A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging
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Abstract
BACKGROUND: To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: One hundred two patients with histopathologically proven PDAC were retrospectively assessed as training cohort, and 30 prospectively accrued and retrospectively enrolled patients served as independent validation cohort (IVC). Tumors were segmented on preoperative apparent diffusion coefficient (ADC) maps, and radiomic features were extracted. A random forest ML algorithm was fit to the training cohort and tested in the IVC. Histopathological subtype of tumor samples was assessed by immunohistochemistry in 21 IVC patients. Individual radiomic feature importance was evaluated by assessment of tree node Gini impurity decrease and recursive feature elimination. Fisher's exact test, 95% confidence intervals (CI), and receiver operating characteristic area under the curve (ROC-AUC) were used. RESULTS: The ML algorithm achieved 87% sensitivity (95% IC 67.3-92.7), 80% specificity (95% CI 74.0-86.7), and ROC-AUC 90% for the prediction of above- versus below-median OS in the IVC. Heterogeneity-related features were highly ranked by the model. Of the 21 patients with determined histopathological subtype, 8/9 patients predicted to experience below-median OS exhibited the quasi-mesenchymal subtype, whilst 11/12 patients predicted to experience above-median OS exhibited a non-quasi-mesenchymal subtype (p < 0.001). CONCLUSION: ML application to ADC radiomics allowed OS prediction with a high diagnostic accuracy in an IVC. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging in PDAC pre-operative subtyping and prognosis.
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Kaissis et al. showed a strong correlation between clinically relevant histopathological subtypes and model predictions.
“One of the papers by Kaissis et al. from the beforementioned review showed a strong correlation between clinically relevant histopathological subtypes and model predictions, highlighting the potential of quantitative imaging for the pre-operative subtyping and prognosis of PDAC [18].”
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Self-citationKünstliche Intelligenz und maschinelles Lernen in der onkologischen Bildgebung
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Self-citationAn XAI Approach to Predictive Analytics of Pancreatic Cancer
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Künstliche Intelligenz und maschinelles Lernen in der onkologischen Bildgebung
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The integration of artificial intelligence models to augment imaging modalities in pancreatic cancer
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Artificial Intelligence in Medicine and Privacy Preservation
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Optimal Elman Neural Network for Pancreatic Cancer Classification Using Computed Tomography Images
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Toward diffusion MRI in the diagnosis and treatment of pancreatic cancer
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Artificial Intelligence in Medicine and Privacy Preservation
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Schnittbilddiagnostik beim duktalen Adenokarzinom des Pankreas
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Pancreatic Ductal Adenocarcinoma
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Self-citationCurrent Concepts and Future Directions in Pancreatic Adenocarcinoma Staging
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Self-citationEnhancing Pancreatic Cancer Diagnosis Precision through Early Detection with ResUnet
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Predictive imaging in abdominal oncology: current trends and future directions
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Artificial Intelligence for treatment outcomes in Pancreatic Cancer: a Scoping ReviewPreprint
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- Authors
- Kaissis, G., Ziegelmayer, S., Lohöfer, F., Algül, H., Eiber, M., Weichert, W., Schmid, R., Friess, H., Rummeny, E., Ankerst, D., Siveke, J., & Braren, R.
- Affiliations
- TUM Klinikum (DE)
- Technical University of Munich (DE)
- University of Duisburg-Essen (DE)
- Corresponding author
- Braren, Rickmer
- ORCID
- Venue
- European Radiology Experimental
- Published
- 2019-10-17
- Volume
- 3
- Issue
- 1
- Pages
- 41-41
- ISSN
- 2509-9280 (ISSN-L)
- PMC
- PMC6797674
- OpenAlex
- W2981208442
- Cited by
- 79
- References
- 34
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 4 · 2023: 11 · 2022: 16 · 2021: 19 · 2020: 13 · 2019: 4
- FWCI
- 8.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- European Radiology Experimental (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
- mediaTUM (Technical University of Munich) (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (SFB 824 Project C6, SFB 824/ Project Z2)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
- Keywords
- pancreatic ductal adenocarcinoma, diffusion-weighted imaging, apparent diffusion coefficient, radiomics, overall survival prediction, tumor subtypes, random forest, preoperative imaging, tumor heterogeneity, prognosis
- MeSH terms
- Machine Learning, Humans, Models, Theoretical, Pancreatic Neoplasms, Predictive Value of Tests, Retrospective Studies, Survival Rate, Carcinoma, Pancreatic Ductal, Diffusion Magnetic Resonance Imaging, Preoperative Period
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