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Machine-learning approach for prediction of pT3a upstaging and outcomes of localized renal cell carcinoma (UroCCR-15).

Astrid Boulenger de HauteclocqueLoïc FerrerDamien AmbrosettiSolene RicardPierre BigotKarim BensalahFrançois HenonNicolas DoumercArnaud MéjeanVirginie VerkarreCharles DarianeStéphane LarréCécile ChampyAlexandre de La TailleFranck BruyereMorgan RouprêtPhilippe PaparelStéphane DroupyAlexis FontenilJean-Jacques PatardXavier DurandThibaut WaeckelHerve LangCedric LebacleLaurent GuyGeraldine PignotMatthieu DurandJean-Alexandre LongThomas CharlesEvanguelos XylinasRomain BoissierMokrane YacoubThierry ColinJean-Christophe Bernhard
Published in: BJU international (2023)
Our study shows that machine-learning technology can play a useful role in the evaluation and prognosis of upstaged RCC. In the context of incidental upstaging, PN does not compromise oncological outcomes, even for large tumour sizes.
Keyphrases
  • machine learning
  • renal cell carcinoma
  • artificial intelligence
  • big data
  • prostate cancer
  • deep learning
  • type diabetes
  • radical prostatectomy
  • adipose tissue
  • skeletal muscle
  • insulin resistance