3D Breast Research Articles

Evaluation of Intraoperative Volumetric Assessment of Breast Volume Using 3D Handheld Stereo Photogrammetric Device.

Methods for assessing three-dimensional (3D) breast volume are becoming increasingly popular in breast surgery. However, the precision of intraoperative volumetric assessment is still unclear. Until now, only non-validated scanning systems have been used for intraoperative volumetric analyses. This study aimed to assess the feasibility, handling, and accuracy of a commercially available, validated, and portable device for intraoperative 3D volumetric evaluation. All patients who underwent breast surgery from 2020 to 2022 were identified from our institutional database. Intraoperative 3D volumetric assessments of 103 patients were included in this study. Standardized 3D volumetric measurements were obtained 3 months postoperatively to compare the intraoperatively generated volumetric assessment. All of the study participants were women with a mean age of 48.3 ± 14.7 years (range: 20-89). The mean time for intraoperative volumetric assessment was 8.7 ± 2.6 min. The postoperative 3D volumetric assessment, with a mean volume of 507.11 ± 206.29 cc, showed no significant difference from the intraoperative volumetric measurements of 504.24 ± 276.61 cc (p = 0.68). The mean absolute volume difference between the intraoperative simulations and postoperative results was 27.1 cc. Intraoperative 3D volumetric assessment using the VECTRA H2 imaging system seems to be a feasible, reliable, and accurate method for measuring breast volume. Based on this finding, we plan to investigate whether volumetric objective evaluations will help to improve breast symmetry in the future.

Mimicking 3D breast tumor-stromal interactions to screen novel cancer therapeutics

Most of the 3D breast tumor models used in drug screening studies only comprise tumor cells, keeping out other essential cell players of the tumor microenvironment. Tumor-associated macrophages and fibroblasts are frequently correlated with tumor progression and therapy resistance, and targeting these cells at the tumor site has been appointed as a promising therapeutic strategy. However, the translation of new therapies to the clinic has been hampered by the absence of cellular models that more closely mimic the features of in vivo breast tumor microenvironment. Therefore, the development of innovative 3D models able to provide consistent and predictive responses about the in vivo efficacy of novel therapeutics is still an unmet preclinical need.Herein, we have established an in vitro 3D heterotypic spheroid model including MCF-7 breast tumor cells, human mammary fibroblasts and human macrophages. To establish this model, different cell densities have been combined and characterized through the evaluation of the spheroid size and metabolic activity, as well as histological and immunohistochemistry analysis of the 3D multicellular structures. The final optimized 3D model consisted in a multicellular spheroid seeded at the initial density of 5000 cells and cell ratio of 1:2:1 (MCF-7:monocytes:fibroblasts). Our model recapitulates several features of the breast tumor microenvironment, including the formation of a necrotic core, spatial organization, and extracellular matrix production. Further, it was validated as a platform for drug screening studies, using paclitaxel, a currently approved drug for breast cancer treatment, and Gefitinib, a chemotherapeutic approved for lung cancer and in preclinical evaluation for breast cancer. Generally, the impact on the cell viability of the 3D model was less evident than in 2D model, reinforcing the relevance of such complex 3D models in addressing novel treatment approaches. Overall, the use of a 3D heterotypic spheroid of breast cancer could be a valuable tool to predict the therapeutic effect of new treatments for breast cancer patients, by recapitulating key features of the breast cancer microenvironment.

Mitigating Under-Sampling Artifacts in 3D Photoacoustic Imaging Using Res-UNet Based on Digital Breast Phantom.

In recent years, photoacoustic (PA) imaging has rapidly grown as a non-invasive screening technique for breast cancer detection using three-dimensional (3D) hemispherical arrays due to their large field of view. However, the development of breast imaging systems is hindered by a lack of patients and ground truth samples, as well as under-sampling problems caused by high costs. Most research related to solving these problems in the PA field were based on 2D transducer arrays or simple regular shape phantoms for 3D transducer arrays or images from other modalities. Therefore, we demonstrate an effective method for removing under-sampling artifacts based on deep neural network (DNN) to reconstruct high-quality PA images using numerical digital breast simulations. We constructed 3D digital breast phantoms based on human anatomical structures and physical properties, which were then subjected to 3D Monte-Carlo and K-wave acoustic simulations to mimic acoustic propagation for hemispherical transducer arrays. Finally, we applied a 3D delay-and-sum reconstruction algorithm and a Res-UNet network to achieve higher resolution on sparsely-sampled data. Our results indicate that when using a 757 nm laser with uniform intensity distribution illuminated on a numerical digital breast, the imaging depth can reach 3 cm with 0.25 mm spatial resolution. In addition, the proposed DNN can significantly enhance image quality by up to 78.4%, as measured by MS-SSIM, and reduce background artifacts by up to 19.0%, as measured by PSNR, even at an under-sampling ratio of 10%. The post-processing time for these improvements is only 0.6 s. This paper suggests a new 3D real time DNN method addressing the sparse sampling problem based on numerical digital breast simulations, this approach can also be applied to clinical data and accelerate the development of 3D photoacoustic hemispherical transducer arrays for early breast cancer diagnosis.

Frequency constraint-based adversarial attack on deep neural networks for medical image classification

The security of AI systems has gained significant attention in recent years, particularly in the medical diagnosis field. To develop a secure medical image classification system based on deep neural networks, it is crucial to design effective adversarial attacks that can embed hidden, malicious behaviors into the system. However, designing a unified attack method that can generate imperceptible attack samples with high content similarity and be applied to diverse medical image classification systems is challenging due to the diversity of medical imaging modalities and dimensionalities. Most existing attack methods are designed to attack natural image classification models, which inevitably corrupt the semantics of pixels by applying spatial perturbations. To address this issue, we propose a novel frequency constraint-based adversarial attack method capable of delivering attacks in various medical image classification tasks. Specially, our method introduces a frequency constraint to inject perturbation into high-frequency information while preserving low-frequency information to ensure content similarity. Our experiments include four public medical image datasets, including a 3D CT dataset, a 2D chest X-Ray image dataset, a 2D breast ultrasound dataset, and a 2D thyroid ultrasound dataset, which contain different imaging modalities and dimensionalities. The results demonstrate the superior performance of our model over other state-of-the-art adversarial attack methods for attacking medical image classification tasks on different imaging modalities and dimensionalities.

How Camera-to-Subject Distance and Height Affect Breast Measurement and Follow-Up Assessment in Plastic Surgery.

Due to several factors that affect photograph quality, bias is inevitably present in two-dimensional (2D) breast photography. The principal variables affecting image performance at a fixed focus length are the distance between the camera and the subjects and the photography angles. This study aimed to investigate the effects of camera-to-subject distances and camera height on breast measurement parameters to understand the trend of breast deformation and provide guidance for the accurate evaluation of planar follow-up. We enlisted 16 volunteers with various breast cup sizes (A-D). Frontal and lateral photos were obtained with a steady focus of 50 mm at distances between 1.10 m and 2.20 m and at heights between 30 cm above the nipple and 30 cm below the nipple at intervals of 10 cm. Two researchers independently evaluated each volunteer's breast aesthetic parameters, including 11 linear parameters, 3 area parameters, and 3 ratio parameters, using Vernier calipers and Photoshop. The correlation coefficient of the two investigators ranged from 0.922 to 0.999. The results measured by Photoshop were 29.67 ± 5.23% greater than those of the Vernier caliper (p < 0.01). In contrast to ratio parameters, which showed no significant changes in each distance group (p = 1.00), linear parameters and area parameters significantly increased as object distance decreased (p < 0.05). The lower pole of the breast grew wider and flatter and occupied a larger proportion of the breast as height declined. Camera-to-subject distances of 1.5-1.7 m are recommended for stabilized and uniform breast photography. Varying shooting height affects breast distortion. Quantifying the relationship between photographic conditions and breast morphology enables plastic surgeons to conduct more comprehensive and accurate assessments. 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. Bullet point list: 1. The breast morphology will get more distortion with a smaller camera-to-subject distance. 2. Camera-to-subject distances of 1.5~1.7m are recommended for stabilized and uniform breast photography. 3. Height rather than distance affects the breast proportion.

Robust 3D breast reconstruction based on monocular images and artificial intelligence for robotic guided oncological interventions.

Breast cancer is a global public health concern. For women with suspicious breast lesions, the current diagnosis requires a biopsy, which is usually guided by ultrasound (US). However, this process is challenging due to the low quality of the US image and the complexity of dealing with the US probe and the surgical needle simultaneously, making it largely reliant on the surgeon's expertise. Some previous works employing collaborative robots emerged to improve the precision of biopsy interventions, providing an easier, safer, and more ergonomic procedure. However, for these equipment to be able to navigate around the breast autonomously, 3D breast reconstruction needs to be available. The accuracy of these systems still needs to improve, with the 3D reconstruction of the breast being one of the biggest focuses of errors. The main objective of this work is to develop a method to obtain a robust 3D reconstruction of the patient's breast, based on RGB monocular images, which later can be used to compute the robot's trajectories for the biopsy. To this end, depth estimation techniques will be developed, based on a deep learning architecture constituted by a CNN, LSTM, and MLP, to generate depth maps capable of being converted into point clouds. After merging several from multiple points of view, it is possible to generate a real-time reconstruction of the breast as a mesh. The development and validation of our method was performed using a previously described synthetic dataset. Hence, this procedure takes RGB images and the cameras' position and outputs the breasts' meshes. It has a mean error of 3.9 mm and a standard deviation of 1.2 mm. The final results attest to the ability of this methodology to predict the breast's shape and size using monocular images.Clinical Relevance- This work proposes a method based on artificial intelligence and monocular RGB images to obtain the breast's volume during robotic guided breast biopsies, improving their execution and safety.

Technical Note: Realization and uncertainty analysis for an adjustable 3D structured breast phantom in digital breast tomosynthesis.

Projection imaging phantoms are often optimized for 2-dimensional image characteristics in homogeneous backgrounds. Therefore, evaluation of image quality in tomosynthesis (DBT) lacks accepted and establishedphantoms. We describe a 3D breast phantom with a structured, variable background. The phantom is an adaptable and advanced version of the L1 phantom by Cockmartin etal. Phantom design and its use for quality assurance measurements for DBT devices are described. Four phantoms were compared to assess theobjectivity. The container size was increased to a diameter of 24cm and a total height of 53.5 mm. Spiculated masses were replaced by five additional non-spiculated masses for higher granularity in threshold diameter resolution. These patterns are adjustable to the imaging device. The masses were printed in one session with a base layer using two-component 3D printing. New materials compared to the L1 phantom improved the attenuation difference between the lesion models and the background. Four phantoms were built and intra-human observer, inter-human observer and inter-phantom variations were determined. The latter assess the reproducibility of the phantom production. Coefficients of variance (V) were calculated for all threevariations. The difference of the attenuation coefficients between the lesion models and the background was 0.20cm-1 (with W/Al at 32kV, equivalent to 19-20keV effective energy) compared to 0.21cm-1 for 50/50 glandular/adipose breast tissue and cancerous lesions. PMMA equivalent thickness of the phantom was 47.0mm for the Siemens Mammomat Revelation. For the masses, the for the intra-observer variation was 0.248, the averaged inter-observer variation, was 0.383. for phantom variance was 0.321. For the micro-calcifications, was 0.0429, 0.0731 and 0.0759. Position, orientation and shape of the masses are reproducible and attenuation differences appropriate. The phantom presented proved to be a candidate test object for qualitycontrol.

Role of tomosynthesis and ultrasound in the assessment of asymmetric breast densities: a comparative prospective study

BackgroundTomosynthesis is a recent advancement of full-field digital mammography involves transforming two-dimensional (2D) breast images into three dimensions (3D) images. It reduces the adverse effect of tissue superimposition on conventional 2D- mammography, therefore having high potential enhancing identification and assessment of asymmetric breast densities. The aim of the study was to assess and compare the diagnostic performance of breast ultrasound and 3D digital breast tomosynthesis in the assessment of asymmetric breast densities.ResultsIn the current study, 80 patients with 80 mammographically and/or tomosynthesized breast asymmetries were included. The patients' ages ranged from 30 to 70 years old, with a mean age of 47.2 ± 9.2 SD. Breast ultrasound outperformed digital breast tomosynthesis in terms of diagnostic performance. Tomosynthesis had a sensitivity of 86.4%, specificity of 93.1%, positive predictive value of 82.6%, negative predictive value of 94.7%, and accuracy of 91.3% compared to ultrasounds' sensitivity of 100.00%, specificity of 93.1%, positive predictive value of 84.6%, negative predictive value of 100.00%, and accuracy of 95%.ConclusionsIncorporating ultrasonography in the assessment of asymmetric breast densities outperformed tomosynthesis and shown to be more precise in characterisation of lesions underlying asymmetric breast density.

Sophorolipid Candidates Demonstrate Cytotoxic Efficacy against 2D and 3D Breast Cancer Models.

Sophorolipids are biosurfactants derived from the nonpathogenic yeasts such as Starmerella bombicola with potential efficacy in anticancer applications. Simple and cost-effective synthesis of these drugs makes them a promising alternative to traditional chemotherapeutics, pending their success in preliminary drug-screening. Drug-screening typically utilizes 2D cell monolayers due to their simplicity and ease of high-throughput assessment. However, 2D assays fail to capture the complexity and 3D context of the tumor microenvironment and have consequently been implicated in the high percentage of drugs investigated in vitro that later fail in clinical trials. Herein, we screened two sophorolipid candidates and a clinically-used chemotherapeutic, doxorubicin, on in vitro breast cancer models ranging from 2D monolayers to 3D spheroids, employing optical coherence tomography to confirm these morphologies. We calculated corresponding IC50 values for these drugs and found one of the sophorolipids to have comparable toxicities to the chemotherapeutic control. Our findings show increased drug resistance associated with model dimensionality, such that all drugs tested showed that 3D spheroids exhibited higher IC50 values than their 2D counterparts. These findings demonstrate promising preliminary data to support the use of sophorolipids as a more affordable alternative to traditional clinical interventions and demonstrate the importance of 3D tumor models in assessing drug response.

Development of PCL PolyHIPE Substrates for 3D Breast Cancer Cell Culture.

Cancer is a becoming a huge social and economic burden on society, becoming one of the most significant barriers to life expectancy in the 21st century. In particular, breast cancer is one of the leading causes of death for women. One of the most significant difficulties to finding efficient therapies for specific cancers, such as breast cancer, is the efficiency and ease of drug development and testing. Tissue-engineered (TE) in vitro models are rapidly developing as an alternative to animal testing for pharmaceuticals. Additionally, porosity included within these structures overcomes the diffusional mass transfer limit whilst enabling cell infiltration and integration with surrounding tissue. Within this study, we investigated the use of high-molecular-weight polycaprolactone methacrylate (PCL-M) polymerised high-internal-phase emulsions (polyHIPEs) as a scaffold to support 3D breast cancer (MDA-MB-231) cell culture. We assessed the porosity, interconnectivity, and morphology of the polyHIPEs when varying mixing speed during formation of the emulsion, successfully demonstrating the tunability of these polyHIPEs. An ex ovo chick chorioallantoic membrane assay identified the scaffolds as bioinert, with biocompatible properties within a vascularised tissue. Furthermore, in vitro assessment of cell attachment and proliferation showed promising potential for the use of PCL polyHIPEs to support cell growth. Our results demonstrate that PCL polyHIPEs are a promising material to support cancer cell growth with tuneable porosity and interconnectivity for the fabrication of perfusable 3D cancer models.

Altered expression of anti-apoptotic protein Api5 affects breast tumorigenesis

BackgroundApoptosis or programmed cell death plays a vital role in maintaining homeostasis and, therefore, is a tightly regulated process. Deregulation of apoptosis signalling can favour carcinogenesis. Apoptosis inhibitor 5 (Api5), an inhibitor of apoptosis, is upregulated in cancers. Interestingly, Api5 is shown to regulate both apoptosis and cell proliferation. To address the precise functional significance of Api5 in carcinogenesis here we investigate the role of Api5 in breast carcinogenesis.MethodsInitially, we carried out in silico analyses using TCGA and GENT2 datasets to understand expression pattern of API5 in breast cancer patients followed by investigating the protein expression in Indian breast cancer patient samples. To investigate the functional importance of Api5 in breast carcinogenesis, we utilised MCF10A 3D breast acinar cultures and spheroid cultures of malignant breast cells with altered Api5 expression. Various phenotypic and molecular changes induced by altered Api5 expression were studied using these 3D culture models. Furthermore, in vivo tumorigenicity studies were used to confirm the importance of Api5 in breast carcinogenesis.ResultsIn-silico analysis revealed elevated levels of Api5 transcript in breast cancer patients which correlated with poor prognosis. Overexpression of Api5 in non-tumorigenic breast acinar cultures resulted in increased proliferation and cells exhibited a partial EMT-like phenotype with higher migratory potential and disruption in cell polarity. Furthermore, during acini development, the influence of Api5 is mediated via the combined action of FGF2 activated PDK1-Akt/cMYC signalling and Ras-ERK pathways. Conversely, Api5 knock-down downregulated FGF2 signalling leading to reduced proliferation and diminished in vivo tumorigenic potential of the breast cancer cells.ConclusionTaken together, our study identifies Api5 as a central player involved in regulating multiple events during breast carcinogenesis including proliferation, and apoptosis through deregulation of FGF2 signalling pathway.

Characterization of a polychromatic microfocus X-ray fluorescence imaging setup with metallic contrast agents in a microphysiological tumor model

Introduction: Accurate diagnosis and personalized treatments involving site-targeted cancer localization, drug delivery, therapeutic strategy, and disease pathways identification, rely on a precise understanding of biomarker kinetics, drug pharmacokinetics, and mechanistic behaviour of functionalized tracers through in vitro and in vivo studies. X-ray fluorescence (XRF) computed tomography (XFCT) offers a potential alternative to current 3D imaging techniques for spatiotemporal localization of nanoparticle-tracers with high spatial resolution and sensitivity. In this work, the applicability of a benchtop cone-beam system with a polychromatic X-ray source was examined with regard to physical constraints of engineered tissue models.Methods: A tissue engineering approach based on a decellularized scaffold was used to establish a 3D breast cancer model with MDA-MB-231 cells in co-culture with primary human fibroblasts. The 3D breast cancer system, in combination with small-animal-sized phantoms, was used to demonstrate the novel integrated pre-clinical imaging approach to perform in vitro surrogate investigations and non-destructive analysis on biophantoms. These models are adopted to evaluate the functionality and optimize the setup for high-spatial-resolution, fast, and fully-3D quantitative imaging. Polychromatic X-rays from a microfocus source are used for XRF stimulation from conventional Gadolinium (Gd) and nanoparticle-based Molybdenum (MoNPs) contrast agents.Results and Discussion: The intestinal scaffold allowed the invasion of the breast cancer cells over this barrier and therefore provides a valuable tool to study metastasis formation of tumor cells from epithelial origin. The breast cancer model was well suited for the development and validation of the proposed XRF imaging, with spatial resolution under &amp;lt;2 mm and contrast dose in the order of a few 100 μg/mL (∼0.3 mg/mL for Gd and ∼0.5 mg/mL for MoNPs), radiation dose in the order of a few 100 cGy (280 cGy for Gd and 94 cGy for MoNPs, with a possible reduction of an order of magnitude for Gd and 67% for MoNPs), and imaging time in the order of 10 min for Gd (33 min total) and 100 min (2.8 h total) for MoNPs, approaching in vivo conform conditions for pre-clinical studies. High-resolution XFCT for tissue-engineered cancer models would be of significant interest in biomedical research and diagnostic imaging, e.g., for an increased mechanistic understanding of molecular processes in tumor formation or early cancer detection.

The Variety of 3D Breast Cancer Models for the Study of Tumor Physiology and Drug Screening.

Breast cancer is the most common cancer in women and responsible for multiple deaths worldwide. 3D cancer models enable a better representation of tumor physiology than the conventional 2D cultures. This review summarizes the important components of physiologically relevant 3D models and describes the spectrum of 3D breast cancer models, e.g., spheroids, organoids, breast cancer on a chip and bioprinted tissues. The generation of spheroids is relatively standardized and easy to perform. Microfluidic systems allow control over the environment and the inclusion of sensors and can be combined with spheroids or bioprinted models. The strength of bioprinting relies on the spatial control of the cells and the modulation of the extracellular matrix. Except for the predominant use of breast cancer cell lines, the models differ in stromal cell composition, matrices and fluid flow. Organoids are most appropriate for personalized treatment, but all technologies can mimic most aspects of breast cancer physiology. Fetal bovine serum as a culture supplement and Matrigel as a scaffold limit the reproducibility and standardization of the listed 3D models. The integration of adipocytes is needed because they possess an important role in breast cancer.

Abstract 6186: Patient-derived tumor organoids with p53 mutations, and not wild-type p53, demonstrate synergistic sensitivity to treatment with temozolomide in combination with talazoparib PARP inhibitor

Abstract Mutations in the TP53 gene can be found in more than 50% of tumors. We recently showed in 2D breast cancer cell lines that mutant p53 (mtp53) proteins tightly associate with replicating DNA, chromatin and Poly (ADP-ribose) polymerase (PARP) protein. In addition, missense mtp53 R273H causes an increase in the chromatin association of replication proteins including PARP, and mini-chromosome maintenance complex 2-7 (MCM2-7). The expression of mtp53 R273H enhances overall MCM2 levels, promotes cell proliferation, and improves the synergistic cytotoxicity of treatment with the alkylating agent temozolomide in combination with the PARP inhibitor (PARPi) talazoparib. Currently, PARPis are indicated for patients that present BRCA1/2 mutations, but there might be patients with other alterations associated with DNA damage repair that could benefit from PARPi treatment; this includes expression of mtp53. Patient-derived Tumor Organoids (PDTO) are 3D culture models that retain cell-cell and cell-matrix interactions and are shown to reproduce drug responses observed in patients. Here, we evaluated the sensitivity of wild-type BRCA1/2 breast and lung PDTO with either mtp53 or wild-type p53 (wtp53) to the combination of DNA damaging agent temozolomide plus the PARPi talazoparib. First, we tested the sensitivity of the organoids to talazoparib and temozolomide individually. To determine if there was synergy between the two treatments, the organoids were treated with an inhibitor matrix to evaluate multiple combinatorial concentrations. Three breast cancer and two lung cancer PDTO with mtp53 presented synergistic cytotoxicity of the combination treatment. Two breast organoid lines with wtp53 showed no synergistic interaction between the two drugs, and the same was observed in a wtp53 lung organoid line. We analyzed wtp53 and mtp53 PDTO cell extracts by western blot to assess the activation of downstream p53 effectors and DNA damage markers after treatment with temozolomide and/or talazoparib. Combination of talazoparib and temozolomide induced higher DNA double-strand breaks in mtp53 organoids as shown by increased gamma-H2AX expression. The results obtained demonstrated that in mtp53 PDTO synergism is achieved with combined talazoparib-temozolomide treatment supporting the idea that tumors expressing mtp53 may be potential candidates for this combination PARPi treatment. Citation Format: Florencia P. Madorsky Rowdo, Gu Xiao, Melissa B. Davis, Laura Martin, Olivier Elemento, Jill Bargonetti. Patient-derived tumor organoids with p53 mutations, and not wild-type p53, demonstrate synergistic sensitivity to treatment with temozolomide in combination with talazoparib PARP inhibitor. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6186.

A 3D Mathematical Breast Texture Model With Parameters Automatically Inferred From Clinical Breast CT Images.

A numerical realistic 3D anthropomorphic breast model is useful for evaluating breast imaging applications. A method is proposed to model small and medium-scale fibroglandular and intra-glandular adipose tissues observed in the center part of clinical breast CT images. The method builds upon a previously proposed model formulated as stochastic geometric processes with mathematically tractable parameters. In this work, the medium-scale parameters were automatically and objectively inferred from breast CT images. We hypothesized that a set of random ellipsoids exhibiting cluster interaction is representative to model the medium-scale intra-glandular adipose compartments. The ellipsoids were reconstructed using a multiple birth, death and shift algorithm. Then, a Matérn cluster process was used to fit the reconstructed ellipsoid centers. Finally, distributions of the ellipsoid shapes and orientations were estimated using maximum likelihood estimators. Feasibility was demonstrated on 16 volumes of interests (VOI). To assess the realism of the 3D breast texture model, β and LFE metrics computed in simulated projection images of simulated texture realizations and clinical images were compared. Visual realism was illustrated. For 12 out of 16 VOIs, our hypothesis on clustering interaction process is confirmed. The average β values from simulated texture images (3.7 to 4.2) of the 12 different VOIs are higher than the average β value from 2D clinical images (2.87). LFE of simulated texture images and clinical mammograms are similar. Compared to our previous model, whereby simulation parameters were based upon empirical observations, our inference method substantially augments the ability to generate textures with higher visual realism and larger morphological variety.

Progestin-related breast volume changes in a woman with complete androgen insensitivity syndrome (CAIS).

A 31-year-old woman with complete androgen insensitivity syndrome (CAIS) experienced breast volume fluctuations during biphasic hormone replacement therapy consisting of estradiol and cyclical dydrogesterone, a progestin. 3D breast volume measurements showed a 100 cc volume (17%) difference between estradiol monotherapy and combined estradiol and dydrogesterone treatment. Progestogen-dependent breast volume changes have not been reported in the literature. Our findings suggest a correlation between progestogen use and breast volume. Due to the rapid cyclical changes, we hypothesize that the effect is caused by fluid retention. There is limited reports available on the effects of progesterone on breast development and volume. 3D imaging provides an easy-to-use method to quantify breast volume. The patient in our case description clearly showed that cyclic progesterone use might induce substantial cyclic changes in breast volume. In women with complete androgen insensitivity syndrome (CAIS), monotherapy with estrogen or continuous supplementation of progesterone might be preferable over cyclic progesterone use.

Synergistic Effect of Co‐Culturing Breast Cancer Cells and Fibroblasts in the Formation of Tumoroid Clusters and Design of In Vitro 3D Models for the Testing of Anticancer Agents (Adv. Biology 4/2023)

In Vitro Tumor Model In article number 2200141, Joaquim M. Oliveira and co-workers show the development of a silk hydrogel-based 3D in vitro breast cancer model. The model highlights the importance of matrix stiffness and breast cancer cells and fibroblasts interaction for tumor progression, which influence chemotherapeutic drug treatment response. The developed 3D breast cancer model will allow a deeper understanding of tumor-stroma interactions, leading to the possibility of patient-specific therapies in the future.

Report on the AAPM deep-learning spectral CT Grand Challenge.

This Special Report summarizes the 2022 AAPM Grand Challenge on Deep-Learning spectral Computed Tomography (DL-spectral CT) imagereconstruction. The purpose of the challenge is to develop the most accurate image reconstruction algorithm possible for solving the inverse problem associated with a fast kilovolt switching dual-energy CT scan using a three tissue-map decomposition. Participants could choose to use a deep-learning (DL), iterative, or a hybridapproach. The challenge is based on a 2D breast CT simulation, where the simulated breast phantom consists of three tissue maps: adipose, fibroglandular, and calcification distributions. The phantom specification is stochastic so that multiple realizations can be generated for DL approaches. A dual-energy scan is simulated where the x-ray source potential of successive views alternates between 50 and 80 kilovolts (kV). A total of 512 views are generated, yielding 256 views for each source voltage. We generate 50 and 80 kV images by use of filtered back-projection (FBP) on negative logarithm processed transmission data. For participants who develop a DL approach, 1000 cases are available. Each case consists of the three 512 × 512 tissue maps, 50 and 80-kV transmission data sets and their corresponding FBP images. The goal of the DL network would then be to predict the material maps from either the transmission data, FBP images, or a combination of the two. For participants developing a physics-based approach, all of the required modeling parameters are made available: geometry, spectra, and tissue attenuation curves. The provided information also allows for hybrid approaches where physics is exploited as well as information about the scanned object derived from the 1000 training cases. Final testing is performed by computation of root-mean-square error (RMSE) for predictions on the tissue maps from 100 newcases. Test phase submission were received from 18 research groups. Of the 18 submissions, 17 were results obtained with algorithms that involved DL. Only the second place finishing team developed a physics-based image reconstruction algorithm. Both the winning and second place teams had highly accurate results where the RMSE was nearly zero to single floating point precision. Results from the top 10 also achieved a high degree of accuracy; and as a result, this special report outlines the methodology developed by each of thesegroups. The DL-spectral CT challenge successfully established a forum for developing image reconstruction algorithms that address an important inverse problem relevant forspectral CT.

Reprogramming of tumor-associated macrophages by polyaniline-coated iron oxide nanoparticles applied to treatment of breast cancer

Breast cancer is the most commonly diagnosed type of cancer among the female population worldwide. It is a disease with a high incidence and geographic distribution that negatively impacts global public health and deleteriously affect the quality of life of cancer patients. Among the new approaches, cancer immunotherapy is the most promising trend in oncology by stimulating the host's own immune system to efficiently destroy cancer cells. Recent evidence has indicated that iron oxide nanoparticles can promote the reprograming of M2 into M1 macrophages with anti-tumor effects in the tumor microenvironment. Thus, the aim of the present work was to evaluate the ability of polyaniline-coated maghemite (Pani/γ-Fe2O3) nanoparticles to modulate human macrophages in 2D monolayers and 3D multicellular breast cancer models. It was observed that Pani/γ-Fe2O3 NPs re-educated IL-10-stimulated macrophages towards a pro-inflammatory profile, decreasing the proportion of CD163+ and increasing the CD86+ proportion in 2D models. NPs were successfully taken-up by macrophages presented in the 3D model and were also able to induce an increasing in their CD86+ proportion in triple MCTs model. Overall, our findings open new perspectives on the use of Pani/γ-Fe2O3 NPs as an immunomodulatory therapy for macrophage reprogramming towards an anti-tumor M1 phenotype, providing a new tool for breast cancer immunotherapies.

Development of Three-Dimensional Breast Scan and Measurement Application Using Laser Imaging Detection and Ranging Sensor on iPhone.

Laser imaging detection and ranging (LiDAR) is a modern three-dimensional (3D) technology that uses a time-of-flight method based on the round-trip time of an infrared laser beam to detect the presence and features of objects. The iPhone 12 Pro is the first smart mobile device with built-in LiDAR sensors. The authors' team developed a software application based on iOS devices with built-in LiDAR sensors for 3D breast scanning and automatically analyzing the breast's geometric measurement. Breast geometry, including midclavicle-to-nipple distance, sternal notch-to-nipple distance, nipple-to-inframammary fold (IMF) distance, distance between nipples, and body circumference on nipple and IMF level were measured using the software application and tapeline. The relative technical error of measurement (rTEM) value was used to calculate the error ratios between the measurements acquired by the software application and those of the tapeline. Good rTEM values ranging from 2.99% to 5.19% were found in the midclavicle-to-nipple distance, sternal notch-to-nipple distance, distance between nipples, nipple-level circumference, and IMF-level circumference. However, there was a poor rTEM value greater than 10% in the nipple-to-IMF distance. The proposed software application using current iOS devices with built-in LiDAR sensors can provide an ideal 3D scanning system that has a low cost burden, good accuracy, portability, and ease of use.