Deep learning-based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net.

PURPOSE: To evaluate deep learning-based segmentation models for oropharyngeal squamous cell carcinoma (OPSCC) using CT and MRI with nnU-Net. METHODS: This single-center retrospective study included 91 patients with OPSCC. The patients were grouped into the development (n = 56), test 1 (n = 13), and test 2 (n = 22) cohorts. In the development cohort, OPSCC was manually segmented on CT, MR, and co-registered CT-MR images, which served as the ground truth. The multimodal and multichannel input images were then trained using a self-configuring nnU-Net. For evaluation metrics, dice similarity coefficient (DSC) and mean Hausdorff distance (HD) were calculated for test cohorts. Pearson's correlation and Bland-Altman analyses were performed between ground truth and prediction volumes. Intraclass correlation coefficients (ICCs) of radiomic features were calculated for reproducibility assessment. RESULTS: All models achieved robust segmentation performances with DSC of 0.64 ± 0.33 (CT), 0.67 ± 0.27 (MR), and 0.65 ± 0.29 (CT-MR) in test cohort 1 and 0.57 ± 0.31 (CT), 0.77 ± 0.08 (MR), and 0.73 ± 0.18 (CT-MR) in test cohort 2. No significant differences were found in DSC among the models. HD of CT-MR (1.57 ± 1.06 mm) and MR models (1.36 ± 0.61 mm) were significantly lower than that of the CT model (3.48 ± 5.0 mm) (p = 0.037 and p = 0.014, respectively). The correlation coefficients between the ground truth and prediction volumes for CT, MR, and CT-MR models were 0.88, 0.93, and 0.9, respectively. MR models demonstrated excellent mean ICCs of radiomic features (0.91-0.93). CONCLUSION: The self-configuring nnU-Net demonstrated reliable and accurate segmentation of OPSCC on CT and MRI. The multimodal CT-MR model showed promising results for the simultaneous segmentation on CT and MRI. CLINICAL RELEVANCE STATEMENT: Deep learning-based automatic detection and segmentation of oropharyngeal squamous cell carcinoma on pre-treatment CT and MRI would facilitate radiologic response assessment and radiotherapy planning. KEY POINTS: • The nnU-Net framework produced a reliable and accurate segmentation of OPSCC on CT and MRI. • MR and CT-MR models showed higher DSC and lower Hausdorff distance than the CT model. • Correlation coefficients between the ground truth and predicted segmentation volumes were high in all the three models.

Assessment of oncologic and cosmetic outcomes of robotic elective neck dissection in early-stage tongue cancer.

BACKGROUND: Robotic neck dissection is emerging as an alternative to conventional open neck dissection. However, the oncologic safety of robotic elective neck dissection (END) and its indications in early-stage tongue cancer are unclear. METHODS: We retrospectively reviewed the data of 78 patients who underwent transoral excision for T1, T2 squamous cell carcinoma of tongue with simultaneous ipsilateral END. Patients were assigned to two groups: the robotic group (n = 32)-postauricular face-lift -and the conventional group (n = 46)- transcervical incision. We compared the survival, clinical, pathologic and cosmetic outcomes of the two groups, and evaluated the number of retrieved lymph nodes and robot console time in the robotic group. RESULTS: The mean age was lower in the robotic group (43.6 ± 12.8 vs. 55.8 ± 14.0, p < 0.001) and the conventional group had more T2 patients (p = 0.01). The mean operation time was significantly longer in the robotic group than the conventional group (178.81 ± 33.9 vs. 92.28 ± 16.7, p < 0.001). The mean number of retrieved lymph nodes was not significantly different between the two groups (19.22 ± 8.51 vs. 20.7 ± 11.4, p = 0.41). The 5-year disease-free survival rate was not significantly different between the two groups (93.6 % vs. 82.9 %, p = 0.59). Overall scar satisfaction assessed by VAS score, the robotic group showed significantly better results compared to the conventional group (8.38 vs. 5.86, p = 0.033). CONCLUSION: Robotic END by a postauricular facelift approach is a feasible and safe approach for early-stage tongue cancer.