ArticleJCO clinical cancer informatics2021
Toward an Optimized Staging System for Pancreatic Ductal Adenocarcinoma: A Clinically Interpretable, Artificial Intelligence-Based Model.
Article in JCO clinical cancer informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 4 citations in OpenAlex.
- Peak Risk of Recurrence Occurs during the First Two Years after a Pancreatectomy in Patients Receiving Neoadjuvant FOLFIRINOX.Cancers · 2023Article
- An interpretable AI model for recurrence prediction after surgery in gastrointestinal stromal tumour: an observational cohort study.EClinicalMedicine · 2023Article
- ExplainableTranslational cancer research · 2023Article
- Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review.Translational cancer research · 2022Review
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Authors and funding
28 authors at 10 institutions in 6 countries.
Funding
Abstract
purposeThe American Joint Committee on Cancer (AJCC) eighth edition schema for pancreatic ductal adenocarcinoma treats T and N stage as independent factors and uses positive lymph nodes (PLNs) to define N stage, despite data favoring lymph node ratio (LNR). We used artificial intelligence-based techniques to compare PLN with LNR and investigate interactions between tumor size and nodal status.
methodsPatients who underwent pancreatic ductal adenocarcinoma resection between 2000 and 2017 at six institutions were identified. LNR and PLN were compared through shapley additive explanations (SHAP) analysis, with the best predictor used to define nodal status. We trained optimal classification trees (OCTs) to predict 1-year and 3-year risk of death, incorporating only tumor size and nodal status as variables. The OCTs were compared with the AJCC schema and similarly trained XGBoost models. Variable interactions were explored via SHAP.
resultsTwo thousand eight hundred seventy-four patients comprised the derivation and 1,231 the validation cohort. SHAP identified LNR as a superior predictor. The OCTs outperformed the AJCC schema in the derivation and validation cohorts (1-year area under the curve: 0.681
conclusionOur findings highlight the superiority of LNR and the importance of interactions between tumor size and nodal status. These results and the potential of the OCT methodology to combine them into a powerful, visually interpretable model can help inform future staging systems.
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