TEMPy: a toolkit for the modeling of weighted tissue equivalent material in diagnostic imaging.

Objective.Accurate simulation of human tissues is imperative for advancements in diagnostic imaging, particularly in the fields of dosimetry and image quality evaluation. Developing Tissue Equivalent Materials (TEMs) with radiological characteristics akin to those of human tissues is essential for ensuring the reliability and relevance of imaging studies. This study presents the development of a mathematical model and a new toolkit (TEMPy) for obtaining the best composition of materials that mimic the radiological characteristics of human tissues. The model and the toolkit are described, along with an example showcasing its application to obtain desired TEMs.Approach.The methodology consisted of fitting volume fractions of the components of TEM in order to determine its linear attenuation coefficient as close as possible to the linear attenuation coefficient of the reference material. The fitting procedure adopted a modified Least Square Method including a weight function. This function reflects the contribution of the x-ray spectra in the suitable energy range of interest. TEMPy can also be used to estimate the effective atomic number and electron density of the resulting TEM.Main results.TEMPy was used to obtain the chemical composition of materials equivalent to water and soft tissue, in the energy range used in x-ray imaging (10 -150 keV) and for breast tissue using the energy range (5-40 keV). The maximum relative difference between the linear attenuation coefficients of the developed and reference materials was ±5% in the considered energy ranges.Significance.TEMPy facilitates the formulation of TEMs with radiological properties closely mimicking those of real tissues, aiding in the preparation of physical anthropomorphic or geometric phantoms for various applications. The toolkit is freely available to interested readers.

Breast segmentation in infrared thermography from characteristical inframammary shape.

Infrared thermography is gaining relevance in breast cancer assessment. For this purpose, breast segmentation in thermograms is an important task for performing automatic image analysis and detecting possible temperature changes that indicate the presence of malignancy. However, it is not a simple task since the breast limit borders, especially the top borders, often have low contrast, making it difficult to isolate the breast area. Several algorithms have been proposed for breast segmentation, but these highly depend on the contrast at the lower breast borders and on filtering algorithms to remove false edges. This work focuses on taking advantage of the distinctive inframammary shape to simplify the definition of the lower breast border, regardless of the contrast level, which indeed also provides a strong anatomical reference to support the definition of the poorly marked upper boundary of the breasts, which has been one of the major challenges in the literature. In order to demonstrate viability of the proposed technique for an automatic breast segmentation, we applied it to a database with 180 thermograms and compared their results with those reported by others in the literature. We found that our approach achieved a high performance, in terms of Intersection over Union of 0.934, even higher than that reported by artificial intelligence algorithms. The performance is invariant to breast sizes and thermal contrast of the images.

Pseudocalcifications: Radiologic-Pathologic Correlation of Postsurgical Gauze Artifacts.

Artifacts and foreign bodies can mimic microcalcifications. We report a series of 17 postsurgical women in whom mammograms showed fine linear radiodensities at the surgical bed. Vacuum-assisted biopsy histopathology of one of the lesions showed foreign bodies of different sizes with macrophage reaction. After discussion with the surgeons, we ascertained that a particular type of gauze was used that had fragmented, and we reproduced the mammographic appearance in a chicken breast. Furthermore, we showed the same pathology was reproduced in mice implanted with the gauze threads. It is important to be aware of this entity to avoid unnecessary examinations and even biopsy. The presence of foreign body linear gauze fragments at the surgical site can pose challenges in the mammographic follow-up of these patients.

BraNet: a mobil application for breast image classification based on deep learning algorithms.

Mobile health apps are widely used for breast cancer detection using artificial intelligence algorithms, providing radiologists with second opinions and reducing false diagnoses. This study aims to develop an open-source mobile app named "BraNet" for 2D breast imaging segmentation and classification using deep learning algorithms. During the phase off-line, an SNGAN model was previously trained for synthetic image generation, and subsequently, these images were used to pre-trained SAM and ResNet18 segmentation and classification models. During phase online, the BraNet app was developed using the react native framework, offering a modular deep-learning pipeline for mammography (DM) and ultrasound (US) breast imaging classification. This application operates on a client-server architecture and was implemented in Python for iOS and Android devices. Then, two diagnostic radiologists were given a reading test of 290 total original RoI images to assign the perceived breast tissue type. The reader's agreement was assessed using the kappa coefficient. The BraNet App Mobil exhibited the highest accuracy in benign and malignant US images (94.7%/93.6%) classification compared to DM during training I (80.9%/76.9%) and training II (73.7/72.3%). The information contrasts with radiological experts' accuracy, with DM classification being 29%, concerning US 70% for both readers, because they achieved a higher accuracy in US ROI classification than DM images. The kappa value indicates a fair agreement (0.3) for DM images and moderate agreement (0.4) for US images in both readers. It means that not only the amount of data is essential in training deep learning algorithms. Also, it is vital to consider the variety of abnormalities, especially in the mammography data, where several BI-RADS categories are present (microcalcifications, nodules, mass, asymmetry, and dense breasts) and can affect the API accuracy model.

Breast density quantification in dual-energy mammography using virtual anthropomorphic phantoms.

PURPOSE: Breast density is a significant risk factor for breast cancer and can impact the sensitivity of screening mammography. Area-based breast density measurements may not provide an accurate representation of the tissue distribution, therefore volumetric breast density (VBD) measurements are preferred. Dual-energy mammography enables volumetric measurements without additional assumptions about breast shape. In this work we evaluated the performance of a dual-energy decomposition technique for determining VBD by applying it to virtual anthropomorphic phantoms. METHODS: The dual-energy decomposition formalism was used to quantify VBD on simulated dual-energy images of anthropomorphic virtual phantoms with known tissue distributions. We simulated 150 phantoms with volumes ranging from 50 to 709 mL and VBD ranging from 15% to 60%. Using these results, we validated a correction for the presence of skin and assessed the method's intrinsic bias and variability. As a proof of concept, the method was applied to 14 sets of clinical dual-energy images, and the resulting breast densities were compared to magnetic resonance imaging (MRI) measurements. RESULTS: Virtual phantom VBD measurements exhibited a strong correlation (Pearson's r > 0.95 $r > 0.95$ ) with nominal values. The proposed skin correction eliminated the variability due to breast size and reduced the bias in VBD to a constant value of -2%. Disagreement between clinical VBD measurements using MRI and dual-energy mammography was under 10%, and the difference in the distributions was statistically non-significant. VBD measurements in both modalities had a moderate correlation (Spearman's ρ $\rho \ $ = 0.68). CONCLUSIONS: Our results in virtual phantoms indicate that the material decomposition method can produce accurate VBD measurements if the presence of a third material (skin) is considered. The results from our proof of concept showed agreement between MRI and dual-energy mammography VBD. Assessment of VBD using dual-energy images could provide complementary information in dual-energy mammography and tomosynthesis examinations.

Breast Adiposity: Menopausal Status Impact and its Influence on Glycemic and Anthropometric Metabolic Parameters.

CONTEXT: Ectopic fat depots are related to the deregulation of energy homeostasis, leading to diseases related to obesity and metabolic syndrome (MetS). Despite significant changes in body composition over women's lifespans, little is known about the role of breast adipose tissue (BrAT) and its possible utilization as an ectopic fat depot in women of different menopausal statuses. OBJECTIVE: We aimed to assess the relationship between BrAT and metabolic glycemic and lipid profiles and body composition parameters in adult women. METHODS: In this cross-sectional study, we enrolled adult women undergoing routine mammograms and performed history and physical examination, body composition assessment, semi-automated assessment of breast adiposity (BA) from mammograms, and fasting blood collection for biochemical analysis. Correlations and multivariate regression analysis were used to examine associations of BA with metabolic and body composition parameters. RESULTS: Of the 101 participants included in the final analysis, 76.2% were in menopause, and 23.8% were in premenopause. The BA was positively related with fasting plasma glucose, glycated hemoglobin, homeostasis model assessment of insulin resistance, body mass index, waist circumference, body fat percentage, and abdominal visceral and subcutaneous fat when adjusted for age among women in postmenopause. Also, the BA was an independent predictor of hyperglycemia and MetS. These associations were not present among women in premenopause. CONCLUSION: The BA was related to different adverse body composition and metabolic factors in women in postmenopause. The results suggest that there might be a relevant BrAT endocrine role during menopause, with mechanisms yet to be clarified, thus opening up research perspectives on the subject and potential clinical implications.

Towards precision medicine in breast imaging: A novel open mammography database with tailor-made 3D image retrieval for AI and teaching.

This project addresses the global challenge of breast cancer, particularly in low-resource settings, by creating a pioneering mammography database. Breast cancer, identified by the World Health Organization as a leading cause of cancer death among women, often faces diagnostic and treatment resource constraints in low- and middle-income countries. To enhance early diagnosis and address educational setbacks, the project focuses on leveraging artificial intelligence (AI) technologies through a comprehensive database. Developed in collaboration with Ambra Health, a cloud-based medical image management software, the database comprises 941 mammography images from 100 anonymized cases, with 62 % including 3D images. Accessible through http://mamografia.unifesp.br, the platform facilitates a simple registration process and an advanced search system based on 169 clinical and imaging variables. The website, customizable to the user's native language, ensures data security through an automatic anonymization system. By providing high-resolution, 3D digital images and supplementary clinical information, the platform aims to promote education and research in breast cancer diagnosis, representing a significant advancement in resource-constrained healthcare environments.

Transfer learning for the generalization of artificial intelligence in breast cancer detection: a case-control study.

BACKGROUND: Some researchers have questioned whether artificial intelligence (AI) systems maintain their performance when used for women from populations not considered during the development of the system. PURPOSE: To evaluate the impact of transfer learning as a way of improving the generalization of AI systems in the detection of breast cancer. MATERIAL AND METHODS: This retrospective case-control Finnish study involved 191 women diagnosed with breast cancer and 191 matched healthy controls. We selected a state-of-the-art AI system for breast cancer detection trained using a large US dataset. The selected baseline system was evaluated in two experimental settings. First, we examined our private Finnish sample as an independent test set that had not been considered in the development of the system (unseen population). Second, the baseline system was retrained to attempt to improve its performance in the unseen population by means of transfer learning. To analyze performance, we used areas under the receiver operating characteristic curve (AUCs) with DeLong's test. RESULTS: Two versions of the baseline system were considered: ImageOnly and Heatmaps. The ImageOnly and Heatmaps versions yielded mean AUC values of 0.82±0.008 and 0.88±0.003 in the US dataset and 0.56 (95% CI=0.50-0.62) and 0.72 (95% CI=0.67-0.77) when evaluated in the unseen population, respectively. The retrained systems achieved AUC values of 0.61 (95% CI=0.55-0.66) and 0.69 (95% CI=0.64-0.75), respectively. There was no statistical difference between the baseline system and the retrained system. CONCLUSION: Transfer learning with a small study sample did not yield a significant improvement in the generalization of the system.

Comparison of medical image interpretation time between conventional and automated methods of breast ultrasound

Abstract Objective: To compare the medical image interpretation's time between the conventional and automated methods of breast ultrasound in patients with breast lesions. Secondarily, to evaluate the agreement between the two methods and interobservers. Methods: This is a cross-sectional study with prospective data collection. The agreement's degrees were established in relation to the breast lesions's ultrasound descriptors. To determine the accuracy of each method, a biopsy of suspicious lesions was performed, considering the histopathological result as the diagnostic gold standard. Results: We evaluated 27 women. Conventional ultrasound used an average medical time of 10.77 minutes (± 2.55) greater than the average of 7.38 minutes (± 2.06) for automated ultrasound (p<0.001). The degrees of agreement between the methods ranged from 0.75 to 0.95 for researcher 1 and from 0.71 to 0.98 for researcher 2. Among the researchers, the degrees of agreement were between 0.63 and 1 for automated ultrasound and between 0.68 and 1 for conventional ultrasound. The area of the ROC curve for the conventional method was 0.67 (p=0.003) for researcher 1 and 0.72 (p<0.001) for researcher 2. The area of the ROC curve for the automated method was 0. 69 (p=0.001) for researcher 1 and 0.78 (p<0.001) for researcher 2. Conclusion: We observed less time devoted by the physician to automated ultrasound compared to conventional ultrasound, maintaining accuracy. There was substantial or strong to perfect interobserver agreement and substantial or strong to almost perfect agreement between the methods.

MamoRef: an optical mammography device using whole-field CW diffuse reflectance. Presentation, validation and preliminary clinical results.

Objective.MamoRef is an mammography device that uses near-infrared light, designed to provide clinically relevant information for the screening of diseases of the breast. Using low power continuous wave lasers and a high sensitivity CCD (Charge-coupled device) that captures a diffusely reflected image of the tissue, MamoRef results in a versatile diagnostic tool that aims to fulfill a complementary role in the diagnosis of breast cancer providing information about the relative hemoglobin concentrations as well as oxygen saturation.Approach.We present the design and development of an initial prototype of MamoRef. To ensure its effectiveness, we conducted validation tests on both the theoretical basis of the reconstruction algorithm and the hardware design. Furthermore, we initiated a clinical feasibility study involving patients diagnosed with breast disease, thus evaluating the practical application and potential benefits of MamoRef in a real-world setting.Main results.Our study demonstrates the effectiveness of the reconstruction algorithm in recovering relative concentration differences among various chromophores, as confirmed by Monte Carlo simulations. These simulations show that the recovered data correlates well with the ground truth, with SSIMs of 0.8 or more. Additionally, the phantom experiments validate the hardware implementation. The initial clinical findings exhibit highly promising outcomes regarding MamoRef's ability to differentiate between lesions.Significance.MamoRef aims to be an advancement in the field of breast pathology screening and diagnostics, providing complementary information to standard diagnostic techniques. One of its main advantages is the ability of determining oxy/deoxyhemoglobin concentrations and oxygen saturation; this constitutes valuable complementary information to standard diagnostic techniques. Besides, MamoRef is a portable and relatively inexpensive device, intended to be not only used in specific medical imaging facilities. Finally, its use does not require external compression of the breast. The findings of this study underscore the potential of MamoRef in fulfilling this crucial role.

Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review.

BACKGROUND: Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes. Various imaging modalities have been used for years to diagnose breast cancer. Nonetheless, digital breast tomosynthesis (DBT), a state-of-the-art technique, has produced promising results comparatively. DBT, a 3D mammography, is replacing conventional 2D mammography rapidly. This technological advancement is key to AI algorithms for accurately interpreting medical images. OBJECTIVE AND METHODS: This paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis. This review focuses on DBT, its extracted synthetic mammography (SM), and full-field digital mammography (FFDM). Furthermore, this survey provides systematic knowledge about DL, radiomics, and radiogenomics for beginners and advanced-level researchers. RESULTS: A total of 500 articles were identified, with 30 studies included as the set criteria. Parallel benchmarking of radiomics, radiogenomics, and DL models applied to the DBT images could allow clinicians and researchers alike to have greater awareness as they consider clinical deployment or development of new models. This review provides a comprehensive guide to understanding the current state of early breast cancer detection using DBT images. CONCLUSION: Using this survey, investigators with various backgrounds can easily seek interdisciplinary science and new DL, radiomics, and radiogenomics directions towards DBT.

3D phantom for image quality assessment of mammography systems.

Objective.To present an innovative approach for the design of a 3D mammographic phantom for medical equipment quality assessment, estimation of the glandular tissue percentage in the patient's breast, and emulation of microcalcification (µC) breast lesions.Approach.Contrast-to noise ratio (CNR) measurements, as well as spatial resolution and intensity-to-glandularity calibrations under mammography conditions were performed to assess the effectiveness of the phantom. CNR measurements were applied to different groups of calcium hydroxyapatite (HA) and aluminum oxide (AO)µCs ranging from 200 to 600µm. Spatial resolution was characterized using an aluminum plate contained in the phantom and standard linear figures of merit, such as the line spread function and modulation transfer function (MTF). The intensity-to-glandularity calibration was developed using an x-ray attenuation matrix within the phantom to estimate the glandular tissue percentage in a breast with a compressed thickness of 4 cm.Main results.For the prototype studied, the minimum confidence level for detecting HAµCs is 95.4%, while for AOµCs is above 68.3%. It was also possible to determine that the MTF of the commercial mammography machine used for this study at the Nyquist frequency is 41%. Additionally, a one-to-one intensity-to-glandularity calibration was obtained and verified with Monte-Carlo simulation results.Significance.The phantom provides traditional arrangements presented in accreditation phantoms, which makes it competitive with available devices, but excelling in regarding affordability, modularity, and inlays distribution. Moreover, its design allows to be positioned in close proximity to the patient's breast during a medical screening for a simultaneous x-ray imaging, such that the features of the phantom can be used as reference values to specify characteristics of the real breast tissue, such as proportion of glandular/adipose composition and/orµC type and size lesions.

Gray-to-color image conversion in the classification of breast lesions on ultrasound using pre-trained deep neural networks.

Breast ultrasound (BUS) image classification in benign and malignant classes is often based on pre-trained convolutional neural networks (CNNs) to cope with small-sized training data. Nevertheless, BUS images are single-channel gray-level images, whereas pre-trained CNNs learned from color images with red, green, and blue (RGB) components. Thus, a gray-to-color conversion method is applied to fit the BUS image to the CNN's input layer size. This paper evaluates 13 gray-to-color conversion methods proposed in the literature that follow three strategies: replicating the gray-level image to all RGB channels, decomposing the image to enhance inherent information like the lesion's texture and morphology, and learning a matching layer. Besides, we introduce an image decomposition method based on the lesion's structural information to describe its inner and outer complexity. These gray-to-color conversion methods are evaluated under the same experimental framework using a pre-trained CNN architecture named ResNet-18 and a BUS dataset with more than 3000 images. In addition, the Matthews correlation coefficient (MCC), sensitivity (SEN), and specificity (SPE) measure the classification performance. The experimental results show that decomposition methods outperform replication and learning-based methods when using information from the lesion's binary mask (obtained from a segmentation method), reaching an MCC value greater than 0.70 and specificity up to 0.92, although the sensitivity is about 0.80. On the other hand, regarding the proposed method, the trade-off between sensitivity and specificity is better balanced, obtaining about 0.88 for both indices and an MCC of 0.73. This study contributes to the objective assessment of different gray-to-color conversion approaches in classifying breast lesions, revealing that mask-based decomposition methods improve classification performance. Besides, the proposed method based on structural information improves the sensitivity, obtaining more reliable classification results on malignant cases and potentially benefiting clinical practice.

Assessment of Three Breast Volume Measurement Techniques: Single Marking, MRI and Crisalix 3D Software®.

INTRODUCTION: Measuring breast volume is important to obtain satisfactory breast surgery results, and many techniques are used for this purpose. Thus, the aim of the present study was to compare 3 breast volume techniques: Pessoa's single marking technique, magnetic resonance imaging (MRI) and Crisalix 3D software®. METHODS: Fourteen patients indicated for mammoplasty were selected. Three breast volume measurement techniques were compared: Pessoa's single marking technique, MRI and Crisalix 3D software®. The volumes were tabulated and analyzed using R software. RESULTS: Average age was 30.93 ± 10.25 years. The breast volume was 1554.54 ± 512.54 cm3, as measured by the MRI technique (considered the gold standard), 1199.64 ± 403.13 cm3 using Crisalix 3D software® and 1518.04 ± 468.72 cm3 by Pessoa's single marking technique. Comparison between the Crisalix 3D software® and MRI techniques using the pairwise t test demonstrated a statistically significant difference (t = 4.3957, df = 27, p value = 0001543), but no significant difference between the single marking and MRI techniques (t = 1.3841, df = 27, p value = 0.1777). CONCLUSION: When compared to MRI, breast volume measurement using Pessoa's single marking technique showed no statistically significant difference between them. However, the Crisalix 3D® technique exhibited a difference in relation to MRI. Anthropometric measurements are useful in measuring breast volume because they are easy to obtain, practical and inexpensive, and should be part of a plastic surgeon's arsenal. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these evidence-based medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Upgrade risk of image-targeted radial scar and complex sclerosing lesions diagnosed at needle-guided biopsy: a retrospective study.

OBJECTIVES: To determine the upgrade rate of radial scar (RS) and complex sclerosing lesions (CSL) diagnosed with percutaneous biopsy. The secondary objectives were to determine the new atypia rate after surgery and to assess the diagnosis of subsequent malignancy on follow-up. METHODS: This single-institution retrospective study had IRB approval. All image-targeted RS and CSL diagnosed with percutaneous biopsy between 2007 and 2020 were reviewed. Patient demographics, imaging presentation, biopsy characteristics, histological report, and follow-up data were collected. RESULTS: During the study period, 120 RS/CSL were diagnosed in 106 women (median age, 43.5 years; range, 23-74), and 101 lesions were analyzed. At biopsy, 91 (90.1%) lesions were not associated with another atypia or malignancy and 10 (9.9%) were associated with another atypia. Out of the 91 lesions that were not associated with malignancy or atypia, 75 (82.4%) underwent surgical excision, and one upgrade to low-grade CDIS was detected (1.3%). Among the 10 lesions initially associated with another atypia, 9 were surgically excised and no malignancy was detected. After a median follow-up of 47 months (range: 12-143 months), two (1.98%) developed malignancy in a different quadrant; in both cases, another atypia was present at biopsy. CONCLUSION: We found a low upgrade rate on image-detected RS/CSL, with or without another atypia associated. Associated atypia was underdiagnosed at biopsy in almost one-third of cases. Subsequent cancer risk could not be established because the only two cases were associated with another high-risk lesion (HRL), which might have increased the patient's risk of developing malignancy. CLINICAL RELEVANCE STATEMENT: Our upgrade rates of RS/CSL with or without atypia diagnosed with core needle biopsy are almost as low as the ones reported with larger sampling methods. This result has particular importance in places with limited accessibility to US-guided vacuum-assisted biopsy. KEY POINTS: •New evidence is showing lower upgrade rates of RS and CSL after surgery, leading to a more conservative management with extensive sampling using VAB or VAE. •Our study showed only one upgrade to a low-grade DCIS after surgery, yielding an upgrade rate of 1.33%. •During follow-up, no new malignancy was detected in the same quadrant where RS/CSL was diagnosed, including patients without surgery.

A three-sequence dynamic contrast enhanced abbreviated MRI protocol to evaluate response to breast cancer neoadjuvant chemotherapy.

PURPOSE: To develop an ABP-MRI to evaluate response to NAC for invasive breast carcinoma. STUDY TYPE: A single-center, cross-sectional study. SUBJECTS: A consecutive series of 210 women with invasive breast carcinoma who underwent breast MRI after NAC between 2016 and 2020. FIELD STRENGTH/SEQUENCE: 1.5 T / Dynamic contrast-enhanced. ASSESSMENT: MRI scans were independently reevaluated, with access to dynamic contrast-enhanced without contrast and to the first, second, and third post-contrast time (ABP-MRI 1-3). STATISTICAL TESTS: The diagnostic performance of the ABP-MRIs and the Full protocol (FP-MRI) were analyzed. The Wilcoxon non-parametric test (p-value <0.050) was used to compare the capability in measuring the most extensive residual lesion. RESULTS: The median age was 47 (24-80) years. ABP-MRI 1 showed higher specificity (84.6%; 77/91) but a higher probability of false-negatives (16.8%) and lower sensitivity (83.2%; 99/119) than ABP-MRI 2,3 and the FP-MRI, which were identical in specificity (81.3%; 74/91), probability of false-negatives (8.4%), and sensitivity (91.6%; 109/119). ABP-MRI 2 showed a mean underestimation of only 0.03 cm in the measurement of the longest axis of the residual lesion (p = 0.008) with an average reduction in the acquisition time of 75%, compared with the FP-MRI. CONCLUSION: ABP-MRI 2 showed diagnostic performance equivalent to the FP-MRI with a 75% reduction in the acquisition time.

Dynamic Vascular Imaging Using Active Breast Thermography.

Mammography is considered the gold standard for breast cancer screening and diagnostic imaging; however, there is an unmet clinical need for complementary methods to detect lesions not characterized by mammography. Far-infrared 'thermogram' breast imaging can map the skin temperature, and signal inversion with components analysis can be used to identify the mechanisms of thermal image generation of the vasculature using dynamic thermal data. This work focuses on using dynamic infrared breast imaging to identify the thermal response of the stationary vascular system and the physiologic vascular response to a temperature stimulus affected by vasomodulation. The recorded data are analyzed by converting the diffusive heat propagation into a virtual wave and identifying the reflection using component analysis. Clear images of passive thermal reflection and thermal response to vasomodulation were obtained. In our limited data, the magnitude of vasoconstriction appears to depend on the presence of cancer. The authors propose future studies with supporting diagnostic and clinical data that may provide validation of the proposed paradigm.

An in silico study on the detectability of field cancerization through parenchymal analysis of digital mammograms.

BACKGROUND: Parenchymal analysis has shown promising performance for the assessment of breast cancer risk through the characterization of the texture features of mammography images. However, the working principles behind this practice are yet not well understood. Field cancerization is a phenomenon associated with genetic and epigenetic alterations in large volumes of cells, putting them on a path of malignancy before the appearance of recognizable cancer signs. Evidence suggests that it can induce changes in the biochemical and optical properties of the tissue. PURPOSE: The aim of this work was to study whether the extended genetic mutations and epigenetic changes due to field cancerization, and the impact they have on the biochemistry of breast tissues are detectable in the radiological patterns of mammography images. METHODS: An in silico experiment was designed, which implied the development of a field cancerization model to modify the optical tissue properties of a cohort of 60 voxelized virtual breast phantoms. Mammography images from these phantoms were generated and compared with images obtained from their non-modified counterparts, that is, without field cancerization. We extracted 33 texture features from the breast area to quantitatively assess the impact of the field cancerization model. We analyzed the similarity and statistical equivalence of texture features with and without field cancerization using the t-test, Wilcoxon sign rank test and Kolmogorov-Smirnov test, and performed a discrimination test using multinomial logistic regression analysis with lasso regularization. RESULTS: With modifications of the optical tissue properties on 3.9% of the breast volume, some texture features started to fail to show equivalence (p < 0.05). At 7.9% volume modification, a high percent of texture features showed statistically significant differences (p < 0.05) and non-equivalence. At this level, multinomial logistic regression analysis of texture features showed a statistically significant performance in the discrimination of mammograms from breasts with and without field cancerization (AUC = 0.89, 95% CI: 0.75-1.00). CONCLUSIONS: These results support the idea that field cancerization is a feasible underlying working principle behind the distinctive performance of parenchymal analysis in breast cancer risk assessment.

Impact of fibroglandular tissue distribution and breast shape in voxelized breast models for dosimetry in mammography.

Objective.This work proposes to study the impact of different voxelized heterogeneous breast models (gaussian centered - GaussC; gaussian lower - GaussL; and fitted equation patient-based on 3D realistic distribution (Fedonet al2021) - FitPB) for dosimetry in mammography compared to a well-established homogeneous approximation. Influence of breast outer shape also was investigated by comparing semicylindric and anthropomorphic breasts.Approach.By using the PENELOPE (v. 2018) + penEasy (v. 2020) MC code, simulations were performed to evaluate the normalized glandular dose (DgN) and the glandular depth dose (GDD(z)) for different breast characteristics and x-ray beam spectra.Main results.The averageDgNoverestimation caused by homogeneous tissue approximation was 33.0%, with the highest values attributed to GaussLand FitPBmodels, where fibroglandular tissue is concentrated deeper in the breast. The observed variation between anthropomorphic and semicylindrical breast shapes was, on average, 5.6%, legitimizing the latter approximation for breast dosimetry. Thicker breasts and lower energy beams resulted in larger overestimation caused by the homogeneous approach, while variations inDgNvalues among different heterogeneous models were higher for thinner breast and lower energy beams. Moreover, the depth where differences betweenGDD(z) for different breast models became maximum depends on the axial variation of fibroglandular tissue concentration between each model. TheGDD(z) dependence results in a significant variation of the contribution of each breast depth to mean glandular dose (MGD) among the breast models studied.Significance.Intercomparison between different breast models for dosimetry can be useful for estimating more accurateMGDvalues for population-based dosimetry, for exploring the use of 1D gaussian distribution for breast dosimetry, and for understanding the dose distributions inside the fibroglandular tissues, which could be a novel source of information for risk estimations.