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重新审视2021年世界卫生组织分类中少突胶质细胞瘤患者的预后:影像特征的增量价值。

Revisiting prognosis of oligodendroglioma patients in the 2021 WHO classification: incremental value of imaging features.

作者信息

Choi Seo Hee, Lee Narae, Choi Kaeum, Han Kyunghwa, Shin Na-Young, Ahn Sung Soo, Yoon Hong In, Chang Jong Hee, Kim Se Hoon, Lee Seung-Koo, Park Yae Won

机构信息

Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Korea.

Division of Nuclear Medicine, Department of Radiology, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.

出版信息

Eur Radiol. 2025 Jul 9. doi: 10.1007/s00330-025-11768-x.

Abstract

OBJECTIVES

To investigate whether imaging factors can improve the prediction of progression-free survival (PFS) in patients with oligodendroglioma over clinicopathological features.

MATERIALS AND METHODS

A total of 180 patients diagnosed and treated for oligodendroglioma (IDH-mutant and 1p/19q codeleted) between 2005 and 2021 were included. Clinical data and preoperative MRI images were analyzed for qualitative and quantitative characteristics. Qualitative features included tumor location, calcification, gliomatosis cerebri pattern, cystic change, necrosis, and infiltrative pattern, while quantitative features included total, contrast-enhancing (CE), non-enhancing, and necrotic tumor volumes via automatic segmentation. Significant predictors of PFS were identified using univariable and multivariable Cox analyses. Two prognostic models were developed: model 1 (clinicopathological features) and model 2 (addition of imaging features). The prognostic value of the two models was compared.

RESULTS

On univariable analysis, male sex, gliomatosis cerebri pattern, larger total tumor, CE tumor, and non-enhancing tumor volumes, and partial resection or biopsy were unfavorable predictors of PFS. On multivariable analysis, male sex (hazard ratio (HR) = 3.76, p = 0.012), larger CE tumor volume (HR = 1.06, p = 0.003) and partial resection or biopsy (HR = 6.83, p = 0.001) remained as unfavorable predictors for PFS. Compared with the clinicopathological model, the model adding imaging feature demonstrated a higher C-index (0.784 vs. 0.776) and iAUC (0.745 vs. 0.725), with a significantly high time-dependent AUC for PFS at 1 year (0.989 vs. 0.943, p = 0.001).

CONCLUSION

The CE tumor volume on preoperative MRI is an independent prognostic factor in oligodendroglioma patients, potentially guiding follow-up and adjuvant treatment decisions.

KEY POINTS

Question This study examines whether imaging factors can improve the prediction of progression-free survival (PFS) in patients with oligodendroglioma over clinicopathological features. Findings Larger contrast-enhancing (CE) tumor volume, male sex, and lesser resection independently predicted shorter PFS. Incorporating CE tumor volume improved model performance over clinicopathological features alone. Clinical relevance The clinicopathological and imaging features were comprehensively investigated in patients with oligodendroglioma to predict PFS. Incorporating CE tumor volume improved the model's predictive performance, providing valuable information for clinical decision-making in identifying high-risk patients.

摘要

目的

探讨影像因素是否能比临床病理特征更好地预测少突胶质细胞瘤患者的无进展生存期(PFS)。

材料与方法

纳入2005年至2021年间诊断并接受治疗的180例少突胶质细胞瘤(异柠檬酸脱氢酶(IDH)突变且1p/19q共缺失)患者。分析临床资料和术前磁共振成像(MRI)图像的定性和定量特征。定性特征包括肿瘤位置、钙化、大脑胶质瘤病模式、囊性变、坏死和浸润模式,而定量特征包括通过自动分割得到的肿瘤总体积、强化体积、非强化体积和坏死体积。使用单变量和多变量Cox分析确定PFS的显著预测因素。建立了两个预后模型:模型1(临床病理特征)和模型2(增加影像特征)。比较了两个模型的预后价值。

结果

单变量分析显示,男性、大脑胶质瘤病模式、较大的肿瘤总体积、强化肿瘤体积、非强化肿瘤体积以及部分切除或活检是PFS的不良预测因素。多变量分析显示,男性(风险比(HR)=3.76,p=0.012)、较大的强化肿瘤体积(HR=1.06,p=0.003)和部分切除或活检(HR=6.83,p=0.001)仍然是PFS的不良预测因素。与临床病理模型相比,增加影像特征的模型具有更高的C指数(0.784对0.776)和综合判别改善指数(iAUC)(0.745对0.725),1年时PFS的时间依赖性AUC显著更高(0.989对0.943,p=0.001)。

结论

术前MRI上的强化肿瘤体积是少突胶质细胞瘤患者的独立预后因素,可能指导随访和辅助治疗决策。

关键点

问题 本研究探讨影像因素是否能比临床病理特征更好地预测少突胶质细胞瘤患者的无进展生存期(PFS)。发现 较大的强化(CE)肿瘤体积、男性以及较少的切除量独立预测较短的PFS。纳入CE肿瘤体积可提高模型性能,优于单独的临床病理特征。临床意义 对少突胶质细胞瘤患者的临床病理和影像特征进行了综合研究以预测PFS。纳入CE肿瘤体积可提高模型的预测性能,为识别高危患者的临床决策提供有价值的信息。

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