Background:Upper tract urothelial carcinoma (UTUC) often presents with aggressive behaviour, demanding accurate preoperative assessment to guide management. Radiomics-based approaches have shown promise in extracting quantitative features from imaging, yet few studies have explored whether perirenal fat (PRF) radiomics can augment tumour-only models.Methods:A retrospective cohort of 103 UTUC patients undergoing radical nephroureterectomy was analysed. Tumour regions of interest (ROI) and concentric PRF expansions (10–30 mm) were segmented from computed tomography (CT) scans. Radiomic features were extracted using PyRadiomics, filtered by correlation and intraclass correlation coefficients, and integrated with clinical variables (e.g., age, BMI, multifocality). Multiple machine learning models, including MLPClassifier and CatBoost, were evaluated via repeated cross-validation. Performance was assessed using the area under the ROC curve (AUC), sensitivity, specificity, F1-score, and DeLong tests.Results:The best tumour grade model (AUC = 0.961) merged tumour-derived features with a 10 mm PRF margin, exceeding PRF-only (AUC = 0.900) and tumour-only (AUC = 0.934) approaches. However, the improvement over tumour-only was not always statistically significant. For stage prediction, combining tumour and 15 mm PRF features yielded the top AUC of 0.852, surpassing the tumour-alone model (AUC = 0.802) and outperforming PRF-only (AUC ≤ 0.778). PRF features provided an additional predictive value for both grade and stage models.Conclusions:Integrating PRF radiomics with tumour-based analyses enhances predictive accuracy for UTUC grade and stage, suggesting that the tumour microenvironment contains complementary imaging cues. These findings, pending external validation, support the potential for radiomics-driven risk stratification and personalised treatment planning in UTUC.
背景：上尿路尿路上皮癌（UTUC）常表现为侵袭性行为，需要准确的术前评估以指导治疗。基于影像组学的方法在从影像中提取定量特征方面显示出潜力，但少有研究探讨肾周脂肪（PRF）影像组学是否能增强仅基于肿瘤的模型。 方法：本研究回顾性分析了103例接受根治性肾输尿管切除术的UTUC患者。从计算机断层扫描（CT）图像中分割出肿瘤感兴趣区域（ROI）以及同心扩展的PRF区域（10–30 mm）。使用PyRadiomics提取影像组学特征，并通过相关性和组内相关系数进行筛选，然后与临床变量（如年龄、BMI、多灶性）整合。通过重复交叉验证评估了包括MLPClassifier和CatBoost在内的多种机器学习模型。使用受试者工作特征曲线下面积（AUC）、敏感性、特异性、F1分数以及DeLong检验评估模型性能。 结果：最佳的肿瘤分级预测模型（AUC = 0.961）融合了肿瘤特征和10 mm PRF边缘特征，其性能超过了仅使用PRF（AUC = 0.900）和仅使用肿瘤（AUC = 0.934）的模型。然而，与仅使用肿瘤的模型相比，其改善并非总是具有统计学显著性。在分期预测方面，结合肿瘤和15 mm PRF特征的模型获得了最高的AUC（0.852），优于仅使用肿瘤的模型（AUC = 0.802）和仅使用PRF的模型（AUC ≤ 0.778）。PRF特征为分级和分期模型均提供了额外的预测价值。 结论：将PRF影像组学与基于肿瘤的分析相结合，提高了对UTUC分级和分期的预测准确性，表明肿瘤微环境中包含互补的影像学信息。这些发现有待外部验证，支持了影像组学驱动的UTUC风险分层和个体化治疗计划的潜力。
肿瘤(癌症)患者之家