AI-generated R.E.N.A.L.+ Score Surpasses Human-generated Score in Predicting Renal Oncologic Outcomes
Issued Date
2023-01-01
Resource Type
ISSN
00904295
eISSN
15279995
Scopus ID
2-s2.0-85169786878
Pubmed ID
37517681
Journal Title
Urology
Rights Holder(s)
SCOPUS
Bibliographic Citation
Urology (2023)
Suggested Citation
Abdallah N., Wood A., Benidir T., Heller N., Isensee F., Tejpaul R., Corrigan D., Suk-ouichai C., Struyk G., Moore K., Venkatesh N., Ergun O., You A., Campbell R., Remer E.M., Haywood S., Krishnamurthi V., Abouassaly R., Campbell S., Papanikolopoulos N., Weight C.J. AI-generated R.E.N.A.L.+ Score Surpasses Human-generated Score in Predicting Renal Oncologic Outcomes. Urology (2023). doi:10.1016/j.urology.2023.07.017 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/90006
Title
AI-generated R.E.N.A.L.+ Score Surpasses Human-generated Score in Predicting Renal Oncologic Outcomes
Other Contributor(s)
Abstract
Objective: To determine whether we can surpass the traditional R.E.N.A.L. nephrometry score (H-score) prediction ability of pathologic outcomes by creating artificial intelligence (AI)-generated R.E.N.A.L.+ score (AI+ score) with continuous rather than ordinal components. We also assessed the AI+ score components’ relative importance with respect to outcome odds. Methods: This is a retrospective study of 300 consecutive patients with preoperative computed tomography scans showing suspected renal cancer at a single institution from 2010 to 2018. H-score was tabulated by three trained medical personnel. Deep neural network approach automatically generated kidney segmentation masks of parenchyma and tumor. Geometric algorithms were used to automatically estimate score components as ordinal and continuous variables. Multivariate logistic regression of continuous R.E.N.A.L. components was used to generate AI+ score. Predictive utility was compared between AI+, AI, and H-scores for variables of interest, and AI+ score components’ relative importance was assessed. Results: Median age was 60 years (interquartile range 51-68), and 40% were female. Median tumor size was 4.2 cm (2.6-6.12), and 92% were malignant, including 27%, 37%, and 23% with high-stage, high-grade, and necrosis, respectively. AI+ score demonstrated superior predictive ability over AI and H-scores for predicting malignancy (area under the curve [AUC] 0.69 vs 0.67 vs 0.64, respectively), high stage (AUC 0.82 vs 0.65 vs 0.71, respectively), high grade (AUC 0.78 vs 0.65 vs 0.65, respectively), pathologic tumor necrosis (AUC 0.81 vs 0.72 vs 0.74, respectively), and partial nephrectomy approach (AUC 0.88 vs 0.74 vs 0.79, respectively). Of AI+ score components, the maximal tumor diameter (“R”) was the most important outcomes predictor. Conclusion: AI+ score was superior to AI-score and H-score in predicting oncologic outcomes. Time-efficient AI+ score can be used at the point of care, surpassing validated clinical scoring systems.