Cox proportional hazards deep neural network identifies peripheral blood complete remission to be at least equivalent to morphologic complete remission in predicting outcomes of patients treated with azacitidine-A prospective cohort study by the AGMT

Lisa Pleyer, Marc Vaisband, Manuel Drost, Michael Pfeilstöcker, Reinhard Stauder, Sonja Heibl, Heinz Sill, Michael Girschikofsky, Margarete Stampfl-Mattersberger, Angelika Pichler, Bernd Hartmann, Andreas Petzer, Martin Schreder, Clemens A Schmitt, Sonia Vallet, Thomas Melchardt, Armin Zebisch, Petra Pichler, Nadja Zaborsky, Sigrid Machherndl-SpandlDominik Wolf, Felix Keil, Jan Hasenauer, Julian Larcher-Senn, Richard Greil

Research output: Journal article (peer-reviewed) › Journal article

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BONE: Biomarker- based therapeutic prevention of bone metastases in breast cancer: Defining the pathophysiologic impact of the endosteal niche
Vallet, S. (PI)
Forschungsimpulse
01.08.2020 → 31.12.2023
Project: Forschungsimpulse › Research Time Out (RTO)

Cox proportional hazards deep neural network identifies peripheral blood complete remission to be at least equivalent to morphologic complete remission in predicting outcomes of patients treated with azacitidine-A prospective cohort study by the AGMT

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BONE: Biomarker- based therapeutic prevention of bone metastases in breast cancer: Defining the pathophysiologic impact of the endosteal niche