Gopal Karemore

A novel and automatic mammographic texture resemblance marker is an independent risk factor for breast cancer

OBJECTIVE We investigated whether breast cancer is predicted by a breast cancer risk mammographic texture resemblance (MTR) marker. METHODS A previously published case-control study included 495 women of which 245 were diagnosed with breast cancer. In baseline mammograms, 2-4 years prior to diagnosis, the following mammographic parameters were analysed for relation to breast cancer risk: (C) categorical parenchymal pattern scores; (R) radiologists percentage density, (P) computer-based percentage density; (H) computer-based breast cancer risk MTR marker; (E) computer-based hormone replacement treatment MTR marker; and (A) an aggregate of P and H. RESULTS Density scores, C, R, and P correlated (tau=0.3-0.6); no other pair of scores showed large (tau>0.2) correlation. For the parameters, the odds ratios of future incidence of breast cancer comparing highest to lowest categories (146 and 106 subject respectively) were C: 2.4(1.4-4.2), R: 2.4(1.4-4.1), P: 2.5(1.5-4.2), E: non-significant, H: 4.2(2.4-7.2), and A: 5.6(3.2-9.8). The AUC analysis showed a similarly increasing pattern (C: 0.58±0.02, R: 0.57±0.03, P: 0.60±0.03, H: 0.63±0.02, A: 0.66±0.02). The AUC of the aggregate marker (A) surpasses others significantly except H. HRT-MTR (E) did not significantly identify future cancers or correlate with any other marker. CONCLUSIONS Breast cancer risk MTR marker was independent of density scores and more predictive of risk. The hormone replacement treatment MTR marker did not identify patients at risk.

An Anatomically Oriented Breast Coordinate System for Mammogram Analysis

We have developed a breast coordinate system that is based on breast anatomy to register female breasts into a common coordinate frame in 2-D mediolateral (ML) or mediolateral oblique (MLO) view mammograms. The breasts are registered according to the location of the pectoral muscle and the nipple and the shape of the breast boundary because these are the most robust features independent of the breast size and shape. On the basis of these landmarks, we have constructed a nonlinear mapping between the parameter frame and the breast region in the mammogram. This mapping makes it possible to identify the corresponding positions and orientations among all of the ML or MLO mammograms, which facilitates an implicit use of the registration, i.e., no explicit image warping is needed. We additionally show how the coordinate transform can be used to extract Gaussian derivative features so that the feature positions and orientations are registered and extracted without nonlinearly deforming the images. We use the proposed breast coordinate transform in a cross-sectional breast cancer risk assessment study of 490 women, in which we attempt to learn breast cancer risk factors from mammograms that were taken prior to when the breast cancer became visible to a radiologist. The coordinate system provides both the relative position and orientation information on the breast region from which the features are derived. In addition, the coordinate system can be used in temporal studies to pinpoint anatomically equivalent locations between the mammograms of each woman and among the mammograms of all of the women in the study. The results of the cross-sectional study show that the classification into cancer and control groups can be improved by using the new coordinate system, compared to other systems evaluated. Comparisons were performed using the area-under-the-receiver-operating-characteristic-curve score. In general, the new coordinate system makes an accurate anatomical registration of breasts possible, which suggests its wide applicability wherever 2-D mammogram registration is required.

EphrinB1/EphB3b Coordinate Bidirectional Epithelial-Mesenchymal Interactions Controlling Liver Morphogenesis and Laterality.

Summary Positioning organs in the body often requires the movement of multiple tissues, yet the molecular and cellular mechanisms coordinating such movements are largely unknown. Here, we show that bidirectional signaling between EphrinB1 and EphB3b coordinates the movements of the hepatic endoderm and adjacent lateral plate mesoderm (LPM), resulting in asymmetric positioning of the zebrafish liver. EphrinB1 in hepatoblasts regulates directional migration and mediates interactions with the LPM, where EphB3b controls polarity and movement of the LPM. EphB3b in the LPM concomitantly repels hepatoblasts to move leftward into the liver bud. Cellular protrusions controlled by Eph/Ephrin signaling mediate hepatoblast motility and long-distance cell-cell contacts with the LPM beyond immediate tissue interfaces. Mechanistically, intracellular EphrinB1 domains mediate EphB3b-independent hepatoblast extension formation, while EpB3b interactions cause their destabilization. We propose that bidirectional short- and long-distance cell interactions between epithelial and mesenchyme-like tissues coordinate liver bud formation and laterality via cell repulsion.

Classification of protein profiles using fuzzy clustering techniques: An application in early diagnosis of oral, cervical and ovarian cancer

Present study has brought out a comparison of PCA and fuzzy clustering techniques in classifying protein profiles (chromatogram) of homogenates of different tissue origins: Ovarian, Cervix, Oral cancers, which were acquired using HPLC-LIF (High Performance Liquid Chromatography-Laser Induced Fluorescence) method developed in our laboratory. Study includes 11 chromatogram spectra each from oral, cervical, ovarian cancers as well as healthy volunteers. Generally multivariate analysis like PCA demands clear data that is devoid of day-to-day variation, artifacts due to experimental strategies, inherent uncertainty in pumping procedure which is very common activities during HPLC-LIF experiment. Under these circumstances we demonstrate how fuzzy clustering algorithm like Gath Geva followed by Sammon mapping outperform PCA mapping in classifying various cancers from healthy spectra with classification rate up to 95 % from 60%. Methods are validated using various clustering indexes and shows promising improvement in developing optical pathology like HPLC-LIF for early detection of various cancers in all uncertain conditions with high sensitivity and specificity.

Breast density changes associated with postmenopausal hormone therapy: post hoc radiologist- and computer-based analyses

Objective:The aim of this study was to assess the impact of oral hormone therapy (HT) on breast density in postmenopausal women and to compare the use of computer-based automated approaches for the assessment of breast density with reference to traditional methods. Methods:Low-dose oral estrogen (1 mg) continuously combined with drospirenone (2 mg) was administered to postmenopausal women for up to 2 years (26 treatment cycles, 28 d/cycle) in a randomized, placebo-controlled trial. This post hoc analysis assessed the changes in breast density measured from digitized images by two radiologist-based approaches (Breast Imaging Reporting and Data System score and interactive threshold) and one computer-based technique (heterogeneity examination of radiographs). Correlations of temporal changes in breast density with changes in serum estradiol levels, biochemical markers of bone metabolism, and bone mineral density at the spine and femur were also assessed. Results:Breast density assessed by the radiologist-based approaches increased significantly from baseline in the HT group (P < 0.01), with significant divergence from placebo at 2 years (P < 0.01). Heterogeneity examination of radiograph score by computer-based technique was unchanged in the HT group and decreased significantly with placebo (P < 0.001) to produce a significant group divergence (P < 0.05). Changes in mammographic markers by radiologist- and computer-based approaches correlated with each other in the HT group (P < 0.01) but not in the placebo group. Conclusions:HT for 2 years in postmenopausal women significantly increased radiologist-assessed breast density compared with placebo, in addition to significant changes in estrogen levels, markers of bone metabolism, and bone mineral density. Computer-automated techniques may be comparable with and offer advantages over traditional methods.

Fractal Dimension and Lacunarity analysis of mammographic patterns in assessing breast cancer risk related to HRT treated population: A Longitudinal and Cross-sectional study

Structural texture measures are used to address the aspect of breast cancer risk assessment in screening mammograms. The current study investigates whether texture properties characterized by local Fractal Dimension (FD) and Lacunarity contribute to asses breast cancer risk. FD represents the complexity while the Lacunarity characterize the gappiness of a fractal. Our cross-sectional case-control study includes mammograms of 50 patients diagnosed with breast cancer in the subsequent 2-4 years and 50 matched controls. The longitudinal double blind placebo controlled HRT study includes 39 placebo and 36 HRT treated volunteers for two years. ROIs with same dimension (250*150 pixels) were created behind the nipple region on these radiographs. Box counting method was used to calculate the fractal dimension (FD) and the Lacunarity. Paired t-test and Pearson correlation coefficient were calculated. It was found that there were no differences between cancer and control group for FD (P=0.8) and Lacunarity (P=0.8) in crosssectional study whereas earlier published heterogeneity examination of radiographs (BC-HER) breast cancer risk score separated groups (p=0.002). In the longitudinal study, FD decreased significantly (P<0.05) in the HRT treated population while Lacunarity remained insignificant (P=0.2). FD is negatively correlated to Lacunarity (-0.74, P<0.001), BIRADS (-0.34, P<0.001) and Percentage Density (-0.41, P<0.001). FD is invariant to the mammographic texture change from control to cancer population but marginally varying in HRT treated population. This study yields no evidence that lacunarity or FD are suitable surrogate markers of mammographic heterogeneity as they neither pick up breast cancer risk, nor show good sensitivity to HRT.

Anisotropic diffusion tensor applied to temporal mammograms: An application to breast cancer risk assessment

Breast density is considered a structural property of a mammogram that can change in various ways explaining different effects of medicinal treatments. The aim of the present work is to provide a framework for obtaining more accurate and sensitive measurements of breast density changes related to specific effects like Hormonal Replacement Therapy (HRT) and aging. Given effect-grouped patient data, we demonstrated how the diffusion tensor and its coherence features computed in an anatomically oriented breast coordinate system followed by statistical learning scheme provides non subjective and reproducible measure, as compared to the traditional BIRADS and computer aided percent density measure. We also demonstrate how orientation of breast tissue changes in temporal study. This framework facilitates radiologist to assess breast tissue change and guide them to evaluate individual risk of having breast cancer.

Classification of Laser Induced Fluorescence spectra from normal and malignant tissues using Learning Vector Quantization neural network in bladder cancer diagnosis

In the present work we discuss the potential of recently developed classification algorithm, learning vector quantization (LVQ), for the analysis of laser induced fluorescence (LIF) spectra, recorded from normal and malignant bladder tissue samples. The algorithm is prototype based and inherently regularizing, which is desirable, for the LIF spectra because of its high dimensionality and features being settled at widely spaced intervals (sparseness). We discuss the effect of different parameters influencing the performance of LVQ in LIF data classification. Further, we compare and cross validate the classification accuracy of LVQ with other classifiers (eg. SVM and multi layer perception) for the same data set. Good agreement has been obtained between LVQ based classification of spectroscopy data and histopathology results which demonstrate the use of LVQ classifier in bladder cancer diagnosis.

Serum protein profile study of clinical samples using high performance liquid chromatography-laser induced fluorescence: case of cervical and oral cancers

The serum protein profiles of normal subjects, patients diagnosed with cervical cancer, and oral cancer were recorded using High Performance Liquid Chromatography combined with Laser Induced Fluorescence detection (HPLC-LIF). Serum protein profiles of the above three classes were tested for establishing the ability of HPLC-LIF protein profiling technique for discrimination, using hard clustering and Fuzzy clustering methods. The clustering algorithms have quite successfully classified the profiles as belonging to normal, cancer of cervix, and oral cancer conditions.

Higher eukaryote-specific APC/C composition is a determinant of spindle assembly checkpoint importance

The multisubunit ubiquitin ligase APC/C (anaphase promoting complex/cyclosome) is essential for mitosis by promoting timely degradation of cyclin B1. Proper timing of APC/C activation is regulated by the spindle assembly checkpoint (SAC), which is initiated by the kinase MPS1 and implemented by MAD2-dependent inhibition of the APC/C. Here we analysed the contribution of the higher eukaryote-specific APC/C subunits APC7 and APC16 to APC/C composition, function and regulation. APC16 is required for APC7 assembly into the APC/C, while APC16 assembles independently of APC7. ΔAPC7 and ΔAPC16 cells display no major defects in mitotic progression, cyclin B1 degradation or SAC response. Strikingly, however, deletion of either APC7 or APC16 is sufficient to provide synthetic viability to MAD2 deletion. ΔAPC7ΔMAD2 cells display an accelerated mitosis and require SAC-independent MPS1 function for maintaining their genome stability. Overall, these results show how human APC/C composition critically influences the cellular fate upon loss of SAC activity.