Deep learning enables automated localization of the metastatic lymph node for thyroid cancer on 131I post-ablation whole-body planar scans.

The accurate detection of radioactive iodine-avid lymph node (LN) metastasis on 131I post-ablation whole-body planar scans (RxWBSs) is important in tracking the progression of the metastatic lymph nodes (mLNs) of patients with papillary thyroid cancer (PTC). However, severe noise artifacts and the indiscernible location of the mLN from adjacent tissues with similar gray-scale values make clinical decisions extremely challenging. This study aims (i) to develop a multilayer fully connected deep network (MFDN) for the automatic recognition of mLNs from thyroid remnant tissue by utilizing the dataset of RxWBSs and (ii) to evaluate its diagnostic performance using post-ablation single-photon emission computed tomography. Image patches focused on the mLN and remnant tissues along with their variations of probability of pixel positions were fed as inputs to the network. With this efficient automatic approach, we achieved a high F1-score and outperformed the physician score (P < 0.001) in detecting mLNs. Competitive segmentation networks on RxWBS displayed moderate performance for the mLN but remained robust for the remnant tissue. Our results demonstrated that the generalization performance with the multiple layers by replicating signal transmission overcome the constraint of local minimum optimization, it can be suitable to localize the unstable location of mLN region on RxWBS and therefore MFDN can be useful in clinical decision-making to track mLN progression for PTC.