ArticleCancers2019
Use of Machine-Learning Algorithms in Intensified Preoperative Therapy of Pancreatic Cancer to Predict Individual Risk of Relapse.
Article in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review.Journal of clinical epidemiology · 2023Pooled it
- Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.BMC medical research methodology · 2022Pooled it
- Development of an artificial intelligence-based prediction platform for early recurrence of resectable pancreatic cancer after curative surgery-toward future use as an indication for neoadjuvant treatment: a retrospective multicenter cohort study.Annals of surgical treatment and research · 2026Article
- Prediction of 12-month recurrence of pancreatic cancer using machine learning and prognostic factors.BMC medical informatics and decision making · 2024Article
- Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study.BMC medical informatics and decision making · 2024Article
- Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review.Journal of medical Internet research · 2023Article
- Machine learning in pancreas surgery, what is new? literature review.Frontiers in surgery · 2023Review
- Integrated Analysis of Glutathione Metabolic Pathway in Pancreatic Cancer.Frontiers in cell and developmental biology · 2022Article
- Preoperative recurrence prediction in pancreatic ductal adenocarcinoma after radical resection using radiomics of diagnostic computed tomography.EClinicalMedicine · 2022Article
- PrACTiC: A Predictive Algorithm for Chemoradiotherapy-Induced Cytopenia in Glioblastoma Patients.Journal of oncology · 2022Article
- Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection.Journal of translational medicine · 2021Article
- Artificial intelligence for early detection of pancreatic adenocarcinoma: The future is promising.World journal of gastroenterology · 2021Review
- Role of artificial intelligence in hepatobiliary and pancreatic surgery.World journal of gastrointestinal surgery · 2021Review
- Machine Learning Approach to Raman Spectrum Analysis of MIA PaCa-2 Pancreatic Cancer Tumor Repopulating Cells for Classification and Feature Analysis.Life (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
16 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundAlthough surgical resection is the only potentially curative treatment for pancreatic cancer (PC), long-term outcomes of this treatment remain poor. The aim of this study is to describe the feasibility of a neoadjuvant treatment with induction polychemotherapy (IPCT) followed by chemoradiation (CRT) in resectable PC, and to develop a machine-learning algorithm to predict risk of relapse.
methodsForty patients with resectable PC treated in our institution with IPCT (based on mFOLFOXIRI, GEMOX or GEMOXEL) followed by CRT (50 Gy and concurrent Capecitabine) were retrospectively analyzed. Additionally, clinical, pathological and analytical data were collected in order to perform a 2-year relapse-risk predictive population model using machine-learning techniques.
resultsA R0 resection was achieved in 90% of the patients. After a median follow-up of 33.5 months, median progression-free survival (PFS) was 18 months and median overall survival (OS) was 39 months. The 3 and 5-year actuarial PFS were 43.8% and 32.3%, respectively. The 3 and 5-year actuarial OS were 51.5% and 34.8%, respectively. Forty-percent of grade 3-4 IPCT toxicity, and 29.7% of grade 3 CRT toxicity were reported. Considering the use of granulocyte colony-stimulating factors, the number of resected lymph nodes, the presence of perineural invasion and the surgical margin status, a logistic regression algorithm predicted the individual 2-year relapse-risk with an accuracy of 0.71 (95% confidence interval [CI] 0.56-0.84,
conclusionAn intensified multimodal neoadjuvant approach (IPCT + CRT) in resectable PC is feasible, with an encouraging long-term outcome. Machine-learning algorithms might be a useful tool to predict individual risk of relapse. A small sample size and therapy heterogeneity remain as potential limitations.
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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.