Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters
Rationale: Pancreatic ductal adenocarcinoma (PDAC) remains a tumor entity of exceptionally poor prognosis, and several biomarkers are under current investigation for the prediction of patient prognosis. Many studies focus on promoting newly developed imaging biomarkers without a rigorous comparison...
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MDPI AG
2020-04-01
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Series: | Journal of Clinical Medicine |
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Online Access: | https://www.mdpi.com/2077-0383/9/5/1250 |
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doaj-708a3dff742e45ecbfb86f26b825ff78 |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Georgios A. Kaissis Friederike Jungmann Sebastian Ziegelmayer Fabian K. Lohöfer Felix N. Harder Anna Melissa Schlitter Alexander Muckenhuber Katja Steiger Rebekka Schirren Helmut Friess Roland Schmid Wilko Weichert Marcus R. Makowski Rickmer F. Braren |
spellingShingle |
Georgios A. Kaissis Friederike Jungmann Sebastian Ziegelmayer Fabian K. Lohöfer Felix N. Harder Anna Melissa Schlitter Alexander Muckenhuber Katja Steiger Rebekka Schirren Helmut Friess Roland Schmid Wilko Weichert Marcus R. Makowski Rickmer F. Braren Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters Journal of Clinical Medicine pancreatic ductal adenocarcinoma survival analysis multiparametric modelling genetics molecular phenotyping image-derived features |
author_facet |
Georgios A. Kaissis Friederike Jungmann Sebastian Ziegelmayer Fabian K. Lohöfer Felix N. Harder Anna Melissa Schlitter Alexander Muckenhuber Katja Steiger Rebekka Schirren Helmut Friess Roland Schmid Wilko Weichert Marcus R. Makowski Rickmer F. Braren |
author_sort |
Georgios A. Kaissis |
title |
Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters |
title_short |
Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters |
title_full |
Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters |
title_fullStr |
Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters |
title_full_unstemmed |
Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters |
title_sort |
multiparametric modelling of survival in pancreatic ductal adenocarcinoma using clinical, histomorphological, genetic and image-derived parameters |
publisher |
MDPI AG |
series |
Journal of Clinical Medicine |
issn |
2077-0383 |
publishDate |
2020-04-01 |
description |
Rationale: Pancreatic ductal adenocarcinoma (PDAC) remains a tumor entity of exceptionally poor prognosis, and several biomarkers are under current investigation for the prediction of patient prognosis. Many studies focus on promoting newly developed imaging biomarkers without a rigorous comparison to other established parameters. To assess the true value and leverage the potential of all efforts in this field, a multi-parametric evaluation of the available biomarkers for PDAC survival prediction is warranted. Here we present a multiparametric analysis to assess the predictive value of established parameters and the added contribution of newly developed imaging features such as biomarkers for overall PDAC patient survival. Methods: 103 patients with resectable PDAC were retrospectively enrolled. Clinical and histopathological data (age, sex, chemotherapy regimens, tumor size, lymph node status, grading and resection status), morpho-molecular and genetic data (tumor morphology, molecular subtype, tp53, kras, smad4 and p16 genetics), image-derived features and the combination of all parameters were tested for their prognostic strength based on the concordance index (CI) of multivariate Cox proportional hazards survival modelling after unsupervised machine learning preprocessing. Results: The average CIs of the out-of-sample data were: 0.63 for the clinical and histopathological features, 0.53 for the morpho-molecular and genetic features, 0.65 for the imaging features and 0.65 for the combined model including all parameters. Conclusions: Imaging-derived features represent an independent survival predictor in PDAC and enable the multiparametric, machine learning-assisted modelling of postoperative overall survival with a high performance compared to clinical and morpho-molecular/genetic parameters. We propose that future studies systematically include imaging-derived features to benchmark their additive value when evaluating biomarker-based model performance. |
topic |
pancreatic ductal adenocarcinoma survival analysis multiparametric modelling genetics molecular phenotyping image-derived features |
url |
https://www.mdpi.com/2077-0383/9/5/1250 |
work_keys_str_mv |
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doaj-708a3dff742e45ecbfb86f26b825ff782020-11-25T03:14:01ZengMDPI AGJournal of Clinical Medicine2077-03832020-04-0191250125010.3390/jcm9051250Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived ParametersGeorgios A. Kaissis0Friederike Jungmann1Sebastian Ziegelmayer2Fabian K. Lohöfer3Felix N. Harder4Anna Melissa Schlitter5Alexander Muckenhuber6Katja Steiger7Rebekka Schirren8Helmut Friess9Roland Schmid10Wilko Weichert11Marcus R. Makowski12Rickmer F. Braren13Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, GermanyDepartment of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, GermanyDepartment of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, GermanyDepartment of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, GermanyDepartment of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, GermanyInstitute for Pathology, School of Medicine, Technical University of Munich, 81675 Munich, GermanyInstitute for Pathology, School of Medicine, Technical University of Munich, 81675 Munich, GermanyInstitute for Pathology, School of Medicine, Technical University of Munich, 81675 Munich, GermanySchool of Medicine, Surgical Clinic and Policlinic, Technical University of Munich, 81675 Munich, GermanySchool of Medicine, Surgical Clinic and Policlinic, Technical University of Munich, 81675 Munich, GermanyDepartment of Internal Medicine II, School of Medicine, Technical University of Munich, 81675 Munich, GermanyInstitute for Pathology, School of Medicine, Technical University of Munich, 81675 Munich, GermanyDepartment of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, GermanyDepartment of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, GermanyRationale: Pancreatic ductal adenocarcinoma (PDAC) remains a tumor entity of exceptionally poor prognosis, and several biomarkers are under current investigation for the prediction of patient prognosis. Many studies focus on promoting newly developed imaging biomarkers without a rigorous comparison to other established parameters. To assess the true value and leverage the potential of all efforts in this field, a multi-parametric evaluation of the available biomarkers for PDAC survival prediction is warranted. Here we present a multiparametric analysis to assess the predictive value of established parameters and the added contribution of newly developed imaging features such as biomarkers for overall PDAC patient survival. Methods: 103 patients with resectable PDAC were retrospectively enrolled. Clinical and histopathological data (age, sex, chemotherapy regimens, tumor size, lymph node status, grading and resection status), morpho-molecular and genetic data (tumor morphology, molecular subtype, tp53, kras, smad4 and p16 genetics), image-derived features and the combination of all parameters were tested for their prognostic strength based on the concordance index (CI) of multivariate Cox proportional hazards survival modelling after unsupervised machine learning preprocessing. Results: The average CIs of the out-of-sample data were: 0.63 for the clinical and histopathological features, 0.53 for the morpho-molecular and genetic features, 0.65 for the imaging features and 0.65 for the combined model including all parameters. Conclusions: Imaging-derived features represent an independent survival predictor in PDAC and enable the multiparametric, machine learning-assisted modelling of postoperative overall survival with a high performance compared to clinical and morpho-molecular/genetic parameters. We propose that future studies systematically include imaging-derived features to benchmark their additive value when evaluating biomarker-based model performance.https://www.mdpi.com/2077-0383/9/5/1250pancreatic ductal adenocarcinomasurvival analysismultiparametric modellinggeneticsmolecular phenotypingimage-derived features |