Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, P. R. China.
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, P. R. China.
Technol Cancer Res Treat. 2024 Jan-Dec;23:15330338241284845. doi: 10.1177/15330338241284845.
The intricate task of diagnosing and managing small renal masses (SRMs) has become progressively convoluted within the realm of clinical practice. Contemporary clinical prediction instruments may succumb to a gradual decay in precision, coupled with an absence of unambiguous guidelines to navigate patient management.
This investigation was devised to formulate and authenticate nomograms for the overall survival (OS) and cancer- specific survival (CSS) among patients afflicted with SRMs. The study encompassed a cohort of 2558 pediatric patients diagnosed with SRMs over the period of 2000 to 2019. Independent prognostic indicators for OS and CSS, encompassing historical staging, chemotherapy regimens, surgical interventions, and pathological classifications, were ascertained through the employment of multivariate Cox proportional hazards regression analysis and backward stepwise selection.
Through the utilization of multivariate Cox regression models, nomograms for OS and CSS were meticulously crafted, demonstrating commendable discrimination and calibration within the training set (OS C-index: 0.762, CSS C-index: 0.779). The validation set further corroborated the exemplary discrimination and calibration of the nomograms. Moreover, these nomograms adeptly differentiated between patient groups at elevated and diminished risk levels.
The nomograms delineated in this research provide propitious predictive accuracy for overall survival and cancer-specific survival in patients suffering from pediatric SRMs, thereby contributing to refined risk stratification and steering the optimal therapeutic course of action. The necessity for supplementary validation prevails before the translation of these findings into clinical practice.
在临床实践中,诊断和管理小肾肿瘤(SRM)的复杂任务变得越来越复杂。目前的临床预测工具可能会逐渐失去精度,而且缺乏明确的指导方针来管理患者。
本研究旨在为患有 SRM 的患者制定并验证总生存(OS)和癌症特异性生存(CSS)的列线图。该研究纳入了 2000 年至 2019 年间诊断为 SRM 的 2558 名儿科患者的队列。通过使用多变量 Cox 比例风险回归分析和向后逐步选择,确定了 OS 和 CSS 的独立预后指标,包括历史分期、化疗方案、手术干预和病理分类。
通过使用多变量 Cox 回归模型,精心制作了 OS 和 CSS 的列线图,在训练集(OS C 指数:0.762,CSS C 指数:0.779)中表现出良好的区分度和校准度。验证集进一步证实了列线图的出色区分度和校准度。此外,这些列线图能够熟练地区分风险水平较高和较低的患者群体。
本研究中制定的列线图为患有儿科 SRM 的患者的总生存和癌症特异性生存提供了有利的预测准确性,有助于细化风险分层并指导最佳治疗方案。在将这些发现转化为临床实践之前,需要进行额外的验证。