ArticleESMO open2022
Multimodal survival prediction in advanced pancreatic cancer using machine learning.
Article in ESMO open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Global research trends and hotspots in prognostic prediction models for pancreatic cancer: a bibliometric analysis.Frontiers in oncology · 2025Pooled it
- Predictive criteria for overall survival and treatment duration of 2nd-line chemotherapy in patients with advanced pancreatic adenocarcinoma (AIO-PAK-0216).British journal of cancer · 2026Trial
- Artificial intelligence in pancreatic cancer: applications in early detection, tumor staging, and survival prediction-a comprehensive review.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Large language models enable prognostic stratification of cancer patients using real-world clinical notes.PLOS digital health · 2026Article
- Construction and validation of a nomogram for overall survival prognosis in patients with advanced (stage III/IV) pancreatic cancer.Scientific reports · 2026Article
- From Bench to Bedside: The Path Toward Real-World Translation for Artificial Intelligence in Pancreatic Cancer Detection.Korean journal of radiology · 2026Review
- Explainable artificial intelligence for personalized prognosis in pancreatic cancer: A nationwide study from Taiwan.PLOS digital health · 2026Article
- Review
- Article
- Survival rate in patients with pancreatic cancer in Guilan province, Iran.Caspian journal of internal medicine · 2026Article
- The development of a multimodal prediction model based on CT and MRI for the prognosis of pancreatic cancer.BMC gastroenterology · 2025Article
- [Implementation of artificial intelligence (AI) in healthcare: historical development, current technologies and challenges].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Review
- Preoperative arterial and venous CT radiomics for survival prediction after pylorus preserving pancreatoduodenectomy in pancreatic head cancer.Scientific reports · 2025Article
- Development of PDAC diagnosis and prognosis evaluation models based on machine learning.BMC cancer · 2025Article
- Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review.BMC medical research methodology · 2025Article
- Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence.Nature cancer · 2025Article
- Construction of feature selection and efficacy prediction model for transformation therapy of locally advanced pancreatic cancer based on CT,Cancer cell international · 2025Article
- Article
- Explainable artificial intelligence in pancreatic cancer prediction: from transparency to clinical decision-making.Frontiers in oncology · 2025Review
- The tumor immune microenvironment: implications for cancer immunotherapy, treatment strategies, and monitoring approaches.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundExisting 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.
methodsIn 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.
resultsOver 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).
conclusionsThe combination of multimodal data and machine-learning algorithms holds potential for personalized prognostication in advanced PDAC already at diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.