Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer globally, especially when lymph node metastasis (LNM) occurs. Molecular heterogeneity, driven by genetic alterations and tumor microenvironment components, contributes to the complexity of PTC. Understanding these complexities is essential for precise risk stratification and therapeutic decisions.

The incidence of thyroid carcinoma (THCA), the most common endocrine tumor, is continuously increasing worldwide. Although the overall prognosis of THCA is good, patients with distant metastases exhibit a mortality rate of 5-20%.

The incidence of thyroid cancer (TC) is increasing in China, largely due to overdiagnosis from widespread screening and improved ultrasound technology. Identifying precise TC biomarkers is crucial for accurate diagnosis and effective treatment.TC patient data were obtained from TCGA. DEGs were analyzed using DESeq2, and WGCNA identified gene modules associated with TC. Machine learning algorithms (XGBoost, LASSO, RF) identified key biomarkers, with ROC and AUC > 0.95 indicating strong diagnostic performance. Immune cell infiltration and biomarker correlation were analyzed using CIBERSORT.Four key genes (P4HA2, TFF3, RPS6KA5, EYA1) were found as potential biomarkers. High P4HA2 expression was associated with suppressed anti-tumor immune responses and promoted disease progression. In vitro studies showed that P4HA2 upregulation increased TC cell growth and migration, while its suppression reduced these activities.Through bioinformatics and experimental validation, we identified P4HA2 as a key potential thyroid cancer biomarker. This finding provides new molecular targets for diagnosis and treatment. P4HA2 has the potential to be a diagnostic or therapeutic target, which could have significant implications for improving clinical outcomes in thyroid cancer patients.© 2025. The Author(s).

This study aimed to develop an artificial intelligence-assisted model for the preoperative prediction of lateral cervical lymph node metastasis (LCLNM) in papillary thyroid carcinoma (PTC) using computed tomography (CT) radiomics, providing a new noninvasive and accurate diagnostic tool for PTC patients with LCLNM.This retrospective study included 389 confirmed PTC patients, randomly divided into a training set (n = 272) and an internal validation set (n = 117), with an additional 40 patients from another hospital as an external validation set. Patient demographics were evaluated to establish a clinical model. Radiomic features were extracted from preoperative contrast-enhanced CT images (venous phase) for each patient. Feature selection was performed using analysis of variance and the least absolute shrinkage and selection operator algorithm. We employed support vector machine, random forest (RF), logistic regression, and XGBoost algorithms to build CT radiomic models for predicting LCLNM. A radiomics score (Rad-score) was calculated using a radiomic signature-based formula. A combined clinical-radiomic model was then developed. The performance of the combined model was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).A total of 1724 radiomic features were extracted from each patient's CT images, with 13 features selected based on nonzero coefficients related to LCLNM. Four clinically relevant factors (age, tumor location, thyroid capsule invasion, and central cervical lymph node metastasis) were significantly associated with LCLNM. Among the algorithms tested, the RF algorithm outperformed the others with five-fold cross-validation on the training set. After integrating the best algorithm with clinical factors, the areas under the ROC curves for the training, internal validation, and external validation sets were 0.910 (95% confidence interval [CI]: 0.729-0.851), 0.876 (95% CI: 0.747-0.911), and 0.821 (95% CI: 0.555-0.802), respectively, with DCA demonstrating the clinical utility of the combined radiomic model.This study successfully established a clinical-CT radiomic combined model for predicting LCLNM, which may significantly enhance surgical decision-making for lateral cervical lymph node dissection in patients with PTC.Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc.

Artificial intelligence (AI) is increasingly being applied in pathology and cytology, showing promising results. We collected a large dataset of whole slide images (WSIs) of thyroid fine-needle aspiration cytology (FNA), incorporating z-stacking, from institutions across the nation to develop an AI model. We conducted a multicenter retrospective diagnostic accuracy study using thyroid FNA dataset from the Open AI Dataset Project that consists of digitalized images samples collected from 3 university hospitals and 215 Korean institutions through extensive quality check during the case selection, scanning, labeling, and reviewing process. Multiple z-layer images were captured using three different scanners and image patches were extracted from WSIs and resized after focus fusion and color normalization. We pretested six AI models, determining Inception ResNet v2 as the best model using a subset of dataset, and subsequently tested the final model with total datasets. Additionally, we compared the performance of AI and cytopathologists using randomly selected 1031 image patches and reevaluated the cytopathologists' performance after reference to AI results. A total of 10,332 image patches from 306 thyroid FNAs, comprising 78 malignant (papillary thyroid carcinoma) and 228 benign from 86 institutions were used for the AI training. Inception ResNet v2 achieved highest accuracy of 99.7%, 97.7%, and 94.9% for training, validation, and test dataset, respectively (sensitivity 99.9%, 99.6%, and 100% and specificity 99.6%, 96.4%, and 90.4% for training, validation, and test dataset, respectively). In the comparison between AI and human, AI model showed higher accuracy and specificity than the average expert cytopathologists beyond the two-standard deviation (accuracy 99.71% [95% confidence interval (CI), 99.38-100.00%] vs. 88.91% [95% CI, 86.99-90.83%], sensitivity 99.81% [95% CI, 99.54-100.00%] vs. 87.26% [95% CI, 85.22-89.30%], and specificity 99.61% [95% CI, 99.23-99.99%] vs. 90.58% [95% CI, 88.80-92.36%]). Moreover, after referring to the AI results, the performance of all the experts (accuracy 96%, 95%, and 96%, respectively) and the diagnostic agreement (from 0.64 to 0.84) increased. These results suggest that the application of AI technology to thyroid FNA cytology may improve the diagnostic accuracy as well as intra- and inter-observer variability among pathologists. Further confirmatory research is needed.

A well display of the spatial location of thyroid nodules in the thyroid is important for surgical path planning and surgeon‐patient communication. The aim of this study was to establish a three‐dimensional (3D) reconstruction method of the thyroid gland, thyroid nodule, and carotid artery with automatic detection based on two‐dimensional (2D) ultrasound videos, and to evaluate its clinical value.

Increased robotic surgery is attended by increased reports of complications, largely due to limited operative view and lack of tactile sense. These kinds of obstacles, which seldom occur in open surgery, are challenging for beginner surgeons. To enhance robotic surgery safety, we created an augmented reality (AR) model of the organs around the thyroid glands, and tested the AR model applicability in robotic thyroidectomy.We created AR images of the thyroid gland, common carotid arteries, trachea, and esophagus using preoperative CT images of a thyroid carcinoma patient. For a preliminary test, we overlaid the AR images on a 3-dimensional printed model at five different angles and evaluated its accuracy using Dice similarity coefficient. We then overlaid the AR images on the real-time operative images during robotic thyroidectomy.The Dice similarity coefficients ranged from 0.984 to 0.9908, and the mean of the five different angles was 0.987. During the entire process of robotic thyroidectomy, the AR images were successfully overlaid on the real-time operative images using manual registration.We successfully demonstrated the use of AR on the operative field during robotic thyroidectomy. Although there are currently limitations, the use of AR in robotic surgery will become more practical as the technology advances and may contribute to the enhancement of surgical safety.

The value of serum thyroglobulin/anti-thyroglobulin (Tg/ATg) for papillary thyroid carcinoma (PTC) surveillance after lobectomy was investigated. We aimed to examine the association between post-lobectomy serum Tg/ATg and PTC structural recurrence and define applicable values for stratification.PTC Patients who underwent lobectomy with adequate serum Tg/ATg data during 2000-2014 were selected. Predictive classifiers of recurrence using random forest were established combining different variables related to serum Tg (ATg-negative patients) or ATg (ATg-positive patients). Cutoff values were determined with receiver operating characteristic curves when applicable. Kaplan-Meier curve and cox regression were performed to examine the predictive value of elevated Tg/ATg.Of 1451 patients enrolled, 66 (6.3%) and 26 (6.5%) patients in the ATg-negative group (n=1050) and ATg-positive group (n=401) experienced recurrence. The established classifier of serum Tg (n=1050) showed a favorable association with recurrence (AUC=0.81), while serum ATg did not (AUC=0.72). The optimal cutoff values of the first Tg (FTg, measured 6-12 months after lobectomy) and last Tg (LTg, measured most recently) were 5.3 and 11.0 ng/ml, respectively. Elevated LTg patients had significantly higher recurrence rates than normal LTg patients (23.5% vs. 4.4%, P<0.05). Patients with elevated FTg had significantly compromised RFS rates compared with patients with normal FTg in all ATg-negative patients, low-risk patients and intermediate- to high-risk patients (according to the ATA initial risk stratification) (n=1050, 583 and 467, all P<0.05). Multivariate analysis by cox regression indicated patients with elevated FTg had twice recurrent risk of those with normal FTg (HR=2.052).Post-lobectomy serum Tg has favorable value for prediction recurrence in PTC patients, and reasonable thresholds could differentiate patients well during follow-up.

在概览国际数字医学战略部署和代表性智慧医院建设的基础上，对数字生物学和数字医学的进展与突破进行了阐述。重点讨论了人工智能、大数据等新兴信息技术对医学科研与诊疗突破的推动作用，具体包括：蛋白质结构预测、人类错义突变发现问题、类器官构建及微生理系统发展、癌症肿瘤诊断与中医临床进展等。在讨论数字医学挑战与发展前景的基础上，提出我国在该领域的发展建议：1）充分发挥交叉科学领域和新兴信息技术的关键作用，促进前沿技术与卫生健康领域的结合；2）重视数据监管以保障数字生物学和数字医学的规范发展；3）推进数据标准化和数据库建设，推动数字疗法发展；4）加强数字医学领域的复合型人才培养，重视多种教育方式对人才培养的补充作用；5）促进数字医疗产业发展，推动数字医学临床落地。