Prediction of Maxillary Bone Invasion in Hard Palate/Upper Alveolus Cancer: A Multi-Center Retrospective Study.

BACKGROUND: maxillary bone invasion (MBI) is not uncommon in hard palate or upper alveolus (HP/UA) cancer; however, there have been relatively few reports about the MBI of HP/UA cancer. PATIENTS AND METHODS: this was a multi-center retrospective study, enrolling 144 cases of HP/UA cancer. MBI was defined by surgical pathology or radiology follow-up. The multiple prediction models for MBI were developed in total cases and in cases having primary bone resection, using clinical and radiological variables. RESULTS: computerized tomography (CT) alone predicted MBI, with an area under receiver operating curve (AUC) of 0.779 (95% confidence interval (CI) = 0.712-0.847). The AUC was increased in a model that combined tumor dimensions and clinical factors (male sex and nodal metastasis) (0.854 (95%CI = 0.790-0.918)). In patients who underwent 18fluorodeoxyglucose positron emission tomography/CT (PET/CT), the discrimination performance of a model including the maximal standardized uptake value (SUVmax) had an AUC of 0.911 (95%CI = 0.847-0.975). The scoring system using CT finding, tumor dimension, and clinical factors, with/without PET/CT SUVmax clearly distinguished low-, intermediate-, and high-risk groups for MBI. CONCLUSION: using information from CT, tumor dimension, clinical factors, and the SUVmax value, the MBI of HP/UA cancer can be predicted with a relatively high discrimination performance.

Morphological Analysis of the Adult Eustachian Tube: A Fresh-Frozen Human Cadaver Study.

OBJECTIVES: To evaluate the dilated Eustachian tube (ET) anatomy configuration using fresh human cadavers. METHODS: Fourteen ears from human cadavers were used to identify the ET configuration. The cadaver head was cut in the sagittal plane parallel to the nasal septum, dividing it into right and left sides. Silicone was then inserted into the ET through the nasopharyngeal orifice (NO). The volume and length of the impression were measured using 3D computed tomography imaging. RESULTS: The ET lumen was found to narrow from the NO to the isthmus, and the ET surface was concave anteriorly and convex posteriorly. The lower portion of the ET lumen was the most dilated and displayed a narrow top. The average volume of the ET impression was 1.4 ±â€Š0.5 ml. The total length of the posterior side was 30.5 ±â€Š3.6 mm, and that of the anterior side was 26.3 ±â€Š3.4 mm. The widest ET area of the NO was 10.1 ±â€Š0.9 mm in height and 8.0 ±â€Š1.5 mm in width. The preisthmus was 2.4 ±â€Š0.4 mm in height and 1.3 ±â€Š0.5 mm in width. The height and width were 8.37 and 5.33 mm at the 5 mm point from the NO, and 5.51 and 1.94 mm at the 20 mm point from the NO, respectively. CONCLUSION: We evaluated the configuration of the cartilaginous ET lumen, which is the main target of balloon dilation, and our findings may give insights into this dilation process and assist with the further development of ET balloons and stents.

Feasibility of a deep learning-based algorithm for automated detection and classification of nasal polyps and inverted papillomas on nasal endoscopic images.

BACKGROUND: Discrimination of nasal cavity mass lesions is a challenging work requiring extensive experience. A deep learning-based automated diagnostic system may help clinicians to classify nasal cavity mass lesions. We demonstrated the feasibility of a convolutional neural network (CNN)-based diagnosis system for automatic detection and classification of nasal polyps (NP) and inverted papillomas (IP). METHODS: We developed a CNN-based algorithm using a transfer learning strategy and trained it on nasal endoscopic images. A total of 99 nasal endoscopic images with normal findings, 98 images with NP, and 100 images with IP were analyzed using the developed CNN. Six otolaryngologists participated in clinical visual assessment. Image-based classification performance was measured by calculating the accuracy and area under the receiver operating characteristic curve (AUC). The diagnostic performance was compared between the CNN and clinical visual assessment by human experts. RESULTS: The algorithm achieved an overall accuracy of 0.742 ± 0.058 with the following class accuracies: normal, 0.81± 0.14; IP, 0.57 ± 0.07; and NP, 0.83 ± 0.21. The AUC values for normal, IP, and NP were 0.91 ± 0.06, 0.82 ± 0.09, and 0.84 ± 0.06, respectively. The overall accuracy of the CNN model was comparable with the average performance of human experts (0.742 vs. 0.749; p = 0.11). CONCLUSIONS: The trained CNN model appears to reliably classify NP and IP of the nasal cavity from nasal endoscopic images; it also yields a reliable reference for diagnosing nasal cavity mass lesions during nasal endoscopy. However, further studies with more test data are warranted to improve the diagnostic accuracy of our CNN model.