Immunoexpression Analysis and Prognostic Value of BLCAP in Breast Cancer

Bladder Cancer Associated Protein (BLCAP, formerly Bc10), was identified by our laboratory as being down-regulated in bladder cancer with progression. BLCAP is ubiquitously expressed in different tissues, and several studies have found differential expression of BLCAP in various cancer types, such as cervical and renal cancer, as well as human tongue carcinoma and osteosarcoma. Here we report the first study of the expression patterns of BLCAP in breast tissue. We analyzed by immunohistochemistry tissue sections of normal and malignant specimens collected from 123 clinical high-risk breast cancer patients within the Danish Center for Translational Breast Cancer Research (DCTB) prospective study dataset. The staining pattern, the distribution of the immunostaining, and its intensity were studied in detail. We observed weak immunoreactivity for BLCAP in mammary epithelial cells, almost exclusively localizing to the cytoplasm and found that levels of expression of BLCAP were generally higher in malignant cells as compared to normal cells. Quantitative IHC analysis of BLCAP expression in breast tissues confirmed this differential BLCAP expression in tumor cells, and we could establish, in a 62-patient sample matched cohort, that immunostaining intensity for BLCAP was increased in tumors relative to normal tissue, in more than 45% of the cases examined, indicating that BLCAP may be of value as a marker for breast cancer. We also analyzed BLCAP expression and prognostic value using a set of tissue microarrays comprising an independent cohort of 2,197 breast cancer patients for which we had follow-up clinical information.     

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.     

Macroaggregated calcifications in cancer of the breast

Breast cancer is characterized by the presence of multiple aggregated microcalcinates. However, the authors managed to detect 6 cases of breast cancer with macroaggregate calcifications, of which 3 were not adequately imaged on mammograms. Morphological investigations in all cases have shown that macroaggregate calcifications that are typical of benign lesions, are also detectable in breast cancer. The absence of an image of calcification on mammograms is probably accountable for by its various density.     

Differential methylation of TSP50 and mTSP50 genes in different types of human tissues and mouse spermatic cells

Earlier studies identified human TSP50 as a testis-specific gene that encoded a threonine protease. Most importantly, TSP50 could be a cancer/testis antigen since there was a high frequency of reactivation in breast cancer biopsies. It was also found to be negatively regulated by the p53 gene. To further characterize this gene, we recently examined the DNA methylation patterns of the TSP50 gene promoter in normal human testis, as well as breast tissue and a testicular embryonic carcinoma cell line (HTECCL). Bisulfite genomic sequencing results demonstrated that the promoter exhibited mixed DNA methylation patterns in normal human testis, mainly non-methylation versus slight methylation, which could be attributed to the different stages spermatic cells go through during spermatogenesis. In contrast, it was methylated to a much greater extent in both breast tissue and HTECCL. To find out whether DNA methylation status was related to spermatogenesis stages, we analyzed DNA methylation patterns of the mTSP50 (the mouse ortholog of TSP50) promoter in spermatocytes and spermatozoa isolated from sexually mature mice. The results clearly demonstrated that each group of cells exhibited its preferential DNA methylation pattern that apparently was consistent with the gene expression status observed before. Taken together, our findings suggested that DNA methylation might regulate the TSP50 and mTSP50 gene expressions in different types of tissues and spermatic cells.     

Breast reduction with ultrasound-assisted lipoplasty

Ultrasound-assisted lipoplasty for reduction of fatty breasts and fixation has been found to be a safe technique with promising aesthetic results when it is applied in selected patients and performed by a surgeon with expertise with ultrasound-assisted body contouring. From 1995 to 2000, 120 patients were treated with ultrasound energy to decrease the fatty component of the breast tissue and at the same time to lift the breast mound. Each patient was evaluated preoperatively with mammograms for correct assessment of the nature and consistency of the breast tissue. Only patients with fibrofatty and fatty breast parenchyma were selected for breast reduction and fixation with ultrasound-assisted lipoplasty. Patients with suspect mammograms (calcification) and a strong family history of breast cancer were not considered. All the prescreening and the postoperative long-term mammographic evaluations were conducted by a radiologist with high competence in breast tissue resonance. Patients' age ranged from 17 to 53 years. Total aspirate ranged from 300 to 1200 ml for size, of which 65 percent was supranatant (fat) and 35 percent was infranatant (tumescence solution and blood). Patients were operated on while they were under general anesthesia; more recently, pure tumescent anesthesia was tried with success in minor cases. Breast dimensions were assessed with breast sizers (before and after the operation), and breast measurements were assessed using a classic breast drawing. Minimum follow-up of patients was 4 years. Particular care was given to evaluating long-term breast tissue appearance through mammographic studies and to looking for suspected calcifications. No evidence of a suspect mass or calcifications was found during the 4-year follow-up. The main advantages of the technique are a significant reduction in breast volume (up to three cup sizes), significant breast lift (up to 5 cm), and nearly invisible scars (1.5 cm in length at the inframammary sulcus and at the axilla).     

Deep Learning-Based Multi-Label Tissue Segmentation and Density Assessment from Mammograms

Objectives: Breast cancer is the most commonly diagnosed type of cancer among women and a common cause of cancer-related deaths. Early diagnosis and treatment of breast cancer is critical in disease prognosis. Breast density is known to have a correlation with breast cancer. In recent years, there has been an increasing interest in the investigation of computer-aided methods for early diagnosis of breast cancer. In this study, a new fully-automated deep learning-based cascaded model was proposed for breast density assessment. In the first stage, the segmentation of adipose, fibroglandular, and pectoral muscle tissues from the digitized film mammograms of the Digital Database for Screening Mammography (DDSM) was investigated using various types of U-nets. Features extracted from the breast tissue segmentation predictions were then used to assess breast density in the second stage. Material and methods: 66 and 296 mediolateral oblique mammograms were selected from DDSM dataset for segmentation and breast density assessment systems, respectively. Different U-nets with varying number of layers and filters were implemented and the model having the highest performance was determined. U-net performance was investigated using categorical cross-entropy, Dice, Tversky, Focal Tversky, and logarithmic cosine-hyperbolic Dice loss functions. The performances of U-nets having different types of connections were investigated. The performances of U-nets having pre-trained weights from VGG16, VGG19, and ResNet50 networks in the encoding path were also investigated. Segmentation results were improved by using an image processing pipeline based on morphological operators. Segmentation performance was presented in terms of accuracy, balanced accuracy, intersection over union, and Dice's similarity coefficient (DSC) metrics. The segmentation system predictions were then used to estimate mammographic density using a machine learning pipeline by extracting features related to the fibroglandular tissue percentage. Results: Using ResNet50-U-net on the test data, average DSC scores of 82.71%, 73.39%, and 95.30% were obtained for adipose, fibroglandular, and pectoral muscle tissue segmentation, respectively. The mammogram segmentation results are 3%-12% better than the current state-of-the-art DSC in the literature when considering all of the foreground tissues concurrently. A breast density classification accuracy of 76.01% was achieved on a separate mammogram dataset, which is comparable to the recent studies in the literature. Conclusion: The proposed system can be used for automatic segmentation of mammogram into adipose, fibroglandular, and pectoral muscle tissues. The segmentation model enables the estimation of the fibroglandular-adipose tissue interface, which is recently found to be an important region for breast cancer investigations. The proposed fully-automatic breast density assessment system has a comparable performance to the ones in the literature.     

Radiotransparency of the triglyceride mammary prosthesis: a quantitative analysis with mastectomy specimens.

Breast cancer is the most frequent malignant tumor in women. It is estimated that 10 percent of women will present with a breast cancer during their lives. It is well known that mammography is the best technique for the early diagnosis of nonpalpable tumors, thus improving life expectancy. However, mammary prostheses may hide between 23 and 82 percent of the normal mammary tissue in mammography, and thus may delay the diagnosis of malignant mammary tumors, making prognosis worse. To solve this problem, oil-filled prostheses have been developed. In this study, 14 mastectomy specimens were used. Mammograms of the tissue pieces alone and also mammograms of the tissue pieces covering a 270-cc Trilucent prosthesis were used to verify whether the prosthesis allows observation of malignant signs in mammography. Mammograms were evaluated by an independent experienced radiologist. The following variables were studied: number of mammograms necessary to examine each specimen; kilovoltage and milliamperage necessary for each mammogram; number of microcalcification groups (malignant); number of macroscopic calcifications (benign); and rarefaction areas that were suspected for malignancy. All of these variables were measured for both mammograms for which the mastectomy specimens were covering and those for which the specimens were not covering the prothesis. Finally, the kilovoltage and milliamperage increases necessary to visualize the mammograms with mastectomy specimens covering the prosthesis were determined. Statistical analysis of the results obtained was performed. There were no significant differences in the number of mammograms (p = 0.391), the number of microcalcifications (p = 0.890), the number of macrocalcifications (p = 0.239), and finally in the presence of rarefaction areas (p = 1.000) observed in the mammograms in specimens either covering or not covering the prosthesis. However, there were significant differences (p < 0.001) between the kilovoltage and milliamperage applied to carry out the mammograms of specimens with and without the prosthesis. Thus, Trilucent prostheses allow visualization of the microscopic and macroscopic calcifications as well as rarefaction areas in mammograms. However, these mammograms required a higher kilovoltage and milliamperage compared with specimens not covering the prosthesis. To explore the whole gland, it might be necessary to perform two series of mammograms: one to detect the area shadowed by the prosthesis and one to observe the rest of the peripheral gland.     

Promising techniques for breast cancer detection,diagnosis, and staging using non-ionizing radiation imaging techniques

Traditional imaging for the diagnosis and staging of breast cancer has relied on the tissue morphology of cancers in the background of normal patterns of fibroglandular breast tissue. X-ray mammography and ultrasound have been the primary modalities for the diagnosis and the work-up of breast cancer. New modalities have been validated including magnetic resonance imaging (MRI) and positron emission tomography (PET). New pulse sequences in MRI combined with contrast enhancement kinetic perfusion curves have greatly enhanced detection of mammographically occult cancers. New modalities on the horizon include optical imaging, exploiting again the differential perfusion properties of cancers in a background of normal glandular tissue. Even more specificity can be ach eved with the addition of ductal or intravenous introduction of optical probes specific to tumor associated antigens such as the HER-2/neu receptor in aggressive breast cancers. Quantum dots and other fluorescent dyes coupled to peptides or other probes will greatly enhance our ability to detect cancers earlier and without ionizing radiation.     

Effective radiation attenuation calibration for breast density: compression thickness influences and correction

Background  

Calibrating mammograms to produce a standardized breast density measurement for breast cancer risk analysis requires an accurate spatial measure of the compressed breast thickness. Thickness inaccuracies due to the nominal system readout value and compression paddle orientation induce unacceptable errors in the calibration.     

Area and Volumetric Density Estimation in Processed Full-Field Digital Mammograms for Risk Assessment of Breast Cancer

Introduction

Mammographic density, the white radiolucent part of a mammogram, is a marker of breast cancer risk and mammographic sensitivity. There are several means of measuring mammographic density, among which are area-based and volumetric-based approaches. Current volumetric methods use only unprocessed, raw mammograms, which is a problematic restriction since such raw mammograms are normally not stored. We describe fully automated methods for measuring both area and volumetric mammographic density from processed images.

Methods

The data set used in this study comprises raw and processed images of the same view from 1462 women. We developed two algorithms for processed images, an automated area-based approach (CASAM-Area) and a volumetric-based approach (CASAM-Vol). The latter method was based on training a random forest prediction model with image statistical features as predictors, against a volumetric measure, Volpara, for corresponding raw images. We contrast the three methods, CASAM-Area, CASAM-Vol and Volpara directly and in terms of association with breast cancer risk and a known genetic variant for mammographic density and breast cancer, rs10995190 in the gene ZNF365. Associations with breast cancer risk were evaluated using images from 47 breast cancer cases and 1011 control subjects. The genetic association analysis was based on 1011 control subjects.

Results

All three measures of mammographic density were associated with breast cancer risk and rs10995190 (p<0.025 for breast cancer risk and p<1×10⁻⁶ for rs10995190). After adjusting for one of the measures there remained little or no evidence of residual association with the remaining density measures (p>0.10 for risk, p>0.03 for rs10995190).

Conclusions

Our results show that it is possible to obtain reliable automated measures of volumetric and area mammographic density from processed digital images. Area and volumetric measures of density on processed digital images performed similar in terms of risk and genetic association.     

Cardioprotective Effects of an Aminothiazole Compound on Isoproterenol-Induced Myocardial Injury in Mice

Lactate dehydrogenase (LDH), marker of anaerobic metabolism, is associated with highly invasive and metastatic breast cancer. Novel studies show that increased anaerobic metabolism (LDH), as well as activity of antioxidative enzymes (superoxide dismutase (SOD) and catalase (CAT)), is correlated with higher mammographic density, as known predictor of breast cancer risk. In this study, we measured LDH, MDH, and SOD activity in tumor and adjacent tissues of breast cancer patients by spectrophotometric assay. Mammograms were evaluated according to the American College of Radiology Breast Imaging Reporting and Data system. Mammographically dense breast tissue is associated with higher activity of LDH in tumor tissue of breast cancer patients. Moreover, patients with masses have significantly higher activity of LDH compared to patients with focal asymmetries or architectural distortion. Patients with spiculated mass margin had higher activity of LDH compared to patients with focal asymmetries or architectural distortion. Activity of LDH in patients significantly increases, while activity of CAT significantly decreases with the increase of BIRADS category. These results suggest that the association of activity of LDH and CAT in tumor tissue with mammographic characteristics could help in defining aggressive breast cancers.     

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of life and survival of breast cancer patients. In this paper a new approach for the detection of breast cancer is described, based on tracking the mammary architectural elements using diffusion tensor imaging (DTI). The paper focuses on the scanning protocols and image processing algorithms and software that were designed to fit the diffusion properties of the mammary fibroglandular tissue and its changes during malignant transformation. The final output yields pixel by pixel vector maps that track the architecture of the entire mammary ductal glandular trees and parametric maps of the diffusion tensor coefficients and anisotropy indices. The efficiency of the method to detect breast cancer was tested by scanning women volunteers including 68 patients with breast cancer confirmed by histopathology findings. Regions with cancer cells exhibited a marked reduction in the diffusion coefficients and in the maximal anisotropy index as compared to the normal breast tissue, providing an intrinsic contrast for delineating the boundaries of malignant growth. Overall, the sensitivity of the DTI parameters to detect breast cancer was found to be high, particularly in dense breasts, and comparable to the current standard breast MRI method that requires injection of a contrast agent. Thus, this method offers a completely non-invasive, safe and sensitive tool for breast cancer detection.     

Automatic Detection and Classification of Mammograms Using Improved Extreme Learning Machine with Deep Learning

Background and objectiveBreast cancer, the most intrusive form of cancer affecting women globally. Next to lung cancer, breast cancer is the one that provides a greater number of cancer deaths among women. In recent times, several intelligent methodologies were come into existence for building an effective detection and classification of such noxious type of cancer. For further improving the rate of early diagnosis and for increasing the life span of victims, optimistic light of research is essential in breast cancer classification. Accordingly, a new customized method of integrating the concept of deep learning with the extreme learning machine (ELM), which is optimized using a simple crow-search algorithm (ICS-ELM). Thus, to enhance the state-of-the-art workings, an improved deep feature-based crow-search optimized extreme learning machine is proposed for addressing the health-care problem. The paper pours a light-of-research on detecting the input mammograms as either normal or abnormal. Subsequently, it focuses on further classifying the type of abnormal severities i.e., benign type or malignant.Materials and methodsThe digital mammograms for this work are taken from the Curated Breast Imaging Subset of DDSM (CBIS-DDSM), Mammographic Image Analysis Society (MIAS), and INbreast datasets. Herein, the work employs 570 digital mammograms (250 normal, 200 benign and 120 malignant cases) from CBIS-DDSM dataset, 322 digital mammograms (207 normal, 64 benign and 51 malignant cases) from MIAS database and 179 full-field digital mammograms (66 normal, 56 benign and 57 malignant cases) from INbreast dataset for its evaluation. The work utilizes ResNet-18 based deep extracted features with proposed Improved Crow-Search Optimized Extreme Learning Machine (ICS-ELM) algorithm.ResultsThe proposed work is finally compared with the existing Support Vector Machines (RBF kernel), ELM, particle swarm optimization (PSO) optimized ELM, and crow-search optimized ELM, where the maximum overall classification accuracy is obtained for the proposed method with 97.193% for DDSM, 98.137% for MIAS and 98.266% for INbreast datasets, respectively.ConclusionThe obtained results reveal that the proposed Computer-Aided-Diagnosis (CAD) tool is robust for the automatic detection and classification of breast cancer.     

乳腺癌CXCR1的表达特征及其与新辅助化疗疗效的相关性

目的:研究趋化因子受体CXCR1在乳腺癌组织中的表达特征,并探讨新辅助化疗前后其表达变化与化疗疗效的关系。方法:选取20例正常乳腺组织,20例乳腺纤维腺瘤,104例乳腺癌标本,免疫组化染色后统计每例标本CXCR1的阳性表达积分,分析不同乳腺疾病CXCR1的表达差异。统计新辅助化疗前后乳腺癌标本CXCR1的阳性表达积分的变化,分析其与化疗病理反应的关系。结果:CXCR1在正常乳腺组织、乳腺纤维腺瘤中低表达,在乳腺癌组织上高表达;其表达与患者的年龄、原发肿瘤的大小无关(P0.05),与病理分期、癌细胞的分化程度、淋巴结转移个数、激素受体状态及Her2表达情况相关,P0.05,有统计学意义。新辅助化疗后,癌组织中CXCR1的表达下降,下降幅度越大,其化疗疗效越好。结论:CXCR1的检测对于乳腺疾病的良恶性判断有指导意义,可辅助判断乳腺癌的恶性程度、侵袭性以及预后,CXCR1表达的下降与化疗疗效相关,抑制其表达可能可以提高化疗疗效。     

Raman spectroscopy can differentiate malignant tumors from normal breast tissue and detect early neoplastic changes in a mouse model

Raman spectroscopy shows potential in differentiating tumors from normal tissue. We used Raman spectroscopy with near-infrared light excitation to study normal breast tissue and tumors from 11 mice injected with a cancer cell line. Spectra were collected from 17 tumors, 18 samples of adjacent breast tissue and lymph nodes, and 17 tissue samples from the contralateral breast and its adjacent lymph nodes. Discriminant function analysis was used for classification with principal component analysis scores as input data. Tissues were examined by light microscopy following formalin fixation and hematoxylin and eosin staining. Discriminant function analysis and histology agreed on the diagnosis of all contralateral normal, tumor, and mastitis samples, except one tumor which was found to be more similar to normal tissue. Normal tissue adjacent to each tumor was examined as a separate data group called tumor bed. Scattered morphologically suspicious atypical cells not definite for tumor were present in the tumor bed samples. Classification of tumor bed tissue showed that some tumor bed tissues are diagnostically different from normal, tumor, and mastitis tissue. This may reflect malignant molecular alterations prior to morphologic changes, as expected in preneoplastic processes. Raman spectroscopy not only distinguishes tumor from normal breast tissue, but also detects early neoplastic changes prior to definite morphologic alteration.     

Elevation of SIPL1 (SHARPIN) Increases Breast Cancer Risk

SIPL1 (Sharpin) or Sharpin plays a role in tumorigenesis. However, its involvement in breast cancer tumorigenesis remains largely unknown. To investigate this issue, we have systemically analyzed SIPL1 gene amplification and expression data available from Oncomine datasets, which were derived from 17 studies and contained approximately 20,000 genes, 3438 breast cancer cases, and 228 normal individuals. We found a SIPL1 gene amplification in invasive ductal breast cancers compared to normal breast tissues and a significant elevation of SIPL1 mRNA in breast cancers in comparison to non-tumor breast tissues. These results collectively reveal that increases in SIPL1 expression occur during breast cancer tumorigenesis. To further investigate this association, we observed increases in the SIPL1 gene and mRNA in the breast cancer subtypes of estrogen receptor (ER)+, progesterone receptor (PR)+, HER2+, or triple negative. Additionally, a gain of the SIPL1 gene correlated with breast cancer grade and the levels of SIPL1 mRNA associated with both breast cancer stages and grades. Elevation of SIPL1 gene copy and mRNA is linked to a decrease in patient survival, especially for those with PR+, ER+, or HER2- breast cancers. These results are supported by our analysis of SIPL1 protein expression using a tissue microarray containing 224 breast cancer cases, in which higher levels of SIPL1 relate to ER+ and PR+ tumors and AKT activation. Furthermore, we were able to show that progesterone significantly reduced SIPL1 mRNA and protein expression in MCF7 cells. As progesterone enhances breast cancer tumorigenesis in a context dependent manner, inhibition of SIPL1 expression may contribute to progesterone''s non-tumorigenic function which might be countered by SIPL1 upregulation. Taken together, we demonstrate a positive correlation of SIPL1 with BC tumorigenesis.     

A computer-aided diagnosis scheme of breast lesion classification using GLGLM and shape features: Combined-view and multi-classifiers

PurposeTo address high false-positive results of FFDM issue, we make the first effort to develop a computer-aided diagnosis (CAD) scheme to analyze and distinguish breast lesions.MethodThe breast lesion regions were first segmented and depicted on FFDM images from 106 patients. In this work, 11 gray-level gap-length matrix texture features and 12 shape features were extracted form craniocaudal view and mediolateral oblique view, and then Student’s t-test, Fisher-score and Relief-F were introduced to select features. We also investigated the effect of three factors, i.e., discretisation, selection methods and classifier methods, of the classification performance via analysis of variance. Finally, a classification model was constructed. Spearman’s correlation coefficient analysis was conducted to assess the internal relevance of features.ResultsThe proposed scheme using Student’s t-test achieved an area under the receiver operating characteristic curve (AUC) value of 0.923 at 512 bins. The AUC values are 0.884, 0.867, 0.874 and 0.901 for the low gray-level gaps emphasis (LGGE), solidity, extent, and the combined set, respectively. Solidity and extent depicts the correlation coefficient of 0.86 (P < 0.05).ConclusionsWe present a new CAD scheme based on the contribution of the significant factors. The experimental results demonstrate that the presented scheme can be used to successfully distinguish breast carcinoma lesions and benign fibroadenoma lesions in our FFDM dataset and the MIAS dataset, which may provide a CAD method to assist radiologists in diagnosing and interpreting screening mammograms. Moreover, we found that LGGE, solidity and extent features show great potential for breast lesion classification.     

A Method for Detecting Positive Growth Autocorrelation without Marking Individuals

In most ecological studies, within-group variation is a nuisance that obscures patterns of interest and reduces statistical power. However, patterns of within-group variability often contain information about ecological processes. In particular, such patterns can be used to detect positive growth autocorrelation (consistent variation in growth rates among individuals in a cohort across time), even in samples of unmarked individuals. Previous methods for detecting autocorrelated growth required data from marked individuals. We propose a method that requires only estimates of within-cohort variance through time, using maximum likelihood methods to obtain point estimates and confidence intervals of the correlation parameter. We test our method on simulated data sets and determine the loss in statistical power due to the inability to identify individuals. We show how to accommodate nonlinear growth trajectories and test the effects of size-dependent mortality on our method''s accuracy. The method can detect significant growth autocorrelation at moderate levels of autocorrelation with moderate-sized cohorts (for example, statistical power of 80% to detect growth autocorrelation ρ ² = 0.5 in a cohort of 100 individuals measured on 16 occasions). We present a case study of growth in the red-eyed tree frog. Better quantification of the processes driving size variation will help ecologists improve predictions of population dynamics. This work will help researchers to detect growth autocorrelation in cases where marking is logistically infeasible or causes unacceptable decreases in the fitness of marked individuals.     

Photoacoustic imaging of breast microcalcifications: A validation study with 3‐dimensional ex vivo data and spectrophotometric measurement

This paper investigates whether photoacoustic imaging (PAI) can provide the visualization of microcalcifications in breast tissue. For this, the geometrical correlation between the 3‐D PA images of breast microcalcifications within ex vivo specimens and the corresponding mammograms was ascertained. Also, the optical absorbance of the calcification compositions (i.e., hydroxyapatite and calcium oxalate) was measured and compared with the PA responses of the microcalcifications. The experimental results demonstrated that the PA images discriminated between the microcalcifications and the surrounding tissue, and their locations in PA images reasonably meshed with those of the microcalcifications appeared in the mammograms. Also, the change in PA signal amplitude along the laser wavelength agreed with the absorbance of hydroxyapatite associated with the relatively high potential of malignant cancers, but not calcium oxalate with only benign cases. (© 2013 WILEY‐VCH Verlag GmbH &Co. KGaA, Weinheim)     

Directional features for automatic tumor classification of mammogram images

One way for breast cancer diagnosis is provided by taking radiographic (X-ray) images (termed mammograms) for suspect patients, images further used by physicians to identify potential abnormal areas thorough visual inspection. When digital mammograms are available, computer-aided based diagnostic may help the physician in having a more accurate decision. This implies automatic abnormal areas detection using segmentation, followed by tumor classification. This work aims at describing an approach to deal with the classification of digital mammograms. Patches around tumors are manually extracted to segment the abnormal areas from the remaining of the image, considered as background. The mammogram images are filtered using Gabor wavelets and directional features are extracted at different orientation and frequencies. Principal Component Analysis is employed to reduce the dimension of filtered and unfiltered high-dimensional data. Proximal Support Vector Machines are used to final classify the data. Superior mammogram image classification performance is attained when Gabor features are extracted instead of using original mammogram images. The robustness of Gabor features for digital mammogram images distorted by Poisson noise with different intensity levels is also addressed.