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...
Main Authors: | Frederik Wessels, Max Schmitt, Eva Krieghoff-Henning, Jakob N Kather, Malin Nientiedt, Maximilian C Kriegmair, Thomas S Worst, Manuel Neuberger, Matthias Steeg, Zoran V Popovic, Timo Gaiser, Christof von Kalle, Jochen S Utikal, Stefan Fröhling, Maurice S Michel, Philipp Nuhn, Titus J Brinker |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2022-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0272656 |
Similar Items
-
An m6A-Driven Prognostic Marker Panel for Renal Cell Carcinoma Based on the First Transcriptome-Wide m6A-seq
by: Frank Waldbillig, et al.
Published: (2023-02-01) -
Uncertainty Estimation in Medical Image Classification: Systematic Review
by: Alexander Kurz, et al.
Published: (2022-08-01) -
Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study
by: Schmitt, Max, et al.
Published: (2021-02-01) -
Reducing the Impact of Confounding Factors on Skin Cancer Classification via Image Segmentation: Technical Model Study
by: Maron, Roman C, et al.
Published: (2021-03-01) -
Changes in neutrophile-to-lymphocyte ratio as predictive and prognostic biomarker in metastatic prostate cancer treated with taxane-based chemotherapy
by: Manuel Neuberger, et al.
Published: (2022-12-01)