Prediction of tumor cellularity in resectable pdac from preoperative computed tomography imaging

Friederike Jungmann; Georgios A. Kaissis; Sebastian Ziegelmayer; Felix Harder; Clara Schilling; Hsi Yu Yen; Katja Steiger; Wilko Weichert; Rebekka Schirren; Ishan Ekin Demir; Helmut Friess; Markus R. Makowski; Rickmer F. Braren; Fabian K. Lohöfer

doi:10.3390/cancers13092069

Prediction of tumor cellularity in resectable pdac from preoperative computed tomography imaging

Friederike Jungmann, Georgios A. Kaissis, Sebastian Ziegelmayer, Felix Harder, Clara Schilling, Hsi Yu Yen, Katja Steiger, Wilko Weichert, Rebekka Schirren, Ishan Ekin Demir, Helmut Friess, Markus R. Makowski, Rickmer F. Braren, Fabian K. Lohöfer

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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.

Original language	English
Article number	2069
Journal	Cancers
Volume	13
Issue number	9
DOIs	https://doi.org/10.3390/cancers13092069
State	Published - 1 May 2021

Keywords

Computed tomography
PDAC
Pancreatic ductal adenocarcinoma
Tumor cellularity

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Jungmann, F., Kaissis, G. A., Ziegelmayer, S., Harder, F., Schilling, C., Yen, H. Y., Steiger, K., Weichert, W., Schirren, R., Demir, I. E., Friess, H., Makowski, M. R., Braren, R. F., & Lohöfer, F. K. (2021). Prediction of tumor cellularity in resectable pdac from preoperative computed tomography imaging. Cancers, 13(9), Article 2069. https://doi.org/10.3390/cancers13092069

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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.",

keywords = "Computed tomography, PDAC, Pancreatic ductal adenocarcinoma, Tumor cellularity",

author = "Friederike Jungmann and Kaissis, {Georgios A.} and Sebastian Ziegelmayer and Felix Harder and Clara Schilling and Yen, {Hsi Yu} and Katja Steiger and Wilko Weichert and Rebekka Schirren and Demir, {Ishan Ekin} and Helmut Friess and Makowski, {Markus R.} and Braren, {Rickmer F.} and Loh{\"o}fer, {Fabian K.}",

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doi = "10.3390/cancers13092069",

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AU - Jungmann, Friederike

AU - Kaissis, Georgios A.

AU - Ziegelmayer, Sebastian

AU - Harder, Felix

AU - Schilling, Clara

AU - Yen, Hsi Yu

AU - Steiger, Katja

AU - Weichert, Wilko

AU - Schirren, Rebekka

AU - Demir, Ishan Ekin

AU - Friess, Helmut

AU - Makowski, Markus R.

AU - Braren, Rickmer F.

AU - Lohöfer, Fabian K.

PY - 2021/5/1

Y1 - 2021/5/1

N2 - 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.

AB - 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.

KW - Computed tomography

KW - PDAC

KW - Pancreatic ductal adenocarcinoma

KW - Tumor cellularity

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ER -

Prediction of tumor cellularity in resectable pdac from preoperative computed tomography imaging

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