A nomogram including body composition parameters for predicting recurrence of pT1 clear cell renal cell carcinoma: a multicenter retrospective study.

IF 4.5 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Insights into Imaging Pub Date : 2026-02-02 DOI:10.1186/s13244-025-02202-3

Haonan Chen, Lingkai Cai, Juntao Zhuang, Yiran Tao, Zhengye Tan, Hao Yu, Chang Chen, Qikai Wu, Qiang Cao, Bo Liang, Pengchao Li, Xiao Yang, Qiang Lu

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引用次数: 0

Abstract

Objective: To develop and validate a body composition parameters (BCPs)-based nomogram for predicting recurrence in T1-stage clear cell renal cell carcinoma (ccRCC), comparing its performance with established models while exploring potential biological mechanisms.

Materials and methods: 536 patients from three institutions (training cohort: 343, external validation cohort: 193) were included. Univariate and multivariate Cox regression analyses identified independent prognostic factors for recurrence-free survival (RFS), which were incorporated into the nomogram. The model performance was evaluated, and potential biological mechanisms were explored.

Results: The postoperative nomogram included three independent adverse prognostic factors for RFS: high Leibovich score (HR = 2.18, 95% CI: 1.44-3.31), high visceral adipose tissue density (VATD; HR = 2.34, 95% CI: 1.33-4.12), and high intramuscular adipose tissue content (IMAC; HR = 3.60, 95% CI: 1.29-10.07). The nomogram demonstrated superior discrimination, with a C-index of 0.732 (95% CI: 0.655-0.810) in the training cohort and 0.766 (95% CI: 0.677-0.855) in the validation cohort. The area under the curves (AUCs) for predicting 3- and 5-year RFS were 0.761 and 0.709 (training), and 0.844 and 0.765 (validation), outperforming the TNM, Leibovich, and SSIGN models. Through 5-fold cross-validation within the training cohort, the model achieved mean AUCs of 0.761 (3-year) and 0.683 (5-year). Calibration curves showed good consistency. Decision curve analysis indicated favorable clinical utility. Risk stratification (cutoff = 94.18) based on nomogram scores revealed significant RFS differences. Exploratory in silico analyses of transcriptomic data suggested enrichment in distinct cancer-related and metabolic pathways, as well as varying drug sensitivities between cohorts.

Conclusion: The BCPs-based nomogram effectively predicts recurrence of T1 ccRCC and significantly improves upon existing prognostic models.

Critical relevance statement: The nomogram, combining body composition parameters and Leibovich score, outperformed established prognostic models in predicting T1 ccRCC recurrence, enabling personalized risk stratification.

Key points: Body composition parameters correlate with oncological outcomes in RCC, but remain underexplored in the T1 clear cell subtype. Elevated Leibovich score, visceral adipose tissue density, and intramuscular adipose tissue content independently predicted reduced RFS, linked to cancer-related and metabolic pathways enrichment. The body composition parameters-based nomogram could effectively predict postoperative recurrence in T1 ccRCC patients.

Abstract Image

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一项多中心回顾性研究：预测pT1透明细胞肾细胞癌复发的体成分参数图。

目的：建立并验证基于身体成分参数（bcp）的预测t1期透明细胞肾细胞癌（ccRCC）复发的nomogram方法，并将其与已建立的模型进行比较，同时探索潜在的生物学机制。材料和方法：纳入来自3家机构的536例患者（培训队列：343例，外部验证队列：193例）。单因素和多因素Cox回归分析确定了无复发生存（RFS）的独立预后因素，并将其纳入nomogram。评估了模型的性能，并探讨了潜在的生物学机制。结果：术后nomogram包括三个独立的RFS不良预后因素：高Leibovich评分（HR = 2.18, 95% CI: 1.44-3.31），高内脏脂肪组织密度（VATD; HR = 2.34, 95% CI: 1.33-4.12），高肌内脂肪组织含量（IMAC; HR = 3.60, 95% CI: 1.29-10.07）。训练组和验证组的c指数分别为0.732 （95% CI: 0.655-0.810）和0.766 （95% CI: 0.677-0.855）。预测3年和5年RFS的曲线下面积（auc）分别为0.761和0.709（训练），0.844和0.765（验证），优于TNM、Leibovich和SSIGN模型。通过训练队列的5倍交叉验证，模型的平均auc为0.761（3年）和0.683（5年）。标定曲线一致性好。决策曲线分析显示具有良好的临床应用价值。基于nomogram评分的风险分层（cut = 94.18）显示RFS存在显著差异。对转录组数据的探索性计算机分析表明，在不同的癌症相关和代谢途径中富集，并且在队列之间存在不同的药物敏感性。结论：基于bcps的nomogram可有效预测T1期ccRCC的复发，较现有预后模型有明显改善。关键相关性声明：结合身体成分参数和Leibovich评分的nomogram预测T1期ccRCC复发，优于已建立的预后模型，实现了个性化的风险分层。重点：体成分参数与RCC的肿瘤预后相关，但在T1透明细胞亚型中仍未得到充分研究。升高的Leibovich评分、内脏脂肪组织密度和肌内脂肪组织含量独立地预测了RFS的降低，这与癌症相关和代谢途径的富集有关。基于体成分参数的诺图能有效预测T1期ccRCC患者术后复发。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Insights into Imaging Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

7.30

自引率

4.30%

发文量

182

审稿时长

13 weeks

期刊介绍： Insights into Imaging (I³) is a peer-reviewed open access journal published under the brand SpringerOpen. All content published in the journal is freely available online to anyone, anywhere! I³ continuously updates scientific knowledge and progress in best-practice standards in radiology through the publication of original articles and state-of-the-art reviews and opinions, along with recommendations and statements from the leading radiological societies in Europe. Founded by the European Society of Radiology (ESR), I³ creates a platform for educational material, guidelines and recommendations, and a forum for topics of controversy. A balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes I³ an indispensable source for current information in this field. I³ is owned by the ESR, however authors retain copyright to their article according to the Creative Commons Attribution License (see Copyright and License Agreement). All articles can be read, redistributed and reused for free, as long as the author of the original work is cited properly. The open access fees (article-processing charges) for this journal are kindly sponsored by ESR for all Members. The journal went open access in 2012, which means that all articles published since then are freely available online.