Development of a Risk Predictive Model for Evaluating Immune Infiltration Status in Invasive Thyroid Carcinoma.

Aims: This study aimed to reveal the molecular characteristics and potential biomarker of immune-activated and immunosuppressive invasive thyroid carcinoma. Methods: Expression and clinical data for invasive thyroid carcinoma were obtained from the TCGA database. Tumor samples were divided into immune-activated or immunosuppressive groups based on the immune enrichment score calculated by ssGSEA. Differentially expressed genes (DEGs) between tumor vs. normal groups or between immune-activated vs. immunosuppressive groups were screened, followed by functional enrichment. Immune infiltration was evaluated using the ESTIMATE, CIBERSORTx, and EPIC algorithms, respectively. A random forest algorithm and Lasso cox analysis were used to identify gene signatures for risk model construction. Results: Totally 1171 DEGs were screened between tumor vs. normal groups, and multiple tumorigenesis-associated pathways were significantly activated in invasive thyroid carcinoma. Compared to immune-activated samples, immunosuppressive samples showed higher tumor purity, lower immune/stromal scores, and lower expression of immune markers, as well as lower infiltration abundance of CD4+ T cells and CD8+ T cells. A risk model based on a 12-immune signature (CCR7, CD1B, CD86, CSF2RB, HCK, HLA-DQA1, LTA, LTB, LYZ, NOD2, TNFRSF9, and TNFSF11) was developed to evaluate the immune infiltration status (AUC = 0.998; AUC of 0.958 and 0.979 in the two external validation datasets), which showed a higher clinical benefit and high accuracy. Immune-activated samples presented lower IC50 value for bortezomib, MG.132, staurosporine, and AZD8055, indicating sensitivity to these drugs. Conclusion: A 12-gene-based immune signature was developed to predict the immune infiltration status for invasive thyroid carcinoma patients and then to identify the subsets of invasive thyroid carcinoma patients who might benefit from immunotherapy.

Deep learning to diagnose Hashimoto's thyroiditis from sonographic images.

Hashimoto's thyroiditis (HT) is the main cause of hypothyroidism. We develop a deep learning model called HTNet for diagnosis of HT by training on 106,513 thyroid ultrasound images from 17,934 patients and test its performance on 5051 patients from 2 datasets of static images and 1 dataset of video data. HTNet achieves an area under the receiver operating curve (AUC) of 0.905 (95% CI: 0.894 to 0.915), 0.888 (0.836-0.939) and 0.895 (0.862-0.927). HTNet exceeds radiologists' performance on accuracy (83.2% versus 79.8%; binomial test, p < 0.001) and sensitivity (82.6% versus 68.1%; p < 0.001). By integrating serologic markers with imaging data, the performance of HTNet was significantly and marginally improved on the video (AUC, 0.949 versus 0.888; DeLong's test, p = 0.004) and static-image (AUC, 0.914 versus 0.901; p = 0.08) testing sets, respectively. HTNet may be helpful as a tool for the management of HT.

Improved diagnosis of thyroid cancer aided with deep learning applied to sonographic text reports: a retrospective, multi-cohort, diagnostic study.

OBJECTIVE: Large volume radiological text data have been accumulated since the incorporation of electronic health record (EHR) systems in clinical practice. We aimed to determine whether deep natural language processing algorithms could aid radiologists in improving thyroid cancer diagnosis. METHODS: Sonographic EHR data were obtained from the EHR database. Pathological reports were used as the gold standard for diagnosing thyroid cancer. We developed thyroid cancer diagnosis based on natural language processing (THCaDxNLP) to interpret unstructured sonographic text reports for thyroid cancer diagnosis. We used the area under the receiver operating characteristic curve (AUROC) as the primary metric to measure the performance of the THCaDxNLP. We compared the performance of thyroid ultrasound radiologists aided with THCaDxNLP vs. those without THCaDxNLP using 5 independent test sets. RESULTS: We obtained a total number of 788,129 sonographic radiological reports. The number of thyroid sonographic data points was 132,277, 18,400 of which were thyroid cancer patients. Among the 5 test sets, the numbers of patients per set were 439, 186, 82, 343, and 171. THCaDxNLP achieved high performance in identifying thyroid cancer patients (the AUROC ranged from 0.857-0.932). Thyroid ultrasound radiologists aided with THCaDxNLP achieved significantly higher performances than those without THCaDxNLP in terms of accuracy (93.8% vs. 87.2%; one-sided t-test, adjusted P = 0.003), precision (92.5% vs. 86.0%; P = 0.018), and F1 metric (94.2% vs. 86.4%; P = 0.007). CONCLUSIONS: THCaDxNLP achieved a high AUROC for the identification of thyroid cancer, and improved the accuracy, sensitivity, and precision of thyroid ultrasound radiologists. This warrants further investigation of THCaDxNLP in prospective clinical trials.

Granulomatous mastitis in multiparae during pregnancy and lactation: Observational study (STROBE compliant).

ABSTRACT: The incidence of granulomatous mastitis (GLM) in multiparae as seriously affected the quality of life and breastfeeding of pregnant women after delivery, but the treatment is rarely reported. In this article, the development, healing, and lactation of 13 cases were reported and a retrospective analysis was performed. 10 cases of GLM were treated at the Breast Disease Prevention and Treatment Center of Haidian Maternal & Child Health Hospital of Beijing and 3 cases of GLM were treated in the Breast Department of Weihai Municipal Hospital of Shandong province from February 2017 to May 2019.Among the 13 patients, conservative symptomatic treatment was adopted during pregnancy and lactation: anti-infective therapy consisting of oral cephalosporin antibiotic for patients; ultrasound-guided puncture and drainage of pus or incision and drainage after abscess formation. Observation continued during the sinus tract phase. Postpartum breastfeeding was encouraged, especially on the affected side. In this study, the median healing time was 20 months and the average healing time was 30.4 months in 5 healthy breast lactation cases. In 8 cases of bilateral breast lactation, the median healing time was 30 months and the average healing time was 26.5 months. Linear regression test analysis: whether the affected breast was breast-fed after delivery had no effect on the postpartum wound healing time, P = .792. The wounds of 13 patients healed well after lactation, and none of them recurred since the last follow-up visit. There were no adverse events in all infants.Conservative symptomatic treatment for GLM of multiparous women during pregnancy and lactation and encouraging breastfeeding after delivery have no effect on infant health and the recovery time of patients.

MCM3AP-AS1 regulates proliferation, apoptosis, migration, and invasion of breast cancer cells via binding with ZFP36.

BACKGROUND: Previous studies suggest that long noncoding RNA (lncRNA) maintenance complex component 3 associated protein (MCM3AP) antisense RNA 1 (MCM3AP-AS1) has a wide range of functions in several cancers. However, its expression and functions in breast cancer are unclear. METHODS: Reverse transcription polymerase chain reaction (RT-PCR) was used to detect the expression of MCM3AP-AS1. MTT, colony formation and anchorage-independent growth assay were used to examine the effect of MCM3AP-AS1 on growth of breast cancer cells. TUNEL and flow cytometry assay were applied to investigate the role of MCM3AP-AS1 on cell apoptosis. Wound heading and transwell matric penetration assay were used to detect the role of MCM3AP-AS1 on cell motility. RNA pull-down and RIP assay were used to examine the interaction of MCM3AP-AS1 and ZFP36. RESULTS: We found that lncRNA MCM3AP-AS1 was significantly upregulated in breast cancer, which correlated with patients' clinicopathological characteristics. Downregulation of MCM3AP-AS1 substantially inhibited the proliferation, apoptosis, migration, and invasion of breast cancer cells. Further analysis clarified that MCM3AP-AS1 binds with the RNA-binding protein ZFP36 ring finger protein (ZFP36) to regulate the levels of cyclin D1 (CCND1), c-myc (MYC), and matrix metalloproteinase 1 (MMP1). CONCLUSIONS: Our findings show lncRNA MCM3AP-AS1 might be a prognostic factor and a promising therapeutic target for breast cancer therapy.

High Expression of Asparagine Synthetase Is Associated with Poor Prognosis of Breast Cancer in Chinese Population.

Aims: This study aimed to determine the expression of asparagine synthetase (ASNS) in breast cancer (BC) tissues and estimate its prognostic value for BC patients. Besides, the roles of ASNS in the proliferation of BC cells were also examined in the study. Methods: Quantitative real-time PCR was conducted to detect the expression of ASNS mRNA in BC tissues and normal controls. The relationship between ASNS expression and clinical characteristics of BC patients was analyzed using χ-square test. MTT assay was performed to explore the effect of ASNS expression on the proliferation of BC cells. Kaplan-Meier curves were plotted to describe the overall survival rate of BC patients. Cox regression analyses were implemented to investigate prognostic factors. Results: ASNS mRNA overexpression was observed in BC tissues (p < 0.05). High expression of ASNS was significantly related to histological grade (p = 0.017), vascular invasion (p = 0.009), and PR status (p = 0.014). The downregulation of ASNS affected the proliferation of BC cells (p < 0.05). Kaplan-Meier survival showed that patients with high ASNS expression lived shorter than those with low expressions (p < 0.001). Finally, Cox regression analyses revealed that ASNS could act as a prognostic marker for BC patients (p < 0.001, HR = 3.293, 95% CI = 1.790-6.058). Conclusion: Taken together, ASNS is a valuable prognostic biomarker for BC patients.

Connection between SOX7 Expression and Breast Cancer Prognosis.

BACKGROUND SOX7 exerts a repressing effect against tumors and imposes vital influences on malignancies. Our research discussed the importance of SOX7 in breast cancer prognoses. MATERIAL AND METHODS SOX7 mRNA expression in breast cancer tissues samples and matched adjacent normal controls of breast cancer patients was measured by quantitative real-time-polymerase chain reaction (qRT-PCR). The relationship of SOX7 with clinicopathological characteristics were analyzed via chi-square test. The association of SOX7 levels with clinical outcomes was evaluated adopting the Kaplan-Meier method and multivariate Cox proportional hazards regression model. RESULTS SOX7 mRNA degree of expression exhibited a declining tendency in breast cancer tissue compared to paired bordering normal tissue specimens (P<0.001). In addition, the reduced SOX7 degree of expression had a strong correlation to larger cancer mass dimension (P=0.006) and lymph node metastasis (P=0.001). Survival analysis revealed that the overall survival (OS) time was much shorter among cases harboring low SOX7 degree of expression compared to high degree of expression (P=0.005). Moreover, SOX7 expression alone could predict OS among breast cancer patients (hazard ratio=3.956, 95% confidence interval=1.330-11.772, P=0.013). CONCLUSIONS SOX7 expression was downregulated in breast cancer tissues, and it could function as a useful prognostic marker in breast cancer.

Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study.

BACKGROUND: The incidence of thyroid cancer is rising steadily because of overdiagnosis and overtreatment conferred by widespread use of sensitive imaging techniques for screening. This overall incidence growth is especially driven by increased diagnosis of indolent and well-differentiated papillary subtype and early-stage thyroid cancer, whereas the incidence of advanced-stage thyroid cancer has increased marginally. Thyroid ultrasound is frequently used to diagnose thyroid cancer. The aim of this study was to use deep convolutional neural network (DCNN) models to improve the diagnostic accuracy of thyroid cancer by analysing sonographic imaging data from clinical ultrasounds. METHODS: We did a retrospective, multicohort, diagnostic study using ultrasound images sets from three hospitals in China. We developed and trained the DCNN model on the training set, 131 731 ultrasound images from 17 627 patients with thyroid cancer and 180 668 images from 25 325 controls from the thyroid imaging database at Tianjin Cancer Hospital. Clinical diagnosis of the training set was made by 16 radiologists from Tianjin Cancer Hospital. Images from anatomical sites that were judged as not having cancer were excluded from the training set and only individuals with suspected thyroid cancer underwent pathological examination to confirm diagnosis. The model's diagnostic performance was validated in an internal validation set from Tianjin Cancer Hospital (8606 images from 1118 patients) and two external datasets in China (the Integrated Traditional Chinese and Western Medicine Hospital, Jilin, 741 images from 154 patients; and the Weihai Municipal Hospital, Shandong, 11 039 images from 1420 patients). All individuals with suspected thyroid cancer after clinical examination in the validation sets had pathological examination. We also compared the specificity and sensitivity of the DCNN model with the performance of six skilled thyroid ultrasound radiologists on the three validation sets. FINDINGS: Between Jan 1, 2012, and March 28, 2018, ultrasound images for the four study cohorts were obtained. The model achieved high performance in identifying thyroid cancer patients in the validation sets tested, with area under the curve values of 0·947 (95% CI 0·935-0·959) for the Tianjin internal validation set, 0·912 (95% CI 0·865-0·958) for the Jilin external validation set, and 0·908 (95% CI 0·891-0·925) for the Weihai external validation set. The DCNN model also showed improved performance in identifying thyroid cancer patients versus skilled radiologists. For the Tianjin internal validation set, sensitivity was 93·4% (95% CI 89·6-96·1) versus 96·9% (93·9-98·6; p=0·003) and specificity was 86·1% (81·1-90·2) versus 59·4% (53·0-65·6; p<0·0001). For the Jilin external validation set, sensitivity was 84·3% (95% CI 73·6-91·9) versus 92·9% (84·1-97·6; p=0·048) and specificity was 86·9% (95% CI 77·8-93·3) versus 57·1% (45·9-67·9; p<0·0001). For the Weihai external validation set, sensitivity was 84·7% (95% CI 77·0-90·7) versus 89·0% (81·9-94·0; p=0·25) and specificity was 87·8% (95% CI 81·6-92·5) versus 68·6% (60·7-75·8; p<0·0001). INTERPRETATION: The DCNN model showed similar sensitivity and improved specificity in identifying patients with thyroid cancer compared with a group of skilled radiologists. The improved technical performance of the DCNN model warrants further investigation as part of randomised clinical trials. FUNDING: The Program for Changjiang Scholars and Innovative Research Team in University in China, and National Natural Science Foundation of China.

Expression of NAMPT is associated with breast invasive ductal carcinoma development and prognosis.

Nicotinamide phosphoribosyltransferase (NAMPT) possesses various functions in human cells, and altered NAMPT expression is associated with human carcinogenesis. The present study detected the expression of NAMPT in normal and cancerous breast tissues from 83 patients using immunohistochemistry, and analyzed its association with the clinicopathological and survival data of the patients. NAMPT was significantly overexpressed in the breast invasive ductal carcinoma tissues compared with adjacent normal mammary gland tissues. Upregulated NAMPT expression was associated with a larger tumor size, lymph node metastasis, advanced clinical tumor-node-metastasis stages, and estrogen receptor and progesterone receptor expression. Furthermore, NAMPT expression was associated with poor overall and disease-free survival in patients with breast cancer. In conclusion, NAMPT increased protein expression in tumor cells may contribute to the development and progression of breast invasive ductal carcinoma. Thus, detection of NAMPT expression might be useful as a biomarker for the early detection and prognosis prediction of breast cancer.

Serum Dickkopf-1 expression level positively correlates with a poor prognosis in breast cancer.

BACKGROUND: The different expression level of Dickkopf-1 (DKK-1) in different cancers shows that the function of DKK-1 depends on the histological type of the cancer cells and the tissue microenvironment. To our knowledge, the serum expression level of DKK-1 in breast cancer is little known. METHODS: Blood samples from 125 consecutive patients diagnosed with breast cancer and 53 control subjects from March 2008 to August 2013 were investigated. Serum DKK-1 expression levels were measured by enzyme-linked immunosorbent assay (ELISA). The overall survival (OS) and relapse-free survival (RFS) analyzed by log-rank test, and survival curves were plotted according to Kaplan-Meier. RESULTS: The mean serum level of DKK-1 in patients with breast cancer was 4.99 ± 1.50 ng/mL, and was significantly higher than that in healthy individuals (1.88 ± 0.81 ng/mL, P < 0.001). DKK-1 level correlated significantly with TNM stage (P = 0.009), tumor grade (P = 0.02), lymph node metastasis (P = 0.001), and expression of HER2 (P = 0.002). The DKK-1 expression level was classified as high or low in relation to the median value, and patients with breast cancer (n = 125) were divided into a high expression group (n = 63) and a low expression group (n = 62). The Kaplan-Meier method for survival analysis showed that the patients with a high serum DKK-1 level had a poorer OS (48.7% vs. 81.3%, p = 0.01) and RFS (24.3% vs. 71.6%, p = 0.003) than those with a low expression level. The multivariate Cox regression analysis indicated that serum DKK-1 level was independent prognostic factors for OS and RFS. CONCLUSIONS: Serum DKK-1 level can be used as a noninvasive biomarker for the prognosis of breast cancer. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_161.