Microcalcification on mammography: approaches to interpretation and biopsy

Unveiling the potential of breast MRI: a game changer for BI-RADS 4A microcalcifications

PURPOSE

To assess the diagnostic performance of breast MRI for BI-RADS 4A microcalcifications on mammography and propose a potential clinical pathway to avoid unnecessary biopsies.

METHODS

Bibliometrics analysis of breast MRI and BI-RADS 4 was provided. A retrospective analysis was conducted on 139 women and 142 cases of BI-RADS 4A microcalcifications on mammography from Fudan University Shanghai Cancer Center. The mammographic BI-RADS level and the MRI reports were compared with the final pathological diagnosis.

RESULTS

Much attention has been given to breast MRI and BI-RADS 4 in the literature. However, studies on BI-RADS 4A are limited. Pathological results showed 117 cases (82.4%) were benign lesions, malignant cases of 25 (17.6%) in our study. The positive predictive values (PPV), specificity, sensitivity and negative predictive values (NPV) of MRI were 44.2% (23/52), 75.2% (88/117), 92.0% (23/25), and 97.8% (88/90), respectively. Therefore, 75.2% (88/117) of biopsies for benign lesions could potentially be avoided. There were 2.2% (2/90) malignant lesions missed. Logistic regression indicated that patients who are postmenopausal (HR = 2.655, p = 0.012), have a history of breast cancer (family history) (HR = 2.833, p = 0.029), and exhibit clustered microcalcifications (HR = 2.179, p = 0.046) are more likely to have a higher MRI BI-RADS level.

CONCLUSIONS

Breast MRI has the potential to improve the diagnosis of BI-RADS 4A microcalcifications on mammography. We propose a potential clinical pathway that patients with BI-RADS 4A on mammography who are premenopausal, have no personal history of breast cancer (family history) or have non-clustered distribution of calcifications can undergo MRI to avoid unnecessary biopsies.

Ductal carcinoma in situ of the breast: finding the balance between overtreatment and undertreatment

Ductal carcinoma in situ (DCIS) accounts for 15-25% of all breast cancer diagnoses. Its prognosis is excellent overall, the main risk being the occurrence of local breast events, as most cases of DCIS do not progress to invasive cancer. Systematic screening has greatly increased the incidence of this non-obligate precursor of invasion, lending urgency to the need to identify DCIS that is prone to invasive progression and distinguish it from non-invasion-prone DCIS, as the latter can be overdiagnosed and therefore overtreated. Treatment strategies, including surgery, radiotherapy, and optional endocrine therapy, decrease the risk of local events, but have no effect on survival outcomes. Active surveillance is being evaluated as a possible new option for low-risk DCIS. Considerable efforts to decipher the biology of DCIS have led to a better understanding of the factors that determine its variable natural history. Given this variability, shared decision making regarding optimal, personalised treatment strategies is the most appropriate course of action. Well designed, risk-based de-escalation studies remain a major need in this field.

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

BACKGROUND

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 in 3D.

PURPOSE

The main purpose of the study was to explore the impact of image resolution (8 µm, 16 µm, 32 µm, 64 µm) when diagnosing breast cancer using radiomics features extracted from individual high resolution 3D micro-CT MC images.

METHODS

Breast MCs extracted from 86 female patients were analyzed at four different spatial resolutions: 8 µm (original resolution) and 16 µm, 32 µm, 64 µm (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 8 µm, 16 µm, 32 µm, and 64 µm resolution; (b) the same MCs visible in 8 µm, 16 µm, and 32 µm resolution; (c) the same MCs visible in 8 µm and 16 µm resolution; (d) all MCs visible in 8 µm, 16 µm, 32 µm, and 64 µm resolution. Accuracy, sensitivity, specificity, AUC, and F1 score were computed for each experiment.

RESULTS

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 8 µm resolution. For the individual MC classifications we report for the F1 scores: a 2.29% drop when using 16 µm instead of 8 µm, a 4.01% drop when using 32 µm instead of 8 µm, a 10.69% drop when using 64 µm instead of 8 µm. The sample results yielded an accuracy and F1 score of 81.4%, sensitivity of 80.43%, and specificity value of 82.5% at 8 µm. For the sample classifications we report for F1 score values: a 6.3% drop when using 16 µm instead of 8 µm, a 4.91% drop when using 32 µm instead of 8 µm, and a 6.3% drop when using 64 µm instead of 8 µm.

CONCLUSIONS

The highest classification results are obtained at the highest resolution (8 µm). If breast MCs characteristics could be visualized/captured in 3D at a higher resolution compared to what is used nowadays in digital mammograms (approximately 70 µm), breast cancer diagnosis will be improved.

Optical Emission Spectroscopy for the Real-Time Identification of Malignant Breast Tissue

Breast conserving resection with free margins is the gold standard treatment for early breast cancer recommended by guidelines worldwide. Therefore, reliable discrimination between normal and malignant tissue at the resection margins is essential. In this study, normal and abnormal tissue samples from breast cancer patients were characterized ex vivo by optical emission spectroscopy (OES) based on ionized atoms and molecules generated during electrosurgical treatment. The aim of the study was to determine spectroscopic features which are typical for healthy and neoplastic breast tissue allowing for future real-time tissue differentiation and margin assessment during breast cancer surgery. A total of 972 spectra generated by electrosurgical sparking on normal and abnormal tissue were used for support vector classifier (SVC) training. Specific spectroscopic features were selected for the classification of tissues in the included breast cancer patients. The average classification accuracy for all patients was 96.9%. Normal and abnormal breast tissue could be differentiated with a mean sensitivity of 94.8%, a specificity of 99.0%, a positive predictive value (PPV) of 99.1% and a negative predictive value (NPV) of 96.1%. For 66.6% patients all classifications reached 100%. Based on this convincing data, a future clinical application of OES-based tissue differentiation in breast cancer surgery seems to be feasible.

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

OBJECTIVES

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).

MATERIAL AND METHODS

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.

RESULTS

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).

CONCLUSION

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.

Multiscale X-ray phase-contrast tomography: From breast CT to micro-CT for virtual histology

Phase-contrast imaging techniques address the issue of poor soft-tissue contrast encountered in traditional X-ray imaging. This can be accomplished with the propagation-based phase-contrast technique by employing a coherent photon beam, which is available at synchrotron facilities, as well as long sample-to-detector distances. This study demonstrates the optimization of propagation-based phase-contrast computed tomography (CT) techniques for multiscale X-ray imaging of the breast at the Elettra synchrotron facility (Trieste, Italy). Two whole breast mastectomy samples were acquired with propagation-based breast-CT using a monochromatic synchrotron beam at a pixel size of 60 µm. Paraffin-embedded blocks sampled from the same tissues were scanned with propagation-based micro-CT imaging using a polychromatic synchrotron beam at a pixel size of 4 µm. Images of both methodologies and of the same sample were spatially registered. The resulting images showed the transition from whole breast imaging with propagation-based breast-CT methodology to virtual histology with propagation-based micro-CT imaging of the same sample. Additionally, conventional histological images were matched to virtual histology images. Phase-contrast images offer a high resolution with low noise, which allows for a highly precise match between virtual and conventional histology. Furthermore, those techniques allow a clear discernment of breast structures, lesions, and microcalcifications, being a promising clinically-compatible tool for breast imaging in a multiscale approach, to either assist in the detection of cancer in full volume breast samples or to complement structure identification in paraffin-embedded breast tissue samples.

Subtypes of high-grade breast ductal carcinoma in situ (DCIS): incidence and potential clinical impact

OBJECTIVE

The purpose of this study was to investigate and classify the molecular subtypes of high-grade ductal carcinoma in situ (DCIS) and identify possible high-risk subtypes. The heterogenicity of DCIS with variable clinical and histopathological presentations has been recognized. Nevertheless, only histopathological grading and diameter are currently implemented in clinical decision-making following the diagnosis of DCIS. The molecular subtypes of DCIS and their IHC surrogate markers have not been defined in conventional treatment guidelines and recommendations. We applied the definitions of molecular subtypes according to the IHC surrogate markers defined for IBC and subclassified high-grade DCIS, accordingly.

METHODS

Histopathological specimens were collected, revised, and regraded from 494 patients diagnosed with DCIS between 1996 and 2018. Other in situ and papillary lesions observed in breast biopsies were excluded from this study. 357 high-grade DCIS cases were submitted to IHC analysis. The markers investigated were ER, PR, HER2, and Ki67.

RESULTS

45 cases were classified as grade 1, 19 as grade 2, and 430 as grade 3. Sixty patients with high-grade DCIS had an additional invasive component in the surgical specimen. Thirty-three patients were diagnosed with recurrent DCIS or invasive cancer (minimum one year after their primary DCIS diagnosis). The proportions of luminal A and luminal B HER2-negative subtypes varied depending on whether 2011 or 2013 St. Gallen Consensus Conference guidelines were adopted. Luminal A was the most prevalent subtype, according to both classifications. The luminal B HER2-positive subtype was found in 22.1% of cases, HER2-enriched subtype in 21.8%, and TPN subtype in 5.6%. There were strong indications that HER2-enriched subtype was significantly more frequent among DCIS with invasive component (p = 0.0169).

CONCLUSIONS

High-grade DCIS exhibits all the molecular subtypes previously identified in IBC, but with a somewhat different distribution in our cohort. HER2-enriched subtype is substantially related to the presence of an invasive component in DCIS; consequently, it is regarded as a high-risk entity.

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.

Classification of breast microcalcifications with GaAs photon-counting spectral mammography using an inverse problem approach

The purpose of this study was to investigate the use of a Gallium Arsenide (GaAs) photon-counting spectral mammography system to differentiate between Type I and Type II calcifications. Type I calcifications, consisting of calcium oxalate dihydrate (CO) or weddellite compounds are more often associated with benign lesions in the breast, and Type II calcifications containing hydroxyapatite (HA) are associated with both benign and malignant lesions in the breast. To be able to differentiate between these two calcification types, it is necessary to be able to estimate the full spectrum of the x-ray beam transmitted through the breast. We propose a novel method for estimating the energy-dependent x-ray transmission fraction of a beam using a photon counting detector with a limited number of energy bins. Using the estimated x-ray transmission through microcalcifications, it was observed that calcification type can be accurately estimated with machine learning. The study was carried out on a custom-built laboratory benchtop system using the SANTIS 0804 GaAs detector prototype system from DECTRIS Ltd with two energy thresholds enabled. Four energy thresholds detector was simulated by taking two separate acquisitions in which two energy thresholds were enabled for each acquisition and set at (12 keV, 21 keV) and then (29 keV, 36 keV). Measurements were performed using BR3D (CIRS, Norfolk, VA) breast imaging phantoms mimicking 100% adipose and 100% glandular tissues swirled together in an approximate 50/50 ratio by weight with the addition of in-house-developed synthetic microcalcifications. First, an inverse problem-based approach was used to estimate the full energy x-ray transmission fraction factor using known basis transmission factors from varying thicknesses of aluminum and polymethyl methacrylate (PMMA). Second, the classification of Type I and Type II calcifications was performed using the estimated energy-dependent transmission fraction factors for the pixels containing calcifications. The results were analyzed using receiver operating characteristic (ROC) analysis and demonstrated good discrimination performance with the area under the ROC curve greater than 84%. They indicated that GaAs photon-counting spectral mammography has potential use as a non-invasive method for discrimination between Type I and Type II calcifications. Results from this study suggested that GaAs-based spectral mammography could serve as a non-invasive measure for ruling out malignancy of calcifications found in the breast. Additional studies in more clinically realistic conditions involving breast tissues samples with smaller microcalcification specks should be performed to further explore the feasibility of this approach.

Influence of Tumor Volume on the Fluence Rate Within Human Breast Model Using Continuous-Wave Diffuse Optical Imaging: A Simulation Study

Objective: This article investigates the effect of varying breast tumor size on the fluence rate distribution within a breast model during the diffuse optical imaging procedure. Background: Early detection of breast cancer is of significant importance owing to its wide spread among women worldwide. Mastectomy surgery became very common due to the late detection of breast cancers by the conventional diagnostic methods such as X-ray mammography and magnetic resonance imaging. On the contrary, optical imaging techniques provide a safe and more sensitive methodology, which is suitable for the early detection criteria. Methods: The implementation was performed based on simulating multiple detectors placed on the outer surface of a human breast model to compute the optical fluence rate after probing the breast (normal and different tumor sizes) with laser irradiation. Different laser wavelengths ranging from the red to near-infrared rays spectral range were examined to determine the optimum fluence rate that shows the highest capability to differentiate between normal and cancerous breasts. A three-dimensional breast model was created using the COMSOL multiphysics package where the optical fluence rate was estimated based on the finite-element solution of the diffusion equation. Results: To evaluate the efficiency of the suggested technique for identifying cancers and discriminate them from normal breast at various wavelengths (600-1000 nm) and several tumor sizes. Conclusions: The obtained results reveal different fluence rate distributions in the breast with different radius tumors, especially at 600 nm due to the significant differences in the scattering coefficient between malignancies and healthy tissue.

Detection of microcalcifications in photon-counting dedicated breast-CT using a deep convolutional neural network: Proof of principle

OBJECTIVE

In this study, we investigate the feasibility of a deep Convolutional Neural Network (dCNN), trained with mammographic images, to detect and classify microcalcifications (MC) in breast-CT (BCT) images.

METHODS

This retrospective single-center study was approved by the local ethics committee. 3518 icons generated from 319 mammograms were classified into three classes: "no MC" (1121), "probably benign MC" (1332), and "suspicious MC" (1065). A dCNN was trained (70% of data), validated (20%), and tested on a "real-world" dataset (10%). The diagnostic performance of the dCNN was tested on a subset of 60 icons, generated from 30 mammograms and 30 breast-CT images, and compared to human reading. ROC analysis was used to calculate diagnostic performance. Moreover, colored probability maps for representative BCT images were calculated using a sliding-window approach.

RESULTS

The dCNN reached an accuracy of 98.8% on the "real-world" dataset. The accuracy on the subset of 60 icons was 100% for mammographic images, 60% for "no MC", 80% for "probably benign MC" and 100% for "suspicious MC". Intra-class correlation between the dCNN and the readers was almost perfect (0.85). Kappa values between the two readers (0.93) and the dCNN were almost perfect (reader 1: 0.85 and reader 2: 0.82). The sliding-window approach successfully detected suspicious MC with high image quality. The diagnostic performance of the dCNN to classify benign and suspicious MC was excellent with an AUC of 93.8% (95% CI 87, 4%-100%).

CONCLUSION

Deep convolutional networks can be used to detect and classify benign and suspicious MC in breast-CT images.

Biomineralogical signatures of breast microcalcifications

Microcalcifications, primarily biogenic apatite, occur in cancerous and benign breast pathologies and are key mammographic indicators. Outside the clinic, numerous microcalcification compositional metrics (e.g., carbonate and metal content) are linked to malignancy, yet microcalcification formation is dependent on microenvironmental conditions, which are notoriously heterogeneous in breast cancer. We interrogate multiscale heterogeneity in 93 calcifications from 21 breast cancer patients using an omics-inspired approach: For each microcalcification, we define a "biomineralogical signature" combining metrics derived from Raman microscopy and energy-dispersive spectroscopy. We observe that (i) calcifications cluster into physiologically relevant groups reflecting tissue type and local malignancy; (ii) carbonate content exhibits substantial intratumor heterogeneity; (iii) trace metals including zinc, iron, and aluminum are enhanced in malignant-localized calcifications; and (iv) the lipid-to-protein ratio within calcifications is lower in patients with poor composite outcome, suggesting that there is potential clinical value in expanding research on calcification diagnostic metrics to include "mineral-entrapped" organic matrix.

The Prospects of Using Structural Phase Analysis of Microcalcifications in Breast Cancer Diagnostics

The detection of microcalcifications in the breast by mammography is of great importance for the early diagnostics of breast cancer. This study aimed to establish the basic morphological and crystal-chemical properties of microscopic calcifications and their impact on breast cancer tissue. During the retrospective study, 55 out of 469 breast cancer samples had microcalcifications. The expression of the estrogen and progesterone receptors and Her2-neu showed no significant difference from the non-calcified samples. An in-depth study of 60 tumor samples revealed a higher expression of osteopontin in the calcified breast cancer samples (p ˂ 0.01). The mineral deposits had a hydroxyapatite composition. Within the group of calcified breast cancer samples, we detected six cases of colocalization of oxalate microcalcifications together with biominerals of the usual "hydroxyapatite" phase composition. The simultaneous presence of calcium oxalate and hydroxyapatite was accompanied by a different spatial localization of microcalcifications. Thus, the phase compositions of microcalcifications could not be used as criteria for the differential diagnostics of breast tumors.

Contrast-Enhanced Spectral Mammography in the Evaluation of Breast Microcalcifications: Controversies and Diagnostic Management

The aim of this study was to evaluate the diagnostic performance of contrast-enhanced spectral mammography (CESM) in predicting breast lesion malignancy due to microcalcifications compared to lesions that present with other radiological findings. Three hundred and twenty-one patients with 377 breast lesions that underwent CESM and histological assessment were included. All the lesions were scored using a 4-point qualitative scale according to the degree of contrast enhancement at the CESM examination. The histological results were considered the gold standard. In the first analysis, enhancement degree scores of 2 and 3 were considered predictive of malignity. The sensitivity (SE) and positive predictive value (PPV) were significative lower for patients with lesions with microcalcifications without other radiological findings (SE = 53.3% vs. 82.2%, p-value < 0.001 and PPV = 84.2% vs. 95.2%, p-value = 0.049, respectively). On the contrary, the specificity (SP) and negative predictive value (NPV) were significative higher among lesions with microcalcifications without other radiological findings (SP = 95.8% vs. 84.2%, p-value = 0.026 and NPV = 82.9% vs. 55.2%, p-value < 0.001, respectively). In a second analysis, degree scores of 1, 2, and 3 were considered predictive of malignity. The SE (80.0% vs. 96.8%, p-value < 0.001) and PPV (70.6% vs. 88.3%, p-value: 0.005) were significantly lower among lesions with microcalcifications without other radiological findings, while the SP (85.9% vs. 50.9%, p-value < 0.001) was higher. The enhancement of microcalcifications has low sensitivity in predicting malignancy. However, in certain controversial cases, the absence of CESM enhancement due to its high negative predictive value can help to reduce the number of biopsies for benign lesions.

Inflammatory cytokine-enriched microenvironment plays key roles in the development of breast cancers

As the incidence of breast cancer continues to increase, it is critical to develop prevention strategies for this disease. Inflammation underlies the onset of the disease, and NF-κB is a master transcription factor for inflammation. Nuclear factor-κB (NF-κB) is activated in a variety of cell types, including normal epithelial cells, cancer cells, cancer-associated fibroblasts (CAFs), and immune cells. Ductal carcinoma in situ (DCIS) is the earliest stage of breast cancer, and not all DCIS lesions develop into invasive breast cancers (IBC). Currently, most patients with DCIS undergo surgery with postoperative therapy, although there is a risk of overtreatment. In BRCA mutants, receptor activator of NF-κB (RANK)-positive progenitors serve as the cell of origin, and treatment using the RANK monoclonal antibody reduces the risk of IBC. There is still an unmet need to diagnose malignant DCIS, which has the potential to progress to IBC, and to establish appropriate prevention strategies. We recently demonstrated novel molecular mechanisms for NF-κB activation in premalignant mammary tissues, which include DCIS, and the resultant cytokine-enriched microenvironment is essential for breast cancer development. On the early endosomes in a few epithelial cells, the adaptor protein FRS2β, forming a complex with ErbB2, carries the IκB kinase (IKK) complex and leads to the activation of NF-κB, thereby inducing a variety of cytokines. Therefore, the FRS2β-NFκB axis in the inflammatory premalignant environment could be targetable to prevent IBC. Further analysis of the molecular mechanisms of inflammation in the premalignant microenvironment is necessary to prevent the risk of IBC.

Biomaterial-assisted tumor therapy: A brief review of hydroxyapatite nanoparticles and its composites used in bone tumors therapy

Malignant bone tumors can inflict significant damage to affected bones, leaving patients to contend with issues like residual tumor cells, bone defects, and bacterial infections post-surgery. However, hydroxyapatite nanoparticles (nHAp), the principal inorganic constituent of natural bone, possess numerous advantages such as high biocompatibility, bone conduction ability, and a large surface area. Moreover, nHAp's nanoscale particle size enables it to impede the growth of various tumor cells via diverse pathways. This article presents a comprehensive review of relevant literature spanning the past 2 decades concerning nHAp and bone tumors. The primary goal is to explore the mechanisms responsible for nHAp's ability to hinder tumor initiation and progression, as well as to investigate the potential of integrating other drugs and components for bone tumor diagnosis and treatment. Lastly, the article discusses future prospects for the development of hydroxyapatite materials as a promising modality for tumor therapy.

A New Collaborative Classification Process for Microcalcification Detection Based on Graphs and Knowledge Propagation

In this paper, we propose a new collaborative process that aims to detect macrocalcifications from mammographic images while minimizing false negative detections. This process is made up of three main phases: suspicious area detection, candidate object identification, and collaborative classification. The main concept is to operate on the entire image divided into homogenous regions called superpixels which are used to identify both suspicious areas and candidate objects. The collaborative classification phase consists in making the initial results of different microcalcification detectors collaborate in order to produce a new common decision and reduce their initial disagreements. The detectors share the information about their detected objects and associated labels in order to refine their initial decisions based on those of the other collaborators. This refinement consists of iteratively updating the candidate object labels of each detector following local and contextual analyses based on prior knowledge about the links between super pixels and macrocalcifications. This process iteratively reduces the disagreement between different detectors and estimates local reliability terms for each super pixel. The final result is obtained by a conjunctive combination of the new detector decisions reached by the collaborative process. The proposed approach is evaluated on the publicly available INBreast dataset. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to existing detectors as well as ordinary fusion operators.

Learning to distinguish progressive and non-progressive ductal carcinoma in situ

Ductal carcinoma in situ (DCIS) is a non-invasive breast neoplasia that accounts for 25% of all screen-detected breast cancers diagnosed annually. Neoplastic cells in DCIS are confined to the ductal system of the breast, although they can escape and progress to invasive breast cancer in a subset of patients. A key concern of DCIS is overtreatment, as most patients screened for DCIS and in whom DCIS is diagnosed will not go on to exhibit symptoms or die of breast cancer, even if left untreated. However, differentiating low-risk, indolent DCIS from potentially progressive DCIS remains challenging. In this Review, we summarize our current knowledge of DCIS and explore open questions about the basic biology of DCIS, including those regarding how genomic events in neoplastic cells and the surrounding microenvironment contribute to the progression of DCIS to invasive breast cancer. Further, we discuss what information will be needed to prevent overtreatment of indolent DCIS lesions without compromising adequate treatment for high-risk patients.

Digital breast tomosynthesis-guided vacuum-assisted biopsy of suspicious calcifications at different sites within one breast: Is biopsy of more than one location needed?

OBJECTIVE

To investigate how often biopsy of two sites of morphologically similar or equally suspicious calcifications within the same breast yield differing histopathologic results, and how this may affect clinical management.

MATERIALS AND METHODS

We identified patients with two or more sites of calcifications categorized as Breast Imaging Reporting and Data System (BI-RADS) ≥ 4b within the same breast who underwent digital breast tomosynthesis-guided vacuum-assisted biopsy (DBT-guided VAB). We analyzed how often biopsy of two distinct sites yielded the same or differing histopathologic findings. The histopathologic findings were dichotomized into "actionable" and "non-actionable", depending on the respective further management. We then analyzed how often the consecutive management would have been the same or different.

RESULTS

Of 206 women undergoing DBT-guided VAB at our institution within 24 months, 21 consecutive patients (54 ± 10.2 years; range: 35-71) underwent DBT-guided VAB of two distinct sites of calcifications. Management of histologic findings was the same (both sites actionable or both sites non-actionable) in 12/21 (57 %), different in the remaining 9/21 patients (43 %). Of the nine patients whose differing histologic findings would have led to different clinical management, 4/9 had a high-risk lesion (atypical ductal hyperplasia n = 3, papilloma with epithelial atypia n = 1) vs benign changes (adenosis n = 4), 2/9 had high-grade DCIS vs benign changes (adenosis n = 1, fat necrosis n = 1), and 3/9 had invasive cancer (luminal A n = 2, luminal B n = 1) with high-grade DCIS vs pure high-grade DCIS.

CONCLUSIONS

Multiple sites of calcifications within the same breast, even when morphologically similar or equally suspicious, may represent different histopathologic findings with different clinical management implications. Accordingly, in the presence of suspicious calcifications at multiple distinct sites within the same breast, biopsy of more than one site of calcification should be considered.

Regulatory mechanisms and potential medical applications of HNF1A-AS1 in cancers

Long noncoding RNAs (lncRNAs) are defined as a class of non-protein-coding RNAs that are longer than 200 nucleotides. Previous studies have shown that lncRNAs play a vital role in the progression of multiple diseases, which highlights their potential for medical applications. The lncRNA hepatocyte nuclear factor 1 homeobox A (HNF1A) antisense RNA 1 (HNF1A-AS1) is known to be abnormally expressed in multiple cancers. HNF1A-AS1 exerts its oncogenic roles through a variety of molecular mechanisms. Moreover, aberrant HNF1A-AS1 expression is associated with diverse clinical features in cancer patients. Therefore, HNF1A-AS1 is a promising biomarker for tumor diagnosis and prognosis and thus a potential candidate for tumor therapy. This review summarizes current studies on the role and the underlying mechanisms of HNF1A-AS1 various cancer types, including gastric cancer, liver cancer, glioma, lung cancer, colorectal cancer, breast cancer, bladder cancer, osteosarcoma, esophageal adenocarcinoma, hemangioma, oral squamous cell carcinoma, laryngeal squamous cell carcinoma, cervical cancer, as well as gastroenteropancreatic neuroendocrine neoplasms. We also describe the diagnostic, prognostic, and therapeutic value of HNF1A-AS1 for multiple cancer patients.

Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head

Introduction and Background: Despite fast developments in the medical field, histological diagnosis is still regarded as the benchmark in cancer diagnosis. However, the input image feature extraction that is used to determine the severity of cancer at various magnifications is harrowing since manual procedures are biased, time consuming, labor intensive, and error-prone. Current state-of-the-art deep learning approaches for breast histopathology image classification take features from entire images (generic features). Thus, they are likely to overlook the essential image features for the unnecessary features, resulting in an incorrect diagnosis of breast histopathology imaging and leading to mortality. Methods: This discrepancy prompted us to develop DEEP_Pachi for classifying breast histopathology images at various magnifications. The suggested DEEP_Pachi collects global and regional features that are essential for effective breast histopathology image classification. The proposed model backbone is an ensemble of DenseNet201 and VGG16 architecture. The ensemble model extracts global features (generic image information), whereas DEEP_Pachi extracts spatial information (regions of interest). Statistically, the evaluation of the proposed model was performed on publicly available dataset: BreakHis and ICIAR 2018 Challenge datasets. Result: A detailed evaluation of the proposed model’s accuracy, sensitivity, precision, specificity, and f1-score metrics revealed the usefulness of the backbone model and the DEEP_Pachi model for image classifying. The suggested technique outperformed state-of-the-art classifiers, achieving an accuracy of 1.0 for the benign class and 0.99 for the malignant class in all magnifications of BreakHis datasets and an accuracy of 1.0 on the ICIAR 2018 Challenge dataset. Conclusion: The acquired findings were significantly resilient and proved helpful for the suggested system to assist experts at big medical institutions, resulting in early breast cancer diagnosis and a reduction in the death rate.

Raman spectroscopy: current applications in breast cancer diagnosis, challenges and future prospects

Despite significant improvements in the way breast cancer is managed and treated, it continues to persist as a leading cause of death worldwide. If detected and diagnosed early, when tumours are small and localised, there is a considerably higher chance of survival. However, current methods for detection and diagnosis lack the required sensitivity and specificity for identifying breast cancer at the asymptomatic or very early stages. Thus, there is a need to develop more rapid and reliable methods, capable of detecting disease earlier, for improved disease management and patient outcome. Raman spectroscopy is a non-destructive analytical technique that can rapidly provide highly specific information on the biochemical composition and molecular structure of samples. In cancer, it has the capacity to probe very early biochemical changes that accompany malignant transformation, even prior to the onset of morphological changes, to produce a fingerprint of disease. This review explores the application of Raman spectroscopy in breast cancer, including discussion on its capabilities in analysing both ex-vivo tissue and liquid biopsy samples, and its potential in vivo applications. The review also addresses current challenges and potential future uses of this technology in cancer research and translational clinical application.

Visual bias could impede diagnostic accuracy of breast cancer calcifications

Purpose: Diagnosing breast cancer based on the distribution of calcifications is a visual task and thus prone to visual biases. We tested whether a recently discovered visual bias that has implications for breast cancer diagnosis would be present in expert radiologists, thereby validating the concern of this bias for accurate diagnoses. Approach: We ran a vision experiment with expert radiologists and untrained observers to test the presence of visual bias when judging the spread of dots that resembled calcifications and when judging the spread of line orientations. We calculated visual bias scores for both groups for both tasks. Results: Participants overestimated the spread of the dots and the spread of the line orientations. This bias, referred to as the variability overestimation effect, was of similar magnitudes in both expert radiologists and untrained observers. Even though the radiologists were better at both tasks, they were similarly biased compared with the untrained observers. Conclusions: The results justify the concern of the variability overestimation effect for accurate diagnoses based on breast calcifications. Specifically, the bias is likely to lead to an increased number of false-negative results, thereby leading to delayed treatments.

Breast microcalcifications: Past, present and future (Review)

Mammary microcalcifications (MCs) are calcium deposits that are considered as robust markers of breast cancer when identified on mammography. MCs are frequently associated with premalignant and malignant lesions. The aim of the present review was to describe the MC types and associated radiological and pathological aspects in detail, provide insights and approaches to the topic, and describe specific clinical scenarios. The primary MC types are composed of calcium oxalate, hydroxyapatite and hydroxyapatite associated with magnesium. The first type is usually associated with benign conditions, while the others remain primarily associated with malignancy. Radiologically, MCs are classified as benign or suspicious. MCs may represent an active pathological mineralization process rather than a passive process, such as degeneration or necrosis. Practical management of breast specimens requires finely calibrated radiological pathological procedures. Understanding the molecular and structural development of MCs may contribute to breast lesion detection and treatment.

Artificial intelligence for breast cancer analysis: Trends & directions

Breast cancer is one of the leading causes of death among women. Early detection of breast cancer can significantly improve the lives of millions of women across the globe. Given importance of finding solution/framework for early detection and diagnosis, recently many AI researchers are focusing to automate this task. The other reasons for surge in research activities in this direction are advent of robust AI algorithms (deep learning), availability of hardware that can run/train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths and limitations. It also enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade to detect breast cancer using various imaging modalities. Primarily, in this article we have focused on reviewing frameworks that have reported results using mammograms as it is the most widely used breast imaging modality that serves as the first test that medical practitioners usually prescribe for the detection of breast cancer. Another reason for focusing on mammogram imaging modalities is the availability of its labelled datasets. Datasets availability is one of the most important aspects for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis.

Sonographic focally thick duct of breast found in screening, why is it concerning? A report of three cases

We report three cases of focally thickened ductal lesions found on screening ultrasonography with fine needle aspiration (FNA)-proven benign cytology in order to demonstrate the different fates of this radiographic finding. All three patients, aged 74, 69 and 68 years old, had their first time mammography and concurrent ultrasonography. Their mammograms did not show abnormalities except a focal asymmetry in one case. The sonographic focally thick ducts were the lesions of concern and all the patients had long-term follow-up.One patient had a slightly decreased lesion size on follow-up, likely to be a non-proliferative alteration of the breast. One patient's FNA revealed a benign papillary lesion whose ductal diameter slightly increased in size with internal echo after two years with repeat FNA demonstrating epithelial papillae consistent with intraductal papilloma. The final patient had an alteration of the imaged ductal lesion in the third year of follow-up and the final specimen after surgical wide excision that was done in the fourth year confirmed cancer. We emphasize the importance of focally thickened ductal lesions found on screening sonography and underscore their need for scrutinized characterization and long term follow-up.

Nationwide mammographic screening and breast cancer mortality in Taiwan: an interrupted time-series analysis

BACKGROUND

In Taiwan, breast cancer is the third leading cause of cancer death in women. A nationwide screening program with biennial mammography for women aged 40-69 in Taiwan was implemented since July 2004, but the impact on breast cancer mortality has not been investigated.

METHODS

The interrupted time-series analysis was used to estimate the impact of mammographic screening on temporal trends of breast cancer mortality and to calculate the level of temporal changes due to the mammographic screening.

RESULTS

The annual average percentage changes of the age-standardized breast cancer mortality rates for all women aged 40-69 were 1.06% from 1991 to 2004 (before mammographic screening) and 0.33-0.34% from 2005 to 2019 (after mammographic screening). For all women aged 40-69, the results of interrupted time-series analysis showed that the increasing trends of breast cancer mortality were all attenuated after the implementation of mammographic screening. An estimation of 2114 women prevented from death of breast cancer may be attributable to screening. For women aged 40-44, 55-59, 60-64 and 65-69, the percentage changes in mortality rates were - 12.1% (- 5.1 to - 19.6%), - 20.8% (- 16.5 to - 25.2%), - 12.8% (- 8.5 to - 17.3%) and - 13.0% (- 7.9 to - 18.3%), respectively, after screening. For women aged 45-49 and 50-54, the reduction of deaths and mortality rates of breast cancer were a little.

CONCLUSIONS

This study revealed that the nationwide screening program with biennial mammography may be associated with the attenuation of breast cancer mortality trends in women aged 40-69 in Taiwan.

Cone-Beam Breast Computed Tomography: Time for a New Paradigm in Breast Imaging

It is time to reconsider how we image the breast. Although the breast is a 3D structure, we have traditionally used 2D mammography to perform screening and diagnostic imaging. Mammography has been continuously modified and improved, most recently with tomosynthesis and contrast mammography, but it is still using modifications of compression 2D mammography. It is time to consider 3D imaging for this 3D structure. Cone-beam breast computed tomography (CBBCT) is a revolutionary modality that will assist in overcoming the limitations of current imaging for dense breast tissue and overlapping structures. It also allows easy administration of contrast material for functional imaging. With a radiation dose on par with diagnostic mammography, rapid 10 s acquisition, no breast compression, and true high-resolution isotropic imaging, CBBCT has the potential to usher in a new era in breast imaging. These advantages could translate into lower morbidity and mortality from breast cancer.

Mammographic microcalcifications and risk of breast cancer

BACKGROUND

Mammographic microcalcifications are considered early signs of breast cancer (BC). We examined the association between microcalcification clusters and the risk of overall and subtype-specific BC. Furthermore, we studied how mammographic density (MD) influences the association between microcalcification clusters and BC risk.

METHODS

We used a prospective cohort (n = 53,273) of Swedish women with comprehensive information on BC risk factors and mammograms. The total number of microcalcification clusters and MD were measured using a computer-aided detection system and the STRATUS method, respectively. Cox regressions and logistic regressions were used to analyse the data.

RESULTS

Overall, 676 women were diagnosed with BC. Women with ≥3 microcalcification clusters had a hazard ratio [HR] of 2.17 (95% confidence interval [CI] = 1.57-3.01) compared to women with no clusters. The estimated risk was more pronounced in premenopausal women (HR = 2.93; 95% CI = 1.67-5.16). For postmenopausal women, microcalcification clusters and MD had a similar influence on BC risk. No interaction was observed between microcalcification clusters and MD. Microcalcification clusters were significantly associated with in situ breast cancer (odds ratio: 2.03; 95% CI = 1.13-3.63).

CONCLUSIONS

Microcalcification clusters are an independent risk factor for BC, with a higher estimated risk in premenopausal women. In postmenopausal women, microcalcification clusters have a similar association with BC as baseline MD.

A new conditional region growing approach for microcalcification delineation in mammograms

Microcalcifications (MCs) are considered as the first indicator of breast cancer development. Their morphology, in terms of shape and size, is considered as the most important criterion that determines their malignity degrees. Therefore, the accurate delineation of MC is a cornerstone step in their automatic diagnosis process. In this paper, we propose a new conditional region growing (CRG) approach with the ability of finding the accurate MC boundaries starting from selected seed points. The starting seed points are determined based on regional maxima detection and superpixel analysis. The region growing step is controlled by a set of criteria that are adapted to MC detection in terms of contrast and shape variation. These criteria are derived from prior knowledge to characterize MCs and can be divided into two categories. The first one concerns the neighbourhood searching size. The second one deals with the analysis of gradient information and shape evolution within the growing process. In order to prove the effectiveness and the reliability in terms of MC detection and delineation, several experiments have been carried out on MCs of various types, with both qualitative and quantitative analysis. The comparison of the proposed approach with state-of-the art proves the importance of the used criteria in the context of MC delineation, towards a better management of breast cancer. Graphical Abstract Flowchart of the proposed approach.

Loss of Mammographic Tissue Homeostasis in Invasive Lobular and Ductal Breast Carcinomas vs. Benign Lesions

The 2D wavelet transform modulus maxima (WTMM) method is used to perform a comparison of the spatial fluctuations of mammographic breast tissue from patients with invasive lobular carcinoma, those with invasive ductal carcinoma, and those with benign lesions. We follow a procedure developed and validated in a previous study, in which a sliding window protocol is used to analyze thousands of small subregions in a given mammogram. These subregions are categorized according to their Hurst exponent values (H): fatty tissue (H ≤ 0.45), dense tissue (H ≥ 0.55), and disrupted tissue potentially linked with tumor-associated loss of homeostasis (0.45 < H < 0.55). Following this categorization scheme, we compare the mammographic tissue composition of the breasts. First, we show that cancerous breasts are significantly different than breasts with a benign lesion (p-value ∼ 0.002). Second, the asymmetry between a patient’s cancerous breast and its contralateral counterpart, when compared to the asymmetry from patients with benign lesions, is also statistically significant (p-value ∼ 0.006). And finally, we show that lobular and ductal cancerous breasts show similar levels of disruption and similar levels of asymmetry. This study demonstrates reproducibility of the WTMM sliding-window approach to help detect and characterize tumor-associated breast tissue disruption from standard mammography. It also shows promise to help with the detection lobular lesions that typically go undetected via standard screening mammography at a much higher rate than ductal lesions. Here both types are assessed similarly.

[The HRT follow-up consultation. What to do in case of breast tumour (clinical or radiological) and microcalcifications. Postmenopausal women management: CNGOF and GEMVi clinical practice guidelines]

OBJECTIVE

The objective was to evaluate the diagnostic value of clinical examination and complementary imaging in the exploration of a breast lump or microcalcifications occurring in a postmenopausal woman taking hormonal replacement therapy (HRT), based on a systematic review of the literature in order to make recommendations for HRT management.

METHODS

A literature review was conducted using Medline, Cochrane Library data and international recommendations in French and English until 2020.

RESULTS

In the presence of a clinical breast mass in postmenopausal women, there is no clinical evidence to rule out cancer. A double evaluation by mammography and ultrasound is recommended and allows the imaging to be classified into 5 BI-RADS categories. The diagnostic management of masses classified BI-RADS 4 and 5 should be based on percutaneous sampling, with microbiopsy being the first step. A total of four situations may arise: 1. Clinical examination has detected a breast mass, but there is no imaging abnormality. In this case, the imaging NPV is high (>96%). If the clinical lesion increases in size, a tissue biopsy should be performed, while continued routine breast screening is recommended if the lesion remains stable and HRT can be continued. 2. Clinical examination, mammography, and ultrasound are in favour of a cyst. Simple cysts can be punctured if painful. There is no contraindication to continuing HRT in the case of simple cysts. Management options for complicated and complex cysts are no different from those offered to women without HRT. Continuation of HRT must consider their histological nature. 3. Clinical examination, mammography, and ultrasonography suggest a benign solid tumour. The management of these benign breast lesions (fibroadenoma…) is not different in women taking an HRT and there is no contraindication to continue the HRT. 4: Clinical examination, imaging and microbiopsy diagnose a malignant tumour. It is imperative that the HRT be stopped, whatever the hormonal dependence of the tumour and whether it is invasive or in situ. The management of the cancerous tumour must consider the updated breast cancer treatment guidelines. In the presence of microcalcifications, the course of action to be taken depends on the BI-RADS classification, established according to the morphology and arrangement of the calcifications. In case of suspicious microcalcifications (BI-RADS 4 or 5), a guided macrobiopsy should be performed. Diagnostic and therapeutic management in these patients is no different from that offered to women without HRT. Discontinuation of HRT is necessary in cases of malignancy (in situ or invasive cancer).

CONCLUSION

A rigorous multidisciplinary approach is necessary for the exploration of a breast mass or microcalcifications in a postmenopausal woman.

Is Carboxypeptidase B1 a Prognostic Marker for Ductal Carcinoma In Situ?

Simple Summary

Ductal carcinoma in situ (DCIS) is an early-stage breast cancer (BC), in which tumor cells are growing in a localized duct of the mammary gland. DCIS is considered a precursor disease for invasive BC and, therefore, treated as soon as it is identified. However, low-grade DCIS can be confused with atypical ductal hyperplasia, which is not a malignant lesion, leading to unnecessary surgery in around 70% of women with suspected DCIS. On the other hand, if left untreated, a DCIS has the potential to progress to IDC. In this retrospective study, we identified a gene signature, carboxypeptidase B1 (CPB1), the expression of which could help differentiate DCIS from an ADH lesion and DCIS that may progress to an invasive BC.

Abstract

Ductal carcinoma in situ (DCIS) is considered a non-obligatory precursor for invasive ductal carcinoma (IDC). Around 70% of women with atypical ductal hyperplasia (ADH) undergo unnecessary surgery due to the difficulty in differentiating ADH from low-grade DCIS. If untreated, 14–60% of DCIS progress to IDC, highlighting the importance of identifying a DCIS gene signature. Human transcriptome data of breast tissue samples representing each step of BC progression were analyzed and high expression of carboxypeptidase B1 (CPB1) expression strongly correlated with DCIS. This was confirmed by quantitative PCR in breast tissue samples and cell lines model. High CPB1 expression correlated with better survival outcome, and mRNA level was highest in DCIS than DCIS adjacent to IDC and IDC. Moreover, loss of CPB1 in a DCIS cell line led to invasive properties associated with activation of HIF1α, FN1, STAT3 and SPP1 and downregulation of SFRP1 and OS9. The expression of CPB1 could predict 90.1% of DCIS in a cohort consisting of DCIS and IDC. We identified CPB1, a biomarker that helps differentiate DCIS from ADH or IDC and in predicting if a DCIS is likely to progress to IDC, thereby helping clinicians in their decisions.

Classification of Mammographic Breast Microcalcifications Using a Deep Convolutional Neural Network: A BI-RADS-Based Approach

MATERIALS AND METHODS

Over 56,000 images of 268 mammograms from 94 patients were labeled to 3 classes according to the BI-RADS standard: "no microcalcifications" (BI-RADS 1), "probably benign microcalcifications" (BI-RADS 2/3), and "suspicious microcalcifications" (BI-RADS 4/5). Using the preprocessed images, a dCNN was trained and validated, generating 3 types of models: BI-RADS 4 cohort, BI-RADS 5 cohort, and BI-RADS 4 + 5 cohort. For the final validation of the trained dCNN models, a test data set consisting of 141 images of 51 mammograms from 26 patients labeled according to the corresponding BI-RADS classification from the radiological reports was applied. The performances of the dCNN models were evaluated, classifying each of the mammograms and computing the accuracy in comparison to the classification from the radiological reports. For visualization, probability maps of the classification were generated.

RESULTS

The accuracy on the validation set after 130 epochs was 99.5% for the BI-RADS 4 cohort, 99.6% for the BI-RADS 5 cohort, and 98.1% for the BI-RADS 4 + 5 cohort. Confusion matrices of the "real-world" test data set for the 3 cohorts were generated where the radiological reports served as ground truth. The resulting accuracy was 39.0% for the BI-RADS 4 cohort, 80.9% for BI-RADS 5 cohort, and 76.6% for BI-RADS 4 + 5 cohort. The probability maps exhibited excellent image quality with correct classification of microcalcification distribution.

CONCLUSIONS

The dCNNs can be trained to successfully classify microcalcifications on mammograms according to the BI-RADS classification system in order to act as a standardized quality control tool providing the expertise of a team of radiologists.

Convolutional neural networks for breast cancer detection in mammography: A survey

Despite its proven record as a breast cancer screening tool, mammography remains labor-intensive and has recognized limitations, including low sensitivity in women with dense breast tissue. In the last ten years, Neural Network advances have been applied to mammography to help radiologists increase their efficiency and accuracy. This survey aims to present, in an organized and structured manner, the current knowledge base of convolutional neural networks (CNNs) in mammography. The survey first discusses traditional Computer Assisted Detection (CAD) and more recently developed CNN-based models for computer vision in mammography. It then presents and discusses the literature on available mammography training datasets. The survey then presents and discusses current literature on CNNs for four distinct mammography tasks: (1) breast density classification, (2) breast asymmetry detection and classification, (3) calcification detection and classification, and (4) mass detection and classification, including presenting and comparing the reported quantitative results for each task and the pros and cons of the different CNN-based approaches. Then, it offers real-world applications of CNN CAD algorithms by discussing current Food and Drug Administration (FDA) approved models. Finally, this survey highlights the potential opportunities for future work in this field. The material presented and discussed in this survey could serve as a road map for developing CNN-based solutions to improve mammographic detection of breast cancer further.

Pure Ductal Carcinoma In Situ of the Breast: Analysis of 270 Consecutive Patients Treated in a 9-Year Period

Simple Summary

Ductal carcinoma in situ (DCIS) accounts for 20 to 25% of all breast cancers and its incidence of progression to invasive ductal carcinoma is at least 13 to 50%. The aim of our retrospective observational analysis is to review the issues of this histological type of cancer. We confirmed in a wide population of 270 consecutive patients who underwent surgery in a single institute that the management of DCIS can be difficult and particularly complex. There are many variables to be taken into consideration such as the choice of the diagnostic and bioptical technique. This delicate management must be carried out in specialized centres such as Breast Units involving multiple professional figures to define and guarantee the best possible treatment for each patient.

Abstract

Introduction: Ductal carcinoma in situ (DCIS) is an intraductal neoplastic proliferation of epithelial cells that are confined within the basement membrane of the breast ductal system. This retrospective observational analysis aims at reviewing the issues of this histological type of cancer. Materials and methods: Patients treated for DCIS between 1 January 2009 and 31 December 2018 were identified from a retrospective database. The patients were divided into two groups of 5 years each, the first group including patients treated from 2009 to 2013, and the second group including patients treated from 2014 to 2018. Once the database was completed, we performed a statistical analysis to see if there were significant differences among the 2 periods. Statistical analyses were performed using GraphPad Prism software for Windows, and the level of significance was set at p < 0.05. Results: 3586 female patients were treated for breast cancer over the 9-year study period (1469 patients from 2009 to 2013 and 2117 from 2014 to 2018), of which 270 (7.53%) had pure DCIS in the final pathology. The median age of diagnosis was 59-year-old (range 36–86). In the first period, 81 (5.5%) women out of 1469 had DCIS in the final pathology, in the second, 189 (8.9%) out of 2117 had DCIS in the final pathology with a statistically significant increase (p = 0.0001). From 2009 to 2013, only 38 (46.9%) were in stage 0 (correct DCIS diagnosis) while in the second period, 125 (66.1%) were included in this stage. The number of patients included in clinical stage 0 increased significantly (p = 0.004). In the first period, 48 (59.3%) specimen margins were at a greater or equal distance than 2 mm (negative margins), between 2014 and 2018; 137 (72.5%) had negative margins. Between 2014 and 2018 the number of DCIS patients with positive margins decreased significantly (p = 0.02) compared to the first period examined. The mastectomies number increased significantly (p = 0.008) between the 2 periods, while the sentinel lymph node biopsy (SLNB) numbers had no differences (p = 0.29). For both periods analysed all the 253 patients who underwent the follow up are currently living and free of disease. We have conventionally excluded the 17 patients whose data were lost. Conclusion: The choice of the newest imaging techniques and the most suitable biopsy method allows a better pre-operative diagnosis of the DCIS. Surgical treatment must be targeted to the patient and a multidisciplinary approach discussed in the Breast Unit centres.

Diagnostic accuracy of tomosynthesis-guided vacuum assisted breast biopsy of ultrasound occult lesions

This study aims to evaluate the diagnostic accuracy of digital breast tomosynthesis-guided vacuum assisted breast biopsy (DBT-VABB) of screening detected suspicious mammographic abnormalities comprising of calcifications, asymmetric densities, architectural distortions and spiculated masses. In this institutionally approved study, a total of 170 (n = 170) DBT-VABB were performed, 153 (90%) were for calcifications, 8 (4.7%) for spiculated mass, 5 (2.9%) for asymmetric density and 4 (2.4%) for architectural distortion. All these lesions were not detected on the corresponding ultrasound. Histopathology results revealed 140 (82.4%) benign, 9 (5.3%) borderline and 21 (12.4%) malignant lesions. The total upgrade rate at surgery was 40% for atypical ductal hyperplasia and 5.9% for ductal carcinoma in-situ. 3.6% discordant benign lesions showed no upgrade. DBT-VABB showed 100% specificity, 91.3% sensitivity and 100% positive predictive value (PPV) for detecting malignant lesions. The negative predictive value (NPV) was 80%. 2 (1.2%) patients had mild complications and 1 (0.6%) had severe pain. Our study showed that DBT-VABB was a safe and reliable method, with high sensitivity, specificity, PPV and NPV in the diagnosis of non-palpable benign and malignant breast lesions. Our data also confirmed the accuracy of DBT-VABB in detecting malignant lesions and we suggest further surgical excision in borderline lesions for a more accurate diagnostic evaluation.

Improving the Diagnostic Accuracy of Breast BI-RADS 4 Microcalcification-Only Lesions Using Contrast-Enhanced Mammography

BACKGROUND

Contrast-enhanced mammography (CEM) is a novel breast imaging technique that can provide additional information of breast tissue blood supply. This study aimed to test the possibility of CEM in improving the diagnostic accuracy of Breast Imaging Reporting and Data System (BI-RADS) 4 calcification-only lesions with consideration of morphology and distribution.

PATIENTS AND METHODS

Data of patients with suspicious malignant calcification-only lesions (BI-RADS 4) on low-energy CEM and proved pathologic diagnoses were retrospectively collected. Two junior radiologists independently reviewed the two sets of CEM images, low-energy images (LE) to describe the calcifications by morphology and distribution type, and recombined images (CE) to record the presence of enhancement. Low-risk and high-risk groups were divided by calcification morphology, distribution, and both, respectively. Positive predictive values and misdiagnosis rates (MDR) were compared between LE-only reading and CE reading. Diagnostic performance was also tested using machine learning method.

RESULTS

The study included 74 lesions (26 malignant and 48 benign). Positive predictive values were significantly higher and MDRs were significantly lower using CE images than using LE alone for both the low-risk morphology type and low-risk distribution type (P < .05). MDRs were significantly lower when using CE images (18.18%-24.00%) than using LE images alone in low-risk group (76.36%-80.00%) (P < .05). Using a machine learning method, significant improvements in the area under the receiver operating characteristic curve were observed in both low-risk and high-risk groups.

CONCLUSION

CEM has the potential to aid in the diagnosis of BI-RADS 4 calcification-only lesions; in particular, those presented as low risk in morphology and/or distribution may benefit more.

A Screening CAD Tool for the Detection of Microcalcification Clusters in Mammograms

Breast cancer is the most common cancer diagnosed in women worldwide. Up to 50% of non-palpable breast cancers are detected solely through microcalcification clusters in mammograms. This article presents a novel and completely automated algorithm for the detection of microcalcification clusters in a mammogram. A multiscale 2D non-linear energy operator is proposed for enhancing the contrast between the microcalcifications and the background. Several texture, shape, intensity, and histogram of oriented gradients (HOG)-based features are used to distinguish microcalcifications from other brighter mammogram regions. A new majority class data reduction technique based on data distribution is proposed to counter data imbalance problem. The algorithm is able to achieve 100% sensitivity with 2.59, 1.78, and 0.68 average false positives per image on Digital Database for Screening Mammography (scanned film), INbreast (direct radiography) database, and PGIMER-IITKGP mammogram (direct radiography) database, respectively. Thus, it might be used as a second reader as well as a screening tool to reduce the burden on radiologists.

Heritability of Mammographic Breast Density, Density Change, Microcalcifications, and Masses

Mammographic features influence breast cancer risk and are used in risk prediction models. Understanding how genetics influence mammographic features is important because the mechanisms through which they are associated with breast cancer are not well known. Here, using mammographic screening history and detailed questionnaire data from 56,820 women from the KARMA prospective cohort study, we investigated the association between a genetic predisposition to breast cancer and mammographic features among women with a family history of breast cancer (N = 49,674) and a polygenic risk score (PRS, N = 9,365). The heritability of mammographic features such as dense area (MD), microcalcifications, masses, and density change (MDC, cm²/year) was estimated using 1,940 sister pairs. Heritability was estimated at 58% [95% confidence interval (CI), 48%-67%) for MD, 23% (2%-45%) for microcalcifications, and 13% (1%-25%)] for masses. The estimated heritability for MDC was essentially null (2%; 95% CI, -8% to 12%). The association between a genetic predisposition to breast cancer (using PRS) and MD and microcalcifications was positive, while for masses this was borderline significant. In addition, for MDC, having a family history of breast cancer was associated with slightly greater MD reduction. In summary, we have confirmed previous findings of heritability in MD, and also established heritability of the number of microcalcifications and masses at baseline. Because these features are associated with breast cancer risk and can improve detecting women at short-term risk of breast cancer, further investigation of common loci associated with mammographic features is warranted to better understand the etiology of breast cancer. SIGNIFICANCE: These findings provide novel data on the heritability of microcalcifications, masses, and density change, which are all associated with breast cancer risk and can indicate women at short-term risk.

The predictive value of calcification for the grading of ductal carcinoma in situ in Chinese patients

High-grade ductal carcinoma in situ (DCIS) requires resection due to the high risk of developing invasive breast cancer. The predictive powers of noninvasive predictors for high-grade DCIS remain contradictory. This study aimed to explore the predictive value of calcification for high-grade DCIS in Chinese patients.This was a retrospective study of Chinese DCIS patients recruited from the Women's Hospital, School of Medicine, Zhejiang University between January and December 2018. The patients were divided into calcification and non-calcification groups based on the mammography results. The correlation of calcification with the pathologic stage of DCIS was evaluated using the multivariable analysis. The predictive value of calcification for DCIS grading was examined using the receiver operating characteristics (ROC) curve.The pathologic grade of DCIS was not associated with calcification morphology (P = .902), calcification distribution (P = .252), or breast density (P = .188). The multivariable analysis showed that the presence of calcification was independently associated with high pathologic grade of DCIS (OR = 3.206, 95% CI = 1.315-7.817, P = .010), whereas the age, hypertension, menopause, and mammography BI-RADS were not (all P > .05) associated with the grade of DCIS. The ROC analysis of the predictive value of calcification for DCIS grading showed that the area under the curve was 0.626 (P = .019), with a sensitivity of 73.1%, specificity of 52.2%, positive predictive value of 72.2%, and negative predictive value of 53.3%.The presence of calcification is independently associated with high pathologic grade of DCIS and could predict high-grade DCIS in Chinese patients.

Spontaneously Disappearing Calcifications in the Breast: A Rare Instance Where a Decrease in Size on Mammogram Is Not Good

Spontaneously resolving breast calcification on mammography is a rare radiologic finding. This phenomenon is defined by a decrease in number and/or prominence of breast calcifications on mammogram when compared to prior imaging. The significance of resolving breast calcifications remains unclear, but they have been reported in cases of malignancy. In current literature, patients whose imaging illustrated a decrease in calcifications usually had other concomitant breast complaints. We are presenting a case of invasive ductal carcinoma, in which the patient was asymptomatic on physical examination. Spontaneously resolving breast calcification and lymphadenopathy were the only abnormal findings on screening mammogram.

Can grating interferometry-based mammography discriminate benign from malignant microcalcifications in fresh biopsy samples?

PURPOSE

In addition to absorption imaging, grating interferometry-based mammography (GIM) is capable of detecting differential-phase and scattering signals. In particular, the scattering signal can enable a quantifiable characterization of breast lesions. The purpose of this study was to determine if suspicious microcalcifications associated with benign or malignant lesions can be discriminated based on their absorption and scattering properties.

MATERIALS AND METHODS

In this prospective, ethically approved study, 62 patients (mean age 60 y, range 39-89) with suspicious microcalcifications, who underwent stereotactic biopsies, were included. Biopsies were measured with an experimental GIM device and the ratios of the scattering and absorption signal (R-value) for microcalcifications were calculated. The mean R-values for benign and malignant lesions associated with microcalcifications were compared with the final histopathological diagnosis using a t-test.

RESULTS

Twenty of the 62 participants had microcalcifications associated with malignancy. Comparing the two largest histopathological sub-groups of fibrosis (n = 23) vs. ductal carcinoma in situ (n = 15) resulted in an average R-value of 4.08 for benign and 2.80 for malignant lesions; p = 0.07. All microcalcifications associated with malignancy had an R-value below 4.71. Excluding microcalcifications with an R-value above this threshold would result in an 11 % reduction of false positives.

CONCLUSION

The novel GIM modality has the potential to non-invasively characterize microcalcifications and might aid in the discrimination of benign from malignant lesions in fresh biopsy samples.

Unresolved Complexity in the Gene Regulatory Network Underlying EMT

Epithelial to mesenchymal transition (EMT) is the process whereby a polarized epithelial cell ceases to maintain cell-cell contacts, loses expression of characteristic epithelial cell markers, and acquires mesenchymal cell markers and properties such as motility, contractile ability, and invasiveness. A complex process that occurs during development and many disease states, EMT involves a plethora of transcription factors (TFs) and signaling pathways. Whilst great advances have been made in both our understanding of the progressive cell-fate changes during EMT and the gene regulatory networks that drive this process, there are still gaps in our knowledge. Epigenetic modifications are dynamic, chromatin modifying enzymes are vast and varied, transcription factors are pleiotropic, and signaling pathways are multifaceted and rarely act alone. Therefore, it is of great importance that we decipher and understand each intricate step of the process and how these players at different levels crosstalk with each other to successfully orchestrate EMT. A delicate balance and fine-tuned cooperation of gene regulatory mechanisms is required for EMT to occur successfully, and until we resolve the unknowns in this network, we cannot hope to develop effective therapies against diseases that involve aberrant EMT such as cancer. In this review, we focus on data that challenge these unknown entities underlying EMT, starting with EMT stimuli followed by intracellular signaling through to epigenetic mechanisms and chromatin remodeling.

Quantitative chemical imaging of breast calcifications in association with neoplastic processes

Calcifications play an essential role in early breast cancer detection and diagnosis. However, information regarding the chemical composition of calcifications identified on mammography and histology is limited. Detailed spectroscopy reveals an association between the chemical composition of calcifications and breast cancer, warranting the development of novel analytical tools to better define calcification types. Previous investigations average calcification composition across broad tissue sections with no spatially resolved information or provide qualitative visualization, which prevents a robust linking of specific spatially resolved changes in calcification chemistry with the pathologic process.

Method: To visualize breast calcification chemical composition at high spatial resolution, we apply hyperspectral stimulated Raman scattering (SRS) microscopy to study breast calcifications associated with a spectrum of breast changes ranging from benign to neoplastic processes, including atypical ductal hyperplasia, ductal carcinoma in situ, and invasive ductal carcinoma. The carbonate content of individual breast calcifications is quantified using a simple ratiometric analysis.

Results: Our findings reveal that intra-sample calcification carbonate content is closely associated with local pathological processes. Single calcification analysis supports previous studies demonstrating decreasing average carbonate level with increasing malignant potential. Sensitivity and specificity reach >85% when carbonate content level is used as the single differentiator in separating benign from neoplastic processes. However, the average carbonate content is limiting when trying to separate specific diagnostic categories, such as fibroadenoma and invasive ductal carcinoma. Second harmonic generation (SHG) data can provide critical information to bridge this gap.

Conclusion: SRS, combined with SHG, can be a valuable tool in better understanding calcifications in carcinogenesis, diagnosis, and possible prognosis. This study not only reveals previously unknown large variations of breast microcalcifications in association with local malignancy but also corroborates the clinical value of linking microcalcification chemistry to breast malignancy. More importantly, it represents an important step in the development of a label-free imaging strategy for breast cancer diagnosis with tremendous potential to address major challenges in diagnostic discordance in pathology.