Computer-Generated R.E.N.A.L. Nephrometry Scores Yield Comparable Predictive Results to Those of Human-Expert Scores in Predicting Oncologic and Perioperative Outcomes

Nick Heller, Resha D Tejpaul, F. Isensee, T. Benidir, M. Hofmann, Paul J Blake, Z. Rengal, K. Moore, Niranjan J Sathianathen, Arveen Kalapara, J. Rosenberg, S. Peterson, Edward G Walczak, A. Kutikov, R. G. Uzzo, D. A. Palacios, E. M. Remer, S. C. Campbell, N. Papanikolopoulos, Christopher J Weight

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Purpose: We sought to automate R.E.N.A.L. (for radius, exophytic/endophytic, nearness of tumor to collecting system, anterior/posterior, location relative to polar line) nephrometry scoring of preoperative computerized tomography scans and create an artificial intelligence-generated score (AI-score). Subsequently, we aimed to evaluate its ability to predict meaningful oncologic and perioperative outcomes as compared to expert human-generated nephrometry scores (H-scores). Materials and Methods: A total of 300 patients with preoperative computerized tomography were identified from a cohort of 544 consecutive patients undergoing surgical extirpation for suspected renal cancer at a single institution. A deep neural network approach was used to automatically segment kidneys and tumors, and geometric algorithms were developed to estimate components of R.E.N.A.L. nephrometry score. Tumors were independently scored by medical personnel blinded to AI-scores. AI- and H-score agreement was assessed using Lin’s concordance correlation and their predictive abilities for both oncologic and perioperative outcomes were assessed using areas under the curve. Results: Median age was 60 years (IQE 51e68), and 40% were female. Median tumor size was 4.2 cm and 91.3% had malignant tumors, including 27%, 37% and 24% with high stage, grade and necrosis, respectively. There was significant agreement between H-scores and AI-scores (Lin’s ρ=0.59). Both AI- and H-scores similarly predicted meaningful oncologic outcomes (p <0.001) including presence of malignancy, necrosis, and high-grade and -stage disease (p <0.003). They also predicted surgical approach (p <0.004) and specific perioperative outcomes (p <0.05). Conclusions: Fully automated AI-generated R.E.N.A.L. scores are comparable to human-generated R.E.N.A.L. scores and predict a wide variety of meaningful patient-centered outcomes. This unambiguous artificial intelligence-based scoring is intended to facilitate wider adoption of the R.E.N.A.L. score.

Original languageEnglish (US)
Pages (from-to)1105-1114
Number of pages10
JournalJournal of Urology
Volume207
Issue number5
DOIs
StatePublished - May 1 2022

Bibliographical note

Funding Information:
Support: National Institutes of Health (NIH) and the National Science Foundation.

Publisher Copyright:
© 2022 by AMERICAN UROLOGICAL ASSOCIATION EDUCATION AND RESEARCH, INC.

Keywords

  • artificial intelligence
  • machine learning
  • Computers
  • Kidney Neoplasms/diagnostic imaging
  • Nephrectomy/methods
  • Artificial Intelligence
  • Humans
  • Middle Aged
  • Female
  • Retrospective Studies
  • Necrosis

PubMed: MeSH publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Journal Article
  • Research Support, N.I.H., Extramural

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