A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma

Abstract

Background: This study aimed to evaluate the efficacy of radiomics signatures derived from polyenergetic images (PEIs) and virtual monoenergetic images (VMIs) obtained through dual-layer spectral detector CT (DLCT). Moreover, it sought to develop a clinical-radiomics nomogram based on DLCT for predicting cancer stage (early stage: stage I-II, advanced stage: stage III-IV) in pancreatic ductal adenocarcinoma (PDAC).

Methods: A total of 173 patients histopathologically diagnosed with PDAC and who underwent contrast-enhanced DLCT were enrolled in this study. Among them, 49 were in the early stage, and 124 were in the advanced stage. Patients were randomly categorized into training (n = 122) and test (n = 51) cohorts at a 7:3 ratio. Radiomics features were extracted from PEIs and 40-keV VMIs were reconstructed at both arterial and portal venous phases. Radiomics signatures were constructed based on both PEIs and 40-keV VMIs. A radiomics nomogram was developed by integrating the 40-keV VMI-based radiomics signature with selected clinical predictors. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA).

Results: The PEI-based radiomics signature demonstrated satisfactory diagnostic efficacy, with the areas under the ROC curves (AUCs) of 0.92 in both the training and test cohorts. The optimal radiomics signature was based on 40-keV VMIs, with AUCs of 0.96 and 0.94 in the training and test cohorts. The nomogram, which integrated a 40-keV VMI-based radiomics signature with two clinical parameters (tumour diameter and normalized iodine density at the portal venous phase), demonstrated promising calibration and discrimination in both the training and test cohorts (0.97 and 0.91, respectively). DCA indicated that the clinical-radiomics nomogram provided the most significant clinical benefit.

Conclusions: The radiomics signature derived from 40-keV VMI and the clinical-radiomics nomogram based on DLCT both exhibited exceptional performance in distinguishing early from advanced stages in PDAC, aiding clinical decision-making for patients with this condition.

Keywords: Cancer stage; Dual-layer spectral detector CT; Nomogram; Pancreatic ductal adenocarcinoma; Radiomics.

Fig. 1

Flow chart of the patient selection process

Fig. 2

Radiomics features selection with the LASSO logistic regression model. (A). 40-keV VMI-based and (B). PEI-based radiomics features selection used LASSO with 10-fold cross-validation by the minimum criteria. (C). 40-keV VMI-based and (D). PEI-based radiomics features of the LASSO coefficient profiles. Y-axis indicates binomial deviances. The upper x-axis indicates the average number of radiomics features. The lower x-axis indicates the log(λ) value

Fig. 3

The performance of the radiomics signature for preoperative diagnosis of PDAC stage. ROC curve of the 40-keV VMI-based radiomics signature in (A). the training cohort and (B). the test cohort. ROC curve of the PEI-based radiomics signature in (C). the training cohort and (D). the test cohort

Fig. 4

Development of the 40-keV VMI-based radiomics nomogram and its performance. Mapping a line upwards based on the actual values of each variable onto the Points scale is performed to determine the score for each variable. The scores for all variables are then summed, and a line is drawn downwards to establish the overall score on the column chart, denoted as the Total Points. This process is used to predict the Risk probability of a patient having advanced-stage pancreatic cancer

Fig. 5

Diagnostic performance of clinical-radiomics model for differentiation of early and advanced stage PDAC. The ROC curves of Radscore_40keV, clinical model and clinical-radiomics model in (A) the training cohort and (B). the test cohort. The red line represents the clinical-radiomics model, the blue line represents the Radscore_40keV and the yellow line represents the clinical model

Fig. 6

Decision curves for Radscore_40keV, clinical model and clinical-radiomics model in (A). the training cohort and (B). the test cohort. The red line represents the clinical-radiomics model. The blue line represents the Radscore_40keV and the yellow line represents the clinical model. The X-axis means the threshold probability, Y-axis shows the model benefit