Delta-radiomics features of ADC maps as early predictors of treatment response in lung cancer
Morphological imaging takes time to detect TR in lung cancer, diffusion-weighted MRI might identify response earlier. Several radiomics features are significantly correlated with TR and PFS. Radiomics of diffusion-weighted MRI may facilitate patient stratification and management.
Authors
- K. Senghas
- Oliver Weinheimer (ORCID: https://orcid.org/0000-0002-9507-076X)
- Róbert Stollmayer (ORCID: https://orcid.org/0000-0003-4673-7588)
- Hans‐Ulrich Kauczor (ORCID: https://orcid.org/0000-0002-6730-9462)
- Farastuk Bozorgmehr (ORCID: https://orcid.org/0000-0001-8058-8884)
- Kai Schlamp
- Jonas Bohn (ORCID: https://orcid.org/0000-0003-1792-2725)
- Christian M. Heidt (ORCID: https://orcid.org/0009-0007-5597-7555)
- Claus Peter Heußel (ORCID: https://orcid.org/0000-0001-5809-5733)
- Stephan Rheinheimer
- Frederik Lars Giesel (ORCID: https://orcid.org/0000-0002-9669-5593)
- Gudula Heußel
- Oyunbileg von Stackelberg
Institutions
- German Cancer Research Center (DE)
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- Düsseldorf University Hospital (DE)
- National Center for Tumor Diseases (DE)
- German Center for Lung Research (DE)
- Heinrich Heine University Düsseldorf (DE)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2024-08-26
- DOI
- https://doi.org/10.1186/s13244-024-01787-5
- Citations
- 9
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.64
Funders
- Deutsches Zentrum für Lungenforschung