ArticleAbdominal radiology (New York)2021
Machine learning for MRI radiomics: a study predicting tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma.
Article in Abdominal radiology (New York), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Interpretable prediction of occult lymph node metastasis in pancreatic ductal adenocarcinoma using a model fusing habitat radiomics and deep learning.Abdominal radiology (New York) · 2026Article
- Noninvasive evaluation and clinical value prediction of tumor-infiltrating neutrophil-to-T-cell ratio in pancreatic ductal adenocarcinoma.NPJ digital medicine · 2026Article
- Magnetic resonance imaging radiomics-based machine learning model for preoperative prediction of pathological grade in pancreatic cancer: A preliminary study.European journal of radiology open · 2025Article
- SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data.Frontiers in genetics · 2023Article
- Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer.Journal of translational medicine · 2022Article
- Radiomic Signatures Associated with CD8Cancers · 2022Review
- Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging.Cancers · 2022Review
- Review
- Artificial intelligence in pancreatic cancer.Theranostics · 2022Review
- A novel preoperative MRI-based radiomics nomogram outperforms traditional models for prognostic prediction in pancreatic ductal adenocarcinoma.American journal of cancer research · 2022Article
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop and validate a machine learning classifier based on magnetic resonance imaging (MRI), for the preoperative prediction of tumor-infiltrating lymphocytes (TILs) in patients with pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND
methodsIn this retrospective study, 156 patients with PDAC underwent MR scan and surgical resection. The expression of CD4, CD8 and CD20 was detected and quantified using immunohistochemistry, and TILs score was achieved by Cox regression model. All patients were divided into TILs score-low and TILs score-high groups. The least absolute shrinkage and selection operator method and the extreme gradient boosting (XGBoost) were used to select the features and to construct a prediction model. The performance of the models was assessed using the training cohort (116 patients) and the validation cohort (40 patients), and decision curve analysis (DCA) was applied for clinical use.
resultsThe XGBoost prediction model showed good discrimination in the training (AUC 0.86; 95% CI 0.79-0.93) and validation sets (AUC 0.79; 95% CI 0.64-0.93). The sensitivity, specificity, and accuracy for the training set were 86.67%, 75.00%, and 0.81, respectively, whereas those for the validation set were 84.21%, 66.67%, and 0.75, respectively. Decision curve analysis indicated the clinical usefulness of the XGBoost classifier.
conclusionThe model constructed by XGBoost could predict PDAC TILs and may aid clinical decision making for immune therapy.
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