Radiomics analysis of dual-layer detector spectral CT-derived iodine maps for predicting Ki-67 PI in pancreatic ductal adenocarcinoma

Abstract

Objective: To evaluate the feasibility of radiomics analysis using dual-layer detector spectral CT (DLCT)-derived iodine maps for the preoperative prediction of the Ki-67 proliferation index (PI) in pancreatic ductal adenocarcinoma (PDAC).

Materials and methods: A total of 168 PDAC patients who underwent DLCT examination were included and randomly allocated to the training (n = 118) and validation (n = 50) sets. A clinical model was constructed using independent clinicoradiological features identified through multivariate logistic regression analysis in the training set. The radiomics signature was generated based on the coefficients of selected features from iodine maps in the arterial and portal venous phases of DLCT. Finally, a radiomics-clinical model was developed by integrating the radiomics signature and significant clinicoradiological features. The predictive performance of three models was evaluated using the Receiver Operating Characteristic (ROC) curve and Decision Curve Analysis. The optimal model was then used to develop a nomogram, with goodness-of-fit evaluated through the calibration curve.

Results: The radiomics-clinical model integrating radiomics signature, CA19-9, and CT-reported regional lymph node status demonstrated excellent performance in predicting Ki-67 PI in PDAC, which showed an area under the ROC curve of 0.979 and 0.956 in the training and validation sets, respectively. The radiomics-clinical nomogram demonstrated the improved net benefit and exhibited satisfactory consistency.

Conclusions: This exploratory study demonstrated the feasibility of using DLCT-derived iodine map-based radiomics to predict Ki-67 PI preoperatively in PDAC patients. While preliminary, our findings highlight the potential of functional imaging combined with radiomics for personalized treatment planning.

Keywords: Dual-layer detector spectral computed tomography; Iodine map; Ki-67; Pancreatic ductal adenocarcinoma; Radiomics.

Fig. 1

Flowchart of the study population. PDAC, pancreatic ductal adenocarcinoma; IHC, immunohistochemistry; DLCT, dual-layer detector spectral computed tomography; PI, proliferation index

Fig. 2

Workflow of the key steps in conducting radiomics analysis of iodine maps. DCA, decision curve analysis; KW, Kruskal-Wallis; RFE, recursive feature elimination; ANOVA, analysis of variance; SVM, support vector machines; LDA, linear discriminant analysis; LR, logistic regression; LRLasso, lasso logistic regression; ROC, receiver operating characteristic; VOI, volumes of interest; LN, lymph node; CA19-9, carbohydrate antigen 19–9; AUC, area under the curve

Fig. 3

Radiomics feature selection results. AP, arterial phase; PVP, portal venous phase; GLCM, gray-level co-occurrence matrix; GLRLM, gray-level run length matrix; GLSZM, gray-level size zone matrix

Fig. 4

Radiomics-clinical nomogram developed in the training set, incorporating the radiomics signature, CA19-9, and CT-reported regional LN status. CA19-9, carbohydrate antigen 19–9; LN, lymph node

Fig. 5

ROC curves depicting the predictive performance of the clinical model, radiomics signature, and radiomics-clinical models for Ki-67 PI in PDAC (a, b). AUC, area under the curve; PI, proliferation index; PDAC, pancreatic ductal adenocarcinoma; ROC, receiver operating characteristic

Fig. 6

DCA results for the clinical model, radiomics signature, and radiomics-clinical models (a, b). DCA, decision curve analysis

Fig. 7

Calibration curves of the radiomics-clinical nomogram (a, b)