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Multimodal survival prediction in advanced pancreatic cancer using machine learning

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Multimodal survival prediction in advanced pancreatic cancer using machine learning. / Keyl, J; Kasper, S; Wiesweg, M; Götze, J; Schönrock, M; Sinn, M; Berger, A; Nasca, E; Kostbade, K; Schumacher, B; Markus, P; Albers, D; Treckmann, J; Schmid, K W; Schildhaus, H-U; Siveke, J T; Schuler, M; Kleesiek, J.

In: ESMO OPEN, Vol. 7, No. 5, 100555, 10.2022.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Keyl, J, Kasper, S, Wiesweg, M, Götze, J, Schönrock, M, Sinn, M, Berger, A, Nasca, E, Kostbade, K, Schumacher, B, Markus, P, Albers, D, Treckmann, J, Schmid, KW, Schildhaus, H-U, Siveke, JT, Schuler, M & Kleesiek, J 2022, 'Multimodal survival prediction in advanced pancreatic cancer using machine learning', ESMO OPEN, vol. 7, no. 5, 100555. https://doi.org/10.1016/j.esmoop.2022.100555

APA

Keyl, J., Kasper, S., Wiesweg, M., Götze, J., Schönrock, M., Sinn, M., Berger, A., Nasca, E., Kostbade, K., Schumacher, B., Markus, P., Albers, D., Treckmann, J., Schmid, K. W., Schildhaus, H-U., Siveke, J. T., Schuler, M., & Kleesiek, J. (2022). Multimodal survival prediction in advanced pancreatic cancer using machine learning. ESMO OPEN, 7(5), [100555]. https://doi.org/10.1016/j.esmoop.2022.100555

Vancouver

Keyl J, Kasper S, Wiesweg M, Götze J, Schönrock M, Sinn M et al. Multimodal survival prediction in advanced pancreatic cancer using machine learning. ESMO OPEN. 2022 Oct;7(5). 100555. https://doi.org/10.1016/j.esmoop.2022.100555

Bibtex

@article{6695ae07ee324b66881d07addda51caf,
title = "Multimodal survival prediction in advanced pancreatic cancer using machine learning",
abstract = "BACKGROUND: Existing risk scores appear insufficient to assess the individual survival risk of patients with advanced pancreatic ductal adenocarcinoma (PDAC) and do not take advantage of the variety of parameters that are collected during clinical care.METHODS: In this retrospective study, we built a random survival forest model from clinical data of 203 patients with advanced PDAC. The parameters were assessed before initiation of systemic treatment and included age, CA19-9, C-reactive protein, metastatic status, neutrophil-to-lymphocyte ratio and total serum protein level. Separate models including imaging and molecular parameters were built for subgroups.RESULTS: Over the entire cohort, a model based on clinical parameters achieved a c-index of 0.71. Our approach outperformed the American Joint Committee on Cancer (AJCC) staging system and the modified Glasgow Prognostic Score (mGPS) in the identification of high- and low-risk subgroups. Inclusion of the KRAS p.G12D mutational status could further improve the prediction, whereas radiomics data of the primary tumor only showed little benefit. In an external validation cohort of PDAC patients with liver metastases, our model achieved a c-index of 0.67 (mGPS: 0.59).CONCLUSIONS: The combination of multimodal data and machine-learning algorithms holds potential for personalized prognostication in advanced PDAC already at diagnosis.",
keywords = "Humans, C-Reactive Protein, Retrospective Studies, CA-19-9 Antigen, Proto-Oncogene Proteins p21(ras), Neoplasm Staging, Prognosis, Pancreatic Neoplasms/diagnosis, Adenocarcinoma/pathology, Machine Learning",
author = "J Keyl and S Kasper and M Wiesweg and J G{\"o}tze and M Sch{\"o}nrock and M Sinn and A Berger and E Nasca and K Kostbade and B Schumacher and P Markus and D Albers and J Treckmann and Schmid, {K W} and H-U Schildhaus and Siveke, {J T} and M Schuler and J Kleesiek",
note = "Copyright {\textcopyright} 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.",
year = "2022",
month = oct,
doi = "10.1016/j.esmoop.2022.100555",
language = "English",
volume = "7",
journal = "ESMO OPEN",
issn = "2059-7029",
publisher = "BMJ PUBLISHING GROUP",
number = "5",

}

RIS

TY - JOUR

T1 - Multimodal survival prediction in advanced pancreatic cancer using machine learning

AU - Keyl, J

AU - Kasper, S

AU - Wiesweg, M

AU - Götze, J

AU - Schönrock, M

AU - Sinn, M

AU - Berger, A

AU - Nasca, E

AU - Kostbade, K

AU - Schumacher, B

AU - Markus, P

AU - Albers, D

AU - Treckmann, J

AU - Schmid, K W

AU - Schildhaus, H-U

AU - Siveke, J T

AU - Schuler, M

AU - Kleesiek, J

N1 - Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.

PY - 2022/10

Y1 - 2022/10

N2 - BACKGROUND: Existing risk scores appear insufficient to assess the individual survival risk of patients with advanced pancreatic ductal adenocarcinoma (PDAC) and do not take advantage of the variety of parameters that are collected during clinical care.METHODS: In this retrospective study, we built a random survival forest model from clinical data of 203 patients with advanced PDAC. The parameters were assessed before initiation of systemic treatment and included age, CA19-9, C-reactive protein, metastatic status, neutrophil-to-lymphocyte ratio and total serum protein level. Separate models including imaging and molecular parameters were built for subgroups.RESULTS: Over the entire cohort, a model based on clinical parameters achieved a c-index of 0.71. Our approach outperformed the American Joint Committee on Cancer (AJCC) staging system and the modified Glasgow Prognostic Score (mGPS) in the identification of high- and low-risk subgroups. Inclusion of the KRAS p.G12D mutational status could further improve the prediction, whereas radiomics data of the primary tumor only showed little benefit. In an external validation cohort of PDAC patients with liver metastases, our model achieved a c-index of 0.67 (mGPS: 0.59).CONCLUSIONS: The combination of multimodal data and machine-learning algorithms holds potential for personalized prognostication in advanced PDAC already at diagnosis.

AB - BACKGROUND: Existing risk scores appear insufficient to assess the individual survival risk of patients with advanced pancreatic ductal adenocarcinoma (PDAC) and do not take advantage of the variety of parameters that are collected during clinical care.METHODS: In this retrospective study, we built a random survival forest model from clinical data of 203 patients with advanced PDAC. The parameters were assessed before initiation of systemic treatment and included age, CA19-9, C-reactive protein, metastatic status, neutrophil-to-lymphocyte ratio and total serum protein level. Separate models including imaging and molecular parameters were built for subgroups.RESULTS: Over the entire cohort, a model based on clinical parameters achieved a c-index of 0.71. Our approach outperformed the American Joint Committee on Cancer (AJCC) staging system and the modified Glasgow Prognostic Score (mGPS) in the identification of high- and low-risk subgroups. Inclusion of the KRAS p.G12D mutational status could further improve the prediction, whereas radiomics data of the primary tumor only showed little benefit. In an external validation cohort of PDAC patients with liver metastases, our model achieved a c-index of 0.67 (mGPS: 0.59).CONCLUSIONS: The combination of multimodal data and machine-learning algorithms holds potential for personalized prognostication in advanced PDAC already at diagnosis.

KW - Humans

KW - C-Reactive Protein

KW - Retrospective Studies

KW - CA-19-9 Antigen

KW - Proto-Oncogene Proteins p21(ras)

KW - Neoplasm Staging

KW - Prognosis

KW - Pancreatic Neoplasms/diagnosis

KW - Adenocarcinoma/pathology

KW - Machine Learning

U2 - 10.1016/j.esmoop.2022.100555

DO - 10.1016/j.esmoop.2022.100555

M3 - SCORING: Journal article

C2 - 35988455

VL - 7

JO - ESMO OPEN

JF - ESMO OPEN

SN - 2059-7029

IS - 5

M1 - 100555

ER -