Evidence map›Paper›PMID 41347669›Full record

ArticleCancer medicine2025

Predicting Benefit of Neoadjuvant Chemotherapy and Elective Nodal Irradiation in Pancreatic Adenocarcinoma: A Supervised Machine Learning Approach.

Garrett K Harada, Jino Park, Nicholas Peterson, Akul Munjal, David Imagawa, Zeljka Jutric, Farshid Dayyani, Jennifer Valerin, David S Hong, Steven N Seyedin

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In one paragraph

Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Garrett K HaradaDepartment of Radiation Oncology, Chao Family Comprehensive Cancer Center, University of California, Irvine, California, USA.ORCID https://orcid.org/0000-0002-7024-3783
Jino ParkDepartment of Radiation Oncology, Chao Family Comprehensive Cancer Center, University of California, Irvine, California, USA.
Nicholas PetersonDepartment of Radiation Oncology, Chao Family Comprehensive Cancer Center, University of California, Irvine, California, USA.
Akul MunjalDepartment of Radiation Oncology, Chao Family Comprehensive Cancer Center, University of California, Irvine, California, USA.
David ImagawaDepartment of Surgical Oncology, Chao Family Comprehensive Cancer Center, University of California, Irvine, California, USA.
Zeljka JutricDepartment of Surgical Oncology, Chao Family Comprehensive Cancer Center, University of California, Irvine, California, USA.
Farshid DayyaniDepartment of Medical Oncology, Chao Family Comprehensive Cancer Center, University of California, Irvine, California, USA.ORCID https://orcid.org/0000-0002-4970-5189
Jennifer ValerinDepartment of Medical Oncology, Chao Family Comprehensive Cancer Center, University of California, Irvine, California, USA.
David S HongDepartment of Radiation Oncology, Chao Family Comprehensive Cancer Center, University of California, Irvine, California, USA.
Steven N SeyedinDepartment of Radiation Oncology, Chao Family Comprehensive Cancer Center, University of California, Irvine, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe relative benefit of neoadjuvant therapies remains controversial for patients with (borderline) resectable pancreatic ductal adenocarcinoma (PDAC). The purpose of this study was to create a model to predict response to multiagent neoadjuvant chemotherapy (NAC) followed by radiotherapy with elective nodal irradiation (ENI).

methodsUsing the National Cancer Database (NCDB), we identified patients with cT1-4N0M0 PDAC diagnosed between 2006 and 2020 treated with multiagent NAC, radiation with ENI, followed by curative resection with nodal dissection. A LASSO logistic regression model was used to predict ypN0 status, with generation of a nomogram and assessment of outcomes in training and testing cohorts. Secondary endpoints of negative-margin resection and overall survival were also examined. Out-of-sample predictions were then made on a separate ENI-naïve cohort, with similar assessments of selected outcomes. The threshold for statistical significance was set to p < 0.05.

resultsA total of 1053 patients were identified with a median age of 64.0 years (IQR = 57-70 years). The final model included pancreatic body tumor location, clinical T stage, time from diagnosis to radiation therapy and surgery, ENI dose, and duration of NAC, among others. Patients predicted for treatment response were more likely to be ypN0 (71.5% vs. 29.7%, p < 0.001), had more R0 resections (87.3% vs. 62.6%, p < 0.001), and improved OS after accounting for competing risks of perioperative death (SHR = 0.64, 95% CI = 0.46-0.89, p = 0.008). A similar significant trend was noted in the ENI-naïve cohort (N = 1258). Model AUC was 0.718 and 0.725 in training and testing cohorts, respectively.

conclusionsUsing a machine learning approach, we define a nomogram capable of predicting treatment response to multiagent NAC followed by radiotherapy with or without ENI. Patients selected by this model had higher rates of ypN0, higher R0 resection rates, and improved OS.

Indexed as

Antineoplastic Combined Chemotherapy ProtocolsCarcinoma, Pancreatic DuctalLymphatic IrradiationNeoadjuvant TherapyPancreatic NeoplasmsSupervised Machine LearningAgedFemaleHumansMaleMiddle AgedNomogramsPancreatectomyTreatment Outcomechemotherapyelective nodal irradiationmachine learningneoadjuvantpancreatic ductal adenocarcinoma

Identifiers

PMID41347669
PMCPMC12679486

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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.