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

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 51–68), 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 ⍴=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.

Machine learning in renal cell carcinoma research: the promise and pitfalls of ‘renal‐izing’ the potential of artificial intelligence

“…Zine-Eddine Khene 1,2 , Alexander Kutikov 3 , Riccardo Campi 4,5,6 and on behalf of the EAU-YAU Renal Cancer Working Group…”

“…Thus, limitations of the presented models stemmed, at least in part, from the nature and handicap of the dataset. Indeed, a deep neural network approach is particularly effective when deployed for analysing large, complex and high-dimensional datasets, such as cross-sectional imaging, that are highly granular and are of high fidelity [4].…”

Point of care parenchymal volume analyses to estimate split renal function and predict functional outcomes after radical nephrectomy

“…In RCC, algorithms that perform automated kidney and tumor segmentations produce R.E.N.A.L. scores comparable to expert human-generated scores 30 . These algorithms accurately predicted oncological RCC outcomes, such as the presence of malignancy, necrosis, and high-grade and high-stage disease, among other relevant parameters 30 .…”

Editorial Comment

“…2 Keep in mind that generative artificial intelligence algorithms continue to support our clinical decisions, where they can successfully predict renal mass complexity and subsequent surgical approaches. 4 We will continue to do even better as our robotic iterations and predictive technology continue to refine and converge into one platform. It is uplifting to think that there might even be a lower risk (<4%) of conversion to RN when embarking on RPN in the near future.…”