Purpose: With the advent of big data research supported by AI models, there's a growing need to utilize consistent and standard nomenclature for targets and organs-at-risk (OARs). The AAPM TG-263 group has provided this standardized nomenclature for structure names. We leverage the non-standard names, 3D imaging, and dose information from retrospective DICOM-RT datasets in a CNN-based Deep Learning model that automatically rename them to standard names.
Methods: We used 9,750 structures from 550 prostate patients from 41 VA sites to automatically classify the following structures: Bladder, Rectum, Left Femur, Right Femur, Small Bowel, Large Bowel, PTV, and all other structures. The 3D bitmap structures, imaging and dose data were extracted by centering a bounding box on each structure. The random names were tokenized with BioBERT; the embedding vector was calculated based on our corpus. We built four 3D-CNN models with data inputs from structure, structure plus image, structure plus dose and structure plus image plus dose. Another 1D-CNN model was built for learning textual features and the output was concatenated with the output feature layer of each 3D-CNN model. Finally, the model was completed with a dense layer to make the predictions.
Results: Our results showed that the macro-averaged F1-scores for the combination of structure, image, and dose data were 0.779, 0.745, 0.748, and 0.734, respectively. With 1D-CNN model on non-standard names, the model’s overall performance improved with F1-scores of 0.934, 0.951, 0.924, and 0.949, respectively.
Conclusion: CNN models can learn radiomic features from 3D bitmap structures; however, the use of dose and imaging data did not improve model performance in prior works. With the unavailability of large amounts of annotated datasets to effectively train these 3D-CNN models, we utilized model training from additional textual features to improve the model performance.
Funding Support, Disclosures, and Conflict of Interest: National Radiation Oncology Program- Veteran's Affairs (NROP-VHA) has funded the work.