Pattern classification approaches for breast cancer identification via MRI: state‐of‐the‐art and vision for the future

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI) of breast tissue are discussed. The algorithms are based on recent advances in multidimensional signal processing and aim to advance current state‐of‐the‐art computer‐aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi‐parametric computer‐aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi‐supervised deep learning and self‐supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high‐dimensional medical imaging analysis platform that is based on multi‐task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE‐MRI. Since some of the approaches discussed are also based on time‐lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis

Pattern identification of biomedical images with time series: contrasting THz pulse imaging with DCE-MRIs

Objective We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. Methods Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. Validation Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. Results Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. Conclusion The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community

Comprehensive analysis of clinical significance of stem-cell related factors in renal cell cancer

<p>Abstract</p> <p>Background</p> <p>C-MYC, LIN28, OCT4, KLF4, NANOG and SOX2 are stem cell related factors. We detected whether these factors express in renal cell carcinoma (RCC) tissues to study their correlations with the clinical and pathological characteristics.</p> <p>Methods</p> <p>The expressions of c-MYC, LIN28, SOX2, KLF4, OCT4 and NANOG in 30 RCC patients and 5 non-RCC patients were detected with quantitative real-time reverse transcription-PCR (qRT-PCR). The data were analyzed with Wilcoxon signed rank sum test and x²test.</p> <p>Results</p> <p>In RCC group, c-MYC expression was significantly higher in RCC tissues compared with normal tissues (P < 0.05). The expression levels of OCT4, KLF4, NANOG and SOX2 were significantly lower in RCC tissues compared with normal tissues (P < 0.05). LIN28 expression level was not significant. No difference was observed when it comes to clinical and pathological characteristics such as gender, age, tumor size, cTNM classification and differentiation status (P > 0.05). Also the expression levels of all above factors were not significantly changed in non-RCC group (P > 0.05).</p> <p>Conclusions</p> <p>The present analysis strongly suggests that altered expression of several stem cell related factors may play different roles in RCC. C-MYC may function as an oncogene and OCT4, KLF4, NANOG and SOX2 as tumor suppressors.</p

Association between decreased serum TBIL concentration and immediate memory impairment in schizophrenia patients

© 2019, The Author(s). Cognitive impairment is a core feature of schizophrenia (SCH). In addition to the toxic effect of Bilirubin (BIL), it has antioxidant properties that were associated with the psychopathology and cognitive impairment of psychiatric disorders. The aim of this study was to examine the correlation of serum total BIL (TBIL) concentration with cognitive impairment in SCH patients. We recruited 34 SCH patients and 119 healthy controls (HCs) in this case-control design. Cognition was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Serum TBIL concentration was measured using the immunoturbidimetric method. Serum TBIL concentration was significantly decreased in SCH patients compared to HCs after adjusting for age, gender, and education. Serum TBIL concentration in SCH patients was also positively correlated with the RBANS immediate memory score. Further stepwise multiple regression analysis confirmed the positive association between serum TBIL concentration and immediate memory score in SCH patients. Our findings supported that the decline in serum TBIL concentration was associated with the immediate memory impairment and psychopathology of SCH

Poly[aqua(μ3-5-aza­niumylisophthalato)­(μ-oxalato)neodymium(III)]

The title compound, [Nd(C8H6NO4)(C2O4)(H2O)]n, is a layer-like coordination polymer. The NdIII ion is coordinated by four carboxyl­ate O atoms from three bridging 5-aza­nium­yl­isophthalate (Haip) ligands, four carboxyl­ate O atoms from two oxalate (ox) anions and one ligated water mol­ecule in a tricapped trigonal–prismatic geometry. The Haip anion acts as a μ3-bridge, connecting three NdIII ions through two carboxyl­ate groups; the ox anion adopts a bis-bidentate-bridging mode, linking two NdIII ions. The layer framework is further extended to a three-dimensional supra­molecular structure through N—H⋯O and O—H⋯O hydrogen bonds

Sentinel lymph node biopsy in oral cavity cancer using indocyanine green: A systematic review and meta-analysis

This meta-analysis was conducted to evaluate the value of indocyanine green (ICG) in guiding sentinel lymph node biopsy (SLNB) for patients with oral cavity cancer. An electronic database search (PubMed, MEDLINE, Cochrane Library, Embase, and Web of Science) was performed from their inception to June 2020 to retrieve clinical studies of ICG applied to SLNB for oral cavity cancer. Data were extracted from 14 relevant articles (226 patients), and 9 studies (134 patients) were finally included in the meta-analysis according to the inclusion and exclusion criteria. The pooled sentinel lymph node (SLN) sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 88.0% (95% confidence interval [CI], 74.0-96.0), 64.0% (95% CI, 61.0-66.0), 2.45 (95% CI, 1.31-4.60), 0.40 (95% CI, 0.17-0.90), and 7.30 (95% CI, 1.74-30.68), respectively. The area under the summary receiver operating characteristic curve was 0.8805. In conclusion, ICG applied to SLNB can effectively predict the status of regional lymph nodes in oral cavity cancer

A Robust Optimization Approach to Emergency Vehicle Scheduling

The emergency vehicle scheduling problem is studied under the objective function to minimize the total transportation time with uncertain road travel time. Firstly, we build a stochastic programming model considering the constrained chance. Then, we analyze the model based on robust optimization method and get its equivalent set of uncertainty constraint, which has good mathematical properties with consideration of the robustness of solutions. Finally, we implement a numerical example to compare the results of robust optimization method and that of the particle swarm optimization algorithm. The case study shows that the proposed method achieves better performance on computational complexity and stability