To automate R.E.N.A.L. nephrometry scoring of preoperative computed tomography (CT) scans and create an artificial intelligence generated score (AI-Score). Subsequently, to evaluate its ability to predict meaningful oncologic and perioperative outcomes as compared to expert human-generated nephrometry scores (H-score. )Materials and Methods:300 patients with pre-operative CT's 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. 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.
Median age was 60 years (IQE 51-68), 40% were female. Median tumor size was 4.2 cm, 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 ⍴=0.59). Both AI- and H-scores similarly predicted meaningful oncologic outcomes (p <0.001) including presence of malignancy, necrosis, high grade and high 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 AI-based scoring is intended to facilitate wider adoption of the R.E.N.A.L score.
The Journal of urology. 2021 Dec 30 [Epub ahead of print]
N Heller, R Tejpaul, F Isensee, T Benidir, M Hofmann, P Blake, Z Rengal, K Moore, N Sathianathen, A Kalapara, J Rosenberg, S Peterson, E Walczak, A Kutikov, R G Uzzo, D A Palacios, E M Remer, S C Campbell, N Papanikolopoulos, Christopher J Weight
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota., German Cancer Research Center (DKFZ) Heidelberg, Germany, University of Heidelberg, Heidelberg, Germany., Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, Ohio., University of Minnesota School of Medicine, Minneapolis, Minnesota., Carleton College, Northfield, Minnesota., Department of Urology, University of Melbourne, Melbourne, Australia., Brigham Young University, Provo, Utah., Urology, Fox Chase Cancer Center, Philadelphia, Pennsylvania.