期刊
ABDOMINAL RADIOLOGY
卷 46, 期 10, 页码 4800-4816出版社
SPRINGER
DOI: 10.1007/s00261-021-03159-9
关键词
Pancreatic neoplasm; Carcinoma; CD4 positive T lymphocytes; CD8 positive T lymphocytes; Magnetic resonance imaging; Radiomics; Tumor microenvironment
资金
- National Science Foundation for Scientists of China [81871352, 82003107]
- Clinical Research Plan of SHDC [SHDC2020CR4073]
- 234 Platform Discipline Consolidation Foundation Project [2019YPT001]
- Shanghai Science and Technology Innovation Action Plan Medical Innovation Research Project [20Y11912500]
- Shanghai Committee of Science and Technology [21ZR1478500]
In this study, a machine learning classifier based on MRI was developed and validated for predicting TILs in PDAC patients. The XGBoost model showed good discrimination and clinical usefulness in predicting PDAC TILs, which could aid in clinical decision making for immune therapy.
Objective To 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 methods In 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. Results The 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. Conclusion The model constructed by XGBoost could predict PDAC TILs and may aid clinical decision making for immune therapy.
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