Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types

Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types

Abstract Background Homologous recombination deficiency (HRD) is recognized as a pan-cancer predictive biomarker that potentially indicates who could benefit from treatment with PARP inhibitors (PARPi). Despite its clinical significance, HRD testing is highly complex. Here, we investigated in a proo...

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Bibliographic Details
Main Authors: Chiara Maria Lavinia Loeffler, Omar S. M. El Nahhas, Hannah Sophie Muti, Zunamys I. Carrero, Tobias Seibel, Marko van Treeck, Didem Cifci, Marco Gustav, Kevin Bretz, Nadine T. Gaisa, Kjong-Van Lehmann, Alexandra Leary, Pier Selenica, Jorge S. Reis-Filho, Nadina Ortiz-Bruechle, Jakob Nikolas Kather
Format: Article
Language:English
Published: BMC 2024-10-01
Series:BMC Biology
Subjects:
Homologous recombination deficiency
Deep learning
DNA repair mechanism
Artificial intelligence
Mpathology
Pan-cancer study
Online Access:https://doi.org/10.1186/s12915-024-02022-9

Internet

https://doi.org/10.1186/s12915-024-02022-9

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