RESUMEN
OBJECTIVE: The superior thyroid artery perforator flap (STAPF) was previously presented as a type of locoregional pedicled flap for lateral facial and temple defects. In this study, we aimed to present our clinical experience with this flap for the reconstruction of soft tissue defects after oral cancer surgery. METHODS: From February 2019 to December 2022, 24 patients with oral cancers at the School and Hospital of Stomatology, Peking University were included. Among these patients, 10 had cancers located in the tongue, five in the cheek inside the oral cavity, three in the lower gingiva, two in the upper gingiva, two in the floor of the mouth, and two in the palate. All patients were treated with extended tumor resection, neck dissection, and STAPFs to reconstruct the soft tissue defects. The details of the flap, including the flap size, venous flow, vascular pedicle length, the attatched muscle, and operation time were evaluated. RESULTS: The dimensions of the flap skin paddle ranged from 3 cm × 5 cm to 6 × 14 cm. Fourteen patients had a closely concomitant superior thyroid vein perforator. Ten patients had non-closely concomitant superior thyroid veins perforators which retrograde external jugular vein. The vascular pedicle length ranged from 5 to 9 cm. The infrahyoid muscle group or sternocleidomastoid muscle was included in the flaps in three patients. A total of 23/24 flaps were successful. CONCLUSIONS: The STAPF is a viable reconstructive option for patients with oral cancers. It has the advantages of being robust, being thin, short operation time, and minor donor site complications. LEVEL OF EVIDENCE: 4 Laryngoscope, 2024.
RESUMEN
OBJECTIVES: We aim to develop a YOLOX-based convolutional neural network model for the precise detection of multiple oral lesions, including OLP, OLK, and OSCC, in patient photos. MATERIALS AND METHODS: We collected 1419 photos for model development and evaluation, conducting both a comparative analysis to gauge the model's capabilities and a multicenter evaluation to assess its diagnostic aid, where 24 participants from 14 centers across the nation were invited. We further integrated this model into a mobile application for rapid and accurate diagnostics. RESULTS: In the comparative analysis, our model overperformed the senior group (comprising three most experienced experts with more than 10 years of experience) in macro-average recall (85 % vs 77.5 %), precision (87.02 % vs 80.29 %), and specificity (95 % vs 92.5 %). In the multicenter model-assisted diagnosis evaluation, the dental, general, and community hospital groups showed significant improvement when aided by the model, reaching a level comparable to the senior group, with all macro-average metrics closely aligning or even surpassing with those of the latter (recall of 78.67 %, 74.72 %, 83.54 % vs 77.5 %, precision of 80.56 %, 76.42 %, 85.15 % vs 80.29 %, specificity of 92.89 %, 91.57 %, 94.51 % vs 92.5 %). CONCLUSION: Our model exhibited a high proficiency in detection of oral lesions, surpassing the performance of highly experienced specialists. The model can also help specialists and general dentists from dental and community hospitals in diagnosing oral lesions, reaching the level of highly experienced specialists. Moreover, our model's integration into a mobile application facilitated swift and precise diagnostic procedures.