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000307503 1001_ $$aSchneider, François$$b0
000307503 245__ $$aThe basis for future personalized therapy approaches - Machine learning-generated 1-year survival rate, metastatic status and therapy-dependent survival in pancreatic cancer patients.
000307503 260__ $$aAmsterdam [u.a.]$$bElsevier$$c2026
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000307503 520__ $$aPancreatic adenocarcinoma (PDAC) remains one of the most lethal types of cancer, characterized by its unspecific symptoms, aggressive nature and late-stage diagnosis.In this study, Machine Learning (ML) models were trained and applied on the largest international, monocentric database, comprising over 23 clinical variables from 1040 PDAC patients. In this study, the potential of ML models was assessed for the 1-year survival rate, metastatic M0/M1 status and therapy-dependent survival time generating predictive and prognostic ML-based biomarkers.Consistent predictive performance was achieved for the 1-year survival rate (accuracy, internal/external cohort 72 % / 70 %) and radiological metastatic M0/M1 status (Accuracy, internal/external cohort 79 % / 72 %). Moreover, distinct ML-based survival models were trained for approved first-line adjuvant and palliative chemotherapy regimen (mFOLFIRINOX, gemcitabine with or without nab-paclitaxel) across resected (C-index = 0.60), locally advanced (C-index = 0.71) and metastatic (C-index = 0.71) PDAC subgroups, demonstrating that predictive performance is best for palliative regimens.The models developed in this study may serve as a foundation for supporting tumor board decisions and demonstrate that personalized, ML-based therapy concepts are feasible in the near future. In addition, the models proposed in this study are helpful for generating ML-based synthetic control arms in future clinical trials.
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000307503 650_7 $$2Other$$aClinical Decision Suppor
000307503 650_7 $$2Other$$aGradient Boosting
000307503 650_7 $$2Other$$aMachine learning
000307503 650_7 $$2Other$$aModel Interpretation
000307503 650_7 $$2Other$$aOncology
000307503 650_7 $$2Other$$aPancreatic cancer
000307503 650_7 $$2Other$$aPersonalized medicine
000307503 650_7 $$2Other$$aPrognostic modeling
000307503 650_7 $$2Other$$aRandom Forest
000307503 650_7 $$2Other$$aRandom Survival Forest
000307503 650_7 $$2Other$$aScenario-Based Simulation
000307503 650_7 $$2Other$$aSurvival analysis
000307503 650_7 $$2Other$$aSurvival prediction
000307503 650_7 $$2Other$$aTreatment Simulation
000307503 7001_ $$aChen, Haotian$$b1
000307503 7001_ $$aPelzer, Uwe$$b2
000307503 7001_ $$aCompton, Richard G$$b3
000307503 7001_ $$aDuwe, Gregor$$b4
000307503 7001_ $$0P:(DE-He78)396821e874b632341e4bcabd27bcad3f$$aDragomir, Mihnea P$$b5$$udkfz
000307503 7001_ $$aHilfenhaus, Georg$$b6
000307503 7001_ $$aVecchione, Loredana$$b7
000307503 7001_ $$aAlig, Annabel$$b8
000307503 7001_ $$aFelsenstein, Matthäus$$b9
000307503 7001_ $$aLerchbaumer, Markus$$b10
000307503 7001_ $$aRieke, Damian T$$b11
000307503 7001_ $$aBahra, Marcus$$b12
000307503 7001_ $$aKutsche, Ralf$$b13
000307503 7001_ $$aTschulik, Kristina$$b14
000307503 7001_ $$aStintzing, Sebastian$$b15
000307503 7001_ $$aKeilholz, Ulrich$$b16
000307503 7001_ $$aNeumann, Christopher C M$$b17
000307503 773__ $$0PERI:(DE-600)1468190-0$$a10.1016/j.ejca.2025.116189$$gVol. 234, p. 116189 -$$p116189$$tEuropean journal of cancer$$v234$$x0959-8049$$y2026
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