  The automatic standardization of nomenclature for anatomical structures in
radiotherapy (RT) clinical data is a critical prerequisite for data curation
and data-driven research in the era of big data and artificial intelligence,
but it is currently an unmet need. Existing methods either cannot handle
cross-institutional datasets or suffer from heavy imbalance and poor-quality
delineation in clinical RT datasets. To solve these problems, we propose an
automated structure nomenclature standardization framework, 3D Non-local
Network with Voting (3DNNV). This framework consists of an improved data
processing strategy, namely, adaptive sampling and adaptive cropping (ASAC)
with voting, and an optimized feature extraction module. The framework
simulates clinicians' domain knowledge and recognition mechanisms to identify
small-volume organs at risk (OARs) with heavily imbalanced data better than
other methods. We used partial data from an open-source head-and-neck cancer
dataset to train the model, then tested the model on three cross-institutional
datasets to demonstrate its generalizability. 3DNNV outperformed the baseline
model, achieving higher average true positive rates (TPR) overall categories on
the three test datasets (+8.27%, +2.39%, and +5.53%, respectively). More
importantly, the 3DNNV outperformed the baseline on the test dataset, 28.63% to
91.17%, in terms of F1 score for a small-volume OAR with only 9 training
samples. The results show that 3DNNV can be applied to identify OARs, even
error-prone ones. Furthermore, we discussed the limitations and applicability
of the framework in practical scenarios. The framework we developed can assist
in standardizing structure nomenclature to facilitate data-driven clinical
research in cancer radiotherapy.
