Clinical implications of TPO and AOX1 in pediatric papillary thyroid carcinoma

The clinical and histopathological characteristics of thyroid gland diseases in adolescents requiring surgical treatment: a ten-year follow-up study

The present research aimed to determine the clinical and pathohistological characteristics of thyroid gland diseases in adolescents who were previously surgically treated at the Endocrine Surgery Center of the University Medical Center of Serbia from 01/01/2001 to 01/01/2011. The study covered 170 patients of both sexes from the population of adolescents (aged 16 to 20 years) with various malignant and benign thyroid gland diseases. The data for this study were extracted from the medical histories of patients and the electronic database of the Center for Endocrine Surgery. Detailed data analysis included diagnosis, symptomatology, surgical intervention type, and disease stage. The following thyroid status parameters were analyzed from preoperative data: thyroxine (T4), thyroxine free fraction (FT4), triiodothyronine (T3), triiodothyronine free fraction (FT3), and thyroid stimulating hormone (TSH). In addition, the pathohistological features of diagnosed thyroid diseases were also determined, with a special focus on the presence of well-differentiated cancers. Papillary carcinomas were the most common of well-differentiated cancers in adolescents over the ten-year follow-up period. Based on the assessed data, total thyroidectomy was the most commonly used type of surgical intervention in these patients. The conducted research provides essential information related to both the biological characteristics and diagnostics of these cancers and their surgical treatment in such a sensitive population. Moreover, research showed that the clinical presentation of thyroid cancer in adolescents is almost identical to that in adults.

Shared-Specific Feature Learning With Bottleneck Fusion Transformer for Multi-Modal Whole Slide Image Analysis

The fusion of multi-modal medical data is essential to assist medical experts to make treatment decisions for precision medicine. For example, combining the whole slide histopathological images (WSIs) and tabular clinical data can more accurately predict the lymph node metastasis (LNM) of papillary thyroid carcinoma before surgery to avoid unnecessary lymph node resection. However, the huge-sized WSI provides much more high-dimensional information than low-dimensional tabular clinical data, making the information alignment challenging in the multi-modal WSI analysis tasks. This paper presents a novel transformer-guided multi-modal multi-instance learning framework to predict lymph node metastasis from both WSIs and tabular clinical data. We first propose an effective multi-instance grouping scheme, named siamese attention-based feature grouping (SAG), to group high-dimensional WSIs into representative low-dimensional feature embeddings for fusion. We then design a novel bottleneck shared-specific feature transfer module (BSFT) to explore the shared and specific features between different modalities, where a few learnable bottleneck tokens are utilized for knowledge transfer between modalities. Moreover, a modal adaptation and orthogonal projection scheme were incorporated to further encourage BSFT to learn shared and specific features from multi-modal data. Finally, the shared and specific features are dynamically aggregated via an attention mechanism for slide-level prediction. Experimental results on our collected lymph node metastasis dataset demonstrate the efficiency of our proposed components and our framework achieves the best performance with AUC (area under the curve) of 97.34%, outperforming the state-of-the-art methods by over 1.27%.

Variants in TPO rs2048722, PTCSC2 rs925489 and SEMA4G rs4919510 affect thyroid carcinoma susceptibility risk

BACKGROUND

Thyroid carcinoma (THCA) is a malignant endocrine tumor all around the world, which is influenced by genetic and environmental factors.

OBJECTIVE

To explore the association between TPO rs2048722, PTCSC2 rs925489, SEMA4G rs4919510 polymorphisms and THCA susceptibility in Chinese population.

METHODS

We recruited 365 THCA patients and 498 normal controls for the study. Logistic regression analysis was used to evaluate the association between TPO rs2048722, PTCSC2 rs925489, SEMA4G rs4919510 polymorphisms and THCA susceptibility. MDR was used to assess the genetic interactions among the three SNPs.

RESULTS

Overall analysis demonstrated that rs925489 of PTCSC2 was evidently associated with increased risk of THCA in multiple genetic models (OR = 1.59, 95%CI = 1.12-2.24, p = 0.009). The results of stratified analysis illustrated that rs2048722 of TPO can significantly increase the THCA susceptibility of participants less than or equal to 44 years old and smokers. Similarly, rs925489 of PTCSC2 obviously improved the risk of THCA among participants older than 44 years, males, smokers and drinkers. However, rs4919510 of SEMA4G has a protective effect on the development of THCA among participants with less than or equal to 44 years old and non-drinkers. Interestingly, there was a strong genetic interaction among the three SNPs in the occurrence of THCA risk.

CONCLUSION

TPO rs2048722, PTCSC2 rs925489 and SEMA4G rs4919510 polymorphisms were evidently associated with the risk of THCA in the Chinese population, which was affected by age, gender, smoking and drinking consumption.

Development of a joint diagnostic model of thyroid papillary carcinoma with artificial neural network and random forest

Objective: Papillary thyroid carcinoma (PTC) accounts for 80% of thyroid malignancy, and the occurrence of PTC is increasing rapidly. The present study was conducted with the purpose of identifying novel and important gene panels and developing an early diagnostic model for PTC by combining artificial neural network (ANN) and random forest (RF). Methods and results: Samples were searched from the Gene Expression Omnibus (GEO) database, and gene expression datasets (GSE27155, GSE60542, and GSE33630) were collected and processed. GSE27155 and GSE60542 were merged into the training set, and GSE33630 was defined as the validation set. Differentially expressed genes (DEGs) in the training set were obtained by "limma" of R software. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis as well as immune cell infiltration analysis were conducted based on DEGs. Important genes were identified from the DEGs by random forest. Finally, an artificial neural network was used to develop a diagnostic model. Also, the diagnostic model was validated by the validation set, and the area under the receiver operating characteristic curve (AUC) value was satisfactory. Conclusion: A diagnostic model was established by a joint of random forest and artificial neural network based on a novel gene panel. The AUC showed that the diagnostic model had significantly excellent performance.

Lymph Node Metastasis Prediction From Whole Slide Images With Transformer-Guided Multiinstance Learning and Knowledge Transfer

The gold standard for diagnosing lymph node metastasis of papillary thyroid carcinoma is to analyze the whole slide histopathological images (WSIs). Due to the large size of WSIs, recent computer-aided diagnosis approaches adopt the multi-instance learning (MIL) strategy and the key part is how to effectively aggregate the information of different instances (patches). In this paper, a novel transformer-guided framework is proposed to predict lymph node metastasis from WSIs, where we incorporate the transformer mechanism to improve the accuracy from three different aspects. First, we propose an effective transformer-based module for discriminative patch feature extraction, including a lightweight feature extractor with a pruned transformer (Tiny-ViT) and a clustering-based instance selection scheme. Next, we propose a new Transformer-MIL module to capture the relationship of different discriminative patches with sparse distribution on WSIs and better nonlinearly aggregate patch-level features into the slide-level prediction. Considering that the slide-level annotation is relatively limited to training a robust Transformer-MIL, we utilize the pathological relationship between the primary tumor and its lymph node metastasis and develop an effective attention-based mutual knowledge distillation (AMKD) paradigm. Experimental results on our collected WSI dataset demonstrate the efficiency of the proposed Transformer-MIL and attention-based knowledge distillation. Our method outperforms the state-of-the-art methods by over 2.72% in AUC (area under the curve).