ArticleCureus2024
Predicting Pathological Response to Preoperative Chemotherapy in Pancreatic Ductal Adenocarcinoma Using Post-Chemotherapy Computed Tomography Radiomics.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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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.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Surgical Perspectives on Neoadjuvant Therapy in Borderline Resectable and Locally Advanced Pancreatic Cancer.Cancers · 2026Review
- Surgical decision-making in borderline resectable pancreatic cancer in the era of neoadjuvant therapy: from response assessment to individualized operative strategy.Frontiers in surgery · 2026Review
- Novel machine-learning model for predicting lymph node metastasis in resectable pancreatic ductal adenocarcinoma.Annals of gastroenterological surgery · 2025Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAssessing the response to preoperative treatment in pancreatic cancer provides valuable information for guiding subsequent treatment strategies. The present study aims to develop and validate a computed tomography (CT) radiomics-based machine learning (ML) model for predicting pathological response (PR) to preoperative chemotherapy in pancreatic ductal adenocarcinoma (PDAC).
methodsRetrospective data were analyzed from 86 PDAC patients undergoing neoadjuvant or conversion chemotherapy followed by surgical resection from January 2018 to May 2023. The cohort was randomly divided into training (70%, n = 60) and testing (30%, n = 26) sets. Favorable PR was defined as Evans grade IIb or greater. Radiomic features were extracted from post-chemotherapy CT images, and dimensionality reduction was performed using the least absolute shrinkage and selection operator (LASSO) logistic regression. Four ML classifiers (Light Gradient Boosting Machine (LGBM), Random Forest, AdaBoost, and Quadratic Discriminant Analysis) were evaluated for predicting a favorable PR. Model performance was primarily assessed using the area under the receiver operating characteristic curve (AUC), Brier score, and decision curve analysis.
resultsForty-one (47.7%) patients had a favorable PR. LASSO analysis on the training set identified five radiomic features. The LGBM model demonstrated the best performance, with a training AUC of 0.902 and a testing AUC of 0.923. It also exhibited the lowest Brier scores, both in training (0.136) and testing (0.135). Decision curve analysis further confirmed its clinical potential.
conclusionThe CT radiomics-based ML model exhibited promising performance in predicting PR in PDAC after neoadjuvant/conversion chemotherapy. This suggests clinical utility in optimizing surgical candidates and timing of surgery, leading to personalized treatment strategies.
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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.