Wavelet-based multifractal analysis of dynamic infrared thermograms to assist in early breast cancer diagnosis

Role of Infrared Thermography in Planning and Monitoring of Head and Neck Microvascular Flap Reconstruction

Background

Reconstruction using microvascular free flaps has become the standard of care in head and neck cancer surgery, and their success lies in appropriate planning, adequate revascularization, and early detection of flap compromise so that prompt salvage is possible. This study evaluates the role of infrared thermography in the planning, execution, and postoperative monitoring of microvascular flaps in head and neck reconstructions.

Methods

This is a single institutional, prospective observational study conducted at a tertiary care hospital in South India for 13 months. Twenty patients were included, and their thermographic images were captured in the preoperative, intraoperative, and postoperative settings using the infrared camera FLIR T400. These images were analyzed along with the Doppler, and clinical monitoring findings in all the settings and the temperature difference were calculated postoperatively.

Results

Hotspot perforator marking was made using infrared camera, and perforator marking was made using hand-held Doppler preoperatively, which correlated in 93% of cases. Intraoperatively, flap rewarming was successfully demonstrated in 19 of 20 cases. Postoperatively, flap compromise was observed on infrared thermography during the first 24 hours but not on clinical monitoring in three cases. The temperature difference values recorded were 5.4°C, 2.4°C, and 4.9°C. The mean of temperature difference of the healthy flaps was 1.0°C (range 0.1°C-1.8°C).

Conclusion

Infrared thermography provides simple and reliable imaging, which can be used in perforator marking and flap designing preoperatively and checking the flap perfusion and vascular anastomosis patency intra- and postoperatively.

Nonlinear Statistical Analysis of Normal and Pathological Infant Cry Signals in Cepstrum Domain by Multifractal Wavelet Leaders

Multifractal behavior in the cepstrum representation of healthy and unhealthy infant cry signals is examined by means of wavelet leaders and compared using the Student t-test. The empirical results show that both expiration and inspiration signals exhibit clear evidence of multifractal properties under healthy and unhealthy conditions. In addition, expiration and inspiration signals exhibit more complexity under healthy conditions than under unhealthy conditions. Furthermore, distributions of multifractal characteristics are different across healthy and unhealthy conditions. Hence, this study improves the understanding of infant crying by providing a complete description of its intrinsic dynamics to better evaluate its health status.

Elimination of Image Saturation Effects on Multifractal Statistics Using the 2D WTMM Method

Imaging artifacts such as image saturation can restrict the computational analysis of medical images. Multifractal analyses are typically restricted to self-affine, everywhere singular, surfaces. Image saturation regions in these rough surfaces rob them of these core properties, and their exclusion decreases the statistical power of clinical analyses. By adapting the powerful 2D Wavelet Transform Modulus Maxima (WTMM) multifractal method, we developed a strategy where the image can be partitioned according to its localized response to saturated regions. By eliminating the contribution from those saturated regions to the partition function calculations, we show that the estimation of the multifractal statistics can be correctly calculated even with image saturation levels up to 20% (where 20% is the number of saturated pixels over the total number of pixels in the image).

Somatic point mutations are enriched in non-coding RNAs with possible regulatory function in breast cancer

Non-coding RNAs (ncRNAs) form a large portion of the mammalian genome. However, their biological functions are poorly characterized in cancers. In this study, using a newly developed tool, SomaGene, we analyze de novo somatic point mutations from the International Cancer Genome Consortium (ICGC) whole-genome sequencing data of 1,855 breast cancer samples. We identify 1030 candidates of ncRNAs that are significantly and explicitly mutated in breast cancer samples. By integrating data from the ENCODE regulatory features and FANTOM5 expression atlas, we show that the candidate ncRNAs significantly enrich active chromatin histone marks (1.9 times), CTCF binding sites (2.45 times), DNase accessibility (1.76 times), HMM predicted enhancers (2.26 times) and eQTL polymorphisms (1.77 times). Importantly, we show that the 1030 ncRNAs contain a much higher level (3.64 times) of breast cancer-associated genome-wide association (GWAS) single nucleotide polymorphisms (SNPs) than genome-wide expectation. Such enrichment has not been seen with GWAS SNPs from other cancers. Using breast cell line related Hi-C data, we then show that 82% of our candidate ncRNAs (1.9 times) significantly interact with the promoter of protein-coding genes, including previously known cancer-associated genes, suggesting the critical role of candidate ncRNA genes in the activation of essential regulators of development and differentiation in breast cancer. We provide an extensive web-based resource (https://www.ihealthe.unsw.edu.au/research) to communicate our results with the research community. Our list of breast cancer-specific ncRNA genes has the potential to provide a better understanding of the underlying genetic causes of breast cancer. Lastly, the tool developed in this study can be used to analyze somatic mutations in all cancers.

The SomaGene tool is developed to identify non-coding RNAs (ncRNAs) mutated in breast cancer but can be used for other cancers. Candidate ncRNAs are shown to be enriched for regulatory features and to contain specific trait loci polymorphisms.

Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography

Breast cancer is one of the leading causes of mortality globally, but early diagnosis and treatment can increase the cancer survival rate. In this context, thermography is a suitable approach to help early diagnosis due to the temperature difference between cancerous tissues and healthy neighboring tissues. This work proposes an ensemble method for selecting models and features by combining a Genetic Algorithm (GA) and the Support Vector Machine (SVM) classifier to diagnose breast cancer. Our evaluation demonstrates that the approach presents a significant contribution to the early diagnosis of breast cancer, presenting results with 94.79% Area Under the Receiver Operating Characteristic Curve and 97.18% of Accuracy.

Label-Free Single Cell Viability Assay Using Laser Interference Microscopy

Laser interference microscopy (LIM) is a promising label-free method for single-cell research applicable to cell viability assessment in the studies of mammalian cells. This paper describes the development of a sensitive and reproducible method for assessing cell viability using LIM. The method, based on associated signal processing techniques, has been developed as a result of real-time investigation in phase thickness fluctuations of viable and non-viable MCF-7 cells, reflecting the presence and absence of their metabolic activity. As evinced by the values of the variable vc, this variable determines the viability of a cell only in the attached state (vc exceeds 20 nm2 for viable attached cells). The critical value of the power spectrum slope βc of the phase thickness fluctuations equals 1.00 for attached MCF-7 cells and 0.71 for suspended cells. The slope of the phase fluctuations' power spectrum for MCF-7 cells was determined to exceed the threshold value of βc for a living cell, otherwise the cell is dead. The results evince the power spectrum slope as the most appropriate indicator of cell viability, while the integrated evaluation criterion (vc and βc values) can be used to assay the viability of attached cells.

A hybrid methodology for breast screening and cancer diagnosis using thermography

Breast cancer is the second most common cancer in the world. Early diagnosis and treatment increase the patient's chances of healing. The temperature of cancerous tissues is generally different from that of healthy neighboring tissues, making thermography an option to be considered in the fight against cancer because it does not use ionizing radiation, venous access, or any other invasive process, presenting no damage or risk to the patient. In this paper, we propose a hybrid computational method using the Dynamic Infrared Thermography (DIT) and Static Infrared Thermography (SIT) for abnormality screening and diagnosis of malignant tumor (cancer), applying supervised and unsupervised machine learning techniques. We use the area under receiver operating characteristic curve, sensitivity, specificity, and accuracy as performance measures to compare the hybrid methodology with previous work in the literature. The K-Star classifier achieved accuracy of 99% in the screening phase using DIT images. The Support Vector Machines (SVM) classifier applied on SIT images yielded accuracy of 95% in the diagnosis of cancer. The results confirm the potential of the proposed approaches for screening and diagnosis of breast cancer.

Loss of Mammographic Tissue Homeostasis in Invasive Lobular and Ductal Breast Carcinomas vs. Benign Lesions

The 2D wavelet transform modulus maxima (WTMM) method is used to perform a comparison of the spatial fluctuations of mammographic breast tissue from patients with invasive lobular carcinoma, those with invasive ductal carcinoma, and those with benign lesions. We follow a procedure developed and validated in a previous study, in which a sliding window protocol is used to analyze thousands of small subregions in a given mammogram. These subregions are categorized according to their Hurst exponent values (H): fatty tissue (H ≤ 0.45), dense tissue (H ≥ 0.55), and disrupted tissue potentially linked with tumor-associated loss of homeostasis (0.45 < H < 0.55). Following this categorization scheme, we compare the mammographic tissue composition of the breasts. First, we show that cancerous breasts are significantly different than breasts with a benign lesion (p-value ∼ 0.002). Second, the asymmetry between a patient’s cancerous breast and its contralateral counterpart, when compared to the asymmetry from patients with benign lesions, is also statistically significant (p-value ∼ 0.006). And finally, we show that lobular and ductal cancerous breasts show similar levels of disruption and similar levels of asymmetry. This study demonstrates reproducibility of the WTMM sliding-window approach to help detect and characterize tumor-associated breast tissue disruption from standard mammography. It also shows promise to help with the detection lobular lesions that typically go undetected via standard screening mammography at a much higher rate than ductal lesions. Here both types are assessed similarly.

LncRNAs AK058003 and MVIH Overexpression in the Blood Samples of Iranian Breast Cancer Patients

Background

Long non-coding RNAs (LncRNAs) are considered as novel biological regulators and potential cancer biomarkers. LncRNAs microvascular invasion in hepatocellular carcinoma (HCC; microvascular invasion [MVIH]) and AK058003 are associated with MVIH in HCC. In breast cancer (BC), upregulated MVIH and AK058003 expression levels have been shown to promote cell proliferation, though LncRNA-AK058003 acts as a tumor suppressor in HCC.

Methods

Blood samples were collected from 30 healthy women and 30 female BC patients. RNA was extracted from the blood of both groups, and cDNA was then synthesized. A real-time PCR technique was conducted to measure the expression level of LncRNA-AK058003 and MVIH.

Results

The expression level of two LncRNAs in the blood samples of BC patients increased significantly compared with healthy individuals. The levels of AK058003 and MVIH were not associated with lymph node metastasis (p = 0.402 and p = 0.39), tumor size (p = 0.76 and p = 0.461), and tumor size; lymph nodes, metastasis stage (TNM; p = 0.574 and p = 0.711), respectively.

Conclusion

As per our findings, LncRNA-AK058003 could serve as a suitable indicator for low stage of BC. In addition, the increased level of LncRNA-MVIH could be considered as a biomarker for BC, which needs more evaluation in the future.

A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography

Breast cancer has been the second leading cause of cancer death among women. New techniques to enhance early diagnosis are very important to improve cure rates. This paper proposes and evaluates an image analysis method to automatically detect patients with breast benign and malignant changes (tumors). Such method explores the difference of Dynamic Infrared Thermography (DIT) patterns observed in patients’ skin. After obtaining the sequential DIT images of each patient, their temperature arrays are computed and new images in gray scale are generated. Then the regions of interest (ROIs) of those images are segmented and, from them, arrays of the ROI temperature are computed. Features are extracted from the arrays, such as the ones based on statistical, clustering, histogram comparison, fractal geometry, diversity indices and spatial statistics. Time series that are broken down into subsets of different cardinalities are generated from such features. Automatic feature selection methods are applied and used in the Support Vector Machine (SVM) classifier. In our tests, using a dataset of 68 images, 100% accuracy was achieved.

Aided Hand Detection in Thermal Imaging Using RGB Stereo Vision

Thermal imaging is used in medical diagnosis and preventive screening, e.g. breast cancer, cardiovascular disease, and orthopedics. Segmentation algorithms fail to recognize body parts of interest when the temperature difference between the body parts and the background is insufficient. We propose to perform segmentation in two stereoscopically acquired RGB images and to triangulate corresponding points extracted from those images into world coordinates. The thereby acquired world coordinates are projected into the thermal image plane for a more robust segmentation result. Our worked example is the segmentation of human hands. The extension of the thermal setup with two additional RGB cameras improves segmentation in our particular case, but could also make segmentation of other body parts in thermal images more robust. Comparing significant points like fingertips and the junctions between the fingers and the metacarpus, we come up with an average deviation of 1.03 pixel ± 0.82 pixel in x-axis direction and 1.04 pixel ± 0.62 pixel in y-axis direction, roughly corresponding to a mean Euclidean distance of 1.4 mm on the hands.

Application and Analysis of Biomedical Imaging Technology in Early Diagnosis of Breast Cancer

Breast cancer is the primary malignant tumor that endangers women's health. The incidence of breast cancer is increasing rapidly in recent years. Accurate disease evaluation before treatment is the key to the selection of treatment options. Biomedical imaging technology plays an irreplaceable role in the diagnosis and staging of tumors. Various imaging methods can provide excellent temporal and spatial resolution from multiple levels and perspectives and have become one of the most commonly used means of breast cancer early detection. With the development of radiomics, it has been found that early imaging diagnosis of breast cancer plays an important guiding role in clinical decision-making. The purpose of this study is to explore the characteristics of various breast cancer imaging technologies, promote the development of individualized accurate diagnosis and treatment of imaging, and improve the clinical application value of radiomics in the early diagnosis of breast cancer.

Performance Analysis of Low-Level and High-Level Intuitive Features for Melanoma Detection

This paper presents an intelligent approach for the detection of Melanoma—a deadly skin cancer. The first step in this direction includes the extraction of the textural features of the skin lesion along with the color features. The extracted features are used to train the Multilayer Feed-Forward Artificial Neural Networks. We evaluate the trained networks for the classification of test samples. This work entails three sets of experiments including 50 % , 70 % and 90 % of the data used for training, while the remaining 50 % , 30 % , and 10 % constitute the test sets. Haralick’s statistical parameters are computed for the extraction of textural features from the lesion. Such parameters are based on the Gray Level Co-occurrence Matrices (GLCM) with an offset of 2 , 4 , 8 , 12 , 16 , 20 , 24 and 28, each with an angle of 0 , 45 , 90 and 135 degrees, respectively. In order to distill color features, we have calculated the mean, median and standard deviation of the three color planes of the region of interest. These features are fed to an Artificial Neural Network (ANN) for the detection of skin cancer. The combination of Haralick’s parameters and color features have proven better than considering the features alone. Experimentation based on another set of features such as Asymmetry, Border irregularity, Color and Diameter (ABCD) features usually observed by dermatologists has also been demonstrated. The ‘D’ feature is however modified and named Oblongness. This feature captures the ratio between the length and the width. Furthermore, the use of modified standard deviation coupled with ABCD features improves the detection of Melanoma by an accuracy of 93.7 %

Multifractal Desynchronization of the Cardiac Excitable Cell Network During Atrial Fibrillation. II. Modeling

In a companion paper (I. Multifractal analysis of clinical data), we used a wavelet-based multiscale analysis to reveal and quantify the multifractal intermittent nature of the cardiac impulse energy in the low frequency range ≲ 2Hz during atrial fibrillation (AF). It demarcated two distinct areas within the coronary sinus (CS) with regionally stable multifractal spectra likely corresponding to different anatomical substrates. The electrical activity also showed no sign of the kind of temporal correlations typical of cascading processes across scales, thereby indicating that the multifractal scaling is carried by variations in the large amplitude oscillations of the recorded bipolar electric potential. In the present study, to account for these observations, we explore the role of the kinetics of gap junction channels (GJCs), in dynamically creating a new kind of imbalance between depolarizing and repolarizing currents. We propose a one-dimensional (1D) spatial model of a denervated myocardium, where the coupling of cardiac cells fails to synchronize the network of cardiac cells because of abnormal transjunctional capacitive charging of GJCs. We show that this non-ohmic nonlinear conduction 1D modeling accounts quantitatively well for the "multifractal random noise" dynamics of the electrical activity experimentally recorded in the left atrial posterior wall area. We further demonstrate that the multifractal properties of the numerical impulse energy are robust to changes in the model parameters.

Fuzzy controller design for breast cancer treatment based on fractal dimension using breast thermograms

In this study, three non-linear indices consist of compression, one-dimensional (1D) and two-dimensional (2D) fractal dimensions are used for the determination of the malignancy or benignity of cancer tumours in breast thermograms. On the other hand, by developing the high-precision infrared cameras as well as new methods of image processing, biomedical thermography images have found a prominent position among the others. Furthermore, cancerous tissue can be affected by the laser. In this study, in order to treat the cancerous lesion identified by breast thermograms, the laser parameters are designed. The basis of controller designing is the obtained non-linear indices. If the indices are moved from the chaotic behaviour to normal condition, the treating tissue is going from cancerous to a healthy condition and the treatment process is completed. Radiation frequency and the energy density of laser are designed as two key elements in the cancer treatment. In this study, the type I and type II fuzzy controllers are employed for the control strategies. Using the proposed closed-loop control, the non-linear indices of the cancerous lesion will be reduced during the treatment process. The simulation results on two datasets of breast thermograms indicate the superiority of type II fuzzy controller.

Gene-Level Regulation of Acupuncture Therapy in Spontaneously Hypertensive Rats: A Whole Transcriptome Analysis

Hypertension is a global health problem. It has been reported that acupuncture at Taichong acupoints (LR3) decreases high blood pressure in spontaneously hypertensive rats. A transcriptome analysis can profile gene expression and its relationship with acupuncture. In this study, rats were treated with 2 weeks of acupuncture followed by regular recording of blood pressure (BP). The mRNA changes in the rostral ventrolateral medulla (RVLM) were evaluated to uncover the genetic mechanisms of acupuncture by using a whole transcript array (Affymetrix Rat Gene 1.0 ST array). BP measurements showed that acupuncture significantly decreased systolic blood pressure (SBP), mean arterial pressure (MAP), and heart rate (HR). In the bioinformatics results, 2371 differentially expressed genes (DEGs) were identified, where 83 DEGs were overlapped among Wistar-Kyoto rats (WKYs), spontaneously hypertensive rats (SHRs), and SHRs + acupuncture rats (SHRs+Acu). Gene ontology (GO) and pathway analysis revealed that 279 GO terms and 20 pathways with significant differences were related to oxidative stress, inflammation, and vascular endothelial function. In addition, coexpressed DEGs networks indicated that Cd4 and Il-33 might mediate the cascade of inflammation and oxidative stress responses, which could serve as a potential target of acupuncture treatment. In conclusion, our study demonstrated that acupuncture is a promising therapy for treating hypertension and could regulate multiple biological processes mainly involving oxidative stress, inflammation, and vascular endothelial function.

Automatic detection of perforator vessels using infrared thermography in reconstructive surgery

PURPOSE

Knowing the location of the blood vessels supplying the skin and subcutaneous tissue is a requirement during the planning of tissue transfer in reconstructive surgery. Commonly used imaging techniques such as computed tomography angiography and indocyanine green angiography expose the patient to radiation or a contrast agent, respectively. Infrared thermal imaging was evaluated with success as a non-invasive alternative. To support the interpretation of thermograms, a method to automatically detect the perforators was developed and evaluated.

METHODS

A system consisting of a thermal camera, a PC and custom software was developed. The temperature variations of the skin surface were analysed to extract the perforator locations. A study was conducted to assess the performance of the algorithm by comparing the detection results of the algorithm with manually labelled thermal images by two clinicians of the deep inferior epigastric perforator flap of 20 healthy volunteers.

RESULTS

The F measure, precision and recall were used to evaluate the system performance. The median F measure is 0.833, the median precision is 0.80, and the median recall is 0.907.

CONCLUSION

The results of this study showed that it is possible to automatically and reliably detect the skin perforators in thermograms despite their weak temperature signature. Infrared thermal imaging is a non-invasive and contactless approach suitable for intraoperative use. Combined with a computer-assisted tool for the automatic detection of perforator vessels, it is a relevant alternative intraoperative imaging method to the standard indocyanine green angiography.

Long non-coding RNA HOXA-AS2 promotes proliferation and invasion of breast cancer by acting as a miR-520c-3p sponge

The long non-coding RNA (lncRNA) HOXA cluster antisense RNA2 (HOXA-AS2) has recently been shown to be dysregulated and involved in the progression of several cancers. However, the biological role and clinical significance of HOXA-AS2 in the carcinogenesis of breast cancer are still unclear. In the present study, we found that HOXA-AS2 was up-regulated in human breast cancer tissues and cell lines and associated with clinicopathological characteristics. Silencing of HOXA-AS2 inhibited the progression of breast cancer cells in vitro and in vivo. Furthermore, microarray profiling indicated that HOXA-AS2 serves as an endogenous sponge by directly binding to miR-520c-3p and down-regulating miR-520c-3p expression. We demonstrated that HOXA-AS2 controls the expression of miR-520c-3p target genes, TGFBR2 and RELA, in breast cancer cells. Therefore, our study may provide a better understanding of the pathogenesis of breast cancer and suggests that HOXA-AS2 may be a potential prognostic and therapeutic target in breast cancer.

Multifractal Desynchronization of the Cardiac Excitable Cell Network During Atrial Fibrillation. I. Multifractal Analysis of Clinical Data

Atrial fibrillation (AF) is a cardiac arrhythmia characterized by rapid and irregular atrial electrical activity with a high clinical impact on stroke incidence. Best available therapeutic strategies combine pharmacological and surgical means. But when successful, they do not always prevent long-term relapses. Initial success becomes all the more tricky to achieve as the arrhythmia maintains itself and the pathology evolves into sustained or chronic AF. This raises the open crucial issue of deciphering the mechanisms that govern the onset of AF as well as its perpetuation. In this study, we develop a wavelet-based multi-scale strategy to analyze the electrical activity of human hearts recorded by catheter electrodes, positioned in the coronary sinus (CS), during episodes of AF. We compute the so-called multifractal spectra using two variants of the wavelet transform modulus maxima method, the moment (partition function) method and the magnitude cumulant method. Application of these methods to long time series recorded in a patient with chronic AF provides quantitative evidence of the multifractal intermittent nature of the electric energy of passing cardiac impulses at low frequencies, i.e., for times (≳0.5 s) longer than the mean interbeat (≃ 10-1 s). We also report the results of a two-point magnitude correlation analysis which infers the absence of a multiplicative time-scale structure underlying multifractal scaling. The electric energy dynamics looks like a "multifractal white noise" with quadratic (log-normal) multifractal spectra. These observations challenge concepts of functional reentrant circuits in mechanistic theories of AF, still leaving open the role of the autonomic nervous system (ANS). A transition is indeed observed in the computed multifractal spectra which group according to two distinct areas, consistently with the anatomical substrate binding to the CS, namely the left atrial posterior wall, and the ligament of Marshall which is innervated by the ANS. In a companion paper (II. Modeling), we propose a mathematical model of a denervated heart where the kinetics of gap junction conductance alone induces a desynchronization of the myocardial excitable cells, accounting for the multifractal spectra found experimentally in the left atrial posterior wall area.

Dynamic infrared thermography (DIRT) for assessment of skin blood perfusion in cranioplasty: a proof of concept for qualitative comparison with the standard indocyanine green video angiography (ICGA)

PURPOSE

Complications in wound healing after neurosurgical operations occur often due to scarred dehiscence with skin blood perfusion disturbance. The standard imaging method for intraoperative skin perfusion assessment is the invasive indocyanine green video angiography (ICGA). The noninvasive dynamic infrared thermography (DIRT) is a promising alternative modality that was evaluated by comparison with ICGA.

METHODS

The study was carried out in two parts: (1) investigation of technical conditions for intraoperative use of DIRT for its comparison with ICGA, and (2) visual and quantitative comparison of both modalities in a proof of concept on nine patients. Time-temperature curves in DIRT and time-intensity curves in ICGA for defined regions of interest were analyzed. New perfusion parameters were defined in DIRT and compared with the usual perfusion parameters in ICGA.

RESULTS

The visual observation of the image data in DIRT and ICGA showed that operation material, anatomical structures and skin perfusion are represented similarly in both modalities. Although the analysis of the curves and perfusion parameter values showed differences between patients, no complications were observed clinically. These differences were represented in DIRT and ICGA equivalently.

CONCLUSIONS

DIRT has shown a great potential for intraoperative use, with several advantages over ICGA. The technique is passive, contactless and noninvasive. The practicability of the intraoperative recording of the same operation field section with ICGA and DIRT has been demonstrated. The promising results of this proof of concept provide a basis for a trial with a larger number of patients.

Extracting oscillating components from nonstationary time series: A wavelet-induced method

This paper consists in the description and application of a method called wavelet-induced mode extraction (WIME) in the context of time-frequency analysis. WIME aims to extract the oscillating components that build amplitude modulated-frequency modulated signals. The essence of this technique relies on the successive extractions of the dominant ridges of wavelet-based time-frequency representations of the signal under consideration. Our tests on simulated examples indicate strong decomposition and reconstruction skills, trouble-free handling of crossing trajectories in the time-frequency plane, sharp performances in frequency detection in the case of mode-mixing problems, and a natural tolerance to noise. These results are compared with those obtained with empirical mode decomposition. We also show that WIME still gives meaningful results with real-life data, namely, the Oceanic Niño Index.

Supportive Noninvasive Tool for the Diagnosis of Breast Cancer Using a Thermographic Camera as Sensor

Breast cancer is the leading disease in incidence and mortality among women in developing countries. The opportune diagnosis of this disease strengthens the survival index. Mammography application is limited by age and periodicity. Temperature is a physical magnitude that can be measured by using multiple sensing techniques. IR (infrared) thermography using commercial cameras is gaining relevance in industrial and medical applications because it is a non-invasive and non-intrusive technology. Asymmetrical temperature in certain human body zones is associated with cancer. In this paper, an IR thermographic sensor is applied for breast cancer detection. This work includes an automatic breast segmentation methodology, to spot the hottest regions in thermograms using the morphological watershed operator to help the experts locate the tumor. A protocol for thermogram acquisition considering the required time to achieve a thermal stabilization is also proposed. Breast thermograms are evaluated as thermal matrices, instead of gray scale or false color images, increasing the certainty of the provided diagnosis. The proposed tool was validated using the Database for Mastology Research and tested in a voluntary group of 454 women of different ages and cancer stages with good results, leading to the possibility of being used as a supportive tool to detect breast cancer and angiogenesis cases.

Automatic depth scanning system for 3D infrared thermography

Infrared thermography can be used as a pre-, intra- and post-operative imaging technique during medical treatment of patients. Modern infrared thermal cameras are capable of acquiring images with a high sensitivity of 10 mK and beyond. They provide a planar image of an examined 3D object in which this high sensitivity is only reached within a plane perpendicular to the camera axis and defined by the focus of the lens. Out of focus planes are blurred and temperature values are inaccurate. A new 3D infrared thermography system is built by combining a thermal camera with a depth camera. Multiple images at varying focal planes are acquired with the infrared camera using a motorized system. The sharp regions of individual images are projected onto the 3D object’s surface obtained by the depth camera. The system evaluation showed that deviation between measured temperature values and a ground truth is reduced with our system.

Comparative Multifractal Analysis of Dynamic Infrared Thermograms and X-Ray Mammograms Enlightens Changes in the Environment of Malignant Tumors

There is growing evidence that the microenvironment surrounding a tumor plays a special role in cancer development and cancer therapeutic resistance. Tumors arise from the dysregulation and alteration of both the malignant cells and their environment. By providing tumor-repressing signals, the microenvironment can impose and sustain normal tissue architecture. Once tissue homeostasis is lost, the altered microenvironment can create a niche favoring the tumorigenic transformation process. A major challenge in early breast cancer diagnosis is thus to show that these physiological and architectural alterations can be detected with currently used screening techniques. In a recent study, we used a 1D wavelet-based multi-scale method to analyze breast skin temperature temporal fluctuations collected with an IR thermography camera in patients with breast cancer. This study reveals that the multifractal complexity of temperature fluctuations superimposed on cardiogenic and vasomotor perfusion oscillations observed in healthy breasts is lost in malignant tumor foci in cancerous breasts. Here we use a 2D wavelet-based multifractal method to analyze the spatial fluctuations of breast density in the X-ray mammograms of the same panel of patients. As compared to the long-range correlations and anti-correlations in roughness fluctuations, respectively observed in dense and fatty breast areas, some significant change in the nature of breast density fluctuations with some clear loss of correlations is detected in the neighborhood of malignant tumors. This attests to some architectural disorganization that may deeply affect heat transfer and related thermomechanics in breast tissues, corroborating the change to homogeneous monofractal temperature fluctuations recorded in cancerous breasts with the IR camera. These results open new perspectives in computer-aided methods to assist in early breast cancer diagnosis.

Hybrid analysis for indicating patients with breast cancer using temperature time series

Breast cancer is the most common cancer among women worldwide. Diagnosis and treatment in early stages increase cure chances. The temperature of cancerous tissue is generally higher than that of healthy surrounding tissues, making thermography an option to be considered in screening strategies of this cancer type. This paper proposes a hybrid methodology for analyzing dynamic infrared thermography in order to indicate patients with risk of breast cancer, using unsupervised and supervised machine learning techniques, which characterizes the methodology as hybrid. The dynamic infrared thermography monitors or quantitatively measures temperature changes on the examined surface, after a thermal stress. In the dynamic infrared thermography execution, a sequence of breast thermograms is generated. In the proposed methodology, this sequence is processed and analyzed by several techniques. First, the region of the breasts is segmented and the thermograms of the sequence are registered. Then, temperature time series are built and the k-means algorithm is applied on these series using various values of k. Clustering formed by k-means algorithm, for each k value, is evaluated using clustering validation indices, generating values treated as features in the classification model construction step. A data mining tool was used to solve the combined algorithm selection and hyperparameter optimization (CASH) problem in classification tasks. Besides the classification algorithm recommended by the data mining tool, classifiers based on Bayesian networks, neural networks, decision rules and decision tree were executed on the data set used for evaluation. Test results support that the proposed analysis methodology is able to indicate patients with breast cancer. Among 39 tested classification algorithms, K-Star and Bayes Net presented 100% classification accuracy. Furthermore, among the Bayes Net, multi-layer perceptron, decision table and random forest classification algorithms, an average accuracy of 95.38% was obtained.

Negative regulation of CDC42 expression and cell cycle progression by miR-29a in breast cancer

Objective

The inhibitory role of microRNA-29a (miR-29a) has been assessed in breast cancer cells. Herein, we analyze the underlying mechanisms of its role in cell cycle progression in breast cancer cells.

Methods

We applied real-time polymerase chain reaction (PCR) to detect the expression of miR-29 in breast cancer cell lines. Then one of the cell lines, MDA-MB-453, was transfected with mimics of miR-29a. The cell cycle was analyzed by fluorescence-activated cell sorting after staining the cells with propidium iodide. Real-time PCR, luciferase assay and western blot were used together to verify the regulation of the predicted target, cell division cycle 42 (CDC42) by miR-29a.

Results

MiR-29s were decreased in our selected mammary cell lines, among which miR-29a was the dominant isoform. Overexpression of miR-29a caused cell cycle arrest at the G0/G1 phase. We further found that miR-29a could target the expression of CDC42, which is a small GTPase associated with cell cycle progression.

Conclusion

We suggest that miR-29a exerts its tumor suppressor role in breast cancer cells partially by arresting the cell cycle through negative regulation of CDC42.

Long noncoding RNA CCAT2 promotes breast tumor growth by regulating the Wnt signaling pathway

In addition to protein-coding genes, the human genome makes a large amount of noncoding RNAs, including microRNAs and long noncoding RNAs (lncRNAs). Emerging evidence indicates that lncRNAs could have a critical role in the regulation of cellular processes such as cell growth and apoptosis as well as cancer progression and metastasis. The lncRNA CCAT2 is dysregulated in several cancers such as colon cancer, non-small cell lung cancer, esophageal squamous cell carcinoma, gastric cancer, and breast cancer; however, the contributions of CCAT2 to breast cancer remain largely unknown. In the current paper, we first confirmed the high expression level of CCAT2 in breast cancer tissues and breast cancer cell lines by reverse transcription quantitative polymerase chain reaction (RT-qPCR) assay, and we further analyzed the relationship between CCAT2 expression and clinical prognostic factors. Also, the biological function of CCAT2 was explored and the results showed silencing of CCAT2 could suppress cell growth in vitro and tumor formation in vivo. Finally, our results revealed that the abnormal expression of CCAT2 could influence the Wnt signaling pathway. In conclusion, lncRNA CCAT2 might be considered as a novel molecule involved in breast cancer development, which provides a potential therapeutic target for breast cancer.

The long noncoding RNA SPRY4-IT1 increases the proliferation of human breast cancer cells by upregulating ZNF703 expression

Background

Long noncoding RNAs (lncRNAs) have emerged recently as a new class of genes that regulate cellular processes, such as cell growth and apoptosis. The SPRY4 intronic transcript 1 (SPRY4-IT1) is a 708-bp lncRNA on chromosome 5 with a potential functional role in tumorigenesis. The clinical significance of SPRY4-IT1 and the effect of SPRY4-IT1 on cancer progression are unclear.

Methods

Quantitative reverse transcriptase PCR (qRT-PCR) was performed to investigate the expression of SPRY4-IT1 in 48 breast cancer tissues and four breast cancer cell lines. Gain and loss of function approaches were used to investigate the biological role of SPRY4-IT1 in vitro. Microarray bioinformatics analysis was performed to identify the putative targets of SPRY4-IT1, which were further verified by rescue experiments, and by western blotting and qRT-PCR.

Results

SPRY4-IT1 expression was significantly upregulated in 48 breast cancer tumor tissues comparedwith normal tissues. Additionally, increased SPRY4-IT1 expression was found to be associated with a larger tumor size and an advanced pathological stage in breast cancer patients. The knockdown of SPRY4-IT1 significantly suppressed proliferation and caused apoptosis of breast cancer cells in vitro. Furthermore, we discovered that ZNF703 was a target of SPRY4-IT1 and was downregulated by SPRY4-IT1 knockdown. Moreover, we provide the first demonstration that ZNF703 plays an oncogenic role in ER (−) breast carcinoma cells.

Conclusions

SPRY4-IT1 is a novel prognostic biomarker and a potential therapeutic candidate for breast cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/s12943-015-0318-0) contains supplementary material, which is available to authorized users.

SWDreader: a wavelet-based algorithm using spectral phase to characterize spike-wave morphological variation in genetic models of absence epilepsy

BACKGROUND

Spike-wave discharges (SWD) found in neuroelectrical recordings are pathognomonic to absence epilepsy. The characteristic spike-wave morphology of the spike-wave complex (SWC) constituents of SWDs can be mathematically described by a subset of possible spectral power and phase values. Morlet wavelet transform (MWT) generates time-frequency representations well-suited to identifying this SWC-associated subset.

NEW METHOD

MWT decompositions of SWDs reveal spectral power concentrated at harmonic frequencies. The phase relationships underlying SWC morphology were identified by calculating the differences between phase values at SWD fundamental frequency from the 2nd, 3rd, and 4th harmonics, then using the three phase differences as coordinates to generate a density distribution in a {360°×360°×360°} phase difference space. Strain-specific density distributions were generated from SWDs of mice carrying the Gria4, Gabrg2, or Scn8a mutations to determine whether SWC morphological variants reliably mapped to the same regions of the distribution, and if distribution values could be used to detect SWD.

COMPARISON WITH EXISTING METHODS

To the best of our knowledge, this algorithm is the first to employ spectral phase to quantify SWC morphology, making it possible to computationally distinguish SWC morphological subtypes and detect SWDs.

RESULTS/CONCLUSIONS

Proof-of-concept testing of the SWDfinder algorithm shows: (1) a major pattern of variation in SWC morphology maps to one axis of the phase difference distribution, (2) variability between the strain-specific distributions reflects differences in the proportions of SWC subtypes generated during SWD, and (3) regularities in the spectral power and phase profiles of SWCs can be used to detect waveforms possessing SWC-like morphology.

Wavelet-based 3D reconstruction of microcalcification clusters from two mammographic views: new evidence that fractal tumors are malignant and Euclidean tumors are benign

The 2D Wavelet-Transform Modulus Maxima (WTMM) method was used to detect microcalcifications (MC) in human breast tissue seen in mammograms and to characterize the fractal geometry of benign and malignant MC clusters. This was done in the context of a preliminary analysis of a small dataset, via a novel way to partition the wavelet-transform space-scale skeleton. For the first time, the estimated 3D fractal structure of a breast lesion was inferred by pairing the information from two separate 2D projected mammographic views of the same breast, i.e. the cranial-caudal (CC) and mediolateral-oblique (MLO) views. As a novelty, we define the “CC-MLO fractal dimension plot”, where a “fractal zone” and “Euclidean zones” (non-fractal) are defined. 118 images (59 cases, 25 malignant and 34 benign) obtained from a digital databank of mammograms with known radiologist diagnostics were analyzed to determine which cases would be plotted in the fractal zone and which cases would fall in the Euclidean zones. 92% of malignant breast lesions studied (23 out of 25 cases) were in the fractal zone while 88% of the benign lesions were in the Euclidean zones (30 out of 34 cases). Furthermore, a Bayesian statistical analysis shows that, with 95% credibility, the probability that fractal breast lesions are malignant is between 74% and 98%. Alternatively, with 95% credibility, the probability that Euclidean breast lesions are benign is between 76% and 96%. These results support the notion that the fractal structure of malignant tumors is more likely to be associated with an invasive behavior into the surrounding tissue compared to the less invasive, Euclidean structure of benign tumors. Finally, based on indirect 3D reconstructions from the 2D views, we conjecture that all breast tumors considered in this study, benign and malignant, fractal or Euclidean, restrict their growth to 2-dimensional manifolds within the breast tissue.