Reply to: machine learning in renal cell carcinoma research: promise and pitfalls of ‘renal‐izing’ the potential of artificial intelligence
Author:
Affiliation:
1. Centre Hospitalier Universitaire de Bordeaux Groupe Hospitalier Pellegrin Bordeaux France
2. SOPHiA GENETICS SAS Bidart France
Publisher
Wiley
Subject
Urology
Reference6 articles.
1. Machine learning in renal cell carcinoma research: the promise and pitfalls of ‘renal‐izing’ the potential of artificial intelligence
2. Machine‐learning approach for prediction of pT3a upstaging and outcomes of localized renal cell carcinoma (UroCCR‐15)
3. Decision Curve Analysis: A Novel Method for Evaluating Prediction Models
4. Application of Machine Learning Models to Predict Recurrence After Surgical Resection of Nonmetastatic Renal Cell Carcinoma
5. 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
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