Deep learning can predict survival directly from histology in clear cell renal cell carcinoma

For clear cell renal cell carcinoma (ccRCC) risk-dependent diagnostic and therapeutic algorithms are routinely implemented in clinical practice. Artificial intelligence-based image analysis has the potential to improve outcome prediction and thereby risk stratification. Thus, we investigated whether...

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Veröffentlicht in:	PloS one 2022-08, Vol.17 (8), p.e0272656-e0272656
Hauptverfasser:	Wessels, Frederik, Schmitt, Max, Krieghoff-Henning, Eva, Kather, Jakob N, Nientiedt, Malin, Kriegmair, Maximilian C, Worst, Thomas S, Neuberger, Manuel, Steeg, Matthias, Popovic, Zoran V, Gaiser, Timo, von Kalle, Christof, Utikal, Jochen S, Fröhling, Stefan, Michel, Maurice S, Nuhn, Philipp, Brinker, Titus J
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Analysis Annotations Artificial intelligence Artificial neural networks Biology and Life Sciences Carcinoma, Renal cell Clear cell-type renal cell carcinoma Computer and Information Sciences Confidence intervals Deep learning Diagnosis Feature extraction Genomes Histology Image analysis Image processing Kidney cancer Machine learning Medical diagnosis Medical imaging Medical prognosis Medicine and Health Sciences Metastases Mutation Neural networks Patients Performance evaluation Predictions Risk Sensitivity Statistical analysis Survival Tumors
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