Development of a Clinical-Radiomics Nomogram That Used Contrast-Enhanced Ultrasound Images to Anticipate the Occurrence of Preoperative Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Patients.

Background and Objectives: Papillary thyroid carcinoma (PTC) is a prevalent histological type of thyroid cancer; however, noninvasive assessment of cervical lymph node metastasis (LNM) poses a challenge. This study aims to develop a novel clinical-radiomics nomogram that utilizes ultrasound (US) images to predict the presence of cervical LNM metastasis in patients with PTC. Methods: A total of 423 patients with PTC were recruited to participate in this study between January 2020 and December 2022, of which 282 were classified into the training group and 141 patients were classified into the validation set. Contrast-enhanced ultrasound (CEUS) and B-mode ultrasound (BMUS) images were subjected to radiomic analysis, leading to the extraction of 912 radiomic features. Thereafter, a radiomics score (Radscore) was developed to effectively integrate the information derived from BMUS and CEUS modalities. Univariate and multivariate backward stepwise logistic regression analysis techniques were used to construct the clinical and clinical-radiomics models, respectively. Results: The findings revealed that the clinical-radiomics nomogram incorporated age, sex, CEUS Radscore, and US-reported LNM as risk factors. The nomogram demonstrated good performance using data from the training (AUC = 0.891) and validation (AUC = 0.870) sets. The decision curve analysis implied that this nomogram exhibited good clinical utility, which was further supported by the results of the calibration curves and Hosmer-Lemeshow test. Conclusion: The CEUS Radscore-based clinical radiomics nomogram could serve as a valuable tool for predicting cervical LNM metastasis in patients with PTC, thereby tailoring individualized treatment strategies for them.

Deep learning radiomics for prediction of axillary lymph node metastasis in patients with clinical stage T1-2 breast cancer.

Background: This study investigates whether deep learning radiomics of conventional ultrasound images can predict preoperative axillary lymph node (ALN) status in patients with clinical stages T1-2 breast cancer (BC). Methods: This study retrospectively analyzed the preoperative ultrasound data of 892 patients with BC, who were classified into training (n=535), validation (n=178), and test (n=179) cohorts. Linear combinations of the selected features were weighted by their coefficients to obtain the predicted score. Then, deep learning radiomic features were extracted from the ultrasound images to evaluate the ALN status. Receiver-operating characteristic curves were drawn, followed by the calculation of the area under the curve (AUC) to assess the accuracy of the prediction model in predicting axillary lymph node metastasis (ALNM) in the three cohorts. Results: Deep learning radiomics combined with radiomics and clinical parameters was the optimal diagnostic predictor of the ALN status in the absence and presence of ALNM, with the AUC of 0.920 (95% confidence interval: 0.872 and 0.968, respectively). Additionally, this combination could also differentiate low-load ALNM [N + (1-2)] from heavy-load ALNM with ≥3 positive nodes [N + (≥3)] in the test cohort, with the AUC of 0.819 (95% confidence interval: 0.568 and 1.00, respectively). Conclusions: Conclusively, deep learning radiomics of ultrasound images is a non-invasive approach to predicting preoperative ALNM in BC.

ExpGCN: Review-aware Graph Convolution Network for explainable recommendation.

Existing works in recommender system have widely explored extracting reviews as explanations beyond user-item interactions, and formulated the explanation generation as a ranking task to enhance item recommendation performance. To associate explanations with users and items, graph neural networks (GNN) are usually employed to learn node representations on the heterogeneous user-item-explanation interaction graph. However, modeling heterogeneous graph convolution poses limitations in both message passing styles and computational efficiency, resulting in sub-optimal recommendation performance. To address the limitations, we propose an Explanation-aware Graph Convolution Network (ExpGCN). In particular, the heterogeneous interaction graph is divided to subgraphs regard to the edge types in ExpGCN. By aggregating information from distinct subgraphs, ExpGCN is capable of generating node representations for explanation ranking task and item recommendation task respectively. Task-oriented graph convolution can not only reduce the complexity of heterogeneous node aggregation, but also alleviate the performance degeneration caused by the conflicts between task learning objectives, which has been neglected in current studies. Extensive experiments on four public datasets show that ExpGCN significantly outperforms state-of-the-art baselines with high efficiency, demonstrating the effectiveness of ExpGCN in explainable recommendations.

Modeling Self-Representation Label Correlations for Textual Aspects and Emojis Recommendation.

The rapid development of Internet services and social platforms encourages users to share their opinions. To help users give valuable comments, content providers expect the recommender system to offer appropriate suggestions, including specific features of the item described in texts and emojis, which are all considered aspects of the user reviews. Hence, the review aspect recommendation task has become significant, where the key lies in handling personal preferences and semantic correlations between suggested items. This article proposes a correlation-aware review aspect recommender (CARAR) system model by constructing self-representation correlations between different views of review aspects, including textual aspects and emojis to make a personalized recommendation. The dependencies between different textual aspects and emojis can be identified and utilized to facilitate the factorization process to learn user and item latent factors. The cross-view correlation mapping between textual aspects and emojis can be built to enhance the recommendation performance. Moreover, the additional information in the real-world environment is also applied to our model to adjust the recommendation results. We constructed experiments on five self-collected and public datasets and compared with six existing models. The results show that our model can outperform the existing models on review aspects recommendation tasks, validating the effectiveness of our approach.

Knockdown of ANXA10 inhibits proliferation and promotes apoptosis of papillary thyroid carcinoma cells by down-regulating TSG101 thereby inactivating the MAPK/ERK signaling pathway.

Annexin A10 (ANXA10) is a member of annexin A and has been reported to highly express in papillary thyroid carcinoma (PTC) tissues. Tumor susceptibility gene 101 (TSG101) also plays a role in PTC and is predicted to bind to ANXA10. This study intended to investigate whether ANXA10 could regulate PTC via binding to ANXA10. The expression of ANXA10 and TSG101 in normal thyroid follicular epithelial cell line and several PTC cell lines was analyzed using RT-qPCR and western blotting assays. Subsequently, PTC cell line BCPAP was silenced with ANXA10 followed by TSG101 overexpression or not, and then cell proliferation, apoptosis and mitogen-activated protein kinase (MAPK) signaling expression were assessed via MTT, colony formation, immunofluorescence staining, Tunel staining and western blotting assays. Besides, the interaction between ANXA10 and TSG101 was validated using Co-immunoprecipitation assay. ANXA10 and TSG101 expressions were up-regulated in PTC cell lines. ANXA10 silence inhibited proliferation, promoted apoptosis and inactivated MAPK/ extracellular regulated protein kinases (ERK) signaling pathway of BCPAP cells. Additionally, ANXA10 could bind to TSG101 and regulate its expression. However, the above effects of ANXA10 silence on BCPAP cells were all blocked by TSG101 overexpression. ANXA10 inhibited proliferation and promoted apoptosis of PTC cells via binding to TSG101, and these actions may depend on down-regulating MAPK/ERK pathway expression.