Ko Ching-Chung, Liu Yan-Lin, Hung Kuo-Chuan, Yang Cheng-Chun, Lim Sher-Wei, Yeh Lee-Ren, Chen Jeon-Hor, Su Min-Ying
Department of Medical Imaging, Chi Mei Medical Center, Tainan 71004, Taiwan.
Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan 71710, Taiwan.
Life (Basel). 2024 Oct 11;14(10):1290. doi: 10.3390/life14101290.
A portion of individuals diagnosed with primary central nervous system lymphomas (PCNSL) may experience early relapse or refractory (R/R) disease following treatment. This research explored the potential of MRI-based radiomics in forecasting R/R cases in PCNSL. Forty-six patients with pathologically confirmed PCNSL diagnosed between January 2008 and December 2020 were included in this study. Only patients who underwent pretreatment brain MRIs and complete postoperative follow-up MRIs were included. Pretreatment contrast-enhanced T1WI, T2WI, and T2 FLAIR imaging were analyzed. A total of 107 radiomic features, including 14 shape-based, 18 first-order statistical, and 75 texture features, were extracted from each sequence. Predictive models were then built using five different machine learning algorithms to predict R/R in PCNSL. Of the included 46 PCNSL patients, 20 (20/46, 43.5%) patients were found to have R/R. In the R/R group, the median scores in predictive models such as support vector machine, k-nearest neighbors, linear discriminant analysis, naïve Bayes, and decision trees were significantly higher, while the apparent diffusion coefficient values were notably lower compared to those without R/R ( < 0.05). The support vector machine model exhibited the highest performance, achieving an overall prediction accuracy of 83%, a precision rate of 80%, and an AUC of 0.78. Additionally, when analyzing tumor progression, patients with elevated support vector machine and naïve Bayes scores demonstrated a significantly reduced progression-free survival ( < 0.05). These findings suggest that preoperative MRI-based radiomics may provide critical insights for treatment strategies in PCNSL.
一部分被诊断为原发性中枢神经系统淋巴瘤（PCNSL）的患者在治疗后可能会出现早期复发或难治性（R/R）疾病。本研究探讨了基于MRI的放射组学在预测PCNSL中R/R病例方面的潜力。本研究纳入了2008年1月至2020年12月期间46例经病理确诊的PCNSL患者。仅纳入了接受过治疗前脑部MRI检查和完整术后随访MRI检查的患者。对治疗前的对比增强T1WI、T2WI和T2 FLAIR成像进行了分析。从每个序列中提取了总共107个放射组学特征，包括14个基于形状的特征、18个一阶统计特征和75个纹理特征。然后使用五种不同的机器学习算法建立预测模型，以预测PCNSL中的R/R情况。在纳入的46例PCNSL患者中，发现20例（20/46，43.5%）患者出现R/R。在R/R组中，支持向量机、k近邻、线性判别分析、朴素贝叶斯和决策树等预测模型的中位数得分显著更高，而表观扩散系数值与无R/R的患者相比明显更低（<0.05）。支持向量机模型表现出最高的性能，总体预测准确率达到83%，精确率为80%，AUC为0.78。此外，在分析肿瘤进展时，支持向量机和朴素贝叶斯得分升高的患者无进展生存期显著缩短（<0.05）。这些发现表明，术前基于MRI的放射组学可能为PCNSL的治疗策略提供关键见解。
