AI and Cancer Research ── Pancreatic ── 2026-11-09
Pancreatic cancer: treatment, trials and AI
- Abstract
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) frequently exhibits lymph node (LN) metastasis, making early identification of high-risk patients crucial for optimizing treatment strategies. However, robust predictive models and external validation remain limited. This study aimed to develop and validate an artificial intelligence (AI) model for preoperative detection of LN status in PDAC patients. METHODS: We retrospectively enrolled 233 pathologically confirmed PDAC patients from two high-volume pancreatic surgery centers between July 2015 and July 2022. All patients underwent contrast-enhanced computed tomography (CT) scans and met strict inclusion and exclusion criteria. Two experienced radiologists independently reviewed imaging. Radiomics analysis was performed using MATLAB and Python-based PyRadiomics. Following feature selection, 14 radiomic features were retained to construct a radiomics score. Three predictive models were developed and compared. RESULTS: From 1,652 extracted radiomic features, 14 were selected to build a radiomics score significantly associated with LN metastasis (P<0.001). Multivariate logistic regression identified tumor location, vascular invasion, white blood cell (WBC) count, platelet count, and radiomics score as independent predictors of LN metastasis, and a nomogram was generated. Various machine learning algorithms were evaluated to identify the optimal model combining radiomic and clinical data. Decision curve analysis demonstrated that the extreme gradient boosting (XGBoost) model achieved superior accuracy and stability, with an area under the curve (AUC) of 0.86 in the training set and 0.81 in the validation set. CONCLUSIONS: We successfully developed a LN metastasis prediction model integrating preoperative CT radiomics and clinical variables. The XGBoost-based model exhibited excellent accuracy, robustness, and generalizability. Future studies should focus on constructing more reliable models through standardized pathological sampling and prospective multicenter trials, alongside advancing imaging techniques to improve identification of small LNs.
Journal IF-equivalent: 1.8 (OpenAlex 2-year mean citedness, value as of 2026-11-08, retrieved 2026-11-09; not the official Clarivate IF)Reference: Li Y, Wu S, Qiu J, Meng L, Zhang M, Cai H, et al. Deep learning radiomics model for lymph node metastases prediction: a diagnostic study. Gland Surg. 2026 Oct;15(10):276-6. doi:10.21037/gs-2026-0378.Checked: Abstract only - Abstract
BACKGROUND: Tertiary lymphoid structures (TLS) are emerging prognostic biomarkers in pancreatic ductal adenocarcinoma (PDAC), yet no reliable non-invasive tool exists for their preoperative assessment. This study aimed to develop and validate a contrast-enhanced computed tomography (CECT)-based habitat radiomics model for preoperative prediction of intratumoral TLS status in PDAC. METHODS: In this retrospective study, 204 surgically resected PDAC patients were enrolled and allocated into training (n=142) and validation (n=62) cohorts. TLS status was determined by fluorescent multiplex immunohistochemistry (mIHC), with positive cases subclassified as primary or secondary follicle-like TLS. K-means clustering partitioned the tumor volume of interest (VOI) into spatially distinct subregions; radiomics features were extracted from each subregion and selected via least absolute shrinkage and selection operator (LASSO) regression. Five machine learning classifiers were evaluated. Independent clinical predictors were identified by multivariate logistic regression (LR) and integrated with the optimal imaging model to construct a combined model. RESULTS: TLS positivity was observed in 32.84% of patients. The LR-based habitat model achieved area under the curve (AUC) of 0.939 and 0.873 in the training and validation cohorts, substantially outperforming the conventional radiomics model (AUCs: 0.815 and 0.701). Tumor differentiation and neural invasion were identified as independent clinical predictors. The combined model yielded the highest AUCs of 0.956 and 0.881, with favorable calibration and superior net clinical benefit on decision curve analysis (DCA). CONCLUSIONS: CECT-based habitat radiomics enables robust, non-invasive preoperative prediction of intratumoral TLS in PDAC, offering a practical tool for individualized risk stratification and treatment planning; incorporation of clinical variables yielded numerically higher AUCs but without statistically significant improvement.
Journal IF-equivalent: 1.8 (OpenAlex 2-year mean citedness, value as of 2026-11-08, retrieved 2026-11-09; not the official Clarivate IF)Reference: Li K, Guo R, Wang X, Li L, Yu F, Li Z, et al. Habitat radiomics combined with clinical features for predicting tertiary lymphoid structure status in pancreatic ductal adenocarcinoma: development and validation of a preoperative CECT-based model. Gland Surg. 2026 Oct;15(10):289-9. doi:10.21037/gs-2026-0439.Checked: Abstract only