Mining Domain Knowledge: Improved Framework towards Automatically Standardizing Anatomical Structure Nomenclature in Radiotherapy

Automatically standardizing nomenclature for anatomical structures in radiotherapy (RT) clinical data is an unmet urgent need in the era of big data and artificial intelligence. Existing methods either can hardly 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, 3DNNV, which consists of an improved data processing strategy (ASAC/Voting) and an optimized feature extraction module to simulate clinicians' domain knowledge and recognition mechanisms to identify heavily imbalanced small-volume organs at risk (OARs) better than other methods. We used partial data from an open-source head-and-neck cancer dataset (HN_PETCT) to train the model, then tested the model on three cross-institutional datasets to demonstrate its generalizability. 3DNNV outperformed the baseline model (ResNet50), achieving a significantly higher average true positive rate (TPR) on the three test datasets (+8.27%, +2.39%, +5.53%). More importantly, the 3DNNV outperformed the baseline, 28.63% to 91.17%, on the F1 score of a small-volume OAR with only 9 training samples, when tested on the HN_UTSW dataset. The developed framework can be used to help standardizing structure nomenclature to facilitate data-driven clinical research in cancer radiotherapy.