RESUMEN
BACKGROUND: The recurrence of thyroid cancer poses challenges compounded by postoperative fibrosis and anatomic changes. By overcoming the limitations of current localizing dye techniques, indocyanine green-macroaggregated albumin-hyaluronic acid (ICG-MAA-HA) mixture dye promises improved localization. This study aimed to evaluate the efficacy and safety of the dye for recurrent thyroid cancer. METHODS: The nine patients in this study underwent surgery and postoperative ultrasonography. The dye was injected into recurrent lesions in all the patients preoperatively. During surgery, the lesions were confirmed with an imaging system before and after excision. If the lesion was unidentifiable with the naked eye, surgical excision was performed under the corresponding fluorescent guide. Side effects related to the dye injection and completeness of the surgery were evaluated. RESULTS: No side effects such as bleeding, skin tattooing, or pain during or after the dye injection were reported, and no discoloration occurred that interfered with the surgical field of view during surgery. In three cases (33.3 %), because it was difficult to localize metastatic lesions with the naked eye, the operation was successfully completed using an imaging system. The completeness of the surgical resection was confirmed by ultrasonography after an average of 5 months postoperatively. CONCLUSION: The study found that ICG-MAA-HA dye effectively located metastatic and recurrent thyroid cancer and had favorable results in terms of minimal procedural side effects and potential for assisting the surgeon. A large-scale multi-institutional study is necessary to prove the clinical significance regarding patient survival and disease control.
Asunto(s)
Verde de Indocianina , Neoplasias de la Tiroides , Humanos , Ácido Hialurónico , Colorantes , Neoplasias de la Tiroides/diagnóstico por imagen , Neoplasias de la Tiroides/cirugía , Neoplasias de la Tiroides/patología , Albúminas , Biopsia del Ganglio Linfático Centinela/métodosRESUMEN
OBJECTIVES: This study aimed to evaluate the performance of artificial intelligence (AI) models using connected speech and vowel sounds in detecting benign laryngeal diseases. STUDY DESIGN: Retrospective. METHODS: Voice samples from 772 patients, including 502 with normal voices and 270 with vocal cord polyps, cysts, or nodules, were analyzed. We employed deep learning architectures, including convolutional neural networks (CNNs) and time series models, to process the speech data. The primary endpoint was the area under the receiver's operating characteristic curve for binary classification. RESULTS: CNN models analyzing speech segments significantly outperformed those using vowel sounds in distinguishing patients with and without benign laryngeal diseases. The best-performing CNN model achieved areas under the receiver operating characteristic curve of 0.895 and 0.845 for speech and vowel sounds, respectively. Correlations between AI-generated disease probabilities and perceptual assessments were more pronounced in the connected-speech analyses. However, the time series models performed worse than the CNNs. CONCLUSION: Connected speech analysis is more effective than traditional vowel sound analysis for the diagnosis of laryngeal voice disorders. This study highlights the potential of AI technologies in enhancing the diagnostic capabilities of speech, advocating further exploration, and validation in this field.