Accurate preoperative prediction of nodal metastasis in papillary thyroid microcarcinoma: Towards optimal management of patients.

OBJECTIVE: To enhance the accuracy in predicting lymph node metastasis (LNM) preoperatively in patients with papillary thyroid microcarcinoma (PTMC), refining the "low-risk" classification for tailored treatment strategies. METHODS: This study involves the development and validation of a predictive model using a cohort of 1004 patients with PTMC undergoing thyroidectomy along with central neck dissection. The data was divided into a training cohort (n = 702) and a validation cohort (n = 302). Multivariate logistic regression identified independent LNM predictors in PTMC, leading to the construction of a predictive nomogram model. The model's performance was assessed through ROC analysis, calibration curve analysis, and decision curve analysis. RESULTS: Identified LNM predictors in PTMC included age, tumor maximum diameter, nodule-capsule distance, capsular contact length, bilateral suspicious lesions, absence of the lymphatic hilum, microcalcification, and sex. Especially, tumors larger than 7 mm, nodules closer to the capsule (less than 3 mm), and longer capsular contact lengths (more than 1 mm) showed higher LNM rates. The model exhibited AUCs of 0.733 and 0.771 in the training and validation cohorts respectively, alongside superior calibration and clinical utility. CONCLUSION: This study proposes and substantiates a preoperative predictive model for LNM in patients with PTMC, honing the precision of "low-risk" categorization. This model furnishes clinicians with an invaluable tool for individualized treatment approach, ensuring better management of patients who might be proposed observation or ablative options in the absence of such predictive information.

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.

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.

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.

Prophylactic bilateral central neck dissection should be evaluated based on prospective randomized study of 581 PTC patients.

BACKGROUND: Prophylactic central lymph node dissection (PCND) was a basic consensus for patients with papillary thyroid carcinoma (PTC) in China. However, unilateral or bilateral central lymph node dissection (CND) was still controversial. This study aimed at investigating the safety and long-term benefit for the patients undergone with bilateral central lymph node dissection (BCCD). METHODS: 581 patients were enrolled and divided randomly into the test and control groups according to range of CND. 285 patients were prospectively assigned to undergo thyroid lobectomy plus BCND in the test group, other 296 patients were assigned to undergo thyroid lobectomy plus ipsilateral central lymph node dissection (ICND) in the control group. RESULTS: We found that the numbers of total LN and pN1a in the test group were more than that of the control group (p = 0.002,0.004), but there was no difference in the number of metastasized lymph nodes (p = 0.857) and tumor recurrence (p = 0.308). Additionally, in the aspect of postoperative complication (1 day after surgery), the serum levels of parathyroid hormone in the BCND group were lower than that in the ICND group (P = 0.010), and the numbers of transient laryngeal nerve palsy were more than that(p = 0.033). Meanwhile, we further found that pathological tumor size larger than 1 cm and tumor side lymph node metastasis were independent risk factors for contralateral central lymph node metastasis(p = 0.002,0.001). CONCLUSION: BCND may be an alternative for patients with tumor sizes larger than 1 cm, but it would significantly increase the rate of transient vocal cord palsy, parathyroid auto transplantation and decreased PTH, but the risk of permanent complications was similar to the ICND group.

The formation mechanism for printed silver-contacts for silicon solar cells.

Screen-printing provides an economically attractive means for making Ag electrical contacts to Si solar cells, but the use of Ag substantiates a significant manufacturing cost, and the glass frit used in the paste to enable contact formation contains Pb. To achieve optimal electrical performance and to develop pastes with alternative, abundant and non-toxic materials, a better understanding the contact formation process during firing is required. Here, we use in situ X-ray diffraction during firing to reveal the reaction sequence. The findings suggest that between 500 and 650 °C PbO in the frit etches the SiNx antireflective-coating on the solar cell, exposing the Si surface. Then, above 650 °C, Ag(+) dissolves into the molten glass frit - key for enabling deposition of metallic Ag on the emitter surface and precipitation of Ag nanocrystals within the glass. Ultimately, this work clarifies contact formation mechanisms and suggests approaches for development of inexpensive, nontoxic solar cell contacting pastes.

Rapid thermal processing chamber for in-situ x-ray diffraction.

Rapid thermal processing (RTP) is widely used for processing a variety of materials, including electronics and photovoltaics. Presently, optimization of RTP is done primarily based on ex-situ studies. As a consequence, the precise reaction pathways and phase progression during the RTP remain unclear. More awareness of the reaction pathways would better enable process optimization and foster increased adoption of RTP, which offers numerous advantages for synthesis of a broad range of materials systems. To achieve this, we have designed and developed a RTP instrument that enables real-time collection of X-ray diffraction data with intervals as short as 100 ms, while heating with ramp rates up to 100 °Cs(-1), and with a maximum operating temperature of 1200 °C. The system is portable and can be installed on a synchrotron beamline. The unique capabilities of this instrument are demonstrated with in-situ characterization of a Bi2O3-SiO2 glass frit obtained during heating with ramp rates 5 °C s(-1) and 100 °C s(-1), revealing numerous phase changes.