Optimizing robotic thyroid surgery: lessons learned from an retrospective analysis of 104 cases.

Background: Robotic assistance in thyroidectomy is a developing field that promises enhanced surgical precision and improved patient outcomes. This study investigates the impact of the da Vinci Surgical System on operative efficiency, learning curve, and postoperative outcomes in thyroid surgery. Methods: We conducted a retrospective cohort study of 104 patients who underwent robotic thyroidectomy between March 2018 and January 2022. We evaluated the learning curve using the Cumulative Sum (CUSUM) analysis and analyzed operative times, complication rates, and postoperative recovery metrics. Results: The cohort had a mean age of 36 years, predominantly female (68.3%). The average body mass index (BMI) was within the normal range. A significant reduction in operative times was observed as the series progressed, with no permanent hypoparathyroidism or recurrent laryngeal nerve injuries reported. The learning curve plateaued after the 37th case. Postoperative recovery was consistent, with no significant difference in hospital stay duration. Complications were minimal, with a noted decrease in transient vocal cord palsy as experience with the robotic system increased. Conclusion: Robotic thyroidectomy using the da Vinci system has demonstrated a significant improvement in operative efficiency without compromising safety. The learning curve is steep but manageable, and once overcome, it leads to improved surgical outcomes and high patient satisfaction. Further research with larger datasets and longer follow-up is necessary to establish the long-term benefits of robotic thyroidectomy.

Intraoperative AI-assisted early prediction of parathyroid and ischemia alert in endoscopic thyroid surgery.

BACKGROUND: The preservation of parathyroid glands is crucial in endoscopic thyroid surgery to prevent hypocalcemia and related complications. However, current methods for identifying and protecting these glands have limitations. We propose a novel technique that has the potential to improve the safety and efficacy of endoscopic thyroid surgery. PURPOSE: Our study aims to develop a deep learning model called PTAIR 2.0 (Parathyroid gland Artificial Intelligence Recognition) to enhance parathyroid gland recognition during endoscopic thyroidectomy. We compare its performance against traditional surgeon-based identification methods. MATERIALS AND METHODS: Parathyroid tissues were annotated in 32 428 images extracted from 838 endoscopic thyroidectomy videos, forming the internal training cohort. An external validation cohort comprised 54 full-length videos. Six candidate algorithms were evaluated to select the optimal one. We assessed the model's performance in terms of initial recognition time, identification duration, and recognition rate and compared it with the performance of surgeons. RESULTS: Utilizing the YOLOX algorithm, we developed PTAIR 2.0, which demonstrated superior performance with an AP50 score of 92.1%. The YOLOX algorithm achieved a frame rate of 25.14 Hz, meeting real-time requirements. In the internal training cohort, PTAIR 2.0 achieved AP50 values of 94.1%, 98.9%, and 92.1% for parathyroid gland early prediction, identification, and ischemia alert, respectively. Additionally, in the external validation cohort, PTAIR outperformed both junior and senior surgeons in identifying and tracking parathyroid glands (p < 0.001). CONCLUSION: The AI-driven PTAIR 2.0 model significantly outperforms both senior and junior surgeons in parathyroid gland identification and ischemia alert during endoscopic thyroid surgery, offering potential for enhanced surgical precision and patient outcomes.

Efficacy of anlotinib combined with radioiodine to treat scalp metastasis of papillary thyroid cancer: A case report and review of literature.

BACKGROUND: Papillary thyroid cancer (PTC) is one of the well-differentiated thyroid tumors. Cutaneous metastasis from differentiated thyroid cancers occurs in < 1% of primary thyroid carcinomas but produces the worst survival prognosis. The multi-targeting tyrosine kinase inhibitor anlotinib has been approved to treat refractory advanced non-small-cell lung cancer as well as advanced soft-tissue and clear cell sarcomas in China. CASE SUMMARY: In a patient with scalp metastasis caused by PTC, thyroid and skull metastasis tumor sizes were significantly reduced after a trial of neoadjuvant anlotinib therapy for 3 cycles. Anlotinib maintenance medication after thyroidectomy further reduced the metastatic skull tumor size thereby preventing the requirement for craniotomy. CONCLUSION: The outcome of the present trial confirmed the potential of anlotinib therapy to treat scalp metastasis induced by PTC and point the way for the treatment of similar diseases in the future.

Development of Artificial Intelligence for Parathyroid Recognition During Endoscopic Thyroid Surgery.

OBJECTIVE: We aimed to establish an artificial intelligence (AI) model to identify parathyroid glands during endoscopic approaches and compare it with senior and junior surgeons' visual estimation. METHODS: A total of 1,700 images of parathyroid glands from 166 endoscopic thyroidectomy videos were labeled. Data from 20 additional full-length videos were used as an independent external cohort. The YOLO V3, Faster R-CNN, and Cascade algorithms were used for deep learning, and the optimal algorithm was selected for independent external cohort analysis. Finally, the identification rate, initial recognition time, and tracking periods of PTAIR (Artificial Intelligence model for Parathyroid gland Recognition), junior surgeons, and senior surgeons were compared. RESULTS: The Faster R-CNN algorithm showed the best balance after optimizing the hyperparameters of each algorithm and was updated as PTAIR. The precision, recall rate, and F1 score of the PTAIR were 88.7%, 92.3%, and 90.5%, respectively. In the independent external cohort, the parathyroid identification rates of PTAIR, senior surgeons, and junior surgeons were 96.9%, 87.5%, and 71.9%, respectively. In addition, PTAIR recognized parathyroid glands 3.83 s ahead of the senior surgeons (p = 0.008), with a tracking period 62.82 s longer than the senior surgeons (p = 0.006). CONCLUSIONS: PTAIR can achieve earlier identification and full-time tracing under a particular training strategy. The identification rate of PTAIR is higher than that of junior surgeons and similar to that of senior surgeons. Such systems may have utility in improving surgical outcomes and also in accelerating the education of junior surgeons. LEVEL OF EVIDENCE: 3 Laryngoscope, 132:2516-2523, 2022.

Feasibility and safety of needle-assisted endoscopic thyroidectomy with lateral neck dissection for papillary thyroid carcinoma: a preliminary experience.

BACKGROUND: Endoscopic thyroidectomy is popular among patients with cosmetic requirements. However, when lateral neck dissection (LND) is required, endoscopic surgery may be challenging. Therefore, we introduced needle-assisted endoscopic technique to achieve endoscopic LND procedure and evaluated its safety and feasibility in the present study. METHODS: Medical records of 37 patients who underwent needle-assisted endoscopic thyroidectomy with LND were retrospectively reviewed. RESULTS: All of 37 patients had excellent cosmetic outcomes. The mean operative time was 338.2 ± 58.74 minutes. Mean number of lymph nodes retrieved in the lateral was 33.5 ± 11.69 and 15.9 ± 7.51 in the central neck. The rates of transient and permanent hypocalcemia were 32.4% and 2.7% and the rates of transient and permanent recurrent laryngeal nerve palsy were 8.1% and 0%, respectively. CONCLUSIONS: Needle-assisted endoscopic thyroidectomy with lateral neck dissection shows potential feasibility but further study is needed to better characterize its safety and applicability.

Modified Tumescent Solution for Creating Working Space During Endoscopic Thyroidectomy.

OBJECTIVE: To study the feasibility of gas-liquid mixing tumescent solution for creating a working space (WS) in endoscopic thyroidectomy (ET). MATERIALS AND METHODS: A prospective study was performed on 186 patients with thyroid tumor who had undergone ET via chest and breast approach. Patients were randomly divided into 2 groups to receive traditional tumescent solution as group A and modified tumescent solution (gas-liquid mixing tumescent solution) as group B. This study compares the following surgical outcome parameters between the 2 groups, including changes of blood pressure, heart rate, and oxygen saturation before and after creating a WS, time for creating a WS, operative time, hemorrhage volume for creating a WS, overall hemorrhage volume, overall postoperative drainage volume, postoperative pain score, postoperative hospitalization, number of retrieved lymph nodes, total serum calcium, serum parathyroid hormone, and cases of transient and permanent recurrent laryngeal nerve palsy. RESULTS: No postoperative bleeding, permanent recurrent laryngeal nerve palsy, incision and surgical site infection, air embolism, flap injury occurred in both groups. The mean time for creating a WS and the whole operation in group B was significantly shorter than that in group A ( P < .05). There were no statistically significant differences in both groups in terms of other observation index ( P > .05). CONCLUSION: The clinical application of gas-liquid mixing tumescent solution can effectively reduce the time for creating a WS and whole operative time, and worthy of being widely used in ET as a safe and effective technique.

Analysis on the metabolite composition of serum samples from patients with papillary thyroid carcinoma using nuclear magnetic resonance.

OBJECTIVE: Analysis of the metabolic differences among the papillary thyroid carcinoma (group T) patients, benign thyroid tumor patients (group B) and healthy controls (group H) by nuclear magnetic resonance hydrogen spectrum. METHODS: collect twenty serum specimens each from group T, group B and group H. Collect image archive. Use Topspin software, AMIX software and SIMCA-P+ software to calibrate, integrate with PCA and PLS-DA, research the three groups' serum for endogenous metabolic differences. RESULTS: The data of group T and group H established a discrimination model, and the model is correct (P<0.05). The content of metabolites in the serum of team T increased including valine, leucine, isoleucine, lactic acid, alanine, glutamic acid, lysine, glycine, while the lipids, choline, tyrosine decreased. The data of group B and group H established a discrimination model and the model is correct (P<0.05). The content of metabolites in the serum of team B increased including Trimethyl glycine, tyrosine, phenylalanine, valine, leucine, isoleucine, lactic acid, alanine, glutamic acid, while the Lipids and lysine reduced. CONCLUSION: Compared with team H, there is an obvious metabolic difference in team T and team B. It not only involves glucose metabolism but also the metabolism of lipids, amino acids and nucleic acid.