The influence of thyroid status, serum Tg, TSH, and TgAb on FNA-Tg in cervical metastatic lymph nodes of papillary thyroid carcinoma.

OBJECTIVE: Papillary thyroid carcinoma is treated in China mostly with surgery, including total ablation, lobectomy, and lobe and isthmus resection. Therefore, whether thyroid status affects the FNA-Tg cutoff value in the diagnosis of cervical lymph node metastasis deserves our attention. In addition, we investigated the influence of serum Tg, TSH, and TgAb on the accuracy of using FNA-Tg for diagnosis. METHODS: Our retrospective analysis included 189 suspected cervical lymph nodes, and we determined whether the cutoff value of FNA-Tg was affected by thyroid status, sTg, sTSH, and sTgAb. RESULTS: In thyroid present cases, the optimal cutoff value of FNA-Tg was 2.3 ng/ml (sensitivity 96.2%, specificity 100%), and in the thyroid absent cases, the optimal cutoff value of FNA-Tg was 0.7 ng/ml (sensitivity 97.6%, specificity 96.0%). Although serum Tg, TSH, and TgAb were weakly correlated with FNA-Tg values, they did not affect the diagnostic performance of the optimal cutoff value of FNA-Tg according to thyroid status. CONCLUSIONS: The optimal cutoff value of FNA-Tg should be selected according to the thyroid status (2.3 ng/ml for thyroid present cases and 0.7 ng/ml for thyroid absent cases) to ensure the efficient diagnosis of cervical metastatic lymph nodes of papillary thyroid carcinoma. It was determined that sTg, sTSH, and sTg-Ab cannot influence the diagnostic performance of FNA-Tg. The combination method of FNA-Tg and FNAC is the most optimal choice for the diagnosis of lymph nodes metastasis.

Computer-aided classification of MRI for pathological complete response to neoadjuvant chemotherapy in breast cancer.

Background: To determine suitable optimal classifiers and examine the general applicability of computer-aided classification to compare the differences between a computer-aided system and radiologists in predicting pathological complete response (pCR) from patients with breast cancer receiving neoadjuvant chemotherapy. Methods: We analyzed a total of 455 masses and used the U-Net network and ResNet to execute MRI segmentation and pCR classification. The diagnostic performance of radiologists, the computer-aided system and a combination of radiologists and computer-aided system were compared using receiver operating characteristic curve analysis. Results: The combination of radiologists and computer-aided system had the best performance for predicting pCR with an area under the curve (AUC) value of 0.899, significantly higher than that of radiologists alone (AUC: 0.700) and computer-aided system alone (AUC: 0.835). Conclusion: An automated classification system is feasible to predict the pCR to neoadjuvant chemotherapy in patients with breast cancer and can complement MRI.

Computer-Aided Diagnosis Evaluation of the Correlation Between Magnetic Resonance Imaging With Molecular Subtypes in Breast Cancer.

BACKGROUND: There is a demand for additional alternative methods that can allow the differentiation of the breast tumor into molecular subtypes precisely and conveniently. PURPOSE: The present study aimed to determine suitable optimal classifiers and investigate the general applicability of computer-aided diagnosis (CAD) to associate between the breast cancer molecular subtype and the extracted MR imaging features. METHODS: We analyzed a total of 264 patients (mean age: 47.9 ± 9.7 years; range: 19-81 years) with 264 masses (mean size: 28.6 ± 15.86 mm; range: 5-91 mm) using a Unet model and Gradient Tree Boosting for segmentation and classification. RESULTS: The tumors were segmented clearly by the Unet model automatically. All the extracted features which including the shape features,the texture features of the tumors and the clinical features were input into the classifiers for classification, and the results showed that the GTB classifier is superior to other classifiers, which achieved F1-Score 0.72, AUC 0.81 and score 0.71. Analyzed the different features combinations, we founded that the texture features associated with the clinical features are the optimal features to different the breast cancer subtypes. CONCLUSION: CAD is feasible to differentiate the breast cancer subtypes, automatical segmentation were feasible by Unet model and the extracted texture features from breast MR imaging with the clinical features can be used to help differentiating the molecular subtype. Moreover, in the clinical features, BPE and age characteristics have the best potential for subtype.

GABARAP suppresses EMT and breast cancer progression via the AKT/mTOR signaling pathway.

Few studies have focused on γ-aminobutyric acid type A (GABAA) receptor-associated protein (GABARAP) in tumor progression. We investigated the expression and importance of GABARAP in breast cancer. We analyzed the expression of GABARAP and its relationship with clinicopathological features and prognosis (TCGA). To explain the role and potential mechanism of GABARAP in regulating tumor development, we performed acquisition and loss of function experiments using cell lines and models of mouse xenotransplantation. We found that GABARAP inhibited proliferation, migration and invasion in vitro and in vivo. Notably, low levels of GABARAP induced the epithelial-mesenchymal transition (EMT). Low levels of GABARAP increased p-AKT and p-mTOR levels, and a specific AKT pathway inhibitor reversed the downregulation of GABARAP-induced tumor progression. GABARAP negatively correlated with advanced clinicopathological features in clinical specimens, such as tumor size and TNM stage. Notably, patients with low GABARAP levels had a poor prognosis. Immunohistochemistry (IHC) revealed that GABARAP expression negatively correlated with matrix metalloproteinase (MMP) 2 and MMP14. Conclusively, these data indicate that GABARAP suppresses the malignant behaviors of breast cancer likely via the AKT/mTOR pathway. The targeting of GABARAP may improve the certainty of diagnosis and treatment strategies for breast cancer.

Preliminary results of computer-aided diagnosis for magnetic resonance imaging of solid breast lesions.

PURPOSE: The present study aimed to determine suitable optimal classifiers and investigate the general applicability of computer-aided diagnosis (CAD) to compare magnetic resonance (MR)-CAD with MR imaging (MRI) in distinguishing benign from malignant solid breast masses. METHODS: We analyzed a total of 251 patients (mean age: 44.8 ± 12.3 years; range: 21-81 years) with 274 breast masses (154 benign masses, 120 malignant masses) using a Gaussian mixture model and a random forest machine model for segmentation and classification. RESULTS: The diagnostic performance of MRI alone and MRI plus CAD were compared with respect to sensitivity, specificity, and area under the curve (AUC), using receiver operating characteristic curve analysis. The discriminating power to detect malignancy using MR-CAD with an AUC of 0.955 (sensitivity was 95.8% and the specificity was 92.9%) was significantly higher than that of MRI alone with an AUC of 0.785 (sensitivity was 71.7% and the specificity was 85.7%). CONCLUSION: CAD is feasible to differentiate breast lesions, and it can complement MRI, thereby making it easier to diagnose breast lesions and obviating the need for unnecessary biopsies.

Reaction-induced microsyneresis in oxide-based gels: the assembly of hierarchical microsphere networks.

Rigid and stable networks composed of litchi-shaped microspheres were formed via hierarchical self-assembly (SA) of oxide-based nanoparticles (NPs). The phenomenon of the apparent changes from NPs networks to microspheres networks after the gelation was similar to normal microsyneresis. However, in-situ composition evolution results indicate that the SA is driven by interparticle dehydration, but not affinity difference between the network for itself and for the solvent. In-situ small-angle X-ray scattering (SAXS), UV-vis-NIR, and electric conductivity were used to study the microsyneresis process. To further demonstrate the mechanism, extra complexant was added and successfully restrained the NPs-microsphere transition by inactivating the surface hydroxyl of the NPs. Considering the structural similarity, this work may provide a new approach to control the assemblies of diverse oxide-based NPs.