Multimodal survival prediction in advanced pancreatic cancer using machine learning

Julius Keyl, Stefan Kasper, Marcel Wiesweg, Julian Götze, Martin Schönrock, Marianne Sinn, Aaron Berger, E. Nasca et al.

ESMO Open 2022 article cited by 53

Source Open-access PDF More in Medicine & Health

importance 6/10 partially solved Pancreatic and Hepatic Oncology Research oncology stage: research

Problem

Existing risk scores were insufficient to assess individual survival risk in patients with advanced pancreatic ductal adenocarcinoma.

Why it matters

Accurate survival prediction guides treatment decisions in a highly lethal cancer.

What the AI did

A random survival forest model was trained on pre-treatment clinical parameters of 203 patients to predict individual survival risk.

Results

c-index of 0.71 on clinical parameters; outperformed AJCC staging and mGPS in identifying high- and low-risk subgroups.

Impact

Improved risk stratification over standard scores in one retrospective cohort of 203 patients.

Record

Venue	ESMO Open
Published	2022
Work type	article
Authors	Julius Keyl, Stefan Kasper, Marcel Wiesweg, Julian Götze, Martin Schönrock, Marianne Sinn, Aaron Berger, E. Nasca et al.
Citations	53 (OpenAlex, at import time)
References	66
Model or method	random survival forest
Organization	not stated in source
Primary source	https://doi.org/10.1016/j.esmoop.2022.100555
Open access	gold
Keywords	pancreatic cancer, survival prediction, random survival forest, machine learning, c-index

Importance rating

6 / 10

significant — thousands to millions affected

Survival prediction in advanced PDAC, a cancer the abstract calls highly lethal context; model outperforms standard AJCC/mGPS scores, aiding personalized prognostication, but no disease-scale numbers given.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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