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

## TL;DR

A new nomogram using body composition parameters improves prediction of recurrence in early-stage kidney cancer, outperforming existing models.

## Contribution

A novel nomogram integrating body composition parameters for predicting recurrence in T1 clear cell renal cell carcinoma.

## Key findings

- The nomogram includes high Leibovich score, visceral adipose tissue density, and intramuscular adipose tissue content as independent predictors of recurrence.
- The nomogram outperformed existing models like TNM, Leibovich, and SSIGN in predicting recurrence-free survival.
- In silico analysis linked body composition parameters to cancer-related and metabolic pathways.

## Abstract

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.

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.

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.

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

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

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.

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.