High BRCA1 gene expression increases the risk of early distant metastasis in ER+ breast cancers.

Although the function of the BRCA1 gene has been extensively studied, the relationship between BRCA1 gene expression and tumor aggressiveness remains controversial in sporadic breast cancers. Because the BRCA1 protein is known to regulate estrogen signaling, we selected microarray data of ER+ breast cancers from the GEO public repository to resolve previous conflicting findings. The BRCA1 gene expression level in highly proliferative luminal B tumors was shown to be higher than that in luminal A tumors. Survival analysis using a cure model indicated that patients of early ER+ breast cancers with high BRCA1 expression developed rapid distant metastasis. In addition, the proliferation marker genes MKI67 and PCNA, which are characteristic of aggressive tumors, were also highly expressed in patients with high BRCA1 expression. The associations among high BRCA1 expression, high proliferation marker expression, and high risk of distant metastasis emerged in independent datasets, regardless of tamoxifen treatment. Tamoxifen therapy could improve the metastasis-free fraction of high BRCA1 expression patients. Our findings link BRCA1 expression with proliferation and possibly distant metastasis via the ER signaling pathway. We propose a testable hypothesis based on these consistent results and offer an interpretation for our reported associations.

Autodetect extracranial and intracranial artery stenosis by machine learning using ultrasound.

BACKGROUND: and Purpose: This study proposed a machine learning method for identifying ≥50% stenosis of the extracranial and intracranial arteries. PATIENTS AND METHODS: A total of 8211 patients with both carotid ultrasound and cerebral angiography were enrolled. Support vector machine (SVM) was employed as the machine learning classifier. Carotid Doppler parameters and transcranial Doppler parameters were used as the input features. Feature selection was performed using the Extra-Trees (extremely randomized trees) method. RESULTS: For the machine learning method, the sensitivities and specificities of identifying stenosis of the extracranial arteries were 88.5%-100% and 96.0%-100%, respectively. The sensitivities and specificities of identifying stenosis of the intracranial arteries were 71.7%-100% and 88.9%-100%, respectively. CONCLUSIONS: The SVM classifier with feature selection is an efficient method for identifying the stenosis of both intracranial and extracranial arteries. Comparing with traditional Doppler criteria, this machine learning method achieves up to 20% higher in accuracy and 45% in sensitivity, respectively.

An exploratory study using an openEHR 2-level modeling approach to represent common data elements.

BACKGROUND AND OBJECTIVE: In order to facilitate clinical research across multiple institutions, data harmonization is a critical requirement. Common data elements (CDEs) collect data uniformly, allowing data interoperability between research studies. However, structural limitations have hindered the application of CDEs. An advanced modeling structure is needed to rectify such limitations. The openEHR 2-level modeling approach has been widely implemented in the medical informatics domain. The aim of our study is to explore the feasibility of applying an openEHR approach to model the CDE concept. MATERIALS AND METHODS: Using the National Institute of Neurological Disorders and Stroke General CDEs as material, we developed a semiautomatic mapping tool to assist domain experts mapping CDEs to existing openEHR archetypes in order to evaluate their coverage and to allow further analysis. In addition, we modeled a set of CDEs using the openEHR approach to evaluate the ability of archetypes to structurally represent any type of CDE content. RESULTS: Among 184 CDEs, 28% (51) of the archetypes could be directly used to represent CDEs, while 53% (98) of the archetypes required further development (extension or specialization). A comprehensive comparison between CDEs and openEHR archetypes was conducted based on the lessons learnt from the practical modeling. DISCUSSION: CDEs and archetypes have dissimilar modeling approaches, but the data structure of both models are essentially similar. This study proposes to develop a comprehensive structure to model CDE concepts instead of improving the structure of CED. CONCLUSION: The findings from this research show that the openEHR archetype has structural coverage for the CDEs, namely the openEHR archetype is able to represent the CDEs and meet the functional expectations of the CDEs. This work can be used as a reference when improving CDE structure using an advanced modeling approach.