Convolutional neural network in nasopharyngeal carcinoma: how good is automatic delineation for primary tumor on a non-contrast-enhanced fat-suppressed T2-weighted MRI?

Purpose Convolutional neural networks (CNNs) show potential for delineating cancers on contrast-enhanced MRI (ce-MRI) but there are clinical scenarios in which administration of contrast is not desirable. We investigated performance of the CNN for delineating primary nasopharyngeal carcinoma (NPC) on non-contrast-enhanced images and compared the performance to that on ce-MRI. Materials and methods We retrospectively analyzed primary NPC in 195 patients using a well-established CNN, U-Net, for tumor delineation on the non-contrast-enhanced fat-suppressed (fs)-T2W, ce-T1W and ce-fs-T1W images. The CNN-derived delineations were compared to manual delineations to obtain Dice similarity coefficient (DSC) and average surface distance (ASD). The DSC and ASD on fs-T2W were compared to those on ce-MRI. Primary tumor volumes (PTVs) of CNN-derived delineations were compared to that of manual delineations. Results The CNN for NPC delineation on fs-T2W images showed similar DSC (0.71 +/- 0.09) and ASD (0.21 +/- 0.48 cm) to those on ce-T1W images (0.71 +/- 0.09 and 0.17 +/- 0.19 cm, respectively) (p > 0.05), and lower DSC but similar ASD to ce-fs-T1W images (0.73 +/- 0.09, p < 0.001; and 0.17 +/- 0.20 cm, p > 0.05). The CNN overestimated PTVs on all sequences (p < 0.001). Conclusion The CNN showed promise for NPC delineation on fs-T2W images in cases where it is desirable to avoid contrast agent injection. The CNN overestimated PTVs on all sequences.

Automated summary

Convolutional neural networks (CNNs) show promising results in delineating primary nasopharyngeal carcinoma (NPC) on non-contrast-enhanced images, with similar performance to contrast-enhanced T1W images but slightly lower on contrast-enhanced fat-suppressed T1W images. The CNN consistently overestimated primary tumor volumes (PTVs) on all sequences.