中,验证队列,所有队列,和不同等级的神经胶质瘤队列,F模型(最优模型)可以很好地识别低风险和高风险人群。“弥漫性胶质瘤预后计算器”可在https://github.com/HDCurry/预后获得。
结论:在三个模型中,F-model(radiomicscombinedclinicalfactors)具有最佳的预测功效,可以更准确地评估弥漫性胶质瘤的预后.基于该模型构建的“弥漫性胶质瘤预后计算器”可以帮助临床医生更轻松,更准确地评估弥漫性胶质瘤患者的预后,从而使他们能够制定更合理的治疗策略。
To construct an optimal prognostic model to assess the prognosis of patients with diffuse glioma.
Preoperative magnetic resonance imaging and clinical data were retrospectively collected from 266 patients (training cohort: validation cohort=7:3) with pathologically confirmed diffuse gliomas. A radiomics prognostic model (R-model) based on the radiomics features was constructed. A prognostic model based on clinical factors (C-model) and a fusion model (F-model) was also constructed. Based on the optimal model of three models, the nomogram was constructed. Finally, a \"Prognosis Calculator for Diffuse Glioma\" was constructed based on the nomogram.
The c-index of the R-, C-, and F-models in the validation cohort was 0.742, 0.796, and 0.814, respectively. In the validation cohort, the 1-year area under the curve of the R-, C-, and F-models was 0.749, 0.806, and 0.836, respectively; the 3-year area under the curve was 0.896, 0.966, and 0.963, respectively. In the training cohort, validation cohort, all cohorts, and different grades of glioma cohorts, F-model (optimal model) could identify low- and high-risk groups well. The \"Prognosis Calculator for Diffuse Glioma\" was available at https://github.com/HDCurry/prognosis.
Among the three models, the F-model (radiomics combined with clinical factors) had optimal predictive efficacy and could more accurately assess the prognosis of diffuse glioma. The \"Prognosis Calculator for Diffuse Glioma\" constructed based on this model could assist clinicians in more easily and accurately assessing the prognosis of patients with diffuse glioma, thus enabling them to make more reasonable treatment strategies.