Modern view on the issues of diagnosis and verification of axillary lymph nodes involvement in early breast cancer

The involvement of axillary lymph nodes is one of the most important prognostic factors, significantly affecting the treatment strategy for early breast cancer (BC). The risk of axillary lymph node metastases depends directly on a number of factors (age of women, size of tumor, presence of lymphovascular invasion and biological characteristics of cancer). The evaluation of regional lymph node status in patients with early BC includes the clinical examination of regional zones and the ultrasound study (US), using these methods can help to study lymph nodes shape, borders, margins and structure. The sensitivity of ultrasound in the evaluation of regional lymph nodes status directly depends on the biological subtype of the tumor; the minimum level of ultrasound sensitivity in the evaluation of lymph nodes status is detected for luminal HER2-negative cancer (less than 40%), and maximum sensitivity is detected for triple negative and HER2-positive subtypes (6871%). Clinical examination and modern ultrasound are the most accessible methods for the evaluation of regional lymph nodes status, but the possibility to misjudge metastatic process can be detected in 1/4 of patients. Verification of the diagnosis in the preoperative phase (fine-needle aspiration biopsy/core-needle biopsy under ultrasound guidance) allows minimize the number of errors for the regional staging. The sentinel lymph node biopsy (SLNB) is the gold standard of regional treatment in patients with early stage BC, nowadays. The randomized trials (NSABP B-32, ACOSOG q0011) show the safety of recession of performing regional lymph node dissection in favor of SLNB not only in case of clinically negative lymph nodes, but also in patients with metastases in 2 sentinel lymph nodes, upon condition that organ-conservative treatment and subsequent radiation therapy will be used. High-quality regional staging, the choice of the therapeutic algorithm in accordance with the biological characteristics of carcinoma, the application of the most effective modern drug regimes, the optimal radiation therapy allow not only minimize the extent of surgery, but also achieve high long-term survival results, provide excellent functional results and high quality of life in patients with the involvement of axillary lymph nodes

Interpretative and predictive modelling of Joint European Torus collisionality scans

Transport modelling of Joint European Torus (JET) dimensionless collisionality scaling experiments in various operational scenarios is presented. Interpretative simulations at a fixed radial position are combined with predictive JETTO simulations of temperatures and densities, using the TGLF transport model. The model includes electromagnetic effects and collisions as well as □(→┬E ) X □(→┬B ) shear in Miller geometry. Focus is on particle transport and the role of the neutral beam injection (NBI) particle source for the density peaking. The experimental 3-point collisionality scans include L-mode, and H-mode (D and H and higher beta D plasma) plasmas in a total of 12 discharges. Experimental results presented in (Tala et al 2017 44th EPS Conf.) indicate that for the H-mode scans, the NBI particle source plays an important role for the density peaking, whereas for the L-mode scan, the influence of the particle source is small. In general, both the interpretative and predictive transport simulations support the experimental conclusions on the role of the NBI particle source for the 12 JET discharges

Intratumoral Heterogeneity of Expression of 16 miRNA in Luminal Cancer of the Mammary Gland

The purpose of this work is to determine the intratumoral distribution of miRNA expression profiles in luminal breast cancer (BC). The study included 33 certain BC cases of the luminal A or luminal B (Her2-) subtypes. The relative expression levels of miRNA-20a; -21; -125b; -126; -200b; -181a; -205; -221; -222; -451a; -99a; -145; -200a; -214; -30a; -191; and small nuclear RNAs U6, U54, and U58 were measured by RT-qPCR in four intratumor areas in each of 33 luminal BC specimens and in surrounding normal mammary gland tissues. Comparative analysis of miRNA expression levels between normal mammary gland tissue and different intratumor areas revealed that only four miRNAs (miRNA-21, -200b, -200a, -191) appear as consistently differentiating markers. A comparative analysis of miRNA expression levels between normal mammary gland tissue and the tumor border revealed statistically significant differences for ten miRNAs; 10 miRNAs show differential expression between normal mammary gland tissue and central tumor specimens; 9 miRNAs show differential expression between normal mammary gland tissue and tumor periphery 1; 13 miRNAs show differential expression between normal mammary gland tissue and tumor periphery 2. After comparing the tumor periphery 1 and tumor center, we found statistically significant differences in expression between five miRNAs and after comparing the tumor periphery 2 and tumor center, differences were observed for 12 miRNAs. MiRNA expression levels are subject to considerable variation, depending on the intratumor area. This may explain the inconsistency in miRNA expression estimates in BC coming from different laboratories

Validation of Breast Cancer Margins by Tissue Spray Mass Spectrometry

Current methods for the intraoperative determination of breast cancer margins commonly suffer from the insufficient accuracy, specificity and/or low speed of analysis, increasing the time and cost of operation as well the risk of cancer recurrence. The purpose of this study is to develop a method for the rapid and accurate determination of breast cancer margins using direct molecular profiling by mass spectrometry (MS). Direct molecular fingerprinting of tiny pieces of breast tissue (approximately 1 × 1 × 1 mm) is performed using a home-built tissue spray ionization source installed on a Maxis Impact quadrupole time-of-flight mass spectrometer (qTOF MS) (Bruker Daltonics, Hamburg, Germany). Statistical analysis of MS data from 50 samples of both normal and cancer tissue (from 25 patients) was performed using orthogonal projections onto latent structures discriminant analysis (OPLS-DA). Additionally, the results of OPLS classification of new 19 pieces of two tissue samples were compared with the results of histological analysis performed on the same tissues samples. The average time of analysis for one sample was about 5 min. Positive and negative ionization modes are used to provide complementary information and to find out the most informative method for a breast tissue classification. The analysis provides information on 11 lipid classes. OPLS-DA models are created for the classification of normal and cancer tissue based on the various datasets: All mass spectrometric peaks over 300 counts; peaks with a statistically significant difference of intensity determined by the Mann–Whitney U-test (p < 0.05); peaks identified as lipids; both identified and significantly different peaks. The highest values of Q2 have models built on all MS peaks and on significantly different peaks. While such models are useful for classification itself, they are of less value for building explanatory mechanisms of pathophysiology and providing a pathway analysis. Models based on identified peaks are preferable from this point of view. Results obtained by OPLS-DA classification of the tissue spray MS data of a new sample set (n = 19) revealed 100% sensitivity and specificity when compared to histological analysis, the “gold” standard for tissue classification. “All peaks” and “significantly different peaks” datasets in the positive ion mode were ideal for breast cancer tissue classification. Our results indicate the potential of tissue spray mass spectrometry for rapid, accurate and intraoperative diagnostics of breast cancer tissue as a means to reduce surgical intervention

Synthetic diagnostic for the JET scintillator probe lost alpha measurements

A synthetic diagnostic has been developed for the JET lost alpha scintillator probe, based on the ASCOT fast ion orbit following code and the AFSI fusion source code. The synthetic diagnostic models the velocity space distribution of lost fusion products in the scintillator probe. Validation with experimental measurements is presented, where the synthetic diagnostic is shown to predict the gyroradius and pitch angle of lost DD protons and tritons. Additionally, the synthetic diagnostic reproduces relative differences in total loss rates in multiple phases of the discharge, which can be used as a basis for total loss rate predictions

Control of the hydrogen:deuterium isotope mixture using pellets in JET

Deuterium pellets are injected into an initially pure hydrogen H-mode plasma in order to control the hydrogen: deuterium (H:D) isotope mixture. The pellets are deposited in the outer 20% of the minor radius, similar to that expected in ITER, creating transiently hollow electron density profiles. A H: D isotope mixture of approximately 45%:55% is obtained in the core with a pellet fuelling throughput of Phi(pel) = 0.045P(aux)/T-e,T-ped similar to previous pellet fuelling experiments in pure deuterium. Evolution of the H: D mix in the core is reproduced using a simple model, although deuterium transport could be higher at the beginning of the pellet train compared with the flat-top phase

First principles and integrated modelling achievements towards trustful fusion power predictions for JET and ITER

Predictability of burning plasmas is a key issue for designing and building credible future fusion devices. In this context, an important effort of physics understanding and guidance is being carried out in parallel to JET experimental campaigns in H and D by performing analyses and modelling towards an improvement of the understanding of DT physics for the optimization of the JET-DT neutron yield and fusion born alpha particle physics. Extrapolations to JET-DT from recent experiments using the maximum power available have been performed including some of the most sophisticated codes and a broad selection of models. There is a general agreement that 11-15 MW of fusion power can be expected in DT for the hybrid and baseline scenarios. On the other hand, in high beta, torque and fast ion fraction conditions, isotope effects could be favourable leading to higher fusion yield. It is shown that alpha particles related physics, such as TAE destabilization or fusion power electron heating, could be studied in ITER relevant JET-DT plasmas

Deep neural networks for plasma tomography with applications to JET and COMPASS

Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural networks, can be used for inverse problems such as plasma tomography. In essence, plasma tomography consists in reconstructing the 2D plasma profile on a poloidal cross-section of a fusion device, based on line-integrated measurements from multiple radiation detectors. Since the reconstruction process is computationally intensive, a deconvolutional neural network trained to produce the same results will yield a significant computational speedup, at the expense of a small error which can be assessed using different metrics. In this work, we discuss the design principles behind such networks, including the use of multiple layers, how they can be stacked, and how their dimensions can be tuned according to the number of detectors and the desired tomographic resolution for a given fusion device. We describe the application of such networks at JET and COMPASS, where at JET we use the bolometer system, and at COMPASS we use the soft X-ray diagnostic based on photodiode arrays