Table2_Radiomics of Contrast-Enhanced Computed Tomography: A Potential Biomarker for Pretreatment Prediction of the Response to Bacillus Calmette-Guerin Immunotherapy in Non-Muscle-Invasive Bladder Cancer.xlsx

Background:Bacillus Calmette-Guerin (BCG) instillation is recommended postoperatively after transurethral resection of bladder cancer (TURBT) in patients with high-risk non-muscle-invasive bladder cancer (NMIBC). An accurate prediction model for the BCG response can help identify patients with NMIBC who may benefit from alternative therapy.
Objective: To investigate the value of computed tomography (CT) radiomics features in predicting the response to BCG instillation among patients with primary high-risk NMIBC.
Methods: Patients with pathologically confirmed high-risk NMIBC were retrospectively reviewed. Patients who underwent contrast-enhanced CT examination within one to 2 weeks before TURBT and received ≥5 BCG instillation treatments in two independent hospitals were enrolled. Patients with a routine follow-up of at least 1 year at the outpatient department were included in the final cohort. Radiomics features based on CT images were extracted from the tumor and its periphery in the training cohort, and a radiomics signature was built with recursive feature elimination. Selected features further underwent an unsupervised radiomics analysis using the newly introduced method, non-negative matrix factorization (NMF), to compute factor factorization decompositions of the radiomics matrix. Finally, a robust component, which was most associated with BCG failure in 1 year, was selected. The performance of the selected component was assessed and tested in an external validation cohort.
Results: Overall, 128 patients (training cohort, n = 104; external validation cohort, n = 24) were included, including 12 BCG failures in the training cohort and 11 failures in the validation cohort each. NMF revealed five components, of which component 3 was selected for the best discrimination of BCG failure; it had an area under the curve (AUC) of .79, sensitivity of .79, and specificity of .65 in the training set. In the external validation cohort, it achieved an AUC of .68, sensitivity of .73, and specificity of .69. Survival analysis showed that patients with higher component scores had poor recurrence-free survival (RFS) in both cohorts (C-index: training cohort, .69; validation cohort, .68).
Conclusion: The study suggested that radiomics components based on NMF might be a potential biomarker to predict BCG response and RFS after BCG treatment in patients with high-risk NMIBC.

研究背景：经尿道膀胱肿瘤切除术（transurethral resection of bladder cancer, TURBT）后，针对高危非肌层浸润性膀胱癌（non-muscle-invasive bladder cancer, NMIBC）患者，临床推荐采用卡介苗（Bacillus Calmette-Guerin, BCG）膀胱灌注治疗。精准预测卡介苗应答的模型，有助于甄别出可从替代疗法中获益的NMIBC患者。
研究目的：探究计算机断层扫描（computed tomography, CT）放射组学特征在预测原发性高危NMIBC患者对卡介苗灌注治疗应答中的应用价值。
研究方法：本研究回顾性分析经病理确诊的高危NMIBC患者。纳入标准为：于TURBT术前1~2周内行增强CT检查，且在两家独立医疗机构接受过≥5次卡介苗灌注治疗，并在门诊完成至少1年的常规随访。训练队列中，从肿瘤及其外周区域的CT影像中提取放射组学特征，通过递归特征消除法构建放射组学特征谱。采用最新提出的非负矩阵分解（non-negative matrix factorization, NMF）这一无监督放射组学分析方法，对放射组学矩阵进行因子分解。最终筛选出与1年内卡介苗治疗失败相关性最强的稳健放射组学组分。在外部验证队列中对该筛选出的组分的性能进行评估与验证。
研究结果：最终共纳入128例患者，其中训练队列104例，外部验证队列24例。训练队列中共计12例发生卡介苗治疗失败，验证队列中共计11例发生治疗失败。非负矩阵分解得到5个放射组学组分，其中组分3对卡介苗治疗失败的区分效果最佳：在训练队列中，其受试者工作特征曲线下面积（area under the curve, AUC）为0.79，灵敏度为0.79，特异度为0.65；在外部验证队列中，其AUC为0.68，灵敏度为0.73，特异度为0.69。生存分析结果显示，在两个队列中，组分评分较高的患者无复发生存期（recurrence-free survival, RFS）均更短（训练队列C指数：0.69；验证队列C指数：0.68）。
研究结论：本研究表明，基于非负矩阵分解得到的放射组学组分，有望成为预测高危NMIBC患者卡介苗治疗应答及治疗后无复发生存期的潜在生物标志物。