Primary and Secondary Breast Sarcoma: Clinical and Pathological Characteristics, Prognostic Factors, and Nomograms for Predicting Survival.

BACKGROUND: Breast sarcoma is one of the rare types of breast tumors with different features and outcomes compared to carcinoma. Our study aims to describe the clinical and pathological characteristics of primary breast sarcoma (PBS) and secondary breast sarcoma (SBS) along with determining prognostic factors and developing nomograms for predicting survival. METHODS: Using the Surveillance, Epidemiology, and End Results (SEER) Program, female patients diagnosed with breast sarcoma between 1975 and 2016 were identified. Cox regression was used to evaluate the association between survival and clinical features. RESULTS: Out of 1334 included patients, 816 had PBS and 518 had SBS. PBS had a significantly better overall survival than SBS with median survival months of 107 for PBS and 45 for SBS. The primary tumor site did not have a significant impact on the survival of SBS. Cox regression showed worse survival of PBS patients who were > 60 years (HR 3.04, 95% CI 2.46-3.74) and had tumor size > 50 mm (HR 2.01, 95% CI 1.61-2.51). Being not married was associated with worse survival of PBS (HR 1.29, 95% CI 1.06-1.56) and SBS (HR 1.50, 95% CI 1.19-1.90). Surgery was associated with better survival of PBS (HR 0.60, 95% CI 0.42-0.85) and SBS (HR 0.46, 95% CI 0.31-0.68). The C-indexes of created nomograms were 0.73 for PBS and 0.69 for SBS. CONCLUSION: Age and size were the most important prognostic factors for the survival. Surgery was associated with better survival. However, radiation and chemotherapy did not show significant improvement in survival.

Reporting quality in systematic reviews of in vitro studies: a systematic review.

Background: Systematic reviews (SRs) and/or meta-analyses of in vitro research have an important role in establishing the foundation for clinical studies. In this study, we aimed to evaluate the reporting quality of SRs of in vitro studies using the PRISMA checklist.Method: Four databases were searched including PubMed, Virtual Health Library (VHL), Web of Science (ISI) and Scopus. The search was limited from 2006 to 2016 to include all SRs and/or meta-analyses (MAs) of pure in vitro studies. The evaluation of reporting quality was done using the PRISMA checklist.Results: Out of 7702 search results, 65 SRs were included and evaluated with the PRISMA checklist. Overall, the mean overall quality score of reported items of the PRISMA checklist was 68%. We have noticed an increasing pattern in the numbers of published SRs of in vitro studies over the last 10 years. In contrast, the reporting quality was not significantly improved over the same period (p = .363). There was a positive but not significant correlation between the overall quality score and the journal impact factor of the included studies.Conclusions: The adherence of SRs of in vitro studies to the PRISMA guidelines was poor. Therefore, we believe that using reporting guidelines and journals paying attention to this fact will improve the quality of SRs of in vitro studies.

Structured crowdsourcing enables convolutional segmentation of histology images.

MOTIVATION: While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. RESULTS: We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. AVAILABILITY AND IMPLEMENTATION: Dataset is freely available at: https://goo.gl/cNM4EL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Oncogenic long noncoding RNA MALAT1 and HCV-related hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide. The oncogenic function of the long non-coding RNA; metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in HCC remains unclear. We aimed to evaluate MALAT1 serum expression profile in HCC and explore its relation to the clinicopathological features. Quantitative Real Time-Polymerase Chain Reaction was applied in 70 cohorts (30 HCC, 20 HCV, 20 controls). Further meta-analysis of clinical studies and in vitro validated experiments was employed. Serum MALAT1 showed area under the curve of 0.79 and 0.70 to distinguish patients with cancer from normal and cirrhotic individuals at fold change of 1.0 and 1.26, respectively. Expression level was significantly higher in males (P <0.001) and patients with massive ascites (P = 0.005). Correlation analysis showed positive correlation of MALAT1 with total bilirubin (r = 0.456, P <0.001) and AST (r = 0.280, P = 0.019), and negative correlation with the hemoglobin level (r = 0.312, P = 0.009). Meta-analysis showed that the over-expressed MALAT1 was linked to tumor number [Cohen's d = 0.450, 95% CI (0.21 to 0.68)], clinical stage [Cohen's d = 0.048, 95% CI (-0.83 to 0.74)], and AFP level [Cohen's d = 0.354, 95% CI (0.1 to 0.57)]. In silico data analysis and systematic review confirmed MALAT1 oncogenic function in cancer development and progression. In conclusion, circulatory MALAT1 might represent a putative non-invasive prognostic biomarker indicating worse liver failure score in HCV-related HCC patients with traditional markers. Large-scale verification is warranted in future studies.