Predicting Thyroid Cancer Research Articles

The impact of Hashimoto’s thyroiditis on the diagnostic utility of P63 and CK19 immunohistochemistry markers in predicting thyroid cancer

Background: The incidence of thyroid cancer (TC) and Hashimoto’s thyroiditis (HT) is increasing worldwide. Coexisting HT adds to diagnostic confusion in establishing TC on histopathological examination (HPE). Studies have shown the promising role of immunohistochemistry markers in predicting cancer, but the impact of coexisting HT is unclear. This prospective study determined the impact of HT on the diagnostic utility of p63 and CK19 in predicting thyroid cancer. Methods: Out of 103 patients undergoing total thyroidectomy for benign or malignant thyroid nodules, 31 patients (mean age=39.7 years, Male:Female=4:27) with elevated thyroid autoantibodies were studied. HPE of formalin-fixed paraffin-embedded tissue from surgical specimens confirmed the final diagnosis. Sections 2-4μ were stained for immunohistochemistry using the standard avidin-biotin complex method with antibodies against P63 and CK19. Expression in 10% or more of neoplastic cells qualified as positive while expression in less than 10% was considered negative. Receiver Operating Characteristic (ROC) assessed the diagnostic accuracy. Results: Histopathology comprised 58.1% benign and 41.9% malignant lesions. CK19 exhibited membranous expression in 87.1%, while p63 exhibited focal nuclear expression in 35.5% of cases. In ROC analysis predicting TC for entire cohort, the area under curve (AUC) of P63 was 0.6 and CK19 was 0.532. In subgroup with HT, the AUC of p63 was 0.8. Each P>0.05. Conclusions: P63 expression had better predictability for thyroid cancer with co-existent Hashimoto’s thyroiditis but was not statistically significant. Whereas, CK19 was non-specific and unreliable. Histomorphological features on HPE are the gold standard for diagnostic decisions.

The feasibility of using a multivariate regression model incorporating ultrasound findings and serum markers to predict thyroid cancer metastasis.

This study aimed to assess the viability of a multivariate regression model utilizing ultrasound findings and serum markers for predicting thyroid cancer metastasis. A retrospective analysis of 98 thyroid patients admitted from January 2022 to October 2022 was conducted to categorize them into a metastasis group (n=20) and a non-metastasis group (n=78) based on postoperative pathological results. Both groups underwent ultrasound examination and serum marker testing. Correlative analysis was performed to explore the association between various indicators and thyroid cancer metastasis. A multivariate regression model was developed, and receiver operating characteristic (ROC) curves were used to assess the predictive value of ultrasound findings, serum markers, and their combination for thyroid cancer metastasis. Statistically significant differences were found in the levels of ultrasound findings and serum markers between the two groups. Nodule boundaries, presence or absence of halos, margins, lobulation, capsular invasion, surface smoothness, nodule aspect ratio, uric acid, total cholesterol, triglyceride, and LDL cholesterol levels were predictors of metastasis in thyroid cancer. The AUC value of 0.950 for the prediction of thyroid cancer metastasis by ultrasound signs combined with serologic indicators was significantly higher than 0.728 and 0.711 predicted by ultrasound signs or serologic indicators alone. The multivariate regression model incorporating ultrasound findings and serum markers enhances the predictive accuracy for thyroid cancer metastasis, offering essential guidance for early prediction and intervention in a clinical setting.

Predicting thyroid cancer ablation success: Pre-ablative 99mTc-Pertechnetate and post-ablative 131I scan comparison

IntroductionThe purpose of this study was to evaluate the relationship between pre-ablative 99mTc-pertechnetate scintigraphy, and therapeutic iodine-131 (131I) whole-body scan (TxWBS), with ablation status and to investigate the possible predictive factors for successful ablation in differentiated thyroid carcinoma (DTC) patients. Materials and methodsA total of 330 DTC patients underwent 99mTc-pertechnetate scintigraphy after thyroidectomy. Uptake values were determined using the region-of-interest technique. All patients had neck ultrasonography and TxWBS. Both scintigraphic scans were interpreted visually and qualitatively. The ablation status was evaluated with a diagnostic 131I whole-body scan. ResultsThe success rate for residual thyroid ablation was 88.8%. The sensitivity, specificity, accuracy, PPV, and NPV of 99mTc-Pertechnetate scintigraphy were 82.4%, 87.5%, 82.7%, 99.2%, and 20.2%, respectively. Pre-ablative TG values and 99mTc uptake (%) were significantly lower in successfully ablated patients. The 99mTc uptake was determined as a significant predictive factor for ablation success (P=0.000). The optimal 99mTc uptake cut-off value of 0.75% was demonstrated for successful ablation. There were significant positive correlations between the visual and the calculated uptake (%) of residual tissues on both scintigraphic scans, Considering the number of remnant tissue foci, significant positive correlations were found between 99mTc-Pertechnetate scintigraphy, TxWBS, and USG. ConclusionPre-ablative 99mTc-Pertechnetate uptake (%) value of the remnant tissue can predict the ablation status in DTC patients. 99mTc-Pertechnetate scintigraphy, which is an easily applicable and accessible imaging method, has maintained its place in the postoperative and pre-ablative period in DTC patients over the years and has not lost any of its value.

Thyroid Cancer Recurrence: Risk Factors and Preventive Strategies

<i>Introduction</i>: Several risk factors influence the recurrence of thyroid cancer, including age, tumor size, lymph node involvement, and other prognostic indicators. The purpose of this study is to evaluate the impact of these risk factors on the recurrence rates of thyroid cancer, with the aim of improving diagnostic and treatment strategies for better patient outcomes. <i>Aim of the study</i>: The aim of the study was to evaluate risk factors for thyroid cancer recurrence and preventive strategies. <i>Methods</i>: This retrospective cohort study conducted at the Department of Otolaryngology-Head & Neck Surgery at Bangabandhu Sheikh Mujib Medical University (BSMMU) included 108 thyroid cancer patients treated between April 2023 and March 2024. Patients were categorized into low-risk and high-risk groups based on prognostic factors. Follow-up for three years assessed recurrence and survival through outpatient visits and telephone interviews. Statistical analyses using SPSS version 22.0 included descriptive statistics, chi-square tests for recurrence rates, and Kaplan-Meier survival analysis. Result: The study identified Differentiated Thyroid Carcinoma (DTC) as the most common type, with Papillary Thyroid Carcinoma being the predominant subtype. Recurrence rates were notably higher in patients aged ≥45 and in males. Significant predictors of recurrence included age, gender, tumor diameter, and lymph node metastasis. High-risk patients mostly received Total Thyroidectomy, Radioiodine ablation, and Thyroxin. Follow-up showed recurrence rates of 8.33% in low-risk and 7.55% in high-risk patients over three years. <i>Conclusion</i>: The study highlights the critical role of age, gender, tumor diameter, and lymph node metastasis in predicting thyroid cancer recurrence, emphasizing the need for personalized treatment and monitoring based on risk levels.

Thyroid cancer trends in China and its comparative analysis with G20 countries: Projections for 2020-2040.

Thyroid cancer, a leading type of endocrine cancer, accounts for 3-4% of all cancer diagnoses. This study aims to analyse and compare thyroid cancer patterns in China and the Group twenty (G20) countries, and predict these trend for the upcoming two decades. This observational longitudinal study utilised data from the Global Burden of Disease (GBD) study 2019. We used metrics including incidence, mortality, mortality-incidence ratio (MIR), age-standardised rate (ASR) and average annual percent change (AAPC) to examine thyroid cancer trends. Joinpoint regression analysis was used to identify periods manifesting notable changes. The association between sociodemographic index (SDI) and AAPC were investigated. The autoregressive integrated moving average (ARIMA) model was used to predict thyroid cancer trends from 2020 to 2040. From 1990 to 2019, thyroid cancer incidence cases in China increased by 289.6%, with a higher AAPC of age-standardised incidence rate (ASIR) in men. Contrastingly, the G20 demonstrated a smaller increase, particularly among women over 50. Despite the overall age-standardised mortality rate (ASMR) was higher in the G20, the increase in mortality was less pronounced than in China. Age-standardised incidence rate increased across all age groups and genders, with a notable rise among men aged 15-49. ASMR decreased in specific age groups and genders, especially among women. Conversely, the ASMR significantly increased in group aged over 70. The MIR exhibited a declining trend, but this decrease was less noticeable in men and the group aged over 70. Joinpoint analysis pinpointed significant shifts in overall ASIR and ASMR, with the most pronounced increase in ASIR during 2003-2011 in China and 2003-2010 in the G20. Predictions suggested a continual ASIR uptrend, especially in the 50-69 age group, coupled with a predicted ASMR downturn among the elderly by 2040. Moreover, the proportion of thyroid cancer deaths attributable to high body mass index (BMI) escalated, with significant increase in Saudi Arabia and a rise to 7.4% in China in 2019. Thyroid cancer cases in incidence and mortality are escalating in both China and the G20. The increasing trend may be attributed to factors beyond overdiagnosis, including environmental and genetic factors. These findings emphasise the necessity for augmenting prevention, control, and treatment strategies. They also highlight the significance of international collaboration in addressing the global challenge posed by thyroid cancer.

Artificial Intelligence & Machine Learning Models to Predict Thyroid Cancer during Pregnancy: A Review

Artificial Intelligence & Machine Learning Models to Predict Thyroid Cancer during Pregnancy: A Review

Investigating the Association of Polygenic Risk Scores With Thyroid Cancer Susceptibility in a Han Chinese Population.

Thyroid cancer, the leading endocrine tumor with a rising global incidence, especially in women, is influenced by both genetic and environmental factors. This study examines the relationship between polygenic risk scores (PRS) and thyroid cancer susceptibility in the Han Chinese population, as well as the impact of genetic variants on clinical outcomes. Analyzing data from 57 257 participants in the Taiwan Precision Medicine Initiative, the study employed the Affymetrix Genome-Wide TWB 2.0 SNP Array for genotyping. PRS were calculated using single nucleotide polymorphisms (SNPs) from prior genome-wide association studies, specifically PGS000087 and PGS000797, and correlated with clinical parameters like age, sex, comorbidities, and treatment methods. Among 4063 participants with thyroid tumors (839 malignant, 3224 benign), higher PRS quartiles correlated significantly with increased thyroid cancer incidence. The highest quartile showed a 1.15-fold (PGS000797) and 1.14-fold (PGS000087) greater risk than the lowest quartile. Key findings included an association between higher PRS quartiles and younger onset age, along with a notable link to chronic kidney disease and thyroid hormone levels in specific SNPs. The study demonstrates PRS's utility in predicting thyroid cancer risk in the Han Chinese population, with higher PRS associated with increased risk and distinct clinical features. While this study focuses on the Han Chinese population, we recognize the importance of comparing PRS performance across different ancestries to fully understand ethnic genetic diversity in cancer risk assessment. Future studies should aim to include such comparative analysis.

Prediction of thyroid cancer recurrence with machine learning models

Background: Thyroid cancer recurrence poses a significant challenge in oncology, necessitating effective tools for early prediction. Machine learning models offer the potential to improve prognostic accuracy and guide clinical decision-making. Aim and Objective: This study aims to investigate the efficacy of machine learning models in predicting thyroid cancer recurrence using a publicly available dataset comprising 17 features. Methods: We explored multiple machine learning algorithms, including Logistic Regression, K-Nearest Neighbors (KNN), Random Forest, and AdaBoost, to develop predictive models. The target variable was the "Recurred" column, indicating whether a patient experienced recurrence. Performance evaluation was conducted using metrics such as Accuracy, Precision, Recall, F1 Score, and ROC AUC. A correlation heatmap was generated to assess relationships between features and detect multicollinearity, while feature importance analysis using the Random Forest model identified key predictors. Results: Among the models, the Random Forest classifier achieved the highest performance on the test dataset, with an Accuracy of 0.9818, Precision of 0.9623, Recall of 1.0000, F1 Score of 0.9808, and ROC AUC of 0.9831. The feature importance analysis highlighted critical factors influencing recurrence prediction, while the correlation heatmap provided insights into feature interactions. Conclusion: This study demonstrates the effectiveness of machine learning models, particularly the Random Forest classifier, in predicting thyroid cancer recurrence. The insights gained from feature analysis and correlation studies contribute to model interpretability and future feature selection strategies. These findings emphasize the potential of machine learning in improving patient outcomes through early and accurate recurrence prediction.

Prognosis and therapy in thyroid cancer by gene signatures related to natural killer cells.

Natural killer (NK) cells are crucial to cancer development and prognosis. However, the role of NK cell-related genes in immunotherapy and the tumor immune microenvironment (TIME) is not well understood. This study aimed to develop reliable risk signatures associated with NK cell-related genes for predicting thyroid cancer (THCA). The single-cell RNA sequencing (scRNA-seq) data from seven THCA samples (GSE184362) and bulk-RNA-seq data of 502 THCA patients (TCGA-THCA) were included. The scRNA-seq data was analyzed using the "Seurat" R package to identify differentially expressed genes in NK cells. The clustering analysis was carried out using the R package "ConsensusClusterPlus". The gene set variation analysis (GSVA) algorithm was applied to assess the variations in biological pathways among subtypes. The ESTIMATE algorithm was utilized to calculate the scores for stromal, immune and estimate variables. In addition, we used the single sample Gene Set Enrichment Analysis and CIBERSORT algorithms to assess the degree to which immune cells and pathways related to immunity were enriched based on the meta-cohort. In the TCGA-THCA cohort, the "glmnet" R package was used for the gene selection, and LASSO Cox analysis was used to construct prognostic features. The "maftools" R package was used to examine the somatic mutation landscape of THCA in both low- and high-risk groups. One-hundred and eighty-five NK cell marker genes were screened, and nine genes were associated with the THCA prognosis. KLF2, OSTF1 and TAPBP were finally identified and constructed a risk signature with significant prognostic value. KLF2 and OSTF1 were protective genes, and TAPBP was a risk gene. Patients at high risk had a considerably lower overall survival compared with those at low risk. Mutations in the TCGA-THCA cohort were predominantly C > T. Increased tumor mutation burden (TMB) levels were linked to overall survival. The low-risk H-TMB+ group had a better prognosis, while the high-risk L-TMB+ group had the worst prognosis. Natural killer cell-related genes KLF2, OSTF1 and TAPBP were used to develop a novel prognostic risk signature, offering a new perspective on the prognosis and treatment of THCA.

Clinical value of grayscale ultrasound combined with real-time shear wave elastography nomogram in risk prediction of thyroid cancer

ObjectivesThis study constructed a nomogram based on grayscale ultrasound features and real-time shear wave elastography (SWE) parameters to predict thyroid cancer.MethodsClinical data of 217 thyroid nodules of 201 patients who underwent grayscale ultrasound, real-time SWE, and thyroid function laboratory examination in Ma’anshan People’s Hospital from January 2019 to December 2020 were retrospectively analyzed. The subjects were divided into a benign nodule group (106 nodules) and a malignant nodule group (111 nodules). The differences in grayscale ultrasound features, quantitative parameters of real-time SWE, and laboratory results of thyroid function between benign and malignant thyroid nodules were analyzed. We used a chi-square test for categorical variables and a t-test for continuous variables. Then, the independent risk factors for thyroid cancer were analyzed using multivariate logistic regression. Based on the independent risk factors, a nomogram for predicting thyroid cancer risk was constructed using the RMS package of the R software.ResultsMultivariate logistic regression showed that the grayscale ultrasound features of thyroid nodules were the shape, margin, echogenicity, and echogenic foci of the nodules,the maximum Young’s modulus (SWE-max) of thyroid nodules, and the ratio of thyroid nodule and peripheral gland (SWE-ratio) measured by real-time SWE were independent risk factors for thyroid cancer (all p < 0.05), and the other variables had no statistical difference (p > 0.05). Based on the shape (OR = 5.160, 95% CI: 2.252–11.825), the margin (OR = 9.647, 95% CI: 2.048–45.443), the echogenicity (OR = 6.512, 95% CI: 1.729–24.524), the echogenic foci (OR = 2.049, 95% CI: 1.118–3.756), and the maximum Young’s modulus (SWE-max) (OR = 1.296, 95% CI: 1.140–1.473), the SWE-ratio (OR = 2.001, 95% CI: 1.403–2.854) of the thyroid nodule to peripheral gland was used to establish the related nomogram prediction model. The bootstrap self-sampling method was used to verify the model. The consistency index (C-index) was 0.979, ROC curve was used to analyze the nomogram scores of all patients, and the AUC of nomogram prediction of thyroid cancer was 0.976, indicating that the nomogram model had high accuracy in the risk prediction of thyroid cancer.ConclusionsThe nomogram model of grayscale ultrasound features combined with SWE parameters can accurately predict thyroid cancer.

Machine Learning Approach for Thyroid Cancer Diagnosis Using Clinical Data

Objective: With an early diagnosis of thyroid cancer, one of the world's most significant health issues, it is feasible to treat the nodules before the spread of malignant thyroid gland cells. It has become crucial to develop models for predicting thyroid cancer. In light of this, the purpose of this study is to develop a clinical decision support model using the Bagged CART model, a machine learning (ML) model for the prediction of thyroid cancer. Methods: Between 2010 and 2012, 724 patients who applied to China Median University Shengjing Hospital comprised the study's data set. The dataset comprises information on nodule malignancies, demographic characteristics, ultrasound characteristics, and blood test results for all patients who underwent thyroidectomy. Using this open-access data set, the Bagged CART modeling technique was applied. Negative predictive value (NPV), specificity (Spe), balanced accuracy (BACC), positive predictive value (PPV), accuracy (ACC), sensitivity (Sen), and F1-score performance metrics were used to evaluate the model's predictive performance. In addition, a 10-fold cross-validation method was used to determine the validity of the model. In addition, variable importance was established, which reveals how much the input variables impact the output variable. Results: ACC, BACC, Sen, Spe, PPV, NPV, and F1-score obtained from the model performance metrics were calculated to 99.1%, 98.7%, 99.7%, 97.7%, 99.1%, 99.2%, and 99.4%, respectively, as a result of modeling. According to the variable importance values that were acquired for the input variables in the dataset that was investigated in this study, the seven variable that hold the greatest significance are as follows: size, TSH, blood flow: size, TSH, blood flow: enriched, multilateral: yes, FT4, site: isthmus, and age, in that order. Conclusion: As a result, the Bagged CART model was found to be effective at predicting thyroid cancer based on the findings of this study. In addition, in this study, risk factors for thyroid cancer were evaluated and their importance values were given. With these results, the decision-making process about the disease will be able to accelerate and thus, it will be able to effective in preventive medicine practices.

Deep Learning Approach for Analyzing and Predicting Thyroid Cancer in Computed Tomography Images: Review

Deep Learning Approach for Analyzing and Predicting Thyroid Cancer in Computed Tomography Images: Review

OSTEOCALCIFICATIONS OF THYROID NODULES

According to the world statistics, when performing thyroid ultrasound, calcifications are detected in 20-33% of nodules of various structures. In recent years, there has been a sharp increase in the number of publications devoted to the assessment of this phenomenon, which is the basis for their study and development of their own approach. The aim is to generalize and systematize the materials available in available scientific Russian and foreign publications on the features of the structure and distribution of calcifications in nodular thyroid formations and on the opportunities of ultrasound examination in their detection and identification from the position of oncological risk. Materials and methods. To achieve the aim, methods of comparison, generalization, analysis, synthesis, grouping of classifications and systematization of data contained in publications of domestic and foreign authors were used. Results. Thyroid calcifications were previously rarely detected at the stage of preoperative examination of patients. With the widespread introduction of ultrasound into practice, such "findings" have become frequent. Despite a large number of patients with thyroid calcifications, attempts have not been made to seriously analyze and systematize this ultrasound sign, although some scientists consider them predictors of thyroid cancer. Attempts to seriously study the composition of calcifications in various nodules of the thyroid gland are occasional; they have not yielded practical results yet. Separation of calcium-containing deposits in thyroid nodules into microcalcifications and macrocalcifications significantly increased the diagnostic weight of the first group, which caused the predominant place of the sign "microcalcifications" in papillary thyroid cancer. There is no consensus on macrocalcifications. Various variants of their structure and the duration of their formation do not yet allow them to be adequately systematized from the perspective of predicting the diagnosis. Conclusions. According to the majority of the authors of the sources studied, calcium-containing deposits in the nodules of the thyroid gland indicate a severe irreversible restructuring of tissues with loss of their ability to regulate mineral metabolism. With the help of ultrasound, it is possible to not only get a descriptive presentation of calcifications, but to track their change over time as well.

Thyroid Cancer Detection

Abstract: In the recent world of Digital and Technology,the level of disease rate also increases with time. According to the World Health Organization, the second most common endocrine disorder in the world is relatedto thyroid gland diseases which is next to diabetes. Hypothyroidism or hyperthyroidism is a major issue in India which rises due to non-functional thyroid hormones. This study proposes a machine learning framework to predict thyroid cancer based on our collected clinical dataset. The ten-fold cross-validation, bootstrap analysis, and permutation predictor importance were applied to estimate and interpret the model performance under uncertainty. The comparison between model prediction and expert assessment showsthe advantage of our framework over human judgment in predicting thyroid cancer. Our method is accurate, interpretable, and thus useable as additional evidence in the preoperative diagnosis of thyroid cancer.This diagnosis of Thyroid Cancer is very monotonous and tough tasks at early steps with accuracy. The Accuracy prediction can be done through various Machine Learning Algorithms.

Double Negative T Cell Proportion of CD3+ Cells Present in the Thyroid Microenvironment is an Immunogenomic Marker for Predicting Thyroid Cancer

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Identification of novel immune-related molecular subtypes and a prognosis model to predict thyroid cancer prognosis and drug resistance.

Background: Thyroid cancer is a common malignant tumor of the endocrine system that has shown increased incidence in recent decades. We explored the relationship between tumor-infiltrating immune cell classification and the prognosis of thyroid carcinoma. Methods: RNA-seq, SNV, copy number variance (CNV), and methylation data for thyroid cancer were downloaded from the TCGA dataset. ssGSEA was used to calculate pathway scores. Clustering was conducted using ConsensusClusterPlus. Immune infiltration was assessed using ESTIMATE and CIBERSORT. CNV and methylation were determined using GISTIC2 and the KNN algorithm. Immunotherapy was predicted based on TIDE analysis. Results: Three molecular subtypes (Immune-enrich(E), Stromal-enrich(E), and Immune-deprived(D)) were identified based on 15 pathways and the corresponding genes. Samples in Immune-E showed higher immune infiltration, while those in Immune-D showed increased tumor mutation burden (TMB) and mutations in tumor driver genes. Finally, Immune-E showed higher CDH1 methylation, higher progression-free survival (PFS), higher suitability for immunotherapy, and higher sensitivity to small-molecule chemotherapeutic drugs. Additionally, an immune score (IMScore) based on four genes was constructed, in which the low group showed better survival outcome, which was validated in 30 cancers. Compared to the TIDE score, the IMScore showed better predictive ability. Conclusion: This study constructed a prognostic evaluation model and molecular subtype system of immune-related genes to predict the thyroid cancer prognosis of patients. Moreover, the interaction network between immune genes may play a role by affecting the biological function of immune cells in the tumor microenvironment.

Predicting coexisting thyroid cancer with primary hyperparathyroidism in an endemic region of multinodular goiter: evaluating the effectiveness of preoperative inflammatory markers.

The aim is to examine the efficacy of inflammatory indicators to predict thyroid cancer in patients with primary hyperparathyroidism (PHPT) in an endemic region of nodular goiter. The prospective database was reviewed to identify patients operated on with the diagnosis of PHPT and thyroid disease between April 2015 and June 2021. Permanent pathologic reports were used as the gold standard for diagnosis. Detailed imaging data with peripheral blood inflammation indices were analyzed to assess their predictive values for concomitant PHPT with thyroid cancer. Postoperative complications and the duration of hospitalization were also reviewed. Thyroid malignancy accompanying PHPT was found in 13 patients (26.0%) out of 50 who had concurrent surgery. The analysis regarding inflammatory indexes revealed nothing significant between thyroid cancer and preoperative blood biochemistry (P > 0.05). In the concurrent surgery group, recurrent laryngeal nerve injury was observed in 1 patient (2.0%) and the mean hospital stay was longer. In endemic regions of nodular thyroid disease, thyroid cancer might accompany PHPT. The value of inflammatory indexes to predict thyroid malignancy in PHPT is controversial and should not be employed in the surgical decision-making process.

Strain Versus 2D Shear-Wave Elastography Parameters—Which Score Better in Predicting Thyroid Cancer?

The aim of this study is to assess the diagnostic performance of strain elastography (SE) versus 2D shear-wave elastography (2D-SWE) by providing a head-to-head comparison of the two methods. Ninety-four thyroid nodules were evaluated using conventional ultrasound (B-mode) and SE, namely, real-time elastography (RTE) with a Hitachi Preirus machine (Hitachi Inc., Tokyo, Japan) and consecutively, 2D-SWE with SuperSonic Mach30 equipment (Supersonic Imagine, Aix-en-Provence, France). The results were compared in all cases to the pathology reports. Out of the 94 nodules, 29 (30.9%) were malignant. Both SE and 2D-SWE parameters proved to have excellent diagnostic quality, with comparable results. The mean elasticity index was the best parameter for the 2D-SWE (AUC 0.912); for a cut-off value of 30.5 kPa, it predicts thyroid malignancy with a sensitivity of 79.3%, specificity of 95.38%, NPV of 91.2% and PPV of 88.5%. The best parameter for SE was the strain ratio (cutoff &gt; 3.9; sensitivity 82.7%; specificity 92.3%; AUC 0.905). When integrated in the ultrasound risk algorithm, both elastography methods improved the diagnostic performance: AUC 0.764 vs. 0.886 vs. 0.861 for B-modes: B-mode + 2D-SWE vs. B-mode + SE. We concluded that elastography adds diagnostic value in predicting malignancy, both when Hitachi RTE and SuperSonic 2D-SWE were used.

Thyroglobulin Levels as a Predictor of Papillary Cancer Recurrence After Thyroid Lobectomy.

The effectiveness of using serum thyroglobulin (TG) to predict thyroid cancer recurrence after a thyroid lobectomy is unknown. This study aimed to evaluate the predictive nature of serum TG and TG trends after thyroid lobectomies. We analyzed 514 papillary thyroid cancer (PTC) patients. The pre-, low-, high, and last-TG levels were reviewed and stratified into three groups. An ascending TG trend was defined if the last-TG level was more than 200% as high as the value of low-TG level. A descending trend was defined if the last-TG level decreased by more than 50% and a flat trend as between them. During a median follow-up period of 73.0 months, there were 21 (4.1%) recurrences. Most patients showed a descending (54.1%) or flat (35.6%) TG trend, but 10.3% of patients showed an ascending TG trend. Overall and lateral recurrences were significantly higher in the ascending TG group, indicating that an ascending TG trend was a good predictor for recurrence. Other factors such as positive node ratio (PNR), patient risk, age, and sex were not significant risk factors. In a Kaplan-Meier analysis, ascending TG trend was a good predictor of lateral recurrence. TG levels as a predictor of papillary cancer recurrence after thyroid lobectomy were found to be poor classifiers, and optimal cut-off values were not verified. An ascending TG trend was a good predictor of lateral recurrence. Further studies are warranted to investigate whether an ascending TG trend was due to an incomplete thyroid lobectomy or if the cancer had already metastasized to the lateral compartment.

Identification of the collagen family as prognostic biomarkers in papillary thyroid carcinoma

The aim of this study was to construct a collagen-related prognostic model for thyroid cancer and to investigate prognostic value of collagen family genes for thyroid cancer. A LASSO Cox regression model for thyroid cancer was developed based on the expression profiles of collagen-related genes. Kaplan-Meier survival analysis was performed for high and low risk groups. The ROC method was used to assess its predictive performance. Predictive independence was verified by multivariate Cox regression analysis. The relationship between this feature and immune cell infiltration was analyzed by tumor microenvironment. COL18A1 was validated by immunohistochemistry and RT-PCR in thyroid cancer tissues. The effect of COL18A1 on cell proliferation, migration and invasion ability of tumor cells were further valuated by CCK-8 assay and transwell assay. The effect of COL18A1 on the immune escape ability of tumor cells was further valuated by cytotoxicity assays. A model including 4 collagen family genes was developed to predict thyroid cancer prognosis. Patients with high-risk score had a poorer prognosis than those with low-risk scores for 1-, 2-, 3-, and 5- year survival. The model independently predicted prognosis after adjusting for other prognostic factors. A nomogram combining risk score and age was constructed with high sensitivity and specificity. This feature was significantly associated with immune cell infiltration. COL18A1 was aberrantly over-expressed in thyroid cancer compared with control tissues and significantly increased proliferative capacity, migration capacity, invasion capacity, and immune escape ability of tumor cells. Our findings establish a signature associated with collagen family genes that can be a promising tool to predict the prognosis of thyroid cancer. High COL18A1 expression significantly correlates with the poor prognosis of patients and enhances the immune escape ability of tumor cells.