Clinical value of color Doppler ultrasound combined with serum tumor markers for the diagnosis of medullary thyroid carcinoma

Can ACR TI-RADS predict the malignant risk of medullary thyroid cancer?

Objectives

This study aimed to evaluate the diagnostic performance for medullary thyroid cancer (MTC) based on the 2017 Thyroid Imaging Reporting and Data System by the American College of Radiology (ACR TI-RADS) guideline, and the ability to recommend fine needle aspiration (FNA) for MTC.

Methods

Fifty-six MTCs were included, and 168 benign thyroid nodules (BTNs) and 168 papillary thyroid nodules (PTCs) were matched according to age. Ultrasound (US) features were reviewed according to ACR TI-RADS. US, clinical features and diagnostic performance of cytology of MTC, BTN and PTC were compared. Multivariate logistic regression analysis was performed to assess independent variables to predict MTC.

Results

Multivariate logistic regression showed that position, hypoechoic, AP/T ratio ≥ 0.9 and marked internal blood flow were independent predictors of MTC compared to BTN (P < 0.05) and nodule sizes, AP/T ratio < 1, smooth or ill-defined margin and marked internal blood flow were independent predictors of MTC compared to PTC (P < 0.05). The area under the receiver operating characteristic (ROC) curve (AUC) of MTC based on ACR TI-RADS was inferior to that of PTC (0.687 vs 0.823) (P < 0.001). The recommended rate of FNA for MTC and PTC was 55.4 and 88.7 % respectively. 8 of 14 MTCs with negative FNA results (Bethesda II) had abnormal calcitonin (Ctn) results.

Conclusions

Based on the ACR TI-RADS classification, the malignant risk features of MTC were intermediate between BTN and PTC. The diagnostic efficacy of MTC and FNA recommendation rate were inferior to PTC. Ctn examination would reduce the FNA missed diagnosis of MTC.

Holomics and Artificial Intelligence-Driven Precision Oncology for Medullary Thyroid Carcinoma: Addressing Challenges of a Rare and Aggressive Disease

Background/Objective: Medullary thyroid carcinoma (MTC) is a rare yet aggressive form of thyroid cancer comprising a disproportionate share of thyroid cancer-related mortalities, despite its low prevalence. MTC differs from other differentiated thyroid malignancies due to its heterogeneous nature, presenting complexities in both hereditary and sporadic cases. Traditional management guidelines, which are designed primarily for papillary thyroid carcinoma (PTC), fall short in providing the individualized care required for patients with MTC. In recent years, the sheer volume of data generated from clinical evaluations, radiological imaging, pathological assessments, genetic mutations, and immunological profiles has made it humanly impossible for clinicians to simultaneously analyze and integrate these diverse data streams effectively. This data deluge necessitates the adoption of advanced technologies to assist in decision-making processes. Holomics, which is an integrated approach that combines various omics technologies, along with artificial intelligence (AI), emerges as a powerful solution to address these challenges. Methods: This article reviews how AI-driven precision oncology can enhance the diagnostic workup, staging, risk stratification, management, and follow-up care of patients with MTC by processing vast amounts of complex data quickly and accurately. Articles published in English language and indexed in Pubmed were searched. Results: AI algorithms can identify patterns and correlations that may not be apparent to human clinicians, thereby improving the precision of personalized treatment plans. Moreover, the implementation of AI in the management of MTC enables the collation and synthesis of clinical experiences from across the globe, facilitating a more comprehensive understanding of the disease and its treatment outcomes. Conclusions: The integration of holomics and AI in the management of patients with MTC represents a significant advancement in precision oncology. This innovative approach not only addresses the complexities of a rare and aggressive disease but also paves the way for global collaboration and equitable healthcare solutions, ultimately transforming the landscape of treatment and care of patients with MTC. By leveraging AI and holomics, we can strive toward making personalized healthcare accessible to every individual, regardless of their economic status, thereby improving overall survival rates and quality of life for MTC patients worldwide. This global approach aligns with the United Nations Sustainable Development Goal 3, which aims to ensure healthy lives and promote well-being at all ages.

Exploration of the prognostic value of methylation regulators related to m5C in papillary thyroid carcinoma

The incidence of papillary thyroid carcinoma (PTC) has increased significantly in recent years, and for patients with metastatic and recurrent PTC, the options for treatment currently available are insufficient. To date, the exact molecular mechanism underlying PTC is still not fully understood. 5-Methylcytosine (m5C) RNA methylation is associated with the prognosis of a variety of tumors. However, the molecular mechanisms and biomarkers associated with m5C in the diagnosis, treatment, and prognosis of this disease have not been fully elucidated. Ten m5C regulators with significantly different expression levels were included in this study. Immune infiltration analysis revealed significant negative correlations between most of these regulators and regulatory T cells. TRDMT1, NSUN5, and NSUN6 had high weights and strong correlations in the protein-protein interaction network. Using gene ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis, 1489 differentially expressed genes were screened from The Cancer Genome Atlas messenger RNA matrix, indicating that these differentially expressed genes were significantly enriched in various pathways and functions related to cancers. Four m5C regulators, NSUN2, NSUN4, NSUN6, and DNMT3B, were screened as prognostic markers by least absolute shrinkage and selection operator regression analysis, and NSUN2 and NSUN6 were identified as risk factors for poor prognosis. We found that the prognostic prediction model constructed using the m5C regulators NSUN2, NSUN4, NSUN6, and DNMT3B showed good prognostic prediction ability and diagnostic ability. This model was applied to predict the survival probability of patients with PTC, the prediction ability of 5-year survival was the best. The multi-factor prognostic prediction model combined with the tumor node metastasis stage and risk score grouping showed better prognostic predictive power.

The value of color Doppler ultrasonography combined with serum tumor markers in differential diagnosis of gastric stromal tumor and gastric cancer

This study aimed to explore the value of color Doppler ultrasonography combined with carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) in differential diagnosis of gastric stromal tumor (GST) and gastric cancer (GC). An analysis of the clinical data of 180 patients with clinically suspected gastric space occupying lesions. According to the postoperative pathological results, 180 suspected gastric space-occupying lesion patients were divided into GST group (n = 83) and GC group (n = 97). Color Doppler ultrasonography, serum tumor markers CEA and CA19-9 were compared. The research results showed that serum CEA and CA19-9 levels were lower in patients with GST group than those with GC group (both P < 0.001). With postoperative pathology as the gold standard, detection rates of GST and GC by combination of color Doppler ultrasound (CDUS), serum CEA, and CA19-9 were higher than those of each index alone (both P < 0.001). There was no difference between detection rates of GST and GC by combination of CDUS, serum CEA, and CA19-9 (P = 0.058). Color Doppler ultrasonography combined with serum tumor markers CEA and CA19-9 tests has a certain differential diagnostic value for GST and GC, which may provide a reliable reference basis for clinical diagnosis and treatment.

Update on the Diagnosis and Management of Medullary Thyroid Cancer: What Has Changed in Recent Years?

Medullary thyroid carcinoma (MTC) is a neoplasm originating from parafollicular C cells. MTC is a rare disease, but its prognosis is less favorable than that of well-differentiated thyroid cancers. To improve the prognosis of patients with MTC, early diagnosis and prompt therapeutic management are crucial. In the following paper, recent advances in laboratory and imaging diagnostics and also pharmacological and surgical therapies of MTC are discussed. Currently, a thriving direction of development for laboratory diagnostics is immunohistochemistry. The primary imaging modality in the diagnosis of MTC is the ultrasound, but opportunities for development are seen primarily in nuclear medicine techniques. Surgical management is the primary method of treating MTCs. There are numerous publications concerning the stratification of particular lymph node compartments for removal. With the introduction of more effective methods of intraoperative parathyroid identification, the complication rate of surgical treatment may be reduced. The currently used pharmacotherapy is characterized by high toxicity. Moreover, the main limitation of current pharmacotherapy is the development of drug resistance. Currently, there is ongoing research on the use of tyrosine kinase inhibitors (TKIs), highly specific RET inhibitors, radiotherapy and immunotherapy. These new therapies may improve the prognosis of patients with MTCs.

Radiomics Features of Different Sizes of Medullary Thyroid Carcinoma (MTC) and Papillary Thyroid Carcinoma (PTC) Tumors: A Comparative Study

Background:

Radiomics strategies exhibit great promise in the context of thyroid nodule diagnosis. This study aimed to compare radiomics features of different sizes of medullary thyroid carcinoma (MTC) and papillary thyroid carcinoma (PTC) tumors and to compare the efficiency of radiomics approaches as a means of differentiating between these tumor types.

Methods:

In total, 86 MTC and 330 PTC nodules were divided into the macronodular (>10 mm) and micronodular (⩽10 mm) categories. The radiomics features of these nodules were analyzed to identify independent prognosis factors and evaluate the efficacy of individual and combined indicators as predictors of tumor type.

Results:

In total, 12 radiomics features were found to differ significantly between MTC and PTC macronodules, while 6 differed significantly between MTC and PTC micronodules. Shape 2D_Sphericity, firstorder_Skewness, glrlm_RunLengthNonUniformity, glszm_GrayLevelNonUniformity, and glszm_SizeZoneNonUniformity were features that were independently associated with the differential diagnoses of MTC and PTC macronodules. Receiver operating characteristic (ROC) curve analyses of the efficacy of these 5 single indicators and a combined indicator composed thereof yielded area under the curve (AUC) values of 0.621, 0.678, 0.704, 0.762, 0.747, and 0.824, respectively, with respective sensitivities of 55.3%, 43.0%, 53.1%, 56.3%, 46.9%, and 65.6%, and respective specificity values of 65.6%, 89.1%, 81.6%, 88.8%, 95.0%, and 91.1%. The glrlm_RunEntropy and glszm_SizeZoneNonUniformity features were identified as independent factors associated with the differential diagnoses of MTC and PTC micronodules. Receiver operating characteristic curve analyses of the efficacy of these 2 single indicators and a combined indicator composed thereof yielded respective AUC values of 0.678, 0.678, and 0.771; Sensitivities of 57.0%, 72.7%, and 72.7%; and specificities of 77.3%, 64.2%, and 77.5%.

Conclusions:

A range of different radiomics features can enable effective differentiation between MTC and PTC nodules of different sizes. Moreover, analyses of combinations of radiomics features yielded diagnostic efficiency values higher than those associated with single radiomics features, highlighting a more reliable approach to diagnosing MTC and PTC tumors.

Preoperative Serum Calcitonin Level and Ultrasonographic Characteristics Predict the Risk of Metastatic Medullary Thyroid Carcinoma: Functional Analysis of Calcitonin-Related Genes

Background. Early cervical lymph node (LN) metastasis is an important cause of poor survival in patients with medullary thyroid cancer (MTC). This study evaluated whether the preoperative serum calcitonin level in combination with ultrasonographic features of MTC can be used to assess the LN status as well as predict the risk of metastasis in patients with MTC. Methods. We retrospectively analyzed the clinical data of 95 patients with MTC, and a nomogram model was constructed and validated. Using integrated database analysis of The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx), we mined pathways wherein CALCA is involved, identified calcitonin-related genes, and analyzed their functions. Results. Correlation analysis revealed a significant association between the infiltrating range, diameter, calcification, blood flow, the preoperative serum calcitonin level, and metastasis. The metastasis risk-prediction model showed great accuracy in determining the risk of metastasis in MTC (area under the curve of the receiver operating characteristic [ROC] curve: 0.979 [95% confidence interval 0.946–1.000]). Decision curve analysis (DCA) showed that the model has excellent clinical utilization potential. Significantly, CALCA, the mRNA for calcitonin, was highly expressed in thyroid cancer tissues and associated with the cytokine–cytokine receptor and neuroactive ligand-receptor interaction pathways as well as the cell-adhesion molecules. ROC curve indicated that the CNTFR, CD27, GDF6, and TSLP genes, which are related to the cytokine–cytokine receptor interaction pathway, could indicate the risk of metastasis in MTC. Conclusions. The preoperative serum calcitonin level, in combination with ultrasonographic features, can be used to predict the risk of metastasis in patients with MTC and constitute a noninvasive accurate method for preoperative diagnosis of MTC.

Inorganic Nanomaterial for Biomedical Imaging of Brain Diseases

In the past few decades, brain diseases have taken a heavy toll on human health and social systems. Magnetic resonance imaging (MRI), photoacoustic imaging (PA), computed tomography (CT), and other imaging modes play important roles in disease prevention and treatment. However, the disadvantages of traditional imaging mode, such as long imaging time and large noise, limit the effective diagnosis of diseases, and reduce the precision treatment of diseases. The ever-growing applications of inorganic nanomaterials in biomedicine provide an exciting way to develop novel imaging systems. Moreover, these nanomaterials with special physicochemical characteristics can be modified by surface modification or combined with functional materials to improve targeting in different diseases of the brain to achieve accurate imaging of disease regions. This article reviews the potential applications of different types of inorganic nanomaterials in vivo imaging and in vitro detection of different brain disease models in recent years. In addition, the future trends, opportunities, and disadvantages of inorganic nanomaterials in the application of brain diseases are also discussed. Additionally, recommendations for improving the sensitivity and accuracy of inorganic nanomaterials in screening/diagnosis of brain diseases.