Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
Cancer Med. 2024 Sep;13(17):e70182. doi: 10.1002/cam4.70182.
The rarity of primary central nervous system lymphoma (PCNSL) and treatment heterogeneity contributes to a lack of prognostic models for evaluating posttreatment remission. This study aimed to develop and validate radiomic-based models to predict the durable response (DR) to high-dose methotrexate (HD-MTX)-based chemotherapy in PCNSL patients.
A total of 159 patients pathologically diagnosed with PCNSL between 2011 and 2021 across two institutions were enrolled. According to the NCCN guidelines, the DR was defined as the remission lasting ≥1 year after receiving HD-MTX-based chemotherapy. For each patient, a total of 1218 radiomic features were extracted from prebiopsy T1 contrast-enhanced MR images. Multiple machine-learning algorithms were utilized for feature selection and classification to build a radiomic signature. The radiomic-clinical integrated models were developed using the random forest method. Model performance was externally validated to verify its clinical utility.
A total of 105 PCNSL patients were enrolled after excluding 54 cases with ineligibility. The training and validation cohorts comprised 76 and 29 individuals, respectively. Among them, 65 patients achieved DR. The radiomic signature, consisting of 8 selected features, demonstrated strong predictive performance, with area under the curves of 0.994 in training cohort and 0.913 in validation cohort. This signature was independently associated with the DR in both cohorts. Both the radiomic signature and integrated models significantly outperformed the clinical models in two cohorts. Decision curve analysis underscored the clinical utility of the established models.
This radiomic signature and integrated models have the potential to accurately predict the DR to HD-MTX-based chemotherapy in PCNSL patients, providing valuable therapeutic insights.
原发性中枢神经系统淋巴瘤（PCNSL）较为罕见，且治疗方法存在差异，这导致目前缺乏用于评估化疗后缓解持久性的预后模型。本研究旨在开发和验证基于放射组学的模型，以预测 PCNSL 患者接受高剂量甲氨蝶呤（HD-MTX）化疗后的持久缓解（DR）。
共纳入 2011 年至 2021 年在两个机构经病理诊断为 PCNSL 的 159 例患者。根据 NCCN 指南，DR 定义为接受 HD-MTX 化疗后持续缓解≥1 年。对每位患者，从活检前 T1 增强磁共振成像中提取了 1218 个放射组学特征。利用多种机器学习算法进行特征选择和分类，以构建放射组学特征。使用随机森林方法开发放射组学-临床综合模型，并进行外部验证以验证其临床实用性。
排除 54 例不符合条件的病例后，共纳入 105 例 PCNSL 患者。训练集和验证集分别包含 76 例和 29 例患者，其中 65 例患者达到 DR。由 8 个选定特征组成的放射组学特征具有很强的预测性能，在训练队列中的曲线下面积为 0.994，在验证队列中的曲线下面积为 0.913。该特征在两个队列中均与 DR 独立相关。在两个队列中，放射组学特征和综合模型均显著优于临床模型。决策曲线分析强调了所建立模型的临床实用性。
该放射组学特征和综合模型有可能准确预测 PCNSL 患者接受 HD-MTX 化疗后的 DR，为治疗提供有价值的见解。
