Background and Objective Surgical complexity for renal tumors has traditionally been assessed using manual nephrometry scores, which require unreimbursed physician effort and are subject to interobserver variability. This study introduces an objective, fully automated alternative derived from decades of experience at a large academic center.

Methods We trained a CT classification model to predict whether a patient would ultimately undergo Partial or Radical Nephrectomy (PN or RN). We hypothesized that the model’s confidence in RN (termed the CLARITY score) would serve as a surrogate for the difficulty of nephron-sparing approaches and thus for tumor complexity. This hypothesis was tested using multivariate logistic regression for failure to achieve trifecta, estimated blood loss (EBL) ≥ 500 mL, and length of stay ≥ 3 d. CLARITY was compared with tumor size and R.E.N.A.L. score. External validation in a geographically distinct cohort was performed.

Key Findings and Limitations For predicting RN, CLARITY achieved an AUROC of 0.899 internally and 0.898 externally. In the external PN subgroup, it outperformed tumor size and R.E.N.A.L. score in predicting failure to achieve trifecta (AUROC 0.613), EBL ≥ 500 mL (0.727), and length of stay ≥ 3 d (0.673). In multivariable analysis, CLARITY remained associated with each outcome, whereas R.E.N.A.L. and size were not. This study is limited by its retrospective design.

Conclusions and Clinical Implications CLARITY is an automated CT-derived marker that quantifies renal tumor complexity more effectively than tumor size and R.E.N.A.L. score and may support scalable, objective preoperative complexity assessment. To support reproducibility and external validation, we have released a public inference pipeline and web-based DICOM upload portal for research use.

The authors have declared no competing interest.

This work was supported in part by the Department of Defense under Award Number HT94252310918. Additional funding was provided by Climb 4 Kidney Cancer, a nonprofit organization dedicated to advancing research, education, and advocacy for kidney cancer.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

IRB of The Cleveland Clinic Foundation waived ethical approval for this work (study ID 23-1158)

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To support reproducibility and independent validation, CLARITY score predictions for the KiTS external validation cohort are publicly available as a JSON file at the project repository. Public inference code, model weights, and a containerized implementation are available at the CLARITY inference pipeline repository. The released pipeline accepts DICOM imaging input, performs preprocessing and segmentation, and returns a CLARITY score for each case. A web-based CLARITY upload portal is also available at clarity.aim-hi-lab.org/. Users may upload a preoperative kidney tumor DICOM case and provide an email address to receive a CLARITY score after processing. The portal is intended to support reproducibility, external testing, and prospective workflow evaluation. Because external KiTS validation in this study was performed using the segmentations and NIfTI images provided by the KiTS dataset rather than pipeline-generated segmentations, reproduced results may differ due to variation in segmentation quality and preprocessing. The data from the internal dataset will not be provided as it includes protected health information.

https://github.com/AIM-HI-Lab/axis-inference-pipeline/blob/main/paper_data/inference_aggregated.json

https://github.com/AIM-HI-Lab/axis-inference-pipeline

https://clarity.aim-hi-lab.org