Assessment Of Density Changes Research Articles

Physico-mechanical parameters of rock materials used in open pit reclamation – a case study of CTL Maczki-Bór S.A. mine in Poland

The aim of the research was to assess the variability of selected physical and mechanical properties of Carboniferous barren rocks in the Upper Silesian Coal Basin and to analyze the impact of time and increasing vertical pressure on the mechanical parameters of the mixture of these rocks used in the reclamation of the open pit. Both type of tests were conducted in a strength press. For lithologically and stratigraphically differentiated rock samples, strength parameters in various states of humidity (air-dried and capillary saturation state) were determined, including compressive strength, cohesion and the angle of internal friction. It was found that the strength of all types of rock increases with geological age, however saturation of these rocks with water causes their structural weakening, which is especially noticeable in the case of sandstone and claystone. In the case of mudstone, due to their structure, the reduction of strength after saturation with water is not so significantly noticeable. In the second part of the laboratory tests, 5 samples of Carboniferous post-mining waste with different storage times in the open pit were subjected to the assessment of changes in density and compressibility in relation to increasing values of vertical pressure. It was found that the long-term depositing of waste in the excavation is conducive to the intensive development of hypergenic processes. This contributes to the disintegration of sedimentary rocks, which causes changes in their physical and mechanical parameters. This phenomenon is additionally intensified by the increasing vertical pressure with thickness of waste layers.

Abstract PD12-06: PD12-06 Longitudinal analysis of breast density change assessed by digital mammogram is associated with breast cancer

Abstract Mammographic breast density is a well-established and strong risk factor for breast cancer. Widespread use of digital mammography has opened new potential for assessment of density changes over time. The underlying premise is that changes in breast tissue due to evolving structures that support cancer development should translate to quantifiable differences between the two breasts over time. To address this hypothesis, we draw on extensive digital mammography data and bring repeated measures over up to 10 years to evaluate the association between change in mammographic breast density and risk of breast cancer. Women were recruited from November 2008 to April 2012 through the mammography service at the Joanne Knight Breast Health Center at Washington University in St Louis. Baseline questionnaire risk factors and screening mammograms were collected from 12,153 women. Of these, 1,672 were excluded for prior history of any cancer (except non-melanoma skin) or diagnosis of breast cancer within 6 months of registration for the study, for a total of 10,481 women. Follow-up is through linking to electronic health records, tumor registry and death register. Routine screening mammograms are collected every 1 to 2 years. Follow-up of cohort participants through December 2020 was: 78% seen in 2019 or 2020; a further 4.4% seen most recently in 2018 and a further 2.4% in 2017 giving over 80% active follow-up for women seen within the last 36 months. The median number of mammograms is 5 (min = 1, max = 10; sd = 2.43). The average person-years of follow-up through most recent contact is 9.2 person-years. We have excluded women who are diagnosed within the first 6 month of baseline mammogram date in all analyses, leaving 259 cases and 695 controls with a total number of 8,966 crandiocaudal (CC) view mammograms for analysis. For these 954 women, the mean number of years between mammograms is 1.3 (10th percentile: 1.0, 90th percentile: 2.0). For the cases, the mean number of years from last mammogram date to diagnosis date was 2.0 years (10th percentile: 1.0, 90th percentile: 3.9) after excluding mammograms that are within 6 months of diagnosis. The percentage volumetric density (MD) within each digital CC-view mammogram is estimated with an automated pixel-thresholding algorithm ADAPT implemented within the Division of Public Health at WashU. The skin around the breast is automatically removed using a boundary detection algorithm prior to estimating the dense areas. The percent density (MD) is then estimated using the dense area divided by the total breast area which normalizes the difference in breast size across women. The correlation between average MD generated from our automated algorithm with Volpara is 0.82. Initial analyses use linear mixed effects with average density between breasts (fitted with R package lmer). We performed test of assumptions for all linear mixed effects models, including the normality of residuals, linearity, and homogeneity of residual variance. Assessing the averaging density between 2 breasts controlling for age, BMI, histology confirmed benign breast disease, family history, parity, and alcohol, MD decreases significantly over follow-up (time in years) (P < 0.01). At baseline, postmenopausal women had lower density than premenopausal women after controlling for the same set of risk factors. The average MD over all time points was significantly different for the case vs. control women (P < 0.01). For overweight women, the trajectory of MD over time was significantly different for the case vs. control women (P < 0.01). Drawing on over 10 years of follow-up we observe, for the first time, a dynamic effect for breast density such that divergence in density over time is related to risk for breast cancer. Citation Format: Graham A. Colditz, Shu Jiang. PD12-06 Longitudinal analysis of breast density change assessed by digital mammogram is associated with breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD12-06.

Abstract 2600: Deep-LIBRA: An artificial intelligence approach for fully-automated assessment of breast density in digital mammography

Abstract Accurate estimation of mammographic breast density could aid in augmenting breast cancer risk assessment for women undergoing breast screening with full-field digital mammography (FFDM). Breast density can be estimated from FFDM and is most commonly assessed in the clinic by visual grading into one of the four categories defined by the American College of Radiology BI-RADS. However, BI-RADS density assessment is highly subjective and does not provide a quantitative, continuous measure of percent density (PD), which would allow for more refined risk stratification and assessment of density changes. Here, we introduce Deep-LIBRA, an artificial intelligence (AI) tool for fully-automated assessment of breast PD from FFDM images. Two key modules form the core of Deep-LIBRA: 1) an implementation of a modified U-Net architecture for breast segmentation and 2) a radiomic machine learning module that performs PD estimation within the segmented breast region. To develop and validate Deep-LIBRA, raw (i.e., "For Processing") FFDM images (Selenia Dimensions, Hologic Inc.) acquired at two breast cancer screening practices were retrospectively analyzed. For the breast segmentation module, we used a total of 12,100 FFDM studies from 2,200 individual women and a 90%-10% split-sample training-validation approach, using the Dice coefficient to evaluate the accuracy of Deep-LIBRA versus ground-truth manual breast segmentation. For the PD estimation module we used a total of 3,304 FFDM images from 1,652 individual women; manual PD scores obtained with the widely used Cumulus software were used as the "gold standard" in a three-fold cross-validation setting to assess the accuracy of Deep-LIBRA in PD estimation. PD estimates from Deep-LIBRA were also compared with breast density estimates from the commercially available Volpara software. Breast segmentation had a Dice coefficient of 95.31% when compared to ground-truth manual breast segmentation in the validation set. Deep-LIBRA average differences from ground-truth PD scores in the three cross-validation folds were 4.91%, 4.65%, and 4.22%, while Volpara had corresponding average differences of 6.20%, 6.01%, and 5.94%. Deep-LIBRA PD scores were also significantly different from Volpara PD (t-test p-value < 0.001 in all three folds). Preliminary evaluation results show that Deep-LIBRA is a promising AI approach for accurately assessing PD from FFDM images. Citation Format: Omid Haji Maghsoudi, Scott Christopher, Aimilia Gastounioti, Lauren Pantalone, Fang-Fang Wu, Eric A. Cohen, Winham Stacey, Emily F. Conant, Celine Vachon, Despina Kontos. Deep-LIBRA: An artificial intelligence approach for fully-automated assessment of breast density in digital mammography [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2600.

Evaluation of Corneal Endothelium Function by Combined Measurement of Corneal Volume and Specular Microscopy before and after Phacoemulsification

ABSTRACT Background: Clarity of the cornea is fundamental for pure vision following cataract surgery including phacoemulsification . The pumping action of corneal endothelial cells acts to maintain this clarity through maintaining the corneal stroma dehydrated, so integrity of this cell layer function is very important for corneal clarity. Aim of the Work: to evaluate endothelial cell layer function before and after phacoemulsification by assessment of changes in density of endothelial cell and corneal volume of central corneal ring of 10 mm diameter. Subjects and Methods: A prospective interventional study was performed during the period from September2019 to December 2019 at Sayed Galal University Hospital. A total of 22 eyes from15 subjects, aged 50 -65 years old, both genders were involved. All patients had an uneventful phacoemulsification for immature senile cataract. Patients were evaluated pre and postoperatively by Topcon® SP-1P specular microscope for cell density and by SIRIUS® 3D rotating Scheimpflug & topography system for corneal volume of central 10 mm ring Results: Our results demonstrated that specular endothelial cell density decreased significantly and Central corneal volume increased significantly after phacoemulsification. Cell density changes Showed a reverse proportional correlation with corneal volume changes. Conclusion: The increase in corneal volume following phacoemulsification can indicate the degree of the endothelial damage. A single anatomical or functional test may not be sufficient for definite evaluation of endothelial dysfunction after phacoemulsification. Keywords: ( Corneal Endothelium Function; Corneal Volume; Specular Microscopy; Phacoemulsification).

Breast density measurements using ultrasound tomography for patients undergoing tamoxifen treatment.

Women with high breast density have an increased risk of developing breast cancer. Women treated with the selective estrogen receptor modulator tamoxifen for estrogen receptor positive breast cancer experience a 50% reduction in risk of contralateral breast cancer and overall reduction of similar magnitude has been identified among high-risk women receiving the drug for prevention. Tamoxifen has been shown to reduce mammographic density, and in the IBIS-1 chemoprevention trial, risk reduction and decline in density were significantly associated. Ultrasound tomography (UST) is an imaging modality that can create tomographic sound speed images of the breast. These sound speed images are useful because breast density is proportional to sound speed. The aim of this work is to examine the relationship between UST-measured breast density and the use of tamoxifen. So far, preliminary results for a small number of patients have been observed and are promising. Correlations between the UST-measured density and mammographic density are strong and positive, while relationships between UST density with some patient specific risk factors behave as expected. Initial results of UST examinations of tamoxifen treated patients show that approximately 45% of the patients have a decrease in density in the contralateral breast after only several months of treatment. The true effect of tamoxifen on UST-measured density cannot yet be fully determined until more data are collected. However, these promising results suggest that UST can be used to reliably assess quantitative changes in breast density over short intervals and therefore suggest that UST may enable rapid assessment of density changes associated with therapeutic and preventative interventions.

Density variation of parotid glands during IMRT for head–neck cancer: Correlation with treatment and anatomical parameters

PurposeMeasuring parotid density changes in patients treated with IMRT for head–neck cancer (HNC) and assessing correlation with treatment-related parameters. Patients and materialsData of 84 patients treated with IMRT for different HNC were pooled from three institutions. Parotid deformation and average Hounsfield number changes (ΔHU) were evaluated through MVCT (with Helical Tomotherapy) or diagnostic kVCT images taken at the treatment start/end. Parotids were delineated in the first image and propagated to the last using a previously validated algorithm based on elastic registration. The correlation between ΔHU and several treatment-related parameters was tested; then, logistic uni- and multi-variate analyses taking “large” ΔHU as end-point were carried out. Due to the better image quality, analyses were repeated considering only kVCT data. ResultsΔHU was negative in 116/168 parotids (69%; for kVCT patients: 72/92, 78%). The average ΔHU was significantly different from zero (−7.3, 0.20–0.25HU/fraction, p<0.001). Individual ΔHU was highly correlated with parotid deformation both in terms of volume change and mean value of the Jacobian of the deformation field (Jac_mean), and with neck thickness variation; these correlations were much stronger for kVCT data. Logistic analyses considering ΔHU<−11 (quartile value) as the end-point showed a two-variable model including large deformation (Jac_mean<0.68) and initial neck thickness to be the most predictive variables (p<0.0005, AUC=0.683; AUC=0.776 for kVCT); the odd ratio of large vs moderate/small parotid deformation was 3.8 and 8.0 for the whole and the kVCT population respectively. ConclusionsParotid density reduced in most patients during IMRT and this phenomenon was highly correlated with parotid deformation. The individual assessment of density changes was highly reliable just with diagnostic KvCT. Density changes should be considered as an additional objective measurement of early parotid radiation-induced modifications; further research is warranted.

A mammographic image analysis method to detect and measure changes in breast density

We present an image analysis method that can detect and measure breast density from digitised mammograms. We present initial results on applying our method to characterise breast changes, in particular, changes due to Hormone Replacement Therapy (HRT). It has been established that long-term use of certain hormone replacement therapies can increase the risk of breast cancer, a fact that encourages the notion that objective measures of tissue density can be an important development in breast cancer image analysis. A set of 59 temporal pairs of mammograms of patients undergoing HRT (two images per patient) were used. The clinician’s assessment of density changes constituted the ground truth for evaluating the proposed quantitative measures of density change. The measures we developed are based on the Standard Mammogram Form (SMF) representation of interesting tissue and their performance (agreement with the expert’s description) is also compared to the “interactive thresholding” method that has been used in the past to characterise mammographic density. The results clearly indicate that present methods for measuring mammographic density fail to characterise temporal changes while the proposed measures have the potential to aid the radiologist in assessing temporal density changes both on a global and a local basis.

An in vitro study of the characteristics of a computer-aided radiographic evaluation (CARE) system for longitudinal assessment of density changes.

In recent years, several systems for computerized analysis of radiographs have been introduced, most of which use digital conversion of the image followed by subtraction of consecutive images to assess changes. This paper introduces a computer-based qualitative and quantitative radiographic evaluation system based on the CADIA algorithm. Problems associated with computerized radiographic analysis are discussed and evaluation criteria for this type of system are suggested. These criteria include evaluation of system noise and threshold setting, reproducibility, and establishment of the system working curve (validity). The CARE system noise ranged from a -10 to +10 CADIA value and the threshold was set on 13 for all measurements. The reproducibility was high, both for the radiographic technique and for repeated measurements. The working curve was established and showed the system's ability to detect small density changes of 0.048 O.D. which corresponds to 0.27 mm of aluminium thickness or compact bone equivalent. The linear range of the curve was between 0.7 and 1.8 O.D. By controlling the exposure parameters it was possible to work within the linear range of the curve. Based on these evaluation criteria, the CARE system can be used to quantitatively evaluate small density changes on sequential radiographs for early detection of caries and periodontal disease.