Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics

CDK5 targets p21CIP1 to regulate thyroid cancer cell proliferation and malignancy in patients

Cyclin‑dependent kinase 5 (CDK5), known for its role in neuronal function, has emerged as a key player in cancer biology, particularly in thyroid cancer. The present study explored the interaction between CDK5 and the cyclin‑dependent kinase inhibitor p21CIP1 in thyroid cancer (TC). Bioinformatic tools and immunoprecipitation assays were used to confirm that CDK5 targets p21 for ubiquitin‑mediated degradation, reducing its stability and tumor‑suppressive effects. Data from The Cancer Genome Atlas revealed a significant inverse correlation between CDK5 and p21 expression, with higher CDK5 levels linked to increased tumor malignancy and worse survival outcomes; conversely, higher p21 expression was correlated with an improved prognosis. Immunohistochemistry analysis of TC samples further confirmed that increased CDK5 and reduced p21 expression were associated with more advanced tumor stages and aggressive phenotypes. These findings suggested that CDK5‑mediated degradation of p21 contributes to TC progression and malignancy, highlighting the potential of targeting the CDK5‑p21 axis as a therapeutic strategy for management of TC.

Clinical performance of a machine learning-based model for detecting lymph node metastasis in papillary thyroid carcinoma: A multicenter study

Papillary thyroid carcinoma (PTC) is a common endocrine malignancy with a generally favorable prognosis, but lymph node metastasis (LNM) complicates treatment and increases recurrence risk. Current preoperative methods like neck ultrasound often miss LNM, leading to unnecessary surgeries. This study developed a non-invasive, artificial intelligence (AI)-driven predictive model for LNM using gene expression data from 157 PTC patients and validated it with qRT-PCR across 807 participants from multiple centers. The model focused on three key genes - RPS4Y1, PKHD1L1, and CRABP1 - chosen for their predictive strength. A random forest algorithm achieved high accuracy, with an AUROC of 0.992 in training and 0.911-0.953 in external validation. RPS4Y1 emerged as a standout predictor, showing the strongest distinction between metastatic and non-metastatic cases. The study also identified immune-related pathways, such as TGF-β signaling and cancer-associated fibroblast activation, as critical in metastasis. This gene expression-based model offers a non-invasive, cost-effective solution for predicting LNM, providing valuable insights to guide surgical decisions and reduce unnecessary procedures, ultimately improving patient outcomes.

Prediction of BRAF and TERT status in PTCs by machine learning-based ultrasound radiomics methods: A multicenter study

Background

Preoperative identification of genetic mutations is conducive to individualized treatment and management of papillary thyroid carcinoma (PTC) patients. Purpose: To investigate the predictive value of the machine learning (ML)-based ultrasound (US) radiomics approaches for BRAF V600E and TERT promoter status (individually and coexistence) in PTC.

Methods

This multicenter study retrospectively collected data of 1076 PTC patients underwent genetic testing detection for BRAF V600E and TERT promoter between March 2016 and December 2021. Radiomics features were extracted from routine grayscale ultrasound images, and gene status-related features were selected. Then these features were included to nine different ML models to predicting different mutations, and optimal models plus statistically significant clinical information were also conducted. The models underwent training and testing, and comparisons were performed.

Results

The Decision Tree-based US radiomics approach had superior prediction performance for the BRAF V600E mutation compared to the other eight ML models, with an area under the curve (AUC) of 0.767 versus 0.547-0.675 (p < 0.05). The US radiomics methodology employing Logistic Regression exhibited the highest accuracy in predicting TERT promoter mutations (AUC, 0.802 vs. 0.525-0.701, p < 0.001) and coexisting BRAF V600E and TERT promoter mutations (0.805 vs. 0.678-0.743, p < 0.001) within the test set. The incorporation of clinical factors enhanced predictive performances to 0.810 for BRAF V600E mutant, 0.897 for TERT promoter mutations, and 0.900 for dual mutations in PTCs.

Conclusion

The machine learning-based US radiomics methods, integrated with clinical characteristics, demonstrated effectiveness in predicting the BRAF V600E and TERT promoter mutations in PTCs.

Predictive Value of LncRNA LINC01184 in Papillary Thyroid Cancer Development and Prognosis and Its Regulatory Effect on Cellular Processes

INTRODUCTION

This study assessed the potential of lncRNA LINC01184 in predicting PTC progression and prognosis and its regulatory mechanism in PTC cellular processes, aiming to explore a novel biomarker for PTC.

METHODS

The study enrolled 111 PTC patients and collected paired tissue samples. Using PCR, the expression of LINC01184 was analyzed, and its association with patients' clinicopathological features and prognosis was evaluated. The regulatory effects of LINC01184 on cell growth and metastasis were assessed by CCK8 and Transwell assays.

RESULTS

LINC01184 was significantly downregulated in PTC, which was closely correlated with poor differentiation, advanced TNM stage, the occurrence of lymph node metastasis, and poor overall survival. In PTC cells, LINC01184 negatively regulated miR-296-3p, and its overexpression suppressed cell growth and metastasis of PTC, which was reversed by overexpressing miR-296-3p.

CONCLUSION

Downregulated LINC01184 served as a biomarker for PTC. Overexpressing LINC01184 suppressed PTC cell progression via suppressing miR-296-3p.

Validation of Diagnostic Utility of Washout CYFRA 21-1 in Lymph Node Metastasis of Thyroid Cancer

PURPOSE

Traditional methods, fine-needle aspiration cytology (FNAC), and washout thyroglobulin (Tg) do not always provide sufficient accuracy for diagnosing lymph node (LN) metastasis in thyroid cancer. This study aimed to validate the diagnostic performance of washout cytokeratin fragment 21-1 (CYFRA 21-1) as a complementary biomarker for diagnosing metastatic LN in thyroid cancer and to explore its relationship with molecular analysis and distant metastasis.

EXPERIMENTAL DESIGN

In this retrospective cohort study involving 230 LN in 224 patients with papillary thyroid carcinoma, FNAC, washout Tg, and washout CYFRA 21-1 levels were measured in suspicious LN. The final LN outcomes were confirmed by surgical histology.

RESULTS

Among the 230 LN, 145 (63.0%) were benign and 85 (37.0%) were metastatic. The optimal cutoff value for washout CYFRA 21-1 was established at 1.12 ng/mL (AUC, 0.959; 95% confidence interval, 0.936-0.982) with sensitivity of 93.4% and specificity of 97.8%. The cutoff value for washout Tg was 12.61 ng/mL (AUC, 0.832; 95% confidence interval, 0.772-0.892). The diagnostic performance of CYFRA 21-1 remained consistent across the preoperative (1.14 ng/mL) and postoperative assessment (1.10 ng/mL). The combination of FNAC and washout CYFRA 21-1 showed higher sensitivity (92.5%), specificity (95.9%), negative predictive value (93.7%), and diagnostic accuracy (95.1%) than the combination of FNAC and washout Tg. The washout CYFRA 21-1 level was associated with TERT mutations (OR, 3.35; P < 0.001), LN metastasis (OR, 5.43; P = 0.019), and distant metastasis (OR, 4.27; P = 0.019).

CONCLUSIONS

Incorporating washout CYFRA 21-1 into the diagnostic process improves the accuracy of metastatic LN detection in thyroid cancer.

Construction of a predictive model for cervical lymph node metastasis in papillary thyroid carcinoma

Background

In oncology, the relationships among cervical central lymph node metastasis (CLNM), biochemical tests, and ultrasound characteristics in patients with papillary thyroid cancer (PTC) remain controversial. This association is currently not well supported by evidence, which emphasizes the need for further research. Understanding the connection between CLNM, biochemical testing, and ultrasound features is crucial for clinical practice and public health efforts. Research on this topic is still underway and is now receiving much interest. Our goal was to create and verify a basic cervical lymph node metastasis prediction model.

Methods

In this retrospective cohort study, 685 individuals diagnosed with PTC from First Hospital of Shanxi Medical University (n = 560) and Changzhi Heping Hospital (n = 125) participated in the research from January 2020 to October 2022. Patients were randomly assigned to a training set (n=392), an internal test set (n=168), or an external test set (n=125). Comprehensive clinical information, serological indices, and ultrasonography features were obtained for every participant. LASSO (Least Absolute Shrinkage and Selection Operator) and BSR (Best Subset Regression) to select features for model construction. A logistic regression model with filtered variables was constructed. A nomogram was developed based on six risk factors. Receiver operating characteristic (ROC) curves, decision curve analysis, and calibration curves were used to assess the predictive accuracy, clinical utility, and discriminative ability of the nomogram.

Results

Of the 560 individuals, 54.3% (304/560) did not have lymph node metastases, whereas 45.7% (256/560) did. Age, male, nodule size, multifocal lesions, capsular contact or invasion and ill-defined margins were determined to be risk variables via BSR and multivariate logistic analysis. Nomograms were created using these six risk indicators. The prediction model of CLNM had an AUC of 0.884 (95% CI 0.851, 0.916). Both the internal and the external validation results were highly encouraging. Confirming the model's stability and applicability in different data environments.

Conclusion

We developed a predictive model and nomogram for CLNM in PTC patients, which demonstrated robust performance. This model can guide surgical planning, potentially reducing complications and improving outcomes.

Intra- and Peritumoral Radiomics Based on Ultrasound Images for Preoperative Differentiation of Follicular Thyroid Adenoma, Carcinoma, and Follicular Tumor With Uncertain Malignant Potential

OBJECTIVE

Differentiating between follicular thyroid adenoma (FTA), carcinoma (FTC), and follicular tumor with uncertain malignant potential (FT-UMP) remains challenging due to their overlapping ultrasound characteristics. This retrospective study aimed to enhance preoperative diagnostic accuracy by utilizing intra- and peritumoral radiomics based on ultrasound images.

METHODS

We collected post-thyroidectomy ultrasound images from 774 patients diagnosed with FTA (n = 429), FTC (n = 158), or FT-UMP (n = 187) between January 2018 and December 2023. Six peritumoral regions were expanded by 5%-30% in 5% increments, with the segment-anything model utilizing prompt learning to detect the field of view and constrain the expanded boundaries. A stepwise classification strategy addressing three tasks was implemented: distinguishing FTA from the other types (task 1), differentiating FTC from FT-UMP (task 2), and classifying all three tumors. Diagnostic models were developed by combining radiomic features from tumor and peritumoral regions with clinical characteristics.

RESULTS

Clinical characteristics combined with intratumoral and 5% peritumoral radiomic features performed best across all tasks (Test set: area under the curves, 0.93 for task 1 and 0.90 for task 2; diagnostic accuracy, 79.9%). The DeLong test indicated that all peritumoral radiomics significantly improved intratumoral radiomics performance and clinical characteristics (p < 0.04). The 5% peritumoral regions showed the best performance, though not all results were significant (p = 0.01-0.91).

CONCLUSION

Ultrasound-based intratumoral and peritumoral radiomics can significantly enhance preoperative diagnostic accuracy for FTA, FTC, and FT-UMP, leading to improved treatment strategies and patient outcomes. Furthermore, the 5% peritumoral area may indicate regions of potential tumor invasion requiring further investigation.

Recent Insights into the Roles of PEST-Containing Nuclear Protein

PEST-containing nuclear protein (PCNP), a short-lived small nuclear protein with 178 amino acids, is a nuclear protein containing two PEST sequences. PCNP is highly expressed in several malignant tumors such as cervical cancer, rectal cancer, and lung cancer. It is also associated with cell cycle regulation and the phosphoinositide 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) and Wnt signaling pathways during tumor growth. The present article discuss how PCNP regulates the PI3K/AKT/mTOR and Wnt signaling pathways and related proteins, and the ubiquitination of PCNP regulates tumor cell cycle as well as the progress of the application of PCNP in the pathophysiology and treatment of colon cancer, human ovarian cancer, thyroid cancer, lung adenocarcinoma and oral squamous cell carcinoma. The main relevant articles were retrieved from PubMed, with keywords such as PEST-containing nuclear protein (PCNP), cancer (tumor), and signaling pathways as inclusion/exclusion criteria. Relevant references has been included and cited in the manuscript.

Development of a clinical-molecular prediction model for central lymph node metastasis in cN0 stage papillary thyroid microcarcinoma: a retrospective study

BACKGROUND

Identifying occult central lymph node metastasis (CLNM) is essential for guiding prophylactic lymph node dissection (PLND) in patients with cN0 stage papillary thyroid microcarcinoma (PTMC). This study aimed to identify molecular prognostic biomarkers associated with PTMC and develop a clinical-molecular prediction model for CLNM.

METHODS

Differentially expressed genes (DEGs) in PTMC were identified through bioinformatics analysis of the TCGA database. Prognostic DEGs were selected using Cox and LASSO regression analyses, and a risk-scoring model was constructed based on these genes. The prognostic value of the model was validated using Kaplan-Meier survival analysis and ROC curves. DEG expression levels were compared between patients with CLNM and those without (NCLNM). Clinical data and surgical specimens were collected from 404 patients with cN0 stage PTMC treated at the First Affiliated Hospital of Ningbo University in 2022. The cohort was randomly divided into a derivation cohort (n = 323) and a validation cohort (n = 81). DEG expression was quantified using RT-qPCR. Univariate and multivariate logistic regression analyses were conducted in the derivation cohort to identify predictors of CLNM and develop a predictive model. The model's performance was evaluated using the Hosmer-Lemeshow test, ROC curves, calibration curves, and decision curve analysis (DCA).

RESULTS

In the TCGA database, FN1, MT-1 F, and TFF3 were identified as prognostic biomarkers. Risk scores based on these genes achieved AUCs of 0.789 (5 years) and 0.674 (10 years) for predicting disease-free survival. Furthermore, FN1, MT-1 F, and TFF3 expression levels were significantly higher in the CLNM group compared to the NCLNM group. Among the 404 PTMC patients, the incidence of CLNM was 42.6% (n = 172). RT-qPCR analysis demonstrated significantly elevated expression of FN1 in both PTMC tissues compared to normal tissues and in the CLNM group relative to the NCLNM group, while MT-1 F and TFF3 exhibited markedly reduced expression levels. In the derivation cohort, FN1, MT-1 F, TFF3, tumor size ≥5 mm, calcification, multifocality, and extrathyroidal extension were independent predictors of CLNM. The prediction model based on these factors showed AUCs of 0.736 (derivation cohort) and 0.813 (validation cohort). Moreover, calibration curves, the Hosmer-Lemeshow test (χ² = 2.411, P = 0.966), and DCA confirmed the model's robust performance and clinical utility.

CONCLUSION

FN1, MT-1 F, and TFF3 are valuable prognostic biomarkers for PTMC. The clinical-molecular prediction model incorporating these genes provides a basis for personalized PLND decision-making in cN0 stage PTMC patients.

TRIAL REGISTRATION NUMBER

Not applicable.

Predicting the efficacy of microwave ablation of benign thyroid nodules from ultrasound images using deep convolutional neural networks

BACKGROUND

Thyroid nodules are frequent in clinical settings, and their diagnosis in adults is growing, with some persons experiencing symptoms. Ultrasound-guided thermal ablation can shrink nodules and alleviate discomfort. Because the degree and rate of lesion absorption vary greatly between individuals, there is no reliable model for predicting the therapeutic efficacy of thermal ablation.

METHODS

Five convolutional neural network models including VGG19, Resnet 50, EfficientNetB1, EfficientNetB0, and InceptionV3, pre-trained with ImageNet, were compared for predicting the efficacy of ultrasound-guided microwave ablation (MWA) for benign thyroid nodules using ultrasound data. The patients were randomly assigned to one of two data sets: training (70%) or validation (30%). Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) were all used to assess predictive performance.

RESULTS

In the validation set, fine-tuned EfficientNetB1 performed best, with an AUC of 0.85 and an ACC of 0.79.

CONCLUSIONS

The study found that our deep learning model accurately predicts nodules with VRR < 50% after a single MWA session. Indeed, when thermal therapies compete with surgery, anticipating which nodules will be poor responders provides useful information that may assist physicians and patients determine whether thermal ablation or surgery is the preferable option. This was a preliminary study of deep learning, with a gap in actual clinical applications. As a result, more in-depth study should be undertaken to develop deep-learning models that can better help clinics. Prospective studies are expected to generate high-quality evidence and improve clinical performance in subsequent research.

Development and validation of a CT-based radiomics nomogram for predicting cervical lymph node metastasis in papillary thyroid carcinoma

ObjectiveThis study aimed to develop and validate a radiomics nomogram based on 40 KeV images and iodine density maps derived from dual-layer spectral detector CT (DLSDCT) for predicting cervical lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC).MethodsA total of 214 LNs from 143 patients with histopathologically confirmed PTC in our hospital were included in the study. The LNs were randomly divided into a training group (n = 150) and a validation group (n = 64) in a 7:3 ratio. Radiomics features were extracted from non-enhanced, arterial phase, and venous phase 40 KeV images, as well as arterial phase and venous phase iodine density maps. Recursive feature elimination (RFE) and logistic regression (LR) were used for feature selection and radiomics score construction. A multivariate logistic regression model was established, incorporating the radiomics score and CT image features. The receiver operating characteristic (ROC) curve was used to evaluate the model's performance. The Hosmer-Lemeshow test and calibration curve were used to assess the model's goodness of fit, while decision curve analysis (DCA) evaluated its clinical applicability.ResultsThe radiomics features consisted of 11 LN-related features that exhibited a good predictive effect. The radiomics nomogram, which included radiomics features, lymphatic hilum status, and significant enhancement in the arterial phase, demonstrated excellent calibration and discrimination in both the training set (AUC = 0.955; 95% confidence interval [CI]: 0.924-0.985) and the validation set (AUC = 0.928; 95% CI: 0.861-0.994). The decision curve analysis confirmed the clinical validity of our nomogram. The DeLong test comparing the radiomics-clinical nomogram with the clinical model yielded a p-value of <0.001.ConclusionsThe radiomics nomogram, incorporating radiomics features and CT image features, serves as a non-invasive preoperative prediction tool with high accuracy in predicting cervical lymph node metastasis in patients with PTC.

Multi-region nomogram for predicting central lymph node metastasis in papillary thyroid carcinoma using multimodal imaging: A multicenter study

BACKGROUND AND OBJECTIVE

Central lymph node metastasis (CLNM) is associated with high recurrence rate and low survival in patients with papillary thyroid carcinoma (PTC). However, there is no satisfactory model to predict CLNM in PTC. This study aimed to integrate PTC deep learning feature based on ultrasound (US) images, fat radiomics features based on computed tomography (CT) images and clinical characteristics to construct a multimodal and multi-region nomogram (MMRN) for predicting the CLNM in PTC.

METHODS

We enrolled 661 patients diagnosed with PTC by thyroidectomy from two independent centers. Patients were divided into the primary cohort, internal test cohort (ITC), and external test cohort (ETC), and collected their US images and CT images. Resnet50 was employed to predict the CLNM status of PTC based on US images. Using radiomics feature extraction methods to extract fat radiomics features from CT images. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) regression. The predictive performance of the MMRN was evaluated using five-fold cross-validation. We comprehensively evaluated the DLRCN and compared it with five radiologists.

RESULTS

In the ITC and ETC, the area under the curves (AUCs) of MMRN were 0.829 (95 % CI: 0.822, 0.835) and 0.818 (95 % CI: 0.808, 0.828). The calibration curve revealed good predictive accuracy between the actual probability and predicted probability (P > 0.05). Decision curve analysis showed that the MMRN was clinically useful. Under equal specificity or sensitivity, the performance of MMRN increased by 6.5 % or 2.9 % compared to radiologist assessments. The incorporation of fat radiomics features led to significant net reclassification improvement (NRI) and integrated discrimination improvement (IDI) (NRI=0.174, P < 0.05, IDI=0.035, P < 0.05).

CONCLUSION

The MMRN demonstrated good performance in predicting the CLNM status of PTC, which was comparable to radiologist assessments. The fat radiomics features exhibited supplementary value for predicting CLNM in PTC.

Analysis and prediction of contralateral central lymph node metastasis risk in unilateral papillary thyroid carcinoma with ipsilateral lateral cervical lymph node: a retrospective clinical study

Background

Papillary thyroid carcinoma (PTC) often metastasizes to lymph nodes, increasing recurrence risk and reducing survival. This study identifies predictors for contralateral central lymph node metastasis (Cont-CLNM) in unilateral PTC patients with ipsilateral lateral cervical lymph node metastasis (Ipsi-LLNM).

Methods

We retrospectively analyzed data, preoperative ultrasound features, and thyroglobulin (Tg) levels in unilateral PTC patients with Ipsi-LLNM treated at the Thyroid Surgery Department of Nanjing Drum Tower Hospital from August 2017 to August 2024. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, with independent t-tests and Chi-squared tests assessing differences. Logistic regression analyses identified risk factors for Cont-CLNM, and a nomogram was validated using 1,000 bootstrap resamples. Decision curve analysis (DCA) evaluated clinical impact.

Results

Of 105 PTC patients, 56 (53.3%) had Cont-CLNM. LASSO regression identified three predictors: male sex, lymph node metastasis posterior to the recurrent laryngeal nerve (LN-prRLN), and elevated Tg levels. Multivariate regression confirmed these variables' association with Cont-CLNM. Internal validation yielded an area under the curve of 0.771 [95% confidence interval (CI): 0.684-0.857]. A nomogram was developed and validated through DCA.

Conclusions

Our findings indicate that combining male gender, LN-prRLN, and Tg levels effectively predicts Cont-CLNM, providing a basis for risk assessment in unilateral PTC.

A retrospective study of 17,995 patients investigating the location and recurrence of papillary thyroid cancer

The incidence of papillary thyroid cancer (PTC) has recently increased. Although PTC usually has a good prognosis, patients with advanced or localized metastases experience a high rate of recurrence. Although many studies have investigated PTC recurrence, a correlation between PTC location and recurrence remains unclear. Thus, we aimed to determine whether the location of PTC affects recurrence.Data were obtained from a single thyroid surgery center with > 6000 surgical cases per year. Between 2009 and 2022, 17,995 were enrolled in this study after screening. The location of the cancerous lesions was determined from ultrasound and pathology reports as well as the division of the lateral thyroid lobes into coronal and sagittal perspectives. The coronal plane was equally divided into upper, middle, and lower parts, and the sagittal plane was equally divided into anterior and dorsal aspects. Kaplan-Meier analysis and Cox proportional hazards regression models were used to analyze recurrence and risk factors. This study concluded that the upper part of the coronal plane and the dorsal part of the sagittal plane were most strongly associated with recurrence. Multifactorial analysis showed that lymph node metastatic status, multifocality, and superior and dorsal location of the tumor were significantly associated with PTC recurrence.

A risk stratification model based on ultrasound radiologic features for cervical metastatic lymph nodes in papillary thyroid cancer

BACKGROUND

Accurate preoperative evaluation for metastatic lesions is significant for PTC patients. However, the stratification systems revealed inconsistencies in the ultrasound (US) features of cervical metastatic lymph nodes (LNs). This study aimed to investigate and develop a risk stratification model based on US radiologic features for cervical metastatic lesions in PTC patients.

METHODS

This study retrospectively enrolled 1806 LNs from 1665 PTC patients who underwent US-guided fine-needle aspiration biopsy for cervical LNs from January 2010 to December 2022. Univariable and multivariable logistic regression analyses determined and developed the independent risk US features and a risk stratification model for cervical metastatic LNs. The performance of the risk stratification model was assessed and validated by the Korean Society of Thyroid Radiology and the European Thyroid Association.

RESULTS

Among the 1806 LNs, 1411 LNs were pathologically diagnosed with malignant. Multivariate analysis indicated that the absence of fatty hilum, cystic components, round shape (SD/LD ≥ 0.5), abundant vascularity, hyperechogenicity (including hyper and hypo-echogenicity, and hyper-echogenicity), and calcifications (include microcalcification, and macrocalcification) were independent risk US features associated with malignant LNs. A risk stratification model for cervical metastatic LNs was developed based on these suspicious US features and showed well-predicted performance (C-index 0.840; 95% CI: 0.840-0.923).

CONCLUSION

Our study proposed a new risk stratification system based on US radiologic features to predict cervical metastatic lymph nodes in PTC patients. We identified several risk factors for lymph node (LN) metastasis from PTC including the absence of fatty hilum, cystic components, round shape (SD/LD ≥ 0.5), abnormal vascularity, hyper-echogenicity, hyper- and hypo-echogenicity, microcalcification, and macrocalcification. These features could serve as valuable indicators for surgeons to accurately assess the status of cervical LNs.

Predictive risk-scoring model for lateral lymph node metastasis in papillary thyroid carcinoma

This study aims to evaluate candidate risk factors for lateral lymph node metastasis (LLNM) and develop a predictive model to identify high-risk groups among patients with papillary thyroid carcinoma (PTC). Additionally, we identified risk factors for recurrence to inform postoperative therapeutic decisions and follow-up for physicians and patients. A total of 4107 patients (4884 lesions) who underwent lymph node dissection at our hospital from 2005 to 2014 were evaluated. LLNM risk was stratified, and a risk-scoring model was developed based on identified independent risk factors for LLNM. Cox's proportional hazards regression model was used to investigate the risk factors for recurrence. Lateral Lymph Node (LLN) metastasis was observed in 10.49% (431/4107) of patients. Multivariate analysis identified the following independent risk predictors for LLN metastasis: Age ≤ 35 years (P = 0.002), tumor size > 1.0 cm (P = 0.000), lobe dissemination (+) (P = 0.000), and CLNM (+) (P = 0.000). A 12-point risk-scoring model was constructed to predict stratified LLNM in PTC patients, with an area under the receiver operating characteristic curve (AUROC) of 0.794 (95% CI: 0.774-0.814) (P < 0.01). The Cox regression model indicated that tumor size > 1.0 cm, lobe dissemination (+), multifocality, Central Lymph Node Metastasis (CLNM), and LLNM were significant risk factors associated with poor outcomes. Based on the risk scoring model, additional investigations and comprehensive considerations are recommended for patients with a total score greater than 5, and prophylactic cervical lymph node dissection is performed if necessary. Additionally, more aggressive treatment and more frequent follow-ups should be considered for patients with tumor size > 1.0 cm, lobe dissemination (+), multifocality, CLNM, and LLNM.

Identification of GJC1 as a novel diagnostic marker for papillary thyroid carcinoma using weighted gene co-expression network analysis and machine learning algorithm

BACKGROUND

The incidence of thyroid papillary carcinoma (PTC) is increasing annually, causing both physical and psychological pressure on patients. Therefore, early recognition and specific interventions for PTC are crucial. The objective of this study is to explore novel diagnostic marker and precise intervention targets for PTC.

METHODS

Based on a weighted gene co-expression network analysis (WGCNA), relevant datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were collected. Enrichment analysis was performed on differentially expressed genes (DEGs) using Gene Ontology (GO), Disease Ontology (DO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Subsequently, three machine learning algorithms Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) were used to identify the core genes. Finally, receiver operating characteristic (ROC) curves were used to analyze the clinical diagnostic value of the core genes.

RESULTS

We found, in total, 11,194 DEGs derived the TCGA and GEO datasets, that are primarily enriched in extracellular matrix (ECM) and inflammation related pathways, such as an ECM receptor interaction, cell adhesion molecules (CAMs), Tumor necrosis factor (TNF) signaling, and nucleotide-binding oligomerization domain (NOD) like receptor signaling pathways. Further analysis of the core genes, identified by the protein-protein interaction network, using three machine learning algorithms discovered three intersecting genes GJC1, KLHL4, and NOL4. Of which, GJC1 has good clinical diagnostic ability, which was verified using both the GEO (area under the ROC curve (AUC) = .982) and TCGA databases (AUC = .840).

CONCLUSIONS

GJC1 is highly expressed in PTC. Therefore, it is considered as a potential biomarker and is expected to become a new target for PTC gene therapy. However, it still needs to be supported and verified by more clinical data.

Identification and validation of the common pathogenesis and hub biomarkers in Papillary thyroid carcinoma complicated by rheumatoid arthritis

BACKGROUND

Papillary thyroid carcinoma coexisting with rheumatoid arthritis is frequently observed in clinical patients, yet its pathogenesis has not been fully elucidated. This investigation sought to further explore the molecular underpinnings of these two diseases.

METHODS

Gene expression profiles for thyroid papillary carcinoma and rheumatoid arthritis patients were obtained from the Comprehensive Gene Expression Database (GEO). Following the discovery of shared differentially expressed genes (DEGs) between these two conditions, three separate analyses were conducted. These included functional annotation, the establishment of a protein‒protein interaction (PPI) network and module, and the identification of hub genes via coexpression analysis. The final step involved the validation of target genes via clinical specimens.

RESULTS

This study analyzed datasets from four GEO databases and identified 64 common DEGs. Functional enrichment analysis revealed that these genes are predominantly associated with pathways related to immunity and signal transduction. Protein‒protein interaction (PPI) network analysis revealed complex interactions among these differentially expressed genes and highlighted several genes that may play pivotal roles in shared pathological mechanisms, namely, CCR5, CD4, IL6, CXCL13, FOXM1, CXCL9, and CXCL10.

CONCLUSION

Our study highlights the shared pathogenesis between papillary thyroid cancer and rheumatoid arthritis. Shared pathways and crucial genes could offer novel perspectives for subsequent investigations into the mechanisms of these diseases.

Analysis of features of papillary thyroid carcinoma on color Doppler ultrasound images: implications for lymph node metastasis

BACKGROUND

This study aimed to describe the color Doppler flow features of papillary thyroid carcinoma (PTC) and to further investigate the associations between these features and lymph node metastasis (LNM).

METHODS

A retrospective analysis of the clinical data of 287 PTC patients confirmed by postoperative pathology at the Second Affiliated Hospital of Xi'an Jiaotong University from January 2022 to April 2023 was conducted. The Adler grading system and novel blood flow patterns were used to analyze the vascularity of the PTC lesions on color Doppler images. Univariate and multivariate logistic regression analyses were conducted to evaluate the independent effects of blood flow characteristics on LNM, and a logistic regression model was established to assess their predictive value for PTC-related LNM.

RESULTS

In all, 287 PTC lesions were analyzed using color Doppler ultrasonography, which identified five main reference patterns: avascular (26.13%), dot-line (24.74%), branching (14.29%), garland (11.50%), and rich-disorganized (23.34%). The Adler blood flow grading was as follows: 0 (32.75%), I (18.82%), II (19.16%), and III (29.27%). A univariate analysis revealed that the Adler grade was not significantly associated with LNM (P > 0.05), whereas the garland pattern was significantly associated with LNM (P < 0.05). A multivariate analysis revealed that the garland pattern was an independent protective factor for LNM (OR [95% CI] = 0.386 [0.156-0.893]). The incorporation of the garland pattern into the model improved the predictive accuracy for LNM in PTC patients, and the AUC increased from 0.727 [95% CI: 0.669-0.786] to 0.767 [95% CI: 0.731-0.821].

CONCLUSIONS

This study classifies PTC into five types on the basis of color Doppler flow features and highlights the garland pattern as a potential predictor of LNM risk.

Location based BRAF V600E mutation status and dimension patterns of sporadic thyroid nodules: a population-based study

Fine-Needle Aspiration (FNA) has been routinely used for papillary thyroid carcinoma (PTC) diagnosis. One single liquid based tissue sample collected from FNA can be used for both cytological diagnosis and genetic testing at the same time. BRAF V600E mutation exhibits 100% specificity and high sensitivity for papillary thyroid carcinoma (PTC). However, FNA based studies on the genotypic (BRAF V600E) and ultrasonic (US) imaging characteristics (location and diameter) are exceedingly rare. Here, we aimed to study the genotypic (BRAF V600E) and US imaging characteristics (location and diameter) in a large retrospective cohort. A total of 4808 patients with thyroid nodules from a tertiary center underwent FNA, and 2430 patients underwent molecular testing. Our data demonstrated that the thyroid nodules were predominantly located on the right side (p = 0.0004). Patients diagnosed with Bethesda VI cytology had significantly more right-sided thyroid nodules (p = 0.0041). Interestingly, among patients with PTC with lymph node metastasis (LNM), right-side-affected LNM was significantly more common than left-side-affected LNM, which implies a biased regional LNM of right-side-located thyroid nodules (p = 0.0007). The size of BRAF-V600E mutated or right-lobe located nodules was significantly larger than that in the control group (p = 0.0156), and patients with a BRAF V600E mutation were considerably younger than those with wild-type BRAF.

Deep learning based analysis of dynamic video ultrasonography for predicting cervical lymph node metastasis in papillary thyroid carcinoma

BACKGROUND

Cervical lymph node metastasis (CLNM) is the most common form of thyroid cancer metastasis. Accurate preoperative CLNM diagnosis is of more importance in patients with papillary thyroid cancer (PTC). However, there is currently no unified methods to objectively predict CLNM risk from ultrasonography in PTC patients.This study aimed to develop a deep learning (DL) model to help clinicians more accurately determine the existence of CLNM risk in patients with PTC and then assist them with treatment decisions.

METHODS

Ultrasound dynamic videos of 388 patients with 717 thyroid nodules were retrospectively collected from Zhejiang Cancer Hospital between January 2020 and June 2022. Five deep learning (DL) models were investigated to examine its efficacy for predicting CLNM risks and their performances were also compared with those predicted using two-dimensional ultrasound static images.

RESULTS

In the testing dataset (n = 78), the DenseNet121 model trained on ultrasound dynamic videos outperformed the other four DL models as well as the DL model trained using the two-dimensional (2D) static images across all metrics. Specifically, using DenseNet121, the comparison between the 3D model and 2D model for all metrics are shown as below: AUROC: 0.903 versus 0.828, sensitivity: 0.877 versus 0.871, specificity: 0.865 versus 0.659.

CONCLUSIONS

This study demonstrated that the DenseNet121 model has the greatest potential in distinguishing CLNM from non-CLNM in patients with PTC. Dynamic videos also offered more information about the disease states which have proven to be more efficient and robust in identifying CLNM compared to statis images.

Diagnostic value of microRNA-129-5p and TSH combination for papillary thyroid cancer with cervical lymph node metastasis

ObjectiveThe papillary thyroid cancer (PTC) incidence is on the increase. We explored the diagnostic value of microRNA (miR)-129-5p & serologic indicator thyroid-stimulating hormone (TSH) test in PTC with cervical lymph node metastasis (LNM).MethodsAccording to the pathological "gold standard," 198 PTC patients were assigned into the LNM (n = 93)/non-LNM (n = 105) groups, with their medical records collected. The serum free-triiodothyronine (FT3)/free-thyroxine (FT4)/TSH/thyroglobulin (Tg)/thyroglobulin antibody levels were assessed using an electrochemiluminescence immunoassay device. Serum miR-129-5p expression was determined by reverse transcription quantitative polymerase chain reaction. Correlations between serum miR-129-5p/TSH levels with pathological indicators were analyzed by Spearman correlation coefficient. Independent influencing factors for cervical LNM in PTC patients was analyzed by logistic multivariate regression analysis. Diagnostic value of miR-129-5p combined with serologic indicator TSH test in PTC patients with cervical LNM and lateral cervical LNM was analyzed by the receiver operating characteristic curve.ResultsThe two groups varied obviously in primary tumor size/Tg level. Serum miR-129-5p expression in the LNM group was reduced, and negatively correlated with Tg and primary tumor size, while the serologic indicator TSH level showed positive correlations with Tg and primary tumor size. Independent influencing factors for PTC with cervical LNM were miR-129-5p/TSH/Tg levels. miR-129-5p and serologic indicator TSH levels had high diagnostic value for PTC patients with cervical LNM and lateral cervical LNM, with their combination showing higher diagnostic value.ConclusionmiR-129-5p and serologic indicator TSH had high diagnostic value for diagnosing PTC patients with cervical LNM, providing high reference value for the formulation of thyroid tumor resection.

Programmed cell death-related gene IL20RA facilitates tumor progression and remodels tumor microenvironment in thyroid cancer

Programmed cell death (PCD) is a vital biological process that is essential for regulating cell progression and tumor microenvironment. This study aimed to explore the relationship between PCD-related genes expression and prognosis in thyroid cancer (THCA), especially IL20RA, as a potential prognostic marker for THCA. Data from The Cancer Genome Atlas (TCGA) database was utilized to develop a PCD-related risk prediction model based on LASSO regression along with univariate Cox regression. The correlation between PCD-related genes and immune cell infiltration was also assessed. The prognostic value of the key PCD-related gene for THCA was investigated by immunohistochemistry. The immune regulatory function and biological function of the key PCD related molecules were detected by cellular experiments. We identified four PCD-related genes (NPC2, E2F1, IL20RA, and TREM2) and constructed a risk model that exhibited excellent accuracy in predicting the prognosis of THCA. Moreover, we confirmed that high expression of IL20RA related to the poor prognosis of THCA. IL20RA promoted cell proliferation and IL20RA knockdown increased apoptosis and ferroptosis. Analysis of the immune microenvironment and detection of macrophages polarization showed that IL20RA promoted the polarization of M2 macrophages while reducing the polarization of M1. We constructed a prognostic prediction model and identified several PCD-related genes. The function of IL20RA which could potentially provide a foundation for additional investigations into diagnostic markers and treatment targets for THCA.

Dual-modal radiomics ultrasound model to diagnose cervical lymph node metastases of differentiated thyroid carcinoma: a two-center study

OBJECTIVES

To establish and validate a dual-modal radiomics nomogram from grayscale ultrasound and color doppler flow imaging (CDFI) of cervical lymph nodes (LNs), aiming to improve the diagnostic accuracy of metastatic LNs in differentiated thyroid carcinoma (DTC).

METHODS

DTC patients with suspected cervical LNs in two medical centers were retrospectively enrolled. Pathological results were set as gold standard. We extracted radiomic characteristics from grayscale ultrasound and CDFI images, then applied lasso (least absolute shrinkage and selection operator) regression analysis to analyze radiomics features and calculate the rad-score. A nomogram based on rad-score, clinical data, and ultrasound signs was developed. The performance of the model was evaluated using AUC and calibration curve. We also assessed the model's diagnostic ability in European Thyroid Association (ETA) indeterminate LNs.

RESULTS

377 DTC patients and 726 LNs were enrolled. 37 radiomics features were determined and calculated as rad-score. The dual-modal radiomics model showed good calibration capabilities. The radiomics model displayed higher diagnostic ability than the traditional ultrasound model in the training set [0.871 (95% CI: 0.839-0.904) vs. 0.848 (95% CI: 0.812-0.884), p<0.01], internal test set [0.804 (95% CI: 0.741-0.867) vs. 0.803 (95% CI: 0.74-0.866), p = 0.696], and external validation cohort [0.939 (95% CI: 0.893-0.984) vs. 0.921 (95% CI: 0.857-0.985), p = 0.026]. The radiomics model could also significantly improve the detection rate of metastatic LNs in the ETA indeterminate LN category.

CONCLUSIONS

The dual-modal radiomics nomogram can improve the diagnostic accuracy of metastatic LNs of DTC, especially for LNs in ETA indeterminate classification.

Combination of inflammatory proteins in serum can be used to diagnose papillary thyroid carcinoma with lymph node metastasis

BACKGROUND

Accurately distinguishing lymph node metastases (LNM) from papillary thyroid carcinomas (PTC) is crucial in clinical practice. The role of the immune system in PTC-LNM has attracted increasing attention. The aim of the present study was to evaluate the differential expression of 92 immune-related proteins in the serum and identify their potential diagnostic effects in patients with PTC-LNM.

METHODS

The 92 immune-related proteins were analyzed using a proximity extension assay. In addition, logistic regression and least absolute shrinkage and selection operator regression methods were used to develop combined diagnostic markers for thyroid cancer. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic validity. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to analyze the potential regulatory pathways.

RESULTS

Five proteins, including IL-22RA1, IL-12B, CCL4, CCL3, and IL-1α, were significantly elevated in the serum of patients with LNM. The combined diagnosis of these five proteins demonstrated excellent diagnostic performance in distinguishing patients with PTC-LNM (area under the curve = 0.967, sensitivity = 0.941, and specificity = 0.889). Further analysis revealed that IL12B and IL1A mRNAs were significantly overexpressed in patients with PTC-LNM. This study also showed that the IL12B and IL1A was closely related to the PI3K-AKT, NF-κB, and MAPK signaling pathways.

CONCLUSION

The combination of IL-22RA1, IL-12B, CCL4, CCL3, and IL-1α represents a promising diagnostic panel for PTC-LNM. These findings provide a novel set of diagnostic markers for PTC-LNM based on serum inflammatory protein levels.

Single-cell RNA-sequencing and spatial transcriptomic analysis reveal a distinct population of APOE- cells yielding pathological lymph node metastasis in papillary thyroid cancer

BACKGROUND

Thyroid cancer is one of the most common endocrine tumors worldwide, especially among women and the metastatic mechanism of papillary thyroid carcinoma remains poorly understood.

METHODS

Thyroid cancer tissue samples were obtained for single-cell RNA-sequencing and spatial transcriptomics, aiming to intratumoral and antimetastatic heterogeneity of advanced PTC. The functions of APOE in PTC cell proliferation and invasion were confirmed through in vivo and in vitro assays. Pseudotime analysis and CellChat were performed to explore the the molecular mechanisms of the APOE in PTC progression.

RESULTS

We identified a subpopulation of tumor cells with lower expression levels of APOE, associated with advanced stages of PTC and cervical metastasis. APOE overexpression significantly reduced tumor cell proliferation and invasion, both in vitro and in vivo, by activating the ABCA1-LXR axis. APOE- tumor cells may promote tumor growth by interacting with dendritic cells and CD4+ T cells via CD99- rather than CD6-regulated signaling. We established a machine learning-based scRNA-seq data, 13-gene signature predictive of lymph node metastasis.

CONCLUSIONS

We identified a distinct APOE- tumor cell population associated with cervical metastasis and poor prognosis. Our results and models have potential clinical, prognostic, and therapeutic implications for advanced PTC.

KEY POINTS

A subpopulation of tumor cells with lower expression levels of APOE was strongly associated with more advanced stages and metastasis of PTC. APOE-negative (APOE-) cellsoverall exhibited weaker interactions with immune cells. A machine-learning bioinformatics model based on scRNA-seq data of in-situ thyroid cancer tissue was established to predict lymph node metastasis.

Serum small extracellular vesicles-derived BST2 as a biomarker for papillary thyroid microcarcinoma promotes lymph node metastasis

Papillary thyroid microcarcinoma (PTMC), although frequently indolent, could be aggressive in a few patients, leading to lymph node metastasis (LNM) and worsened prognosis. To explore the role of protein profiling of small extracellular vesicles (sEVs) in the auxiliary diagnosis and risk stratification of PTMC, proteins in serum sEVs isolated from PTMC patients with (N = 10) and without (N = 10) LNM and benign thyroid nodule (BN) patients (N = 9) were profiled via a bioinformatics-integrated data-independent acquisition proteomic technique. The performance of candidate proteins as diagnostic and prognostic biomarkers in PTMC was assessed via receiver operating characteristic analysis. We found that serum sEVs from PTMC patients promoted the proliferation and migration of human papillary thyroid cancer (PTC) cells and tube formation in human lymphatic endothelial cells (HLECs). SEV proteins from PTMC patients with and without LNM have differential expression profiles, with bone marrow stromal cell antigen 2 (BST2) being best associated with PTMC progression. Through knockdown and overexpression, we proved that the high expression of sEV-derived BST2 was bound up with higher proliferation and migration ability of PTC cells as well as stronger lymphangiogenesis in HLECs. This study brought insight into the differential sEV-protein profile with or without LNM in PTMC. The serum sEV-BST2 may contribute to PTMC progression and LNM and may have diagnostic, prognostic, and therapeutic implications.

Human adipose-derived stem cells promote migration of papillary thyroid cancer cell via leptin pathway

INTRODUCTION

Obesity is associated with the incidence and poor prognosis of thyroid cancer, but the mechanism is not fully understood. The aim of this study was to investigate the effects of human adipose-derived stem cells (ADSCs) on the invasion and migration of thyroid cancer cells.

METHODS

ADSCs-conditioned medium (ADSC-CM) was collected to culture thyroid cancer cell lines TPC-1 cells and BCPAP cells. The effects of ADSCs on thyroid cancer cell proliferation were determined by CCK8 and EdU assays, and the effects on migration were determined by Transwell and wound closure assays. Leptin neutralizing antibodies (NAB) were added to ADSC-CM to block leptin. In animal experiments, TPC-1 cells and BCPAP cells were injected into the tail vein of nude mice, and the leptin receptor antagonist peptide allo-aca was injected subcutaneously to block the leptin pathway. The number and size of metastatic lung tumours were observed after 8 weeks.

RESULTS

ADSC-CM significantly promoted the invasion and migration of thyroid cancer cells and upregulated their matrix metalloproteinase 2 (MMP-2) levels, while NAB with the addition of leptin reduced the invasion and migration of thyroid cancer cells and downregulated MMP-2 levels. Allo-aca treatment reduced the number of metastatic lung nodules formed by thyroid cancer cells in nude mice and reduced the diameter of metastatic lesions.

CONCLUSION

ADSCs upregulate MMP-2 levels of thyroid cancer cells through exocrine leptin, thereby promoting cancer cell migration, which may be one of the key mechanisms by which obesity increases the invasiveness of thyroid cancer.

Preoperative Ultrasound Radomics to Predict Posthepatectomy Liver Failure in Patients With Hepatocellular Carcinoma

PURPOSE

Posthepatectomy liver failure (PHLF) is a major cause of postoperative mortality in hepatocellular carcinoma (HCC) patients. The study aimed to develop a method based on the two-dimensional shear wave elastography and clinical data to evaluate the risk of PHLF in HCC patients with chronic hepatitis B.

METHODS

This multicenter study proposed a deep learning model (PHLF-Net) incorporating dual-modal ultrasound features and clinical indicators to predict the PHLF risk. The datasets were divided into a training cohort, an internal validation cohort, an internal independent testing cohort, and three external independent testing cohorts. Based on ResNet50 pretrained on ImageNet, PHLF-Net used a progressive training strategy with images of varying granularity and incorporated conventional B-mode and elastography images and clinical indicators related to liver reserve function.

RESULTS

In total, 532 HCC patients who underwent hepatectomy at five hospitals were enrolled. PHLF occurred in 147 patients (27.6%, 147/532). The PHLF-Net combining dual-modal ultrasound and clinical indicators demonstrated high effectiveness for predicting PHLF, with AUCs of 0.957 and 0.923 in the internal validation and testing sets, and AUCs of 0.950, 0.860, and 1.000 in the other three independent external testing sets. The performance of PHLF-Net outperformed models of single- and dual-modal US.

CONCLUSIONS

Preoperative ultrasound imaging combining clinical indicators can effectively predict the PHLF probability in patients with HCC. In the internal and external validation sets, PHLF-Net demonstrated its usefulness in predicting PHLF.

Bibliometric and visual analysis of radiomics for evaluating lymph node status in oncology

Background

Radiomics, which involves the conversion of digital images into high-dimensional data, has been used in oncological studies since 2012. We analyzed the publications that had been conducted on this subject using bibliometric and visual methods to expound the hotpots and future trends regarding radiomics in evaluating lymph node status in oncology.

Methods

Documents published between 2012 and 2023, updated to August 1, 2024, were searched using the Scopus database. VOSviewer, R Package, and Microsoft Excel were used for visualization.

Results

A total of 898 original articles and reviews written in English and be related to radiomics for evaluating lymph node status in oncology, published between 2015 and 2023, were retrieved. A significant increase in the number of publications was observed, with an annual growth rate of 100.77%. The publications predominantly originated from three countries, with China leading in the number of publications and citations. Fudan University was the most contributing affiliation, followed by Sun Yat-sen University and Southern Medical University, all of which were from China. Tian J. from the Chinese Academy of Sciences contributed the most within 5885 authors. In addition, Frontiers in Oncology had the most publications and transcended other journals in recent 4 years. Moreover, the keywords co-occurrence suggested that the interplay of "radiomics" and "lymph node metastasis," as well as "major clinical study" were the predominant topics, furthermore, the focused topics shifted from revealing the diagnosis of cancers to exploring the deep learning-based prediction of lymph node metastasis, suggesting the combination of artificial intelligence research would develop in the future.

Conclusion

The present bibliometric and visual analysis described an approximately continuous trend of increasing publications related to radiomics in evaluating lymph node status in oncology and revealed that it could serve as an efficient tool for personalized diagnosis and treatment guidance in clinical patients, and combined artificial intelligence should be further considered in the future.

Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical data

Aims

To develop and validate an individualized nomogram for differentiating the histologic subtypes (adenocarcinoma and squamous cell carcinoma) of subpleural non-small cell lung cancer (NSCLC) based on ultrasound parameters and clinical data.

Methods

This study was conducted retrospectively between March 2018 and December 2019. Patients were randomly assigned to a development cohort (DC, n=179) and a validation cohort (VC, n=77). A total of 7 clinical parameters and 16 ultrasound parameters were collected. Least absolute shrinkage and selection operator regression analysis was employed to identify the most significant predictors utilizing a 10-fold cross-validation. The multivariate logistic regression model was applied to investigate the relevant factors. An individualized nomogram was then developed. Receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were applied for model validation in both DC and VC.

Results

Following the final regression analysis, gender, serum carcinoembryonic antigen, lesion size and perfusion defect in contrast-enhanced ultrasound were entered into the nomogram. The model showed moderate predictive ability, with an area under the ROC curve of 0.867 for DC and 0.838 for VC. The calibration curves of the model showed good agreement between actual and predicted probabilities. The ROC and DCA curves demonstrated that the nomogram exhibited a good predictive performance.

Conclusion

We developed a nomogram that can predict the histologic subtypes of subpleural NSCLC. Both internal and external validation revealed optimal discrimination and calibration, indicating that the nomogram may have clinical utility. This model has the potential to assist clinicians in making treatment recommendations.

CT-based deep learning model for predicting the success of extracorporeal shock wave lithotripsy in treating ureteral stones larger than 1 cm

OBJECTIVES

To develop a deep learning (DL) model based on computed tomography (CT) images to predict the success of extracorporeal shock wave lithotripsy (SWL) treatment for patients with ureteral stones larger than 1 cm.

MATERIALS AND METHODS

We enrolled 333 patients who underwent SWL treatment for ureteral stones and randomly divided them into training and test sets. A DL model was built based on CT images of ureteral stones to predict SWL outcomes. The predictive efficacy of the DL model was assessed by comparing it with traditional and radiomics models.

RESULTS

The DL model demonstrated significantly better predictive performance in both training and test sets compared to radiomics (training set, AUC: 0.993 vs. 0.923, P < 0.001; test set AUC: 0.982 vs. 0.846, P < 0.001) and traditional models (training set AUC: 0.993 vs. 0.75, P = 0.005; test set AUC: 0.982 vs. 0.677, P < 0.001). Decision curve analysis (DCA) also proved that the DL model brought more benefit in predicting the success of SWL treatment than other methods.

CONCLUSION

The DL model based on CT images showed excellent ability to predict the probability of success of SWL treatment for patients with ureteral stones larger than 1 cm, providing a new auxiliary tool for clinical treatment decision-making.

The splicing factor QKI inhibits metastasis by modulating alternative splicing of E-Syt2 in papillary thyroid carcinoma

Alternative splicing (AS) plays a crucial role in the hallmarks of cancer and can open new avenues for targeted therapies. However, the aberrant AS events and the metastatic cascade in papillary thyroid carcinoma (PTC) remain largely unclear. Here, we identify the splicing factor, quaking protein (QKI), which was significantly downregulated in PTC and correlated with poor survival outcomes in patients with PTC. Functional studies indicated that low expression of QKI promoted the PTC cell growth and metastasis in vitro and in vivo. Mechanistically, low QKI induced exon 14 retention of extended synaptotagmin 2 (E-Syt2) and produced a long isoform transcript (termed E-Syt2L) that acted as an important oncogenic factor of PTC metastasis. Notably, overexpression of long non-coding RNA eosinophil granule ontogeny transcript (EGOT) physically binds to QKI and suppressed its activity by inhibiting ubiquitin specific peptidase 25 (USP25) mediated deubiquitination and subsequent degradation of QKI. Collectively, these data demonstrate the novel mechanistic links between the splicing factor QKI and splicing event in PTC metastasis and support the potential utility of targeting splicing events as a therapeutic strategy for PTC.

Predictive factors for lymph node metastasis in papillary thyroid cancer patients undergoing neck dissection: insights from a large cohort study

Background

This study aimed to investigate the risk factors and metastatic patterns in papillary thyroid cancer (PTC) patients undergoing lymph node dissection, offering guidance for clinical practice.

Methods

A total of 924 PTC patients who underwent thyroidectomy with central neck dissection (CND) or lateral neck dissection (LND) between January 2021 and November 2022 were included in the analysis. The study investigated the relationships between clinicopathological characteristics, lymph node metastasis, and various risk factor.

Results

Among the 924 PTC patients, the cervical lymph node metastasis rate was 59.1% (546 patients). Of these patients, 381 had central neck metastasis (CNM, 41.2%), while the remaining 165 patients had lateral neck metastasis (LNM, 17.9%). Factors associated with increased risk of CNM and LNM included larger tumor diameter, presence of multiple tumors, and capsular invasion (p<0.05). Male sex, age <55 years, larger tumor diameter (>0.85 cm), multiple tumors, capsular invasion, and absence of Hashimoto's disease were identified as independent risk factors for CNM (p<0.05), with an AUC value of 0.722. CNM, maximum diameter >1.15 cm, and multiple tumors were independent risk factors for LNM (p<0.05), with an AUC of 0.699.

Conclusion

These findings suggest that tailored neck dissection based on individual risk factors is crucial, particularly in cases of suspected LNM with larger tumors, CNM, multiple tumors, and capsular invasion.

Meta-analysis of prediction models for predicting lymph node metastasis in thyroid cancer

BACKGROUND

The purpose of this systematic review and meta-analysis is to assess the efficacy of various machine learning (ML) techniques in predicting preoperative lymph node metastasis (LNM) in patients diagnosed with papillary thyroid carcinoma (PTC). Although prior studies have investigated the potential of ML in this context, the current evidence is not sufficiently strong. Hence, we undertook a thorough analysis to ascertain the predictive accuracy of different ML models and their practical relevance in predicting preoperative LNM in PTC patients.

MATERIALS AND METHODS

In our search, we thoroughly examined PubMed, Cochrane Library, Embase, and Web of Science, encompassing their complete database history until December 3rd, 2022. To evaluate the potential bias in the machine learning models documented in the included studies, we employed the Prediction Model Risk of Bias Assessment Tool (PROBAST).

RESULTS

A total of 107 studies, involving 136,245 patients, were included. Among them, 21,231 patients showed central LNM (CLNM) and 4,637 had lateral LNM (LLNM). The meta-analysis results revealed that the c-index for predicting LNM, CLNM, and LLNM were 0.762 (95% CI: 0.747-0.777), 0.762 (95% CI: 0.747-0.777), and 0.803 (95% CI: 0.773-0.834) in the training set, and 0.773 (95% CI: 0.754-0.791), 0.762 (95% CI: 0.747-0.777), and 0.829 (95% CI: 0.779-0.879) in the validation set, respectively. A total of 134 machine learning-based prediction models were included, covering 10 different types. Logistic Regression (LR) was the most commonly used model, accounting for 81.34% (109/134) of the included models.

CONCLUSIONS

Machine learning methods have shown a certain level of accuracy in predicting preoperative LNM in PTC patients, indicating their potential as a predictive tool. Their use in the clinical management of PTC holds great promise. Among the various ML models investigated, the performance of logistic regression-based nomograms was deemed satisfactory.

Dysregulation of pseudouridylation in small RNAs contributes to papillary thyroid carcinoma metastasis

BACKGROUND

Previous studies have indicated that ψ-modified small RNAs play crucial roles in tumor metastasis. However, the ψ-modified small RNAs during metastasis of PTC are still unclear.

METHODS

We compared the pseudouridine synthase 7 (PUS7) alteration between metastatic and non-metastatic PTCs, and investigated its correlation with clinicopathological features. Additionally, we employed a small RNA ψ modification microarray to examine the small RNA ψ modification profile in both metastatic and non-metastatic PTCs, as well as paired paracancerous tissues. The key molecule involved in ψ modification, pre-miR-8082, was identified and found to regulate the expression of CD47. Experiments in vitro were conducted to further investigate the function of PUS7 and CD47 in PTC.

RESULTS

Our results demonstrated that PUS7 was down-regulated in PTC and was closely associated with metastasis. Moreover, the ψ modification of pre-miR-8082 was found to be decreased, resulting in down-expression of pre-miR-8082 and miR-8082, leading to the loss of the inhibitory effect on CD47, thereby promoting tumor migration.

CONCLUSIONS

Our study demonstrates that PUS7 promotes the inhibition of CD47 and inhibits metastasis of PTC cells by regulating the ψ modification of pre-miR-8082. These results suggest that PUS7 and ψ pre-miR-8082 may serve as potential targets and diagnostic markers for PTC metastasis.

FTO/IGF2BP2-mediated N6 methyladenosine modification in invasion and metastasis of thyroid carcinoma via CDH12

Epigenetic reprogramming plays a critical role in cancer progression of cancer, and N6-methyladenosine (m6A) is the most common RNA modification in eukaryotes. The purpose of this study was to explore the related modification mode of m6A regulator construction and evaluate the invasion and migration of thyroid cancer. Our results showed that m6A levels were significantly increased in papillary thyroid cancer (PTC) and anaplastic thyroid cancer (ATC) samples, which may have been induced by the down-regulation of demethylase fat mass and obesity-associated gene (FTO). Moreover, FTO inhibited PTC and ATC invasion and metastasis through the epithelial-to-mesenchymal transition (EMT) pathway in vivo and in vitro. Mechanistically, an m6A-mRNA epitranscriptomic microarray showed that Cadherin 12 (CDH12) is the key target gene mediated by FTO in an m6A-dependent manner. CDH12 promotes invasion and metastasis through the EMT pathway in thyroid cancer, both in vivo and in vitro. Furthermore, we found that insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) is an important m6A reading protein, that regulates the stability of CDH12 mRNA and mediates EMT progression, thereby promoting the invasion and metastasis of PTC and ATC. Thus, FTO, IGF2BP2 and CDH12 may be effective therapeutic targets for PTC and ATC with significant invasion or distant metastasis. Schematic summary of FTO-IGF2BP2 axis in modulation of CDH12 mRNA m6A and upregulation of CDH12 expression in the invasion and metastasis of thyroid carcinoma.

Computer Vision-Radiomics & Pathognomics

The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.

A comprehensive prediction model for central lymph node metastasis in papillary thyroid carcinoma with Hashimoto's thyroiditis: BRAF may not be a valuable predictor

Purpose

Papillary thyroid carcinoma (PTC) frequently coexists with Hashimoto's thyroiditis (HT), which poses challenges in detecting central lymph node metastasis (CLNM) and determining optimal surgical management. Our study aimed to identify the independent predictors for CLNM in PTC patients with HT and develop a comprehensive prediction model for individualized clinical decision-making.

Patients and methods

In this retrospective study, a total of 242 consecutive PTC patients who underwent thyroid surgery and central lymph node dissection between February 2019 and December 2021 were included. 129 patients with HT were enrolled as the case group and 113 patients without HT as control. The results of patients' general information, laboratory examination, ultrasound features, pathological evaluation, and BRAF mutation were collected. Multivariate logistic regression analysis was used to identify independent predictors, and the prediction model and nomogram were developed for PTC patients with HT. The performance of the model was assessed using the receiver operating characteristic curve, calibration curve, decision curve analysis, and clinical impact curve. In addition, the impact of the factor BRAF mutation was further evaluated.

Results

Multivariate analysis revealed that gender (OR = 8.341, P = 0.013, 95% CI: 1.572, 44.266), maximum diameter (OR = 0.316, P = 0.029, 95% CI: 0.113, 0.888), multifocality (OR = 3.238, P = 0.010, 95% CI: 1.319, 7.948), margin (OR = 2.750, P = 0.046, 95% CI: 1.020, 7.416), and thyrotropin receptor antibody (TR-Ab) (OR = 0.054, P = 0.003, 95% CI: 0.008, 0.374) were identified as independent predictors for CLNM in PTC patients with HT. The area under the curve of the model was 0.82, with accuracy, sensitivity, and specificity of 77.5%, 80.3% and 75.0%, respectively. Meanwhile, the model showed satisfactory performance in the internal validation. Moreover, the results revealed that BRAF mutation cannot further improve the efficacy of the prediction model.

Conclusion

Male, maximum diameter > 10mm, multifocal tumors, irregular margin, and lower TR-Ab level have significant predictive value for CLNM in PTC patients with HT. Meanwhile, BRAF mutation may not have a valuable predictive role for CLNM in these cases. The nomogram constructed offers a convenient and valuable tool for clinicians to determine surgical decision and prognostication for patients.

Enhanced stiffness in peri-cancerous tissue: a marker of poor prognosis in papillary thyroid carcinoma with lymph node metastasis

BACKGROUND

The prognostic significance of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) remains controversial. Notably, there is evidence suggesting an association between tissue stiffness and the aggressiveness of the disease. We therefore aimed to explore the effect of tissue stiffness on LNM-related invasiveness in PTC patients.

METHOD

A total of 2492 PTC patients from 3 hospitals were divided into an LNM group and a non-LNM group based on their pathological results. The effects of interior lesion stiffness (E) and peri-cancerous tissue stiffness (Eshell) on the LNM-related recurrence rate and mortality in each patient with PTC subgroup were analyzed. The activation of cancer-associated fibroblasts (CAFs) and extracellular matrix component type 1 collagen (COL-I) in the lesion were compared and analyzed across different subgroups. The underlying biological basis of differences in each subgroup was identified using RNA sequencing (RNA-seq) data.

RESULTS

The Eshell value and Eshell/E in the LNM group were significantly higher than those in the non-LNM group of patients with PTC (Eshell: 72.72 ± 5.63 vs 66.05 ± 4.46; Eshell/E: 1.20 ± 1.72 vs 1.09 ± 1.10, P < .001). When Eshell/E > 1.412 and LNM were both present, the recurrence rate and mortality were significantly increased compared to those of group of patients with LNM (91.67% and 7.29%, respectively). The CAF activation and COL-I content in the Eshell/E+ group were significantly higher than those in the Eshell/E- group (all P < .001), and the RNA-seq results revealed significant extracellular matrix (ECM) remodeling in the LNM-Eshell/E+ group.

CONCLUSIONS

Stiff peri-cancerous tissue induced CAF activation, COL-I deposition, and ECM remodeling, resulting in a poor prognosis for PTC patients with LNM.

Epigenetics in thyroid cancer: a bibliometric analysis

Background

Epigenetics, which involves regulatory modifications that do not alter the DNA sequence itself, is crucial in the development and progression of thyroid cancer. This study aims to provide a comprehensive analysis of the epigenetic research landscape in thyroid cancer, highlighting current trends, major research areas, and potential future directions.

Methods

A bibliometric analysis was performed using data from the Web of Science Core Collection (WOSCC) up to 1 November 2023. Analytical tools such as VOSviewer, CiteSpace, and the R package 'bibliometrix' were employed for comprehensive data analysis and visualization. This process identified principal research themes, along with influential authors, institutions, and countries contributing to the field.

Results

The analysis reveals a marked increase in thyroid cancer epigenetics research over the past two decades. Emergent key themes include the exploration of molecular mechanisms and biomarkers, various subtypes of thyroid cancer, implications for therapeutic interventions, advancements in technologies and methodologies, and the scope of translational research. Research hotspots within these themes highlight intensive areas of study and the potential for significant breakthroughs.

Conclusion

This study presents an in-depth overview of the current state of epigenetics in thyroid cancer research. It underscores the potential of epigenetic strategies as viable therapeutic options and provides valuable insights for researchers and clinicians in advancing the understanding and treatment of this complex disease. Future research is vital to fully leverage the therapeutic possibilities offered by epigenetics in the management of thyroid cancer.

Application of deep-learning to the automatic segmentation and classification of lateral lymph nodes on ultrasound images of papillary thyroid carcinoma

PURPOSE

It is crucial to preoperatively diagnose lateral cervical lymph node (LN) metastases (LNMs) in papillary thyroid carcinoma (PTC) patients. This study aims to develop deep-learning models for the automatic segmentation and classification of LNM on original ultrasound images.

METHODS

This study included 1000 lateral cervical LN ultrasound images (consisting of 512 benign and 558 metastatic LNs) collected from 728 patients at the Chongqing General Hospital between March 2022 and July 2023. Three instance segmentation models (MaskRCNN, SOLO and Mask2Former) were constructed to segment and classify ultrasound images of lateral cervical LNs by recognizing each object individually and in a pixel-by-pixel manner. The segmentation and classification results of the three models were compared with an experienced sonographer in the test set.

RESULTS

Upon completion of a 200-epoch learning cycle, the loss among the three unique models became negligible. To evaluate the performance of the deep-learning models, the intersection over union threshold was set at 0.75. The mean average precision scores for MaskRCNN, SOLO and Mask2Former were 88.8%, 86.7% and 89.5%, respectively. The segmentation accuracies of the MaskRCNN, SOLO, Mask2Former models and sonographer were 85.6%, 88.0%, 89.5% and 82.3%, respectively. The classification AUCs of the MaskRCNN, SOLO, Mask2Former models and sonographer were 0.886, 0.869, 0.90.2 and 0.852 in the test set, respectively.

CONCLUSIONS

The deep learning models could automatically segment and classify lateral cervical LNs with an AUC of 0.92. This approach may serve as a promising tool to assist sonographers in diagnosing lateral cervical LNMs among patients with PTC.

Automatic substantia nigra segmentation with Swin-Unet in susceptibility- and T2-weighted imaging: application to Parkinson disease diagnosis

Background

Accurately distinguishing between Parkinson disease (PD) and healthy controls (HCs) through reliable imaging method is crucial for appropriate therapeutic intervention. However, PD diagnosis is hindered by the subjective nature of the evaluation. We aimed to develop an automatic deep-learning method that can segment the substantia nigra areas on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) and further differentiate patients with PD from HCs using a machine learning algorithm.

Methods

Magnetic resonance imaging (MRI) data from 83 patients with PD and 83 age- and sex-matched HCs were obtained on the same 3.0-T MRI scanner. A deep learning method with Swin-Unet was developed to segment volumes of interest (VOIs) on SWI and then map the VOIs on SWI to the corresponding T2WI; features were then extracted from the VOIs on SWI and T2WI. Three machine learning models were developed and compared to differentiate those with PD from HCs.

Results

Swin-Unet achieved a better Dice coefficient than did U-Net in SWI segmentation (0.832 vs. 0.712). Machine learning models outperformed visual analysis (P>0.05), and logistic regression (LR) achieved the best performance [area under the curve (AUC) ≥0.819] and the most stable (relative standard deviations in AUC ≤0.05). The test results showed that the AUC of the LR model based on SWI segmentation was 0.894 while that of the LR model based on T2WI segmentation was 0.876. There was no significant difference in VOIs based on manual labeling or automatic segmentation across T2WI, SWI, or a combination of the two (P>0.05). The AUCs of the LR model based on automatic segmentation were close to those of the model based on manual labeling (P>0.05).

Conclusions

Our approach could provide a powerful and useful method for automatically and rapidly diagnosing PD in the clinic with only T2WI.

Proteomic and Phosphoproteomic analysis of thyroid papillary carcinoma: Identification of potential biomarkers for metastasis

Thyroid cancer has emerged as the most rapidly proliferating solid neoplasm. In this study, we included a cohort of patients who underwent sonographic assessment and surgical intervention at the Sir Run Run Shaw Hospital, associated with the School of Medicine at Zhejiang University, spanning from January 2019 to June 2020. Stratification of cases was based on a combination of preoperative ultrasonographic evaluations and postoperative histopathological diagnoses, resulting in three distinct groups: high-risk papillary thyroid carcinoma (PTC) labeled as C1, low-risk PTC designated as C2, and a control group (N) composed of benign thyroid tissue adjacent to the carcinoma. Proteomic and phosphoproteomic analyses were conducted on PTC specimens. The comparative assessment revealed that proteins up-regulated in the C1/N and C2/N groups were predominantly involved in functions such as amino acid binding, binding of phosphorylated compounds, and serine protease activity. Notably, proteins like NADH dehydrogenase, ATP synthase, oxidoreductases, and iron ion channels were significantly elevated in the C1 versus C2 comparative group. Through meticulous analysis of differential expression multiples, statistical significance, and involvement in metabolic pathways, this study identified eight potential biomarkers pertinent to PTC metastasis diagnostics, encompassing phosphorylated myosin 10, phosphorylated proline-directed protein kinase, leucine tRNA synthetase, 2-oxo-isovalerate dehydrogenase, succinic semialdehyde dehydrogenase, ADP/ATPtranslocase, pyruvate carboxylase, and fibrinogen. Therapeutic assays employing metformin, an AMP-activated protein kinase (AMPK) activator, alongside the phosphorylation-specific inhibitor ML-7 targeting Myosin10, demonstrated attenuated cellular proliferation, migration, and invasion capabilities in thyroid cancer cells, accompanied by a reduction in amino acid pools. Cellular colocalization and interaction studies elucidated that AMPK activation imposes an inhibitory influence on Myosin10 levels. The findings of this research corroborate the utility of proteomic and phosphoproteomic platforms in the identification of metastatic markers for PTC and suggest that modulation of AMPK activity, coupled with the inhibition of Myosin10 phosphorylation, may forge novel therapeutic avenues in the management of thyroid carcinoma. SIGNIFICANCE: The significance of our research lies in its potential to transform the current understanding and management of thyroid papillary carcinoma (PTC), particularly in its metastatic form. By integrating both proteomic and phosphoproteomic analyses, our study not only sheds light on the molecular alterations associated with PTC but also identifies eight novel biomarkers that could serve as indicators of metastatic potential.

Using multimodal ultrasound including full-time-series contrast-enhanced ultrasound cines for identifying the nature of thyroid nodules

Background

Based on the conventional ultrasound images of thyroid nodules, contrast-enhanced ultrasound (CEUS) videos were analyzed to investigate whether CEUS improves the classification accuracy of benign and malignant thyroid nodules using machine learning (ML) radiomics and compared with radiologists.

Materials and methods

The B-mode ultrasound (B-US), real-time elastography (RTE), color doppler flow images (CDFI) and CEUS cines of patients from two centers were retrospectively gathered. Then, the region of interest (ROI) was delineated to extract the radiomics features. Seven ML algorithms combined with four kinds of radiomics data (B-US, B-US + CDFI + RTE, CEUS, and B-US + CDFI + RTE + CEUS) were applied to establish 28 models. The diagnostic performance of ML models was compared with interpretations from expert and nonexpert readers.

Results

A total of 181 thyroid nodules from 181 patients of 64 men (mean age, 42 years +/- 12) and 117 women (mean age, 46 years +/- 12) were included. Adaptive boosting (AdaBoost) achieved the highest area under the receiver operating characteristic curve (AUC) of 0.89 in the test set among 28 models when combined with B-US + CDFI + RTE + CEUS data and an AUC of 0.72 and 0.66 when combined with B-US and B-US + CDFI + RTE data. The AUC achieved by senior and junior radiologists was 0.78 versus (vs.) 0.69 (p > 0.05), 0.79 vs. 0.64 (p < 0.05), and 0.88 vs. 0.69 (p < 0.05) combined with B-US, B-US+CDFI+RTE and B-US+CDFI+RTE+CEUS, respectively.

Conclusion

With the addition of CEUS, the diagnostic performance was enhanced for all seven classifiers and senior radiologists based on conventional ultrasound images, while no enhancement was observed for junior radiologists. The diagnostic performance of ML models was similar to senior radiologists, but superior to those junior radiologists.

Higher microRNA-221 and lower microRNA-451 expression are associated with poor prognosis in patients with thyroid papillary carcinoma

BACKGROUND

To analyze the relationship between serum microRNA-221 and microRNA-451 expression and the pathological features and prognosis of patients with thyroid papillary carcinoma.

METHODS

Cross-sectional study that included 120 patients with papillary thyroid cancer treated at the hospital and 120 healthy volunteers selected as the control group who underwent physical examination. The relative expression levels of microRNA-221 and microRNA-451 were compared between the thyroid papillary carcinoma group (prior to treatment) and the control group. Additionally, microRNA-221 and microRNA-451 expression levels were analyzed in patients with papillary thyroid carcinoma across different pathological characteristics.

RESULTS

Serum microRNA-221 relative levels were significantly higher (p<0.001) in the papillary carcinoma group compared to the control group, while microRNA-451 levels were higher in the control group (p<0.001). In the papillary carcinoma group, microRNA-221 expression was significantly higher in patients with extracapsular invasion (p<0.001), lymphatic metastasis (p=0.003), and poor prognosis (p<0.001). Conversely, microRNA-451 expression was significantly lower (p<0.001) in patients with extracapsular invasion, lymphatic metastasis and poor prognosis. In the multivariate logistic regression analysis, morphological features suggestive of an aggressive clinical behavior (extracapsular invasion and lymphatic metastasis) were related to high expression of microRNA-221 and low expression of microRNA-451 in patients with thyroid papillary carcinoma (p<0.001).

CONCLUSIONS

Serum microRNA-221 and microRNA-451 expression levels are significantly higher and lower, respectively, in patients with papillary thyroid carcinoma, particularly in patients with morphological features suggestive of an aggressive clinical behavior (extracapsular invasion and lymphatic metastasis) and, therefore, of a poor prognosis.

Thyroid Cancer Central Lymph Node Metastasis Risk Stratification Based on Homogeneous Positioning Deep Learning

Due to the absence of definitive diagnostic criteria, there remains a lack of consensus regarding the risk assessment of central lymph node metastasis (CLNM) and the necessity for prophylactic lymph node surgery in ultrasound-diagnosed thyroid cancer. The localization of thyroid nodules is a recognized predictor of CLNM; however, quantifying this relationship is challenging due to variable measurements. In this study, we developed a differential isomorphism-based alignment method combined with a graph transformer to accurately extract localization and morphological information of thyroid nodules, thereby predicting CLNM. We collected 88,796 ultrasound images from 48,969 patients who underwent central lymph node (CLN) surgery and utilized these images to train our predictive model, ACE-Net. Furthermore, we employed an interpretable methodology to explore the factors influencing CLNM and generated a risk heatmap to visually represent the distribution of CLNM risk across different thyroid regions. ACE-Net demonstrated superior performance in 6 external multicenter tests (AUC = 0.826), surpassing the predictive accuracy of human experts (accuracy = 0.561). The risk heatmap enabled the identification of high-risk areas for CLNM, likely correlating with lymphatic metastatic pathways. Additionally, it was observed that the likelihood of metastasis exceeded 80% when the nodal margin's minimum distance from the thyroid capsule was less than 1.25 mm. ACE-Net's capacity to effectively predict CLNM and provide interpretable disease-related insights can importantly reduce unnecessary lymph node dissections by 37.9%, without missing positive cases, thus offering a valuable tool for clinical decision-making.

Predictive Values of Clinical Features and Multimodal Ultrasound for Central Lymph Node Metastases in Papillary Thyroid Carcinoma

Papillary thyroid carcinoma (PTC), the predominant pathological type among thyroid malignancies, is responsible for the sharp increase in thyroid cancer. Although PTC is an indolent tumor with good prognosis, 60-70% of patients still have early cervical lymph node metastasis, typically in the central compartment. Whether there is central lymph node metastasis (CLNM) or not directly affects the formulation of preoperative surgical procedures, given that such metastases have been tied to compromised overall survival and local recurrence. However, detecting CLNM before operation can be challenging due to the limited sensitivity of preoperative approaches. Prophylactic central lymph node dissection (PCLND) in the absence of clinical evidence of CLNM poses additional surgical risks. This study aims to provide a comprehensive review of the risk factors related to CLNM in PTC patients. A key focus is on utilizing multimodal ultrasound (US) for accurate prognosis of preoperative CLNM and to highlight the distinctive role of US-based characteristics for predicting CLNM.

Ultrasound-Base Radiomics for Discerning Lymph Node Metastasis in Thyroid Cancer: A Systematic Review and Meta-analysis

PURPOSE

Ultrasound is the imaging modality of choice for preoperative diagnosis of lymph node metastasis (LNM) in thyroid cancer (TC), yet its efficacy remains suboptimal. As radiomics gains traction in tumor diagnosis, its integration with ultrasound for LNM differentiation in TC has emerged, but its diagnostic merit is debated. This study assesses the accuracy of ultrasound-integrated radiomics in preoperatively diagnosing LNM in TC.

METHODS

Literatures were searched in PubMed, Embase, Cochrane, and Web of Science until July 11, 2023. Quality of the studies was assessed by the radiomics quality score (RQS). A meta-analysis was executed using a bivariate mixed effects model, with a subgroup analysis based on modeling variables (clinical features, radiomics features, or their combination).

RESULTS

Among 27 articles (16,410 TC patients, 6356 with LNM), the average RQS was 16.5 (SD:5.47). Sensitivity of the models based on clinical features, radiomics features, and radiomics features plus clinical features were 0.64, 0.76 and 0.69. Specificities were 0.77, 0.78 and 0.82. SROC values were 0.76, 0.84 and 0.81.

CONCLUSION

Ultrasound-based radiomics effectively evaluates LNM in TC preoperatively. Adding clinical features does not notably enhance the model's performance. Some radiomics studies showed high bias, possibly due to the absence of standard application guidelines.

CXCR4 promotes migration, invasion, and epithelial-mesenchymal transition of papillary thyroid carcinoma by activating STAT3 signaling pathway

AIMS

Papillary thyroid cancer (PTC) is a serious threat to human health worldwide, while metastasis in the early phase limits therapeutic success and leads to poor survival outcomes. The CXC chemokine receptor type 4 (CXCR4) plays an important role in many cellular movements such as transcriptional modulation, cell skeleton rearrangement, and cell migration, and the change in CXCR4 levels are crucial in various diseases including cancer. In this study, we explored the role of CXCR4 in the migration and invasion of PTC and investigated the potential mechanisms underlying its effects.

SUBJECTS AND METHODS

We analyzed the expression levels of CXCR4 in PTC tissues and cell lines. Would healing migration, Transwell invasion assay in vitro, and tail-vein lung metastasis assay In vivo were performed to evaluated the migration and invasion abilities of PTC cells with stable CXCR4 knockdown or overexpression. Signal transducers and activators of transcription (STAT3) signaling pathway-related protein expressions were examined by Western blotting assays.

RESULTS

The results showed that CXCR4 was highly expressed in PTC cell lines and PTC tissues. CXCR4 knockdown in PTC cells dampened the migration, invasion, and epithelial-mesenchymal transition (EMT), whereas CXCR4 overexpression enhanced these properties. In vivo, we also found that CXCR4 promoted the metastasis of PTC. Mechanistic studies showed that CXCR4 played these vital roles through the STAT3 signaling pathway. Furthermore, PTC patients with high CXCR4 or p-STAT3 expression correlated with aggressive clinical characteristics such as extrathyroidal extension (ETE), and lymph node metastasis (LNM).

CONCLUSIONS

We provided evidence that CXCR4 might activate the STAT3 signaling pathway and further promote PTC development. Thus, CXCR4 might be a novel therapeutic target for PTC.