Friederike Jungmann1, Georgios A Kaissis1,2,3, Sebastian Ziegelmayer1, Felix Harder1, Clara Schilling1, Hsi-Yu Yen4, Katja Steiger4, Wilko Weichert4, Rebekka Schirren5, Ishan Ekin Demir5, Helmut Friess5, Markus R Makowski1, Rickmer F Braren1,6, Fabian K Lohöfer1. 1. Institute of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany. 2. Department of Computing, Faculty of Engineering, Imperial College of Science, Technology and Medicine, London SW7 2AZ, UK. 3. Institute for Artificial Intelligence in Medicine and Healthcare, School of Medicine and Faculty of Informatics, Technical University of Munich, 81675 Munich, Germany. 4. Institute for Pathology, School of Medicine, Technical University of Munich, 81675 Munich, Germany. 5. Surgical Clinic and Policlinic, School of Medicine, Technical University of Munich, 81675 Munich, Germany. 6. German Cancer Consortium (DKTK) Partner Site Munich, 81675 Munich, Germany.
Abstract
BACKGROUND: PDAC remains a tumor entity with poor prognosis and a 5-year survival rate below 10%. Recent research has revealed invasive biomarkers, such as distinct molecular subtypes, predictive for therapy response and patient survival. Non-invasive prediction of individual patient outcome however remains an unresolved task. METHODS: Discrete cellularity regions of PDAC resection specimen (n = 43) were analyzed by routine histopathological work up. Regional tumor cellularity and CT-derived Hounsfield Units (HU, n = 66) as well as iodine concentrations were regionally matched. One-way ANOVA and pairwise t-tests were performed to assess the relationship between different cellularity level in conventional, virtual monoenergetic 40 keV (monoE 40 keV) and iodine map reconstructions. RESULTS: A statistically significant negative correlation between regional tumor cellularity in histopathology and CT-derived HU from corresponding image regions was identified. Radiological differentiation was best possible in monoE 40 keV CT images. However, HU values differed significantly in conventional reconstructions as well, indicating the possibility of a broad clinical application of this finding. CONCLUSION: In this study we establish a novel method for CT-based prediction of tumor cellularity for in-vivo tumor characterization in PDAC patients.
BACKGROUND: PDAC remains a tumor entity with poor prognosis and a 5-year survival rate below 10%. Recent research has revealed invasive biomarkers, such as distinct molecular subtypes, predictive for therapy response and patient survival. Non-invasive prediction of individual patient outcome however remains an unresolved task. METHODS: Discrete cellularity regions of PDAC resection specimen (n = 43) were analyzed by routine histopathological work up. Regional tumor cellularity and CT-derived Hounsfield Units (HU, n = 66) as well as iodine concentrations were regionally matched. One-way ANOVA and pairwise t-tests were performed to assess the relationship between different cellularity level in conventional, virtual monoenergetic 40 keV (monoE 40 keV) and iodine map reconstructions. RESULTS: A statistically significant negative correlation between regional tumor cellularity in histopathology and CT-derived HU from corresponding image regions was identified. Radiological differentiation was best possible in monoE 40 keV CT images. However, HU values differed significantly in conventional reconstructions as well, indicating the possibility of a broad clinical application of this finding. CONCLUSION: In this study we establish a novel method for CT-based prediction of tumor cellularity for in-vivo tumor characterization in PDAC patients.
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