Data Sheet 1_Machine learning-based radiomics for bladder cancer staging: evaluating the role of imaging timing in differentiating T2 from T3 disease.docx

ObjectivesAccurate preoperative staging of bladder cancer is essential for therapeutic decision-making, particularly in distinguishing between organ-confined (T2) and extravesical (T3) disease. This study aimed to develop a CT-based radiomics model to differentiate T2 from T3 tumors and to evaluate the impact of imaging timing relative to transurethral resection of the bladder (TURB) on model performance. Additionally, we assessed the added diagnostic value of integrating routine clinical biomarkers.
MethodsIn this retrospective study, 97 patients with histologically confirmed bladder cancer who underwent TURB followed by contrast-enhanced CT were included. Tumor segmentation was performed using a semi-automated three-dimensional approach, and radiomic features were extracted according to IBSI standards. A random forest classifier was trained to distinguish between T2 and T3 tumors. Patients were stratified according to the interval between TURB and CT imaging (≤14 days vs >14 days). Performance metrics were assessed for both radiomics-only and combined clinical-radiomics models. Clinical variables included preoperative creatinine, hemoglobin, arterial hypertension, diabetes mellitus, smoking status, and tumor size.
ResultsThe radiomics-only model achieved an AUC of 0.68 in Cohort 1 (≤14 days post-TURB). In Cohort 2 (>14 days post-TURB), model performance improved with an AUC of 0.80. The combined clinical-radiomics model further enhanced performance, yielding an AUC of 0.76 in Cohort 1 and 0.82 in Cohort 2. Delayed imaging was associated with increased radiomic feature stability and improved classification accuracy, suggesting a potential benefit of temporal separation from post-surgical tissue changes.
ConclusionThis study demonstrates the feasibility of CT-based radiomics using full-volume 3D tumor segmentation to distinguish between T2 and T3 bladder cancer. The integration of clinical biomarkers and consideration of imaging timing significantly improved model performance. These findings support the development of temporally optimized, multimodal prediction models for individualized bladder cancer staging and treatment planning.

研究目的：准确的膀胱癌术前分期对治疗决策至关重要，尤其是在区分器官局限性（organ-confined）T2期与膀胱外侵犯（extravesical）T3期肿瘤方面。本研究旨在构建基于计算机断层扫描（CT）的放射组学模型以区分T2与T3期肿瘤，并评估经尿道膀胱肿瘤切除术（TURB, transurethral resection of the bladder）后的成像时间间隔对模型性能的影响。此外，本研究还评估了整合常规临床生物标志物的额外诊断价值。
研究方法：本回顾性研究纳入了97例经组织病理学确诊为膀胱癌、且先接受经尿道膀胱肿瘤切除术（TURB）后行增强CT扫描的患者。采用半自动三维方法进行肿瘤分割，并按照国际生物医学成像研究协会标准（IBSI, Imaging Biomarker Standards Initiative）提取放射组学特征。训练随机森林分类器以区分T2与T3期肿瘤。根据TURB与CT扫描的间隔时间将患者分为两组：≤14天组与＞14天组。分别评估仅使用放射组学特征的模型以及整合临床信息的放射组学联合模型的性能指标。临床变量包括术前肌酐水平、血红蛋白水平、动脉高血压、糖尿病、吸烟史及肿瘤大小。
研究结果：仅使用放射组学特征的模型在队列1（TURB术后≤14天扫描）中获得的曲线下面积（AUC, Area Under Curve）为0.68。在队列2（TURB术后＞14天扫描）中，模型性能得到提升，AUC达到0.80。整合临床信息的放射组学联合模型进一步优化了性能，在队列1与队列2中的AUC分别为0.76与0.82。延迟成像（即间隔时间＞14天）与放射组学特征稳定性提升及分类准确性改善相关，这提示与术后组织改变的时间分离可能存在潜在获益。
研究结论：本研究证实了采用全容积三维肿瘤分割的CT放射组学方法区分T2与T3期膀胱癌的可行性。整合临床生物标志物并考虑成像时间因素可显著提升模型性能。上述研究结果支持开发经时间优化的多模态预测模型，用于个体化膀胱癌分期与治疗方案制定。