Discussion
LNI
BLIND, RECTAL CANCER
Accurate target delineation is crucial for ensuring optimal radiotherapy outcomes in patients with locally advanced mid-low rectal cancer. In this study, we developed a DL-based workflow for fully automated CTV and GTV delineation for rectal cancer neoadjuvant radiotherapy. The workflow used a divide-and-conquer strategy to address CTV segmentation in the CT domain and GTV segmentation in the MRI domain. We used a DpnUnet network as the backend for the DL models. The key advantage and significance for clinical work is, while there are a lot of DL-based studies focusing on OAR segmentation, our work focused on CTV and GTV segmentation for rectal cancer. This approach allowed oncologists to efficiently delineate both CTV and GTV volumes, representing a step forward towards DL-based fully automated target delineation. To validate the workflow, we designed a comprehensive three-level evaluation scheme that was multicenter-involved, blind, and randomized.In Level-1, DSC and 95HD values over the testing cohort were calculated. For CTV segmentation, the DSC and 95HD values (mean ± standard deviation) were 0.85 ± 0.06 and 7.75 ± 6.42 mm. Compared with the previous studies by Wu et al. [In Level-2, multicenter-involved, blind and randomized expert scoring was performed. The proposed DL-based method achieved 96.4% and 100% of clinically viable scores (≥ 2) for CTV and GTV segmentation respectively, indicating promising clinical applicability. While the DL contours showed a significant difference from the GT for GTV segmentation, since 99.5% of the DL contours were clinically viable, the clinical implication of being statistically different is negligible.For the head-to-head Turing test in Level-3 evaluation, the proposed DL-based method not only passed the positive rate threshold (30%), but also was comparable with GT in expert subjective preference for both CTV and GTV segmentation. This excellent performance of the DL-based method indicates substantial potential in clinical application.The evaluation results indicate two limitations of our work. First, we did give special arrangement of cases with LNI for model training and tuning, and consequently the current CTV model exhibits inadequate coverage of surrounding lymph nodes for patients with LNI. We will address this issue in future work to improve the overall capability of the proposed approach. Second, the current GTV model delivered one outlier case with major errors in superior and interior boundaries, indicating uncertainty in GTV boundary definition. The limitations implicate, if the current models were deployed for clinical application, clinicians should be careful of patient enrollment and manual review should be required in case of inadequate or excessive target coverage. In addition, only 20 patient cases were used for model performance testing. Future efforts will be made to enroll more patients to further verify the model robustness.