Department of Biostatistics, School of Public Health & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
Department of Tumor Screening and Prevention, Zhongshan Hospital, Fudan University, Shanghai, China.
BMC Cancer. 2024 Feb 28;24(1):274. doi: 10.1186/s12885-024-11996-2.
Glioma recurrence, subsequent to maximal safe resection, remains a pivotal challenge. This study aimed to identify key clinical predictors influencing recurrence and develop predictive models to enhance neurological diagnostics and therapeutic strategies.
This longitudinal cohort study with a substantial sample size (n = 2825) included patients with non-recurrent glioma who were pathologically diagnosed and had undergone initial surgical resection between 2010 and 2018. Logistic regression models and stratified Cox proportional hazards models were established with the top 15 clinical variables significantly influencing outcomes screened by the least absolute shrinkage and selection operator (LASSO) method. Preoperative and postoperative models predicting short-term (within 6 months) postoperative recurrence in glioma patients were developed to explore the risk factors associated with short- and long-term recurrence in glioma patients.
Preoperative and postoperative logistic models predicting short-term recurrence had accuracies of 0.78 and 0.87, respectively. A range of biological and early symptomatic characteristics linked to short- and long-term recurrence have been pinpointed. Age, headache, muscle weakness, tumor location and Karnofsky score represented significant odd ratios (t > 2.65, p < 0.01) in the preoperative model, while age, WHO grade 4 and chemotherapy or radiotherapy treatments (t > 4.12, p < 0.0001) were most significant in the postoperative period. Postoperative predictive models specifically targeting the glioblastoma and IDH wildtype subgroups were also performed, with an AUC of 0.76 and 0.80, respectively. The 50 combinations of distinct risk factors accommodate diverse recurrence risks among glioma patients, and the nomograms visualizes the results for clinical practice. A stratified Cox model identified many prognostic factors for long-term recurrence, thereby facilitating the enhanced formulation of perioperative care plans for patients, and glioblastoma patients displayed a median progression-free survival (PFS) of only 11 months.
The constructed preoperative and postoperative models reliably predicted short-term postoperative glioma recurrence in a substantial patient cohort. The combinations risk factors and nomograms enhance the operability of personalized therapeutic strategies and care regimens. Particular emphasis should be placed on patients with recurrence within six months post-surgery, and the corresponding treatment strategies require comprehensive clinical investigation.
最大限度安全切除后,胶质瘤复发仍然是一个关键挑战。本研究旨在确定影响复发的关键临床预测因素,并建立预测模型以增强神经诊断和治疗策略。
本研究采用纵向队列研究,样本量较大(n=2825),包括 2010 年至 2018 年间经病理诊断为非复发性胶质瘤且接受初始手术切除的患者。采用最小绝对收缩和选择算子(LASSO)方法筛选出对结局有显著影响的前 15 个临床变量,建立逻辑回归模型和分层 Cox 比例风险模型。建立预测胶质瘤患者短期(6 个月内)术后复发的术前和术后模型,探讨与胶质瘤患者短期和长期复发相关的危险因素。
预测短期复发的术前和术后逻辑模型的准确性分别为 0.78 和 0.87。确定了一系列与短期和长期复发相关的生物学和早期症状特征。年龄、头痛、肌肉无力、肿瘤位置和卡诺夫斯基评分在术前模型中代表显著的比值比(t>2.65,p<0.01),而年龄、世界卫生组织(WHO)分级 4 级和化疗或放疗(t>4.12,p<0.0001)在术后期间是最显著的。还对胶质母细胞瘤和 IDH 野生型亚组的术后预测模型进行了专门研究,其 AUC 分别为 0.76 和 0.80。50 种不同风险因素的组合适应了胶质瘤患者不同的复发风险,列线图可视化了临床实践的结果。分层 Cox 模型确定了许多与长期复发相关的预后因素,从而为患者围手术期护理计划的制定提供了帮助,胶质母细胞瘤患者的中位无进展生存期(PFS)仅为 11 个月。
在大量患者队列中,构建的术前和术后模型可靠地预测了短期术后胶质瘤复发。组合风险因素和列线图增强了个性化治疗策略和护理方案的可操作性。应特别关注术后 6 个月内复发的患者,相应的治疗策略需要全面的临床调查。