# Preoperative predictors of adverse pathology and recurrence‐free survival for patients with renal masses

## TL;DR

This study identifies preoperative CT imaging features that predict adverse outcomes in kidney cancer patients, potentially improving treatment decisions.

## Contribution

The study introduces predictive models for adverse pathology and recurrence-free survival using preoperative CT imaging features.

## Key findings

- Radiological features like tumor size, heterogeneity, and PVR ≥25% are strong predictors of adverse pathology.
- The models achieved AUC values of 0.81 for adverse pathology and 0.84–0.86 for recurrence-free survival.
- Preoperative parameters may reduce the need for kidney mass biopsies in some cases.

## Abstract

Our objective was to develop algorithms to predict adverse pathology (AP) and recurrence‐free survival (RFS) for patients with renal tumours primarily based on multifaceted analysis of preoperative CT imaging.

Seven hundred forty‐eight patients with non‐metastatic renal tumours managed with definitive surgery at Cleveland Clinic (2011–2014) were retrospectively evaluated (median follow‐up 9.1 years). All patients underwent contrast‐enhanced CT and parenchymal volume analysis using semi‐automated software. A variety of conventional radiological features were evaluated in addition to parenchymal volume replacement (PVR) due to invasive tumour growth, using the contralateral kidney as a control. Adverse pathology (AP) was defined as stage ≥pT3a, grade 3/4 or sarcomatoid/rhabdoid features. Multivariable logistic regression and Cox proportional hazards regression analyses were used to develop predictive models.

Overall, 339/748 patients (45%) had AP, which significantly associated with reduced RFS. On univariable analysis, tumour‐size, degree of vascularity, heterogeneity, irregular contour, sinus margin irregularity, necrosis, non‐cystic tumour and increased PVR significantly associated with AP. On multivariable logistic regression, male sex, R.E.N.A.L. Nearness, heterogeneity, necrosis, sinus margin irregularity and PVR ≥ 25% independently associated with AP. Multivariable analysis indicated that tumour size, heterogeneity, necrosis, PVR ≥ 25% and tumour‐related symptoms significantly associated with reduced RFS. Models for AP and RFS at 3, 5 and 10 years showed area under the curve (AUC) values of 0.81 and 0.84–0.86, respectively.

These findings confirm that radiological features and PVR are associated with AP and reduced RFS after definitive renal cancer surgery. Our predictive models are entirely based on preoperative parameters and may improve patient counselling and occasionally preclude the need for renal mass biopsy.