Computed Tomography-Based Radiomics Using Tumor and Vessel Features to Assess Resectability in Cancer of the Pancreatic Head

Abstract

The preoperative prediction of resectability pancreatic ductal adenocarcinoma (PDAC) is challenging. This retrospective single-center study examined tumor and vessel radiomics to predict the resectability of PDAC in chemo-naïve patients. The tumor and adjacent arteries and veins were segmented in the portal-venous phase of contrast-enhanced CT scans, and radiomic features were extracted. Features were selected via stability and collinearity testing, and least absolute shrinkage and selection operator application (LASSO). Three models, using tumor features, vessel features, and a combination of both, were trained with the training set (N = 86) to predict resectability. The results were validated with the test set (N = 15) and compared to the multidisciplinary team's (MDT) performance. The vessel-features-only model performed best, with an AUC of 0.92 and sensitivity and specificity of 97% and 73%, respectively. Test set validation showed a sensitivity and specificity of 100% and 88%, respectively. The combined model was as good as the vessel model (AUC = 0.91), whereas the tumor model showed poor performance (AUC = 0.76). The MDT's prediction reached a sensitivity and specificity of 97% and 84% for the training set and 88% and 100% for the test set, respectively. Our clinician-independent vessel-based radiomics model can aid in predicting resectability and shows performance comparable to that of the MDT. With these encouraging results, improved, automated, and generalizable models can be developed that reduce workload and can be applied in non-expert hospitals.

Keywords: adenocarcinoma; computed tomography; oncology; pancreas; radiomics; resectability.

Figure 1

Study workflow to develop the three models for resectability prediction.

Figure 2

Segmentations of five example cases included in this study. For each case, an axial CECT slice (with and without segmentations) and a 3D view of the segmentation are shown (tumor in red, arteries in blue, and veins in green). First (a–c), a true positive case, without any vessel contact, where the three models and the MDT correctly predicted resectability. Second (d–f), a true negative case, with both arterial and venous tumor–vessel contact, where the three models and the MDT correctly predicted irresectability. Third (g–i), a false positive case, with venous tumor–vessel contact, where the three models and the MDT predicted resectability, but the tumor was irresectable during surgery due to encasement of the VMS. Fourth (j–l), a false negative case, with venous tumor–vessel contact, where the three models predicted irresectability, the MDT predicted resectability, and the tumor was resected. Finally, (m–o), a true negative case, with venous and arterial tumor–vessel contact, where the three models predicted irresectability and the MDT predicted resectability, but the tumor was irresectable during surgery due to the encasement of VMS and AMS.

Figure 3

A schematic representation of the method used to extract (a) the maximum angle (α) of encasement, determined with 3-degree segments, and (b) the maximum tumor–vessel contact length.

Figure 4

Flowchart of patient inclusion and exclusion.

Figure 5

The ROC curves of the three SVM models to predict resectability (training set, N = 86). The optimal point for each classifier according to the Youden index is indicated with a circle.

Figure 6

The ROC curves of the three SVM classifiers to predict resection margin status (R0 vs. R1/2; training set, N = 31). The optimal point for each classifier according to the Youden index is indicated with a circle.