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The field of Radiation Oncology is uniquely positioned to benefit from theuse of artificial intelligence to fully automate the creation of radiationtreatment plans for cancer therapy. This time-consuming and specialized taskcombines patient imaging with organ and tumor segmentation to generate a 3Dradiation dose distribution to meet clinical treatment goals, similar tovoxel-level dense prediction. In this work, we propose Swin UNETR++, thatcontains a lightweight 3D Dual Cross-Attention (DCA) module to capture theintra and inter-volume relationships of each patient's unique anatomy, whichfully convolutional neural networks lack. Our model was trained, validated, andtested on the Open Knowledge-Based Planning dataset. In addition to metrics ofDose Score $\overline{S_{\text{Dose}}}$ and DVH Score$\overline{S_{\text{DVH}}}$ that quantitatively measure the difference betweenthe predicted and ground-truth 3D radiation dose distribution, we propose thequalitative metrics of average volume-wise acceptance rate$\overline{R_{\text{VA}}}$ and average patient-wise clinical acceptance rate$\overline{R_{\text{PA}}}$ to assess the clinical reliability of thepredictions. Swin UNETR++ demonstrates near-state-of-the-art performance onvalidation and test dataset (validation: $\overline{S_{\text{DVH}}}$=1.492 Gy,$\overline{S_{\text{Dose}}}$=2.649 Gy, $\overline{R_{\text{VA}}}$=88.58%,$\overline{R_{\text{PA}}}$=100.0%; test: $\overline{S_{\text{DVH}}}$=1.634 Gy,$\overline{S_{\text{Dose}}}$=2.757 Gy, $\overline{R_{\text{VA}}}$=90.50%,$\overline{R_{\text{PA}}}$=98.0%), establishing a basis for future studies totranslate 3D dose predictions into a deliverable treatment plan, facilitatingfull automation.

Swin UNETR++: Advancing Transformer-Based Dense Dose Prediction Towards Fully Automated Radiation Oncology Treatments