Microcalcification Research Articles

Inhibiting Pathological Calcium Phosphate Mineralization: Implications for Disease Progression.

Pathological calcifications, especially calcium phosphate microcalcifications (MCs), appear in most early breast cancer lesions, and their formation correlates with more aggressive tumors and a poorer prognosis. Hydroxyapatite (HA) is a key MC component that crystallizes in the tumor microenvironment. It is often associated with malignant breast cancer lesions and can trigger tumorigenesis in vitro. Here, we investigate the impact of additives on HA crystallization and inhibition, and how precancerous breast cells respond to minerals that are deposited in the presence of these additives. We show that nonstoichiometric HA spontaneously crystallizes in a solution simulating the tumor microenvironmental fluids and exhibits lump-like morphology similar to breast cancer MCs. In this system, the effectiveness of poly(aspartic acid) and poly(acrylic acid) (PAA) to inhibit HA is examined as a potential route to improve cancer prognosis. In the presence of additives, the formation of HA lumps is associated with the promotion or only minimal inhibition of mineralization, whereas the formation of amorphous calcium phosphate (ACP) lumps is followed by inhibition of mineralization. PAA emerges as a robust HA inhibitor by forming spherical ACP particles. When precancerous breast cells are exposed to various HA and ACP minerals, the most influential factors on cell proliferation are the mineral phase and whether the mineral is in the form of discrete particles or particle aggregates. The tumorigenic effects on cells, ranging from cytotoxicity and suppression of proliferation to triggering of proliferation, can be summarized as HA particles < HA aggregates < ACP particles < ACP aggregates. The cellular response to minerals can be attributed to a combination of factors, including mineral phase, crystallinity, morphology, surface texture, aggregation state, and surface potential. These findings have implications for understanding mineral-cell interactions within the tumor microenvironment and suggest that, in some cases, the byproducts of HA inhibition can contribute to disease progression more than HA itself.

SAH-NET: Structure-Aware Hierarchical Network for Clustered Microcalcification Classification in Digital Breast Tomosynthesis.

Benign and malignant classification of clustered microcalcifications (MCs) in digital breast tomosynthesis (DBT) is an essential task in computer-aided diagnosis. However, due to the anisotropic resolution of DBT, three-dimensional (3-D) convolutional neural network (CNN)-based methods cannot extract hierarchical features efficiently. Moreover, the sparse distribution of MC points in the cluster makes it difficult for the CNN to extract discriminative structural information for classification. To comprehensively address these challenges, we propose a novel structure-aware hierarchical network (SAH-Net) for benign and malignant classification of clustered MC in a DBT volume. Specifically, the two-dimensional (2-D) group convolution is used to extract intraslice features. The one-to-one correspondence between group convolutions and slices ensures the independence of hierarchical feature extraction. Then, a partial deformable Transformer-based 3-D structural feature learning module is proposed to capture the long-range dependency between MC points in the cluster. We evaluate the proposed method on an in-house dataset with 495 clustered MCs collected from 462 DBT images. Experimental results confirm the validity of our proposed modules. The results also show that the proposed SAH-Net outperforms several other representative methods on this topic, and achieves the best classification result, with an area under the receiver operation curve (AUC) of 86.87%. The implementation of the proposed model is available at https://github.com/sunhaotian130911/SAHNet.

A Multi-group similarity-decoding-based method for deep model ensembling applied in the microcalcification classification on digital mammograms

Classifying mammograms scored at Breast Imaging-Reporting and Data System (BI-RADS ) 4 is crucial for the early detection of breast cancer. But it is particularly challenging because microcalcifications (MCs) vary greatly in shape and distribution. Traditional machine learning and deep models are either time-consuming or poorly generalized. Recently, deep ensemble learning, which can improve generalization, has been developed rapidly. However, most ensembling strategies are based on weighted average and often sensitive to biased learners. To achieve data-adaption, they employ complex techniques to implement dynamic weight calculation. In this paper, we proposed a novel multi-group similarity-decoding-based method (MG-SDM) which enables a more robust and easier ensembling of different deep models. Firstly, the multi-group (MG) strategy is designed to transform model ensembling into a process of feature-level fusion, which could alleviate the impact of a single model on overall performance. Specifically, individual features of different models are grouped based on the position, and feature fusion is performed within each group. Secondly, the similarity-decoding-based method (SDM) is developed to fuse features. All individual features are vectorized through linear transformation. Then their similarities with a learnable latent embedding, which is introduced to denote most task-related features, are calculated using inner product. Finally, the similarity values among features within same group are normalized to be weights for a subsequent weighted summation operation. Extensive experiments on the clinical dataset demonstrate the superiority of the proposed over other state-of-the-art methods.

The interplay between crystallinity and the levels of Zn and carbonate in synthetic microcalcifications directs thyroid cell malignancy.

One of the key challenges in diagnosing thyroid cancer lies in the substantial percentage of indeterminate diagnoses of thyroid nodules that have undergone ultrasound-guided fine-needle aspiration (FNA) biopsy for cytological evaluation. This delays the definitive diagnosis and treatment plans. We recently demonstrated that hydroxyapatite microcalcifications (MCs) aspirated from thyroid nodules may aid nodule diagnosis based on their composition. In particular, Zn-enriched MCs have emerged as potential cancer biomarkers. However, a pertinent question remains: is the elevated Zn content within MCs a consequence of cancer, or do the Zn-enriched MCs encourage tumorigenesis? To address this, we treated the human thyroid cancer cell line MDA-T32 with synthetic MC analogs comprising hydroxyapatite crystals with varied pathologically relevant Zn fractions and assessed the cellular response. The MC analogs exhibited an irregular surface morphology similar to FNA MCs observed in cancerous thyroid nodules. These MC analogs displayed an inverse relationship between Zn fraction and crystallinity, as shown by X-ray diffractometry. The zeta potential of the non-Zn-bearing hydroxyapatite crystals was negative, which decreased once Zn was incorporated into the crystal. The MC analogs were not cytotoxic. The cellular response to exposure to these crystals was evaluated in terms of cell migration, proliferation, the tendency of the cells to form multicellular spheroids, and the expression of cancer markers. Our findings suggest that, if thyroid MCs play a role in promoting cancerous behavior in vivo, it is likely a result of the interplay of crystallinity with Zn and carbonate fractions in MCs.

Characterization of dolomite and calcite microcalcifications in human breast tissue.

Pathological crystallization within soft tissues often yields biominerals with properties differing from those of their geological or synthetic counterparts. Microcalcifications (MCs) are abundant in breast tumors, particularly in non-invasive lesions, such as ductal carcinoma in situ (DCIS). Given the challenge of predicting DCIS progression into invasive cancer, it has been suggested that MCs can be leveraged to inform DCIS prognosis. The predominant type of breast MCs are those containing calcium phosphates (CaP), whose crystal properties are commonly held to correlate with malignancy. Less common are non-CaP minerals, which have received less attention, as they are associated mainly with benign lesions. Here, we conducted a retrospective study of tissue samples collected from patients who were originally diagnosed with DCIS and whose current medical status is known. We examined the elemental composition, morphology, and crystal phases of 398 MCs, aiming to investigate potential correlations between MC crystal properties and the progression of DCIS. Our findings revealed primarily non-CaP MCs, an observation that was likely made possible only by the tissue processing methodology employed, which did not involve harsh conditions. We found that non-CaP MCs were abundant in DCIS lesions, that they exhibited diverse morphologies and sizes, and that they were composed of calcite and dolomite. Dolomite formation in cancer has not been reported previously and may be linked to pH fluctuations in the tumor microenvironment. The small size of DCIS lesions often requires pathologists to use the entire sample, thus reducing the number of samples available for further research. Nonetheless, despite our limited sample size, the observed trend indicated an association of dolomite MCs with DCIS lesions that progressed into invasive cancer over time.

Model-based deep CNN-regularized reconstruction for digital breast tomosynthesis with a task-based CNN image assessment approach

Objective. Digital breast tomosynthesis (DBT) is a quasi-three-dimensional breast imaging modality that improves breast cancer screening and diagnosis because it reduces fibroglandular tissue overlap compared with 2D mammography. However, DBT suffers from noise and blur problems that can lower the detectability of subtle signs of cancers such as microcalcifications (MCs). Our goal is to improve the image quality of DBT in terms of image noise and MC conspicuity. Approach. We proposed a model-based deep convolutional neural network (deep CNN or DCNN) regularized reconstruction (MDR) for DBT. It combined a model-based iterative reconstruction (MBIR) method that models the detector blur and correlated noise of the DBT system and the learning-based DCNN denoiser using the regularization-by-denoising framework. To facilitate the task-based image quality assessment, we also proposed two DCNN tools for image evaluation: a noise estimator (CNN-NE) trained to estimate the root-mean-square (RMS) noise of the images, and an MC classifier (CNN-MC) as a DCNN model observer to evaluate the detectability of clustered MCs in human subject DBTs. Main results. We demonstrated the efficacies of CNN-NE and CNN-MC on a set of physical phantom DBTs. The MDR method achieved low RMS noise and the highest detection area under the receiver operating characteristic curve (AUC) rankings evaluated by CNN-NE and CNN-MC among the reconstruction methods studied on an independent test set of human subject DBTs. Significance. The CNN-NE and CNN-MC may serve as a cost-effective surrogate for human observers to provide task-specific metrics for image quality comparisons. The proposed reconstruction method shows the promise of combining physics-based MBIR and learning-based DCNNs for DBT image reconstruction, which may potentially lead to lower dose and higher sensitivity and specificity for MC detection in breast cancer screening and diagnosis.

Construction of a nomogram for central lymph node metastasis in papillary thyroid cancer based on gender grouping.

The status of central lymph nodes is crucial for determining the surgical approach to papillary thyroid carcinoma (PTC). Because of the differences between genders in central lymph node metastasis (CLNM), we aimed to construct separate predictive models for CLNM according to gender. In our study, a total of 1258 PTC patients who underwent thyroid cancer surgery from September 2021 to March 2023 were analyzed retrospectively. The data were analysed univariately and multivariately using SPSS software grouped according to gender and nomograms of CLNM were plotted using R software. The variables included in this study were sex, Age, body mass index, Diabetes, chronic lymphocytic thyroiditis (CLT), Suspicious central lymph node (SCLN), A/T, Margin, Microcalcification (MC), BRAF, Number, Location, CLNM. The preoperative nomogram in male patients included four clinical variables: CLT, Margin, Number, Size. The preoperative nomogram of female patients included six clinical variables: Age, SCLN, Margin, MC, Number, Size. The calibration curves showed great agreement in both the training group and the validation group. The decision curve analysis showed the feasibility of nomogram in predicting CLNM in both man and woman. Based on the successful establishment of nomogram, we can analyze the variability of CLNM between male and female, which may provide clinicians with personalized clinical schemes in the treatment of PTC.

Automatic ultrasound diagnosis of thyroid nodules: a combination of deep learning and KWAK TI-RADS

Objective. There has been a considerable amount of computer-aided diagnosis (CAD) methods highlighted in the field of ultrasonic examination (USE) of thyroid nodules. However, few researches focused on the automatic risk classification, which was the basis for determining whether fine needle aspiration (FNA) was needed. The aim of this work was to implement automatic risk level assessment of thyroid nodules. Approach. Firstly, 1862 cases of thyroid nodules with the results of USE and FNA were collected as the dataset. Then, an improved U-Net++ model was utilized for segmenting thyroid nodules in ultrasound images automatically. Finally, the segmentation result was imported into a multi-task convolutional neural network (MT-CNN), the design of which was based on the clinical guideline called KWAK TI-RADS. Apart from the category of benign and malignant, the MT-CNN also exported the classification result of four malignant features, solid component (SC), hypoechogenicity or marked hypoechogenicity (HMH), microlobulated or irregular margin (MIM), microcalcification (MC), which were used for counting the risk level in KWAK TI-RADS. Main results. The performance of the improved U-Net++ was evaluated on our test set, including 302 cases. The Dice coefficient and intersection over union reached 0.899, 0.816, respectively. The classification accuracy rates of SC, HMH, MIM, MC, were 94.5%, 92.8%, 86.1%, 88.9%, while the false positive (FP) rate was 6.0%, 5.6%, 10.6%, 12.9% respectively. As for the category of benign and malignant, the precision and recall rates were 93.7% and 94.4%. Significance. The proposed CAD method showed favourable performance in the diagnosis of thyroid nodules. Compared with other methods, it could provide reports closer to clinical practice for doctors.

Value of Molybdenum Target X-Ray and High-Frequency Color Doppler Flow Imaging in Early Diagnosis of Breast Carcinoma: A Comparative Analysis.

Breast carcinoma (BC) threatens the physical and mental health of women worldwide, and early diagnosis is important for improving patient outcomes and ensuring successful treatment. This research mainly aims to compare and analyze the value of molybdenum target X-ray and high-frequency color Doppler flow imaging (CDFI) in the early diagnosis of BC. First, 102 patients with suspected early-stage BC (ESBC) admitted to Henan Provincial People's Hospital were examined by molybdenum target X-ray and CDFI. Based on the pathological findings, the diagnostic efficiency data of the two diagnostic modalities such as positive detection rate (PDR), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SEN), specificity (SPE), and accuracy (ACC), as well as imaging information like masses, microcalcifications (MCs), axillary lymph node (LN) metastases, and blood flow signal or vascular sign abnormalities were analyzed. CDFI contributed to higher PDR, PRV, NPV, SEN, and ACC than molybdenum target X-ray in ESBC diagnosis, but similar SPE. The combined diagnosis of molybdenum target X-ray plus CDFI contributed to even higher PDR, PRV, NPV, SEN, and ACC than molybdenum target X-ray alone and higher ACC than CDFI. Imaging inspection revealed that the number of cases of masses, axillary LN metastases, and abnormalities in blood flow signals or vascular signs detected by CDFI was significantly higher than that by molybdenum target X-ray, while the number of MCs was significantly lower. Molybdenum target X-ray plus CDFI is more effective in the diagnosis of ESBC and plays a complementary role in imaging examination, which can synergistically improve the diagnostic ACC of ESBC and is worthy of clinical promotion.

The impact of (simulated) resolution on breast cancer diagnosis based on high-resolution 3D micro-CT microcalcification images.

Breast microcalcifications (MCs) are considered to be a robust marker of breast cancer. A machine learning model can provide breast cancer diagnosis based on properties of individual MCs - if their characteristics are captured at high resolution and in3D. The main purpose of the study was to explore the impact of image resolution (8µm, 16µm, 32µm, 64µm) when diagnosing breast cancer using radiomics features extracted from individual high resolution 3D micro-CT MCimages. Breast MCs extracted from 86 female patients were analyzed at four different spatial resolutions: 8µm (original resolution) and 16µm, 32µm, 64µm (simulated image resolutions). Radiomic features were extracted at each image resolution in an attempt, to find a compact feature signature allowing to distinguish benign and malignant MCs. Machine learning algorithms were used for classifying individual MCs and samples (i.e., patients). For sample diagnosis, a custom-based thresholding approach was used to combine individual MC results into sample results. We conducted classification experiments when using (a) the same MCs visible in 8µm, 16µm, 32µm, and 64µm resolution; (b) the same MCs visible in 8µm, 16µm, and 32µm resolution; (c) the same MCs visible in 8µm and 16µm resolution; (d) all MCs visible in 8µm, 16µm, 32µm, and 64µm resolution. Accuracy, sensitivity, specificity, AUC, and F1 score were computed for eachexperiment. The individual MC results yielded an accuracy of 77.27%, AUC of 83.83%, F1 score of 77.25%, sensitivity of 80.86%, and specificity of 72.2% at 8µm resolution. For the individual MC classifications we report for the F1 scores: a 2.29% drop when using 16µm instead of 8µm, a 4.01% drop when using 32µm instead of 8µm, a 10.69% drop when using 64µm instead of 8µm. The sample results yielded an accuracy and F1 score of 81.4%, sensitivity of 80.43%, and specificity value of 82.5% at 8µm. For the sample classifications we report for F1 score values: a 6.3% drop when using 16µm instead of 8µm, a 4.91% drop when using 32µm instead of 8µm, and a 6.3% drop when using 64µm instead of 8µm. The highest classification results are obtained at the highest resolution (8µm). If breast MCs characteristics could be visualized/captured in 3D at a higher resolution compared to what is used nowadays in digital mammograms (approximately 70µm), breast cancer diagnosis will beimproved.

Detection of microcalcifications using nonlinear beamforming techniques.

Abundant research demonstrates that early detection of cancer leads to improved patient prognoses. By detecting cancer earlier, when tumors are in their primary stages, treatment can be applied before metastases have occurred. The presence of microcalcifications (MCs) is indicative of malignancy in the breast, i.e., 30-50% of all nonpalpable breast cancers detected using mammograms are based on identifying the presence of MCs. Therefore, improving the ability to detect MCs with modern imaging technology remains an important goal. Specifically, improving the sensitivity of ultrasound imaging techniques to detect MCs in the breast will provide an important role for the early detection and diagnosis of breast cancer. In this work, a novel nonlinear beamforming technology for ultrasonic arrays is investigated for its ability to detect MCs. The beamforming technique, called null subtraction imaging (NSI), utilizes nulls in the beam pattern to create images using ultrasound. NSI provides improved lateral resolution, a reduction in side lobes, and an accentuation of bright singular targets. Therefore, it was hypothesized that the use of NSI would result in identification of more MCs in rat tumors having a speckle background. To test this hypothesis, rats with tumors were injected with Hydroxyapatite (HA) particles to mimic MCs. Ultrasound was used to scan the rat tumors and images were constructed using conventional delay and sum and using NSI beamforming. Three readers with experience in diagnostic ultrasound imaging examined the 1,344 images and scored the presence or absence of MCs. In all, 336 different tumor image slices were recorded and each reconstructed using NSI or conventional delay and sum with Hann apodization. In every image where one or MCs were detected in the Hann reconstructions, MCs were detected in the NSI images. In nine rat tumor images, one or more MCs were detected in the NSI images but not in the Hann images. Statistically, the results did support the hypothesis that NSI would increase the number of MCs detected in the rat tumors.

Enhancing Detection of Microcalcifications using FADHECAL for Early Stage Breast Cancer

Microcalcifications (MCCs) are reliable early signs of breast cancer. However, the small size of calcifications and low radiation factors used in digital mammograms cause low and poor quality mammogram images in detecting MCCs. This paper presents an image enhancement technique called Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL) to enhance the details of MCCs in mammogram images by reducing the image noise while conserving contrast and brightness. A total of 23 mammogram images with MCCs were retrieved from the Mammographic Image Analysis Society’s database. The enhancement performance of FADHECAL was compared with Recursive Mean-Separate Histogram Equalization, Histogram Equalization and Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization. Image quality measurement tools of absolute mean brightness error (AMBE), structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) were used. The results showed that FADHECAL had the most superior results among other enhancement techniques, with 6.302 of AMBE, 20.453 of PSNR and 0.851 of SSIM. The proposed FADHECAL exhibited a high accuracy of 91.30% for the detection of MCCs. Hence, FADHECAL can be used as an ideal tool for identifying MCCs in early-stage breast cancer.

Deep learning denoising of digital breast tomosynthesis: Observer performance study of the effect on detection of microcalcifications in breast phantom images.

The noise in digital breast tomosynthesis (DBT) includes x-ray quantum noise and detector readout noise. The total radiation dose of a DBT scan is kept at about the level of a digital mammogram but the detector noise is increased due to acquisition of multiple projections. The high noise can degrade the detectability of subtle lesions, specifically microcalcifications (MCs). We previously developed a deep-learning-based denoiser to improve the image quality of DBT. In the current study, we conducted an observer performance study with breast radiologists to investigate the feasibility of using deep-learning-based denoising to improve the detection of MCs in DBT. We have a modular breast phantom set containing seven 1-cm-thick heterogeneous 50% adipose/50% fibroglandular slabs custom-made by CIRS, Inc. (Norfolk, VA). We made six 5-cm-thick breast phantoms embedded with 144 simulated MC clusters of four nominal speck sizes (0.125-0.150, 0.150-0.180, 0.180-0.212, 0.212-0.250mm) at random locations. The phantoms were imaged with a GE Pristina DBT system using the automatic standard (STD) mode. The phantoms were also imaged with the STD+ mode that increased the average glandular dose by 54% to be used as a reference condition for comparison of radiologists' reading. Our previously trained and validated denoiser was deployed to the STD images to obtain a denoised DBT set (dnSTD). Seven breast radiologists participated as readers to detect the MCs in the DBT volumes of the six phantoms under the three conditions (STD, STD+, dnSTD), totaling 18 DBT volumes. Each radiologist read all the 18 DBT volumes sequentially, which were arranged in a different order for each reader in a counter-balanced manner to minimize any potential reading order effects. They marked the location of each detected MC cluster and provided a conspicuity rating and their confidence level for the perceived cluster. The visual grading characteristics (VGC) analysis was used to compare the conspicuity ratings and the confidence levels of the radiologists for the detection of MCs. The average sensitivities over all MC speck sizes were 65.3%, 73.2%, and 72.3%, respectively, for the radiologists reading the STD, dnSTD, and STD+ volumes. The sensitivity for dnSTD was significantly higher than that for STD (p<0.005, two-tailed Wilcoxon signed rank test) and comparable to that for STD+. The average false positive rates were 3.9±4.6, 2.8±3.7, and 2.7±3.9 marks per DBT volume, respectively, for reading the STD, dnSTD, and STD+ images but the difference between dnSTD and STD or STD+ did not reach statistical significance. The overall conspicuity ratings and confidence levels by VGC analysis for dnSTD were significantly higher than those for both STD and STD+ (p≤0.001). The critical alpha value for significance was adjusted to be 0.025 with Bonferroni correction. This observer study using breast phantom images showed that deep-learning-based denoising has the potential to improve the detection of MCs in noisy DBT images and increase radiologists' confidence in differentiating noise from MCs without increasing radiation dose. Further studies are needed to evaluate the generalizability of these results to the wide range of DBTs from human subjects and patient populations in clinical settings.

The Diagnostic Role of Shear Wave Elastography and Superb Microvascular Imaging in the Evaluation of Suspicious Microcalcifications.

The aim of this study was to investigate the role of superb microvascular imaging (SMI) and shear wave elastography (SWE) in the prediction of malignancy and invasiveness of isolated microcalcifications (MC) that can be visualized by ultrasonography (US). Sixty-seven women with MC, who were considered suspicious on mammography were evaluated. Only those lesions that could be visualized by US and presented as non-mass lesion were included. They were evaluated by B-mode US, SMI, and SWE before US-guided core-needle biopsy. B-mode US, SMI (vascular index (SMIvi)), and SWE (E-mean, E-ratio) findings were compared with histopathologic features. Pathology confirmed 45 malignant (21 invasive and 24 in situ carcinomas) and 22 benign lesions. There was a statistically significant difference between malignant and benign groups in terms of size (P = .015), distortion (P = .028), cystic component (P < .001), E-mean (P < .001), E-ratio (P < .001), and SMIvi (P = .006). For differentiation of invasiveness E-mean (P = .002), E-ratio (P = .002), and SMIvi (P = .030) were statistically significant. According to ROC analysis E-mean (cut-off point at 38 kPa) was the most sensitive (78%) and the most specific (95%) value among four numeric parameters (size, SMI, E-mean, and E-ratio) with AUC = 0.895, PPV = 97%, and NPV = 68% in detecting malignancy. In the evaluation of invasiveness, the most sensitive (71.4%) method was SMI (cut-off point at 3.4) and the most specific (72%) method was E-mean (cut-off point at 91.5 kPa). Our study shows that adding SWE and SMI to the sonographic evaluation of MC would be an advantage for US-guided biopsy. Including suspicious areas according to SMI and SWE in the sampling area can help target the invasive part of the lesion and avoid underestimation of core biopsy.

Abstract 3587: High resolution microCT to analyze the 3D morphology of microcalcifications in benign breast disease and breast cancer biopsy tissues

Abstract Introduction: We used micro-computed tomography (microCT), a high-resolution imaging option, to detect and characterize microcalcifications (MCs) in three dimensions within pathology blocks of benign and malignant breast tissues. Methods: A set of 44 formalin-fixed, paraffin-embedded breast tissue blocks were assessed from n=22 women at two time points: benign breast biopsy and subsequent DCIS or DCIS with invasive disease breast cancer. Blocks were scanned using the Bruker Skyscan 1276 microCT at a resolution of 10 µm. SkyScan analysis CTAn software was utilized to prepare and analyze morphological parameters and multiple MCs contained within each block were averaged. Paired morphological parameters were compared between the benign and subsequent cancer samples within women using Wilcoxon signed-rank tests. Results: Median age of patients was 53 years (range 37-77 years) at initial benign biopsy and developed breast cancer at median of 5.6 years (range: 2.3-12.8 years) later. The initial benign biopsies were for proliferative disease without atypia in the majority (59%), while 23% had non-proliferative lesions and 18% atypical hyperplasia. The average structural model index was consistent with a near rod-like shape (median 2.99, range: 2.71-3.29). Paired comparisons between benign and cancer blocks showed that the average surface area of MCs was significantly higher in the cancer samples (p=0.0467), as was the average volume of MCs (p=0.0298) (Table 1). Conclusions: Preliminarily, we demonstrate that microCT of routinely prepared pathology blocks of breast tissues may be useful in characterizing the 3-D morphometry of MCs with suggestive differences in benign and cancer tissues. Given that MCs are a sentinel radiologic marker of DCIS but are common in benign breast disease, developing improved approaches for classifying MCs may have value in breast cancer screening and management. Table 1. Comparison of MC parameters in biopsy tissues (IQR = Interquartile Range). Benign sample from women who later developed breast cancer Breast cancer after prior benign sample Paired Differences (Cancer - Benign) Wilcoxon signed-rank p value (N=22) (N=22) (N=22) Number of Objects (Median (IQR)) 43.5 (14, 789) 69.5 (33, 289) -6 (-139, 67) 0.6830 Average Surface Area (mm2)(Median (IQR)) 0.0225 (0.0134, 0.0393) 0.0342 (0.0185, 0.0656) 0.0113 (-0.0027, 0.0322) 0.0467 Average Volume (mm3) (Median (IQR)) 0.0003 (0.0001, 0.0007) 0.0007 (0.0003, 0.0022) 0.0003 (0.000002, 0.0020) 0.0298 Average Surface Area/Volume (mm−1)(Median (IQR)) 214.92 (162.25, 328.39) 199.55 (151.89, 230.52) -20.85 (-86.05, 27.04) 0.1433 Average Structural Thickness (mm)(Median (IQR)) 0.0341(0.0196, 0.0446) 0.0395 (0.0336, 0.0505) 0.0074 (-0.0062, 0.0153) 0.1173 Average Structural Model Index (Median (IQR)) 2.9758 (2.8465, 3.1175) 3.0026 (2.9281, 3.0927) 0.0075 (-0.1073, 0.1381) 0.7656 Citation Format: Sarah E. Schrup, Thomas de Bel, Tanya Hoskin, Teresa Allers, Stacey Winham, Derek Radisky, Laura Pacheco-Spann, Lisa Seymour, Amy Degnim, Mark Sherman. High resolution microCT to analyze the 3D morphology of microcalcifications in benign breast disease and breast cancer biopsy tissues. [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 3587.

Clinical and pathological features analysis of invasive breast cancer with microcalcification.

Microcalcification (MC) is a valuable diagnostic indicator to detect invasive breast cancer (IBC). This study aimed to determine the clinicopathological features of IBC with MC and detect biomarkers related to the potential mechanism of the MC formation in IBC. Data from 364 patients with IBC were collected for the clinical characteristic analysis. The analysis of clinical data helped us to establish a predictive model of axillary node metastasis (ANM) before surgery. In addition, 49 tissue samples of IBC patients were collected to test the protein levels of osteocalcin (OCN) and hypoxia-inducible factor-1α (HIF-1α) by immunohistochemistry. Significant differences were observed in tumor size, age, ANM, HER2+ , TNM stage, and mutant P53 between samples from IBC patients with MC and samples from IBC patients without MC. Younger age, a larger tumor size, a higher number of childbirths, and MC were independent predictors for ANM in IBC. HIF-1α protein level was higher in tumor tissue than in normal tissue. High protein levels of OCN and HIF-1α are related to the complication of MC in IBC. Of the patients that exhibited high HIF-1α protein levels, the percentage of high OCN protein levels was larger in patients with ANM. Based on this study, we concluded that patients with MC had a comparatively poor prognosis. MC was an independent predictive factor associated with the risk of ANM. High protein levels of OCN and HIF-1α were associated with MC and ANM, which were also related to poor prognosis. OCN and HIF-1α had a positive correlation in IBC.

Low-Risk Women with Suspicious Microcalcifications in Mammography—Can an Additional Breast MRI Reduce the Biopsy Rate?

In the German Mammography Screening Program, 62% of ductal carcinoma in situ (DCIS) and 38% of invasive breast cancers are associated with microcalcifications (MCs). Vacuum-assisted stereotactic breast biopsies are necessary to distinguish precancerous lesions from benign calcifications because mammographic discrimination is not possible. The aim of this study was to investigate if breast magnetic resonance imaging (MRM) could assist the evaluation of MCs and thus help reduce biopsy rates. In this IRB-approved study, 58 women (mean age 58 +/- 24 years) with 59 suspicious MC clusters in the MG were eligible for this prospective single-center trial. Additional breast magnetic resonance imaging (MRI) was conducted before biopsy. The breast MRI showed a sensitivity of 86%, a specificity of 84%, a positive predictive value (PPV) of 75% and a negative predictive value (NPV) of 91% for the differentiation between benign and malignant in these 59 MCs found with MG. Breast MRI in addition to MG could increase the PPV from 36% to 75% compared to MG alone. The MRI examination led to nine additional suspicious classified lesions in the study cohort. A total of 55% (5/9) of them turned out to be malignant. A total of 32 of 59 (54 %) women with suspicious MCs and benign histology were classified as non-suspicious by MRI. An additionally performed breast MRI could have increased the diagnostic reliability in the assessment of MCs. Further, in our small cohort, a considerable number of malignant lesions without mammographically visible MCs were revealed.

Improving the Quantitative Analysis of Breast Microcalcifications: A Multiscale Approach

Accurate characterization of microcalcifications (MCs) in 2D digital mammography is a necessary step toward reducing the diagnostic uncertainty associated with the callback of indeterminate MCs. Quantitative analysis of MCs can better identify MCs with a higher likelihood of ductal carcinoma in situ or invasive cancer. However, automated identification and segmentation of MCs remain challenging with high false positive rates. We present a two-stage multiscale approach to MC segmentation in 2D full-field digital mammograms (FFDMs) and diagnostic magnification views. Candidate objects are first delineated using blob detection and Hessian analysis. A regression convolutional network, trained to output a function with a higher response near MCs, chooses the objects which constitute actual MCs. The method was trained and validated on 435 screening and diagnostic FFDMs from two separate datasets. We then used our approach to segment MCs on magnification views of 248 cases with amorphous MCs. We modeled the extracted features using gradient tree boosting to classify each case as benign or malignant. Compared to state-of-the-art comparison methods, our approach achieved superior mean intersection over the union (0.670 ± 0.121 per image versus 0.524 ± 0.034 per image), intersection over the union per MC object (0.607 ± 0.250 versus 0.363 ± 0.278) and true positive rate of 0.744 versus 0.581 at 0.4 false positive detections per square centimeter. Features generated using our approach outperformed the comparison method (0.763 versus 0.710 AUC) in distinguishing amorphous calcifications as benign or malignant.

Morphological and Distribution Pattern of Calcifications on Full Field Digital Mammography versus Digital Breast Tomosynthesis and Comparison of Diagnostic Abilities of the Two Modalities: A Retrospective Study

Introduction: Microcalcification (MC) is an effective and sometimes the only indicator of breast cancer. Early detection and characterisation of malignant MC can facilitate early diagnosis and timely treatment of breast cancer. However, due to the small size and low contrast as compared to the background parenchyma, it is difficult and time-consuming for radiologists to accurately evaluate MC. Aim: To compare the diagnostic abilities of Full Field Digital Mammography (FFDM) and Digital Breast Tomosynthesis (DBT) in the detection and characterisation of breast calcifications. Materials and Methods: This retrospective descriptive study was conducted in the year 2022 at the breast imaging unit of Dr. Ram Manohar Lohia Institute of Medical Sciences, Lucknow, Uttar Pradesh, India and data of patients who had undergone FFDM and DBT between March 2019- September 2020 was collected. Mammograms of 702 women with 1217 breasts were evaluated and MC was detected in 622 breasts. Based on the morphology and distribution pattern, Breast Imaging Reporting and Data System (BI-RADS) assessment Category was assigned from 2 to 5. Cases with BI-RADS 2 and 3 were followed-up by repeat Mammograms at 6-month to 1-year intervals. Cases with BI-RADS 4 and 5 were biopsied under stereotactic or ultrasound guidance. Histopathology findings and the stability of the calcifications on sequential mammograms were considered the gold standard for final BI-RADS categorisation. The Chi-square test was applied for the comparison of FFDM and DBT. Results: Typically benign morphology MC was noted in 508 (81.67%) of breasts on FFDM and 505 (80.67%) on DBT. Suspicious morphology MC was noted in 114 (18.33%) of breasts on FFDM and 121 (19.33%) on DBT. Vascular calcification was the most common benign MC seen in 233 cases (37.45%) on FFDM and 244 cases (38.9%) on DBT. Similarly, fine pleomorphic was the most common suspicious morphology MC on FFDM and DBT both seen in 47 cases (7.5%) and 44 cases (7.02%), respectively. The most common distribution pattern was diffuse seen in 582 cases (93.56%) on FFDM and 583 cases (93.13%) on DBT, respectively. No significant difference was observed (p=0.283) in the rate of detection or characterisation of MC by FFDM or DBT. The sensitivity of both modalities were almost similar (97.7% and 97.9%) without any significant difference (p=0.278). Similarly, there was no difference in the specificity (94.9% and 94.4%, respectively, p=0.289). Conclusion: The performance of FFDM and DBT for the detection and characterisation of MC is not significantly different.

Multicellular spheroids containing synthetic mineral particles: an advanced 3D tumor model system to investigate breast precancer malignancy potential according to the mineral type.

Mineral particles that form in soft tissues in association with disease conditions are heterogeneous in their composition and physiochemical properties. Hence, it is challenging to study the effect of mineral type on disease progression in a high-throughput and realistic manner. For example, most early breast precancer lesions, termed ductal carcinoma in situ (DCIS), contain microcalcifications (MCs), calcium-containing pathological minerals. The most common type of MCs is calcium phosphate crystals, mainly carbonated apatite; it is associated with either benign or malignant lesions. In vitro studies indicate that the crystal properties of apatite MCs can affect breast cancer progression. A less common type of MCs is calcium oxalate dihydrate (COD), which is almost always found in benign lesions. We developed a 3D tumor model of multicellular spheroids of human precancer cells containing synthetic MC analogs that link the crystal properties of MCs with the progression of breast precancer to invasive cancer. Using this 3D model, we show that apatite crystals induce Her2 overexpression in DCIS cells. This tumor-triggering effect is increased when the carbonate fraction in the MCs decreases. COD crystals, in contrast, decrease Her2 expression in the spheroids, even compared with a control group with no added MC analogs. Furthermore, COD decreases cell proliferation and migration in DCIS monolayers compared to untreated cells and cells incubated with apatite crystals. This finding suggests that COD is not randomly located only in benign lesions-it may actively contribute to suppressing precancer progression in its surroundings. Our model provides an easy-to-manipulate platform to better understand the interactions between mineral particles and their biological microenvironment. A better understanding of the effect of the crystal properties of MCs on precancer progression will potentially provide new directions for better precancer prognosis and treatment.