In-silico study of the impact of system design parameters on microcalcification detection in wide-angle digital breast tomosynthesis.

Purpose: Accurate detection of microcalcifications ( µ Calcs ) is crucial for the early detection of breast cancer. Some clinical studies have indicated that digital breast tomosynthesis (DBT) systems with a wide angular range have inferior µ Calc detectability compared with those with a narrow angular range. This study aims to (1) provide guidance for optimizing wide-angle (WA) DBT for improving µ Calcs detectability and (2) prioritize key optimization factors. Approach: An in-silico DBT pipeline was constructed to evaluate µ Calc detectability of a WA DBT system under various imaging conditions: focal spot motion (FSM), angular dose distribution (ADS), detector pixel pitch, and detector electronic noise (EN). Images were simulated using a digital anthropomorphic breast phantom inserted with 120 µ m µ Calc clusters. Evaluation metrics included the signal-to-noise ratio (SNR) of the filtered channel observer and the area under the receiver operator curve (AUC) of multiple-reader multiple-case analysis. Results: Results showed that FSM degraded µ Calcs sharpness and decreased the SNR and AUC by 5.2% and 1.8%, respectively. Non-uniform ADS increased the SNR by 62.8% and the AUC by 10.2% for filtered backprojection reconstruction with a typical clinical filter setting. When EN decreased from 2000 to 200 electrons, the SNR and AUC increased by 21.6% and 5.0%, respectively. Decreasing the detector pixel pitch from 85 to 50 µ m improved the SNR and AUC by 55.6% and 7.5%, respectively. The combined improvement of a 50 µ m pixel pitch and EN200 was 89.2% in the SNR and 12.8% in the AUC. Conclusions: Based on the magnitude of impact, the priority for enhancing µ Calc detectability in WA DBT is as follows: (1) utilizing detectors with a small pixel pitch and low EN level, (2) allocating a higher dose to central projections, and (3) reducing FSM. The results from this study can potentially provide guidance for DBT system optimization in the future.

Hybrid Feature Mammogram Analysis: Detecting and Localizing Microcalcifications Combining Gabor, Prewitt, GLCM Features, and Top Hat Filtering Enhanced with CNN Architecture.

Breast cancer is a prevalent malignancy characterized by the uncontrolled growth of glandular epithelial cells, which can metastasize through the blood and lymphatic systems. Microcalcifications, small calcium deposits within breast tissue, are critical markers for early detection of breast cancer, especially in non-palpable carcinomas. These microcalcifications, appearing as small white spots on mammograms, are challenging to identify due to potential confusion with other tissues. This study hypothesizes that a hybrid feature extraction approach combined with Convolutional Neural Networks (CNNs) can significantly enhance the detection and localization of microcalcifications in mammograms. The proposed algorithm employs Gabor, Prewitt, and Gray Level Co-occurrence Matrix (GLCM) kernels for feature extraction. These features are input to a CNN architecture designed with maxpooling layers, Rectified Linear Unit (ReLU) activation functions, and a sigmoid response for binary classification. Additionally, the Top Hat filter is used for precise localization of microcalcifications. The preprocessing stage includes enhancing contrast using the Volume of Interest Look-Up Table (VOI LUT) technique and segmenting regions of interest. The CNN architecture comprises three convolutional layers, three ReLU layers, and three maxpooling layers. The training was conducted using a balanced dataset of digital mammograms, with the Adam optimizer and binary cross-entropy loss function. Our method achieved an accuracy of 89.56%, a sensitivity of 82.14%, and a specificity of 91.47%, outperforming related works, which typically report accuracies around 85-87% and sensitivities between 76 and 81%. These results underscore the potential of combining traditional feature extraction techniques with deep learning models to improve the detection and localization of microcalcifications. This system may serve as an auxiliary tool for radiologists, enhancing early detection capabilities and potentially reducing diagnostic errors in mass screening programs.

Enhancing early breast cancer diagnosis through automated microcalcification detection using an optimized ensemble deep learning framework.

Background: Breast cancer remains a pressing global health concern, necessitating accurate diagnostics for effective interventions. Deep learning models (AlexNet, ResNet-50, VGG16, GoogLeNet) show remarkable microcalcification identification (>90%). However, distinct architectures and methodologies pose challenges. We propose an ensemble model, merging unique perspectives, enhancing precision, and understanding critical factors for breast cancer intervention. Evaluation favors GoogleNet and ResNet-50, driving their selection for combined functionalities, ensuring improved precision, and dependability in microcalcification detection in clinical settings. Methods: This study presents a comprehensive mammogram preprocessing framework using an optimized deep learning ensemble approach. The proposed framework begins with artifact removal using Otsu Segmentation and morphological operation. Subsequent steps include image resizing, adaptive median filtering, and deep convolutional neural network (D-CNN) development via transfer learning with ResNet-50 model. Hyperparameters are optimized, and ensemble optimization (AlexNet, GoogLeNet, VGG16, ResNet-50) are constructed to identify the localized area of microcalcification. Rigorous evaluation protocol validates the efficacy of individual models, culminating in the ensemble model demonstrating superior predictive accuracy. Results: Based on our analysis, the proposed ensemble model exhibited exceptional performance in the classification of microcalcifications. This was evidenced by the model's average confidence score, which indicated a high degree of dependability and certainty in differentiating these critical characteristics. The proposed model demonstrated a noteworthy average confidence level of 0.9305 in the classification of microcalcification, outperforming alternative models and providing substantial insights into the dependability of the model. The average confidence of the ensemble model in classifying normal cases was 0.8859, which strengthened the model's consistent and dependable predictions. In addition, the ensemble models attained remarkably high performances in terms of accuracy, precision, recall, F1-score, and area under the curve (AUC). Conclusion: The proposed model's thorough dataset integration and focus on average confidence ratings within classes improve clinical diagnosis accuracy and effectiveness for breast cancer. This study introduces a novel methodology that takes advantage of an ensemble model and rigorous evaluation standards to substantially improve the accuracy and dependability of breast cancer diagnostics, specifically in the detection of microcalcifications.

Mammography of suspicious calcifications among ductal carcinoma in situ and benign breast disease.

Background: Most ductal carcinoma in situ (DCIS) lesions manifest early as calcifications, which could be benign or malignant. The classified group of suspicious calcifications among DCIS and benign breast disease is clinically important to early evaluate patient risk factors and plan treatment options. Objectives: To compare imaging features of suspicious calcifications between DCIS and benign breast disease. Method: A retrospective study of 101 suspicious calcifications was performed at Thammasat University Hospital from June 2011 to October 2020. The calcifications were surgically excised by mammography-guided wire localisation. The mammographic features of the suspicious calcifications were reviewed according to the fifth edition of the American College of Radiology Breast Imaging-Reporting and Data System lexicon. For comparing between two groups, the student t-test, Fisher's exact test and Mann-Whitney U test were used for statistical analyses. The logistic regression analysis was calculated for DCIS prediction. Results: The pathologic results of all 101 suspicious calcifications were DCIS (30 cases) and benign breast disease (71 cases). Linear morphology and segmental distribution correlated significantly with DCIS (p = 0.003 and p = 0.024, respectively). After multivariable analysis, fine linear calcification still significantly elevated the risk of DCIS (odd ratios, 51.72 [95% confidence interval: 2.61, 1022.89], p-value of 0.01), however, the odds of predicting DCIS was not statistically significant different among any distribution. Conclusion: Ductal carcinoma in situ calcification has contrasting morphology and distribution features compared to benign breast disease. The calcification descriptor is considered an important implement for early diagnosis and distinguishes DCIS from other benign breast conditions. Contribution: Calcification descriptor is considered an important implement for early diagnosis and distinguishment of DCIS from other benign breast conditions.

Mammographic breast features and risk of cardiovascular diseases in korean women.

BACKGROUND: There is a growing amount of evidence on the association between cardiovascular diseases (CVDs) and breast calcification. Thus, mammographic breast features have recently gained attention as CVD predictors. OBJECTIVE: This study assessed the association of mammographic features, including benign calcification, microcalcification, and breast density, with cardiovascular diseases. METHODS: This study comprised 6,878,686 women aged ≥40 who underwent mammographic screening between 2009 and 2012 with follow-up until 2020. The mammographic features included benign calcification, microcalcification, and breast density. The cardiovascular diseases associated with the mammographic features were assessed using logistic regression. RESULTS: The prevalence of benign calcification, microcalcification, and dense breasts were 9.6 %, 0.9 % and 47.3 % at baseline, respectively. Over a median follow-up of 10 years, benign calcification and microcalcification were positively associated with an increased risk of chronic ischaemic heart disease whereas breast density was inversely associated with it; the corresponding aOR (95 % CI) was 1.14 (1.10-1.17), 1.19 (1.03-1.15), and 0.88 (0.85-0.90), respectively. A significantly increased risk of chronic ischaemic heart disease (IHD) was observed among women with benign calcifications (aHR, 1.14; 95 % CI 1.10-1.17) and microcalcifications (aOR, 1.19; 95 % CI 1.06-1.33). Women with microcalcifications had a 1.16-fold (95 % CI 1.03-1.30) increased risk of heart failure. CONCLUSIONS: Mammographic calcifications were associated with an increased risk of chronic ischaemic heart diseases, whereas dense breast was associated with a decreased risk of cardiovascular disease. Thus, the mammographic features identified on breast cancer screening may provide an opportunity for cardiovascular disease risk identification and prevention.

Detection and diagnosis of automated breast ultrasound in patients with BI-RADS category 4 microcalcifications: a retrospective study.

BACKGROUND: Automated Breast Ultrasound (AB US) has shown good application value and prospects in breast disease screening and diagnosis. The aim of the study was to explore the ability of AB US to detect and diagnose mammographically Breast Imaging Reporting and Data System (BI-RADS) category 4 microcalcifications. METHODS: 575 pathologically confirmed mammographically BI-RADS category 4 microcalcifications from January 2017 to June 2021 were included. All patients also completed AB US examinations. Based on the final pathological results, analyzed and summarized the AB US image features, and compared the evaluation results with mammography, to explore the detection and diagnostic ability of AB US for these suspicious microcalcifications. RESULTS: 250 were finally confirmed as malignant and 325 were benign. Mammographic findings including microcalcifications morphology (61/80 with amorphous, coarse heterogeneous and fine pleomorphic, 13/14 with fine-linear or branching), calcification distribution (189/346 with grouped, 40/67 with linear and segmental), associated features (70/96 with asymmetric shadow), higher BI-RADS category with 4B (88/120) and 4 C (73/38) showed higher incidence in malignant lesions, and were the independent factors associated with malignant microcalcifications. 477 (477/575, 83.0%) microcalcifications were detected by AB US, including 223 malignant and 254 benign, with a significantly higher detection rate for malignant lesions (x2 = 12.20, P < 0.001). Logistic regression analysis showed microcalcifications with architectural distortion (odds ratio [OR] = 0.30, P = 0.014), with amorphous, coarse heterogeneous and fine pleomorphic morphology (OR = 3.15, P = 0.037), grouped (OR = 1.90, P = 0.017), liner and segmental distribution (OR = 8.93, P = 0.004) were the independent factors which could affect the detectability of AB US for microcalcifications. In AB US, malignant calcification was more frequent in a mass (104/154) or intraductal (20/32), and with ductal changes (30/41) or architectural distortion (58/68), especially with the both (12/12). BI-RADS category results also showed that AB US had higher sensitivity to malignant calcification than mammography (64.8% vs. 46.8%). CONCLUSIONS: AB US has good detectability for mammographically BI-RADS category 4 microcalcifications, especially for malignant lesions. Malignant calcification is more common in a mass and intraductal in AB US, and tend to associated with architectural distortion or duct changes. Also, AB US has higher sensitivity than mammography to malignant microcalcification, which is expected to become an effective supplementary examination method for breast microcalcifications, especially in dense breasts.

Clinical, Histopathological, and Radiological Profile of Patients Presenting With Thyroid Malignancies Among the Kerala Population.

BACKGROUND AND OBJECTIVE: Thyroid cancer, though relatively uncommon among all cancer types, stands as the primary form of endocrine tumor. Over the last 20 years, there has been a significant uptick in its occurrence. Papillary thyroid carcinoma (PTC), which is well-differentiated, emerges as the dominant subtype, in regions where iodine levels are deemed adequate. The study aimed to study the clinicopathological profile of patients diagnosed with thyroid malignancies at the Muslim Educational Society (MES) Medical College Perinthalmanna. MATERIALS AND METHODS: This is a retrospective study undertaken at the MES Medical College by the Department of General Surgery and Endocrine Surgery. The study focuses on patients who have been diagnosed with thyroid cancer through a biopsy. Case sheets of all those patients diagnosed with thyroid malignancy were referred from the Medical Records Library to collect the relevant medical and sociodemographic data. This data was entered in the proforma, which was transferred to the Excel sheet and processed in IBM SPSS Statistics for Windows, Version 20 (Released 2011; IBM Corp., Armonk, New York, United States). RESULTS: The study included predominantly middle-aged individuals (40-60 years), with 22 (55%) falling within this age range, followed by 14 (35%) aged between 20 and 40 years, and only four (10%) above 60 years. Female patients constituted 82.5% of the study group. Most cases presented with swelling lasting less than six months 23 (57.5%), while only four (10%) had swelling lasting more than five years. Compression symptoms were rare, with only three (7.5%) experiencing dysphagia or dyspnea. Pain was reported in two (5%) of the cases. Hypothyroidism, toxic manifestations, or hoarseness were observed in one (2.5%) of the patients. Regarding swelling characteristics, most were greater than 4 cm in size (29, 72%) and firm in consistency (25, 62.5%). Nodular surfaces were present in 19 (47.5%) of the cases, while 38 (95%) of the swellings were mobile. Palpable lymph nodes were noted in 13 (32.5%) of cases. Radiologically, hypoechoic lesions were observed in 26 (65%) of cases, with microcalcification in 29 (72.5%) and peripheral vascularity in 31 (77.5%). Papillary carcinoma was the most common histological type (34, 85%), with medullary and follicular carcinomas accounting for five (12.5%) and one (2.5%), respectively. An association was found between the duration of swelling and histological type (p = 0.05) and between the mobility of swelling and histological type (p < 0.05). However, no significant associations were observed between imaging findings and histological type (p > 0.05). The gender distribution did not show a statistically significant association with histological type. CONCLUSION: The findings of the study revealed a statistically insignificant association between age, gender, clinical features, and the histological type of thyroid malignancy. Additionally, there was no statistically significant association between the histological type of thyroid malignancy and the size or type of surface or consistency of thyroid swelling or ultrasonographic findings of the swelling like echogenicity, microcalcification, increased peripheral vascularity, or loss of peripheral halo.

Novel echocardiographic pixel intensity quantification method for differentiating transthyretin cardiac amyloidosis from light chain cardiac amyloidosis and other phenocopies.

AIMS: Differentiating cardiac amyloidosis (CA) subtypes is important considering the significantly different therapies for transthyretin (ATTR)-CA and light chain (AL)-CA. Therefore, an echocardiographic method to distinguish ATTR-CA from AL-CA would provide significant value. We assessed a novel echocardiographic pixel intensity method to quantify myocardial calcification to differentiate ATTR-CA from phenocopies of CA and from AL-CA, specifically. METHODS AND RESULTS: 167 patients with ATTR-CA (n=53), AL-CA (n=32), hypertrophic cardiomyopathy (n=37), and advanced chronic kidney disease (n=45) were retrospectively evaluated. The septal reflectivity ratio (SRR) was measured as the average pixel intensity of the visible anterior septal wall divided by the average pixel intensity of the visible posterior lateral wall. SRR and other myocardial strain-based echocardiographic measures were evaluated with receiver operator characteristic analysis to evaluate accuracy in distinguishing ATTR-CA from AL-CA and other forms of left ventricular hypertrophy. Mean septal reflectivity ratio (SRR) was significantly higher in the ATTR-CA cohort compared to the other cohorts (p <0.001). SRR demonstrated the largest AUC (0.91, p<0.0001) for distinguishing ATTR from all other cohorts and specifically for distinguishing ATTR-CA from AL-CA (AUC=0.90, p<0.0001, specificity 96%, sensitivity 63%). There was excellent inter- and intra-operator reproducibility with an ICC of 0.91 (p <0.001) and 0.89 (p <0.001), respectively. CONCLUSION: The SRR is a reproducible and robust parameter for differentiating ATTR-CA from other phenocopies of CA and specifically ATTR-CA from AL-CA.

Role of 18F-sodium fluoride positron emission tomography in imaging atherosclerosis.

Atherosclerosis involving vascular beds across the human body remains the leading cause of death worldwide. Coronary and peripheral artery disease, which are almost universally a result of atherosclerotic plaque, can manifest clinically as myocardial infarctions, ischemic stroke, or acute lower-limb ischemia. Beyond imaging myocardial perfusion and blood-flow, nuclear imaging has the potential to depict the activity of the processes that are directly implicated in the atherosclerotic plaque progression and rupture. Out of several tested tracers to date, the literature is most advanced for 18F-sodium fluoride positron emission tomography. In this review, we present the latest data in the field of atherosclerotic 18F-sodium fluoride positron emission tomography imaging, discuss the advantages and limitation of the techniques, and highlight the aspects that require further research in the future.

Translating microcalcification biomarker information into the laboratory: A preliminary assessment utilizing core biopsies obtained from sites of mammographic calcification.

Rationale and objectives: The potential of breast microcalcification chemistry to provide clinically valuable intelligence is being increasingly studied. However, acquisition of crystallographic details has, to date, been limited to high brightness, synchrotron radiation sources. This study, for the first time, evaluates a laboratory-based system that interrogates histological sections containing microcalcifications. The principal objective was to determine the measurement precision of the laboratory system and assess whether this was sufficient to provide potentially clinical valuable information. Materials and methods: Sections from 5 histological specimens from breast core biopsies obtained to evaluate mammographic calcification were examined using a synchrotron source and a laboratory-based instrument. The samples were chosen to represent a significant proportion of the known breast tissue, mineralogical landscape. Data were subsequently analysed using conventional methods and microcalcification characteristics such as crystallographic phase, chemical deviation from ideal stoichiometry and microstructure were determined. Results: The crystallographic phase of each microcalcification (e.g., hydroxyapatite, whitlockite) was easily determined from the laboratory derived data even when a mixed phase was apparent. Lattice parameter values from the laboratory experiments agreed well with the corresponding synchrotron values and, critically, were determined to precisions that were significantly greater than required for potential clinical exploitation. Conclusion: It has been shown that crystallographic characteristics of microcalcifications can be determined in the laboratory with sufficient precision to have potential clinical value. The work will thus enable exploitation acceleration of these latent microcalcification features as current dependence upon access to limited synchrotron resources is minimized.

Impact of residual microcalcifcations on prognosis after neoadjuvant chemotherapy in breast cancer patients.

BACKGROUND: Residual microcalcifications after neoadjuvant chemotherapy (NAC) are challenging for deciding extent of surgery and questionable for impact on prognosis. We investigated changes in the extent and patterns of microcalcifications before and after NAC and correlated them with pathologic response. We also compared prognosis of patients depending on presence of residual microcalcifications after NAC. METHODS: A total of 323 patients with invasive breast carcinoma treated with neoadjuvant chemotherapy at Kangbuk Samsung Hospital and Samsung Medical center from March 2015 to September 2018 were included. Patients were divided into four groups according to pathologic response and residual microcalcifications. Non-pCRw/mic group was defined as breast non-pCR with residual microcalcifications. Non-pCRw/o mic group was breast non-pCR without residual microcalcifications. pCRw/mic group was breast pCR with residual microcalcifications. pCRw/o mic group was breast pCR without residual microcalcifications. The first aim of this study is to investigate changes in the extent and patterns of microcalcifications before and after NAC and to correlate them with pathologic response. The second aim is to evaluate oncologic outcomes of residual microcalcifications according to pathologic response after NAC. RESULTS: There were no statistical differences in the extent, morphology, and distribution of microcalcifications according to pathologic response and subtype after NAC (all p > 0.05). With a median follow-up time of 71 months, compared to pCRw/o mic group, the hazard ratios (95% confidence intervals) for regional recurrence were 5.190 (1.160-23.190) in non-pCRw/mic group and 5.970 (1.840-19.380) in non-pCRw/o mic group. Compared to pCRw/o mic group, the hazard ratios (95% CI) for distant metastasis were 8.520 (2.130-34.090) in non-pCRw/mic group, 9.120 (2.850-29.200) in non-pCRw/o mic group. Compared to pCRw/o mic, the hazard ratio (95% CI) for distant metastasis in pCRw/mic group was 2.240 (0.230-21.500) without statistical significance (p = 0.486). CONCLUSIONS: Regardless of residual microcalcifications, patients who achieved pCR showed favorable long term outcome compared to non-pCR group.

Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification.

Breast microcalcifications are observed in 80% of mammograms, and a notable proportion can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated and error-prone process due to their diverse sizes, shapes, and subtle variations. In this study, we propose a radiomic signature that effectively differentiates between healthy tissue, benign microcalcifications, and malignant microcalcifications. Radiomic features were extracted from a proprietary dataset, composed of 380 healthy tissue, 136 benign, and 242 malignant microcalcifications ROIs. Subsequently, two distinct signatures were selected to differentiate between healthy tissue and microcalcifications (detection task) and between benign and malignant microcalcifications (classification task). Machine learning models, namely Support Vector Machine, Random Forest, and XGBoost, were employed as classifiers. The shared signature selected for both tasks was then used to train a multi-class model capable of simultaneously classifying healthy, benign, and malignant ROIs. A significant overlap was discovered between the detection and classification signatures. The performance of the models was highly promising, with XGBoost exhibiting an AUC-ROC of 0.830, 0.856, and 0.876 for healthy, benign, and malignant microcalcifications classification, respectively. The intrinsic interpretability of radiomic features, and the use of the Mean Score Decrease method for model introspection, enabled models' clinical validation. In fact, the most important features, namely GLCM Contrast, FO Minimum and FO Entropy, were compared and found important in other studies on breast cancer.

Phenotyping calcification in vascular tissues using artificial intelligence.

Vascular calcification is implicated as an important factor in major adverse cardiovascular events (MACE), including heart attack and stroke. A controversy remains over how to integrate the diverse forms of vascular calcification into clinical risk assessment tools. Even the commonly used calcium score for coronary arteries, which assumes risk scales positively with total calcification, has important inconsistencies. Fundamental studies are needed to determine how risk is influenced by the diverse calcification phenotypes. However, studies of these kinds are hindered by the lack of high-throughput, objective, and non-destructive tools for classifying calcification in imaging data sets. Here, we introduce a new classification system for phenotyping calcification along with a semi-automated, non-destructive pipeline that can distinguish these phenotypes in even atherosclerotic tissues. The pipeline includes a deep-learning-based framework for segmenting lipid pools in noisy µ-CT images and an unsupervised clustering framework for categorizing calcification based on size, clustering, and topology. This approach is illustrated for five vascular specimens, providing phenotyping for thousands of calcification particles across as many as 3200 images in less than seven hours. Average Dice Similarity Coefficients of 0.96 and 0.87 could be achieved for tissue and lipid pool, respectively, with training and validation needed on only 13 images despite the high heterogeneity in these tissues. By introducing an efficient and comprehensive approach to phenotyping calcification, this work enables large-scale studies to identify a more reliable indicator of the risk of cardiovascular events, a leading cause of global mortality and morbidity.

Mammographically detected breast clustered microcalcifications localized by chest thin-section computed tomography.

BACKGROUND: To explore the capability and clinical significance of chest thin-section computed tomography (CT) for localization of mammographically detected clustered microcalcifications. METHODS: A total of 69 patients with 71 mammographically detected clustered microcalcifications received surgical biopsy under the guidance of mammography (MG), CT was used to localize calcifications combined with MG if calcifications can be seen on CT. Intraoperative mammography of the specimens were performed in all cases for identification of the resected microcalcifications. The clinical, imaging and pathological information of these patients were analyzed. RESULTS: A total of 42 (59.15%) cases of calcifications were localized by CT + MG, 29 (40.85%) cases were guided only by the mammography. All suspicious calcifications on the mammography were successfully removed. Pathological results showed 42 cases were cancer, 23 cases were benign, and 6 cases were atypical hyperplasia. The mean age in the CT + MG group was older than that of the MG group (54.12 vs. 49.27 years; P = 0.014). The maximum diameter of clusters of microcalcifications on mammography in the CT + MG group was larger than that of the MG group [(cranio-caudal view, 1.52 vs. 0.61 mm, P = 0.000; mediolateral oblique (MLO) view, 1.53 vs. 0.62 mm, P = 0.000)]. The gray value ratio (calcified area / paraglandular; MLO, P = 0.004) and the gray value difference (calcified area - paraglandular; MLO, P = 0.005) in the CT + MG group was higher than that of the MG group. Multivariate analysis showed that the max diameter of clusters of microcalcifications (MLO view) was a significant predictive factor of localization by CT in total patients (P = 0.001). CONCLUSIONS: About half of the mammographically detected clustered microcalcifications could be localized by thin-section CT. Maximum diameter of clusters of microcalcifications (MLO view) was a predictor of visibility of calcifications by CT. Chest thin-section CT may be useful for localization of calcifications in some patients, especially for calcifications that are only visible in one view on the mammography.

Testicular biopsies in men with testicular microlithiasis and additional risk factors for cancer: A case series.

BACKGROUND: Testicular microlithiasis is the presence of small calcifications in the testicular parenchyma. The association between testicular microlithiasis and germ cell neoplasia in situ, a precursor to testicular cancer, is still unclear. OBJECTIVES: To determine the incidence of germ cell neoplasia in situ in men with testicular microlithiasis and evaluate the indication for testicular biopsy according to risk factors in the form of male infertility/reduced semen quality, testicular atrophy, and history of cryptorchidism. MATERIALS AND METHODS: This retrospective case series included all patients diagnosed with testicular microlithiasis who underwent testicular biopsies at three hospitals in Denmark between 2007 and 2021. The medical records of 167 patients were reviewed, and data on patient demographics, testicular microlithiasis characteristics, risk factors, histological findings, and treatments were collected. The main outcome measure was the incidence of germ cell neoplasia in situ in relation to each risk factor. The data were analyzed using descriptive statistics. Logistic regression was used to examine the odds ratio of germ cell neoplasia in situ in patients with testicular microlithiasis and testicular atrophy. RESULTS: Germ cell neoplasia in situ was found in 13 out of 167 patients (7.8% [95% confidence interval: 4.3, 13.2]). Eleven of these had testicular atrophy resulting in a significantly higher incidence in this group than other risk factors (odds ratio 9.36 [95% confidence interval: 2.41, 61.88]; p = 0.004). DISCUSSION: The study comprises the largest cohort to date of men who have undergone testicular biopsies because of testicular microlithiasis and additional risk factors. Limitations include its retrospective design, and relatively low absolute numbers of patients with germ cell neoplasia in situ on biopsies. CONCLUSION: This study found that men with testicular microlithiasis and testicular atrophy are at an increased risk of germ cell neoplasia in situ. Additionally, our results indicate that biopsies should be considered in men with a combination of subfertility and bilateral testicular microlithiasis. Our findings do not support testicular biopsies for men with testicular microlithiasis and other risk factors.

Network Proteins of Human Sortilin1, Its Expression and Targetability Using Lycopene.

BACKGROUND: Sortilin1 (SORT1) is a ubiquitously expressed transporter involved in sorting or clearing proteins and is pathologically linked to tissue fibrosis and calcification. Targeting SORT1 may have potential clinical efficacy in controlling or reversing cardiovascular fibrosis and/or calcification. Hence, this study assessed the protein-protein network of human SORT1 and its targetability using known nutra-/pharmaceuticals. MATERIAL AND METHODS: Network proteins of human SORT1 were identified using the String database, and the affinity of the protein-protein interaction of this network was analysed using Chimera software (Chimera-1.17.3-mac64). The tissue-specific expression profile of SORT1 was evaluated and assessed for enrichment in different cell types, including immune cells. A library of in-house small molecules and currently used therapeutics for cardiovascular diseases were screened using AutoDock Vina to assess the targetability of human SORT1. The concentration affinity (CA) ratio of the small molecules was estimated to assess the clinical feasibility of targeting SORT1. RESULTS: IGF2R, NTRK2, GRN and GGA1 were identified as high-affinity interaction networks of SORT1. Of these high-affinity interactions, IGF2R and GRN can be considered relevant networks in regulating tissue fibrosis or the microcalcification process due to their influence on T-cell activation, inflammation, wound repair, and the tissue remodelling process. The tissue cell-type enrichment indicated major expression of SORT1 in adipocytes, specialised epithelial cells, monocytes, cardiomyocytes, and thyroid glandular cells. The binding pocket analysis of human SORT1 showed twelve potential drug interaction sites with varying binding scores (0.86 to 5.83) and probability of interaction (0.004 to 0.304). Five of the drug interaction sites were observed to be targetable at the therapeutically feasible concentration of the small molecules evaluated. Empagliflozin, sitagliptin and lycopene showed a superior affinity and CA ratio compared to established inhibitors of SORT1. CONCLUSION: IGF2R and GRN are relevant networks of SORT1, regulating tissue fibrosis or the microcalcification process. SORT1 can be targeted using currently approved small-molecule therapeutics (empagliflozin and sitagliptin) or widely used nutraceuticals (lycopene), which should be evaluated in a randomised clinical trial to assess their efficacy in reducing the cardiac/vascular microcalcification process.

A Mammography-Based Radiomic Nomogram for Predicting Malignancy in Breast Suspicious Microcalcifications.

RATIONALE AND OBJECTIVES: Preoperative accurate identification of benign and malignant breast lesions is vital for patients to achieve individualized treatment. This study aimed to develop and validate a mammography-based radiomic nomogram for predicting malignant risk of breast suspicious microcalcifications (MCs). MATERIALS AND METHODS: 496 patients with histologically confirmed breast suspicious MCs were randomly divided into the training set (n = 346) and validation set (n = 150). Radiomics features was extracted from the craniocaudal and mediolateral oblique images. Least absolute shrinkage and selection operator algorithm were used to select radiomics features, then radiomics score (Rad-score) was calculated. Univariate analysis was used to identify malignant MCs-related clinical independent risk factors. Multivariate logistic regression was used to establish a clinical-radiomics model by incorporating Rad-score and clinic factors. A nomogram was developed to visualize the clinical-radiomics model. The receiver operating characteristic curve, calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. RESULTS: The Rad-score was consisted of 29 optimal radiomics features. We developed a nomogram by incorporating Rad-score, menopause status, MCs morphology and distribution, the area under the curve value of the combined model was 0.926(95% confidence interval [CI]: 0.878-0.975) for the validation set. The calibration curves and DCA indicated the combined model had favorable calibration and clinical utility. CONCLUSION: The combined model could be considered as a potential imaging marker to predict malignant risk of breast suspicious MCs.

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.

Threshold in breast compression reduction for full-field digital mammography and digital breast tomosynthesis.

INTRODUCTION: Breast compression is essential in mammography to improve image quality and reduce radiation dose. However, it can cause discomfort or even pain in women which could discourage them from attending future mammography examinations. Therefore, this study aims to explore the maximum reduction in breast compression in full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) that is achievable without impacting on image quality and dose. METHODS: Ten compression force (CF) levels (20N-110N, with 10N intervals) were assessed on Siemens MAMMOMAT Inspiration with Nuclear Associates 18-228 phantom. Imaging was carried out in craniocaudal projection using Automatic Exposure Control at 28 kVp with a Tungsten/Rhodium anode/filter combination, and at 50° sweep angle for DBT. Using ImageJ software, image quality of the acquired mammograms and central tomosynthesis slices were examined based on mass conspicuity (MC) and microcalcification conspicuity (MicroC). Entrance skin dose (ESD) and mean glandular dose (MGD) were recorded from Digital Imaging and Communication in Medicine image header. Linear regression was performed to examine the relationship between CF with ESD, MGD, MC and MicroC. Differences in image quality and radiation dose were assessed with one-way analysis of variance and Kruskal-Wallis H test. RESULTS: Significant correlations were noted between CF with ESD and MicroC for FFDM and DBT, with DBT also demonstrating associations with MGD and MC. No significant differences were observed for ESD, MGD, MC and MicroC when CF was reduced to 40N and 80N in FFDM and DBT respectively. CONCLUSION: This study demonstrated that CF can be reduced as low as 40N and 80N in FFDM and DBT respectively, without significant impact on image quality and radiation dose. IMPLICATIONS FOR PRACTICE: Reduced mammographic compression may reduce discomfort or pain in women, which may improve attendance rate in breast screening programmes. Findings from this study will provide reference for future work examining breast compression in mammography.

Microcalcification detectability in breast CT images using CNN observers.

BACKGROUND: Breast computed tomography (CT) is an emerging breast imaging modality, and ongoing developments aim to improve breast CT's ability to detect microcalcifications. To understand the effects of different parameters on microcalcification detectability, a virtual clinical trial study was conducted using hybrid images and convolutional neural network (CNN)-based model observers. Mathematically generated microcalcifications were embedded into breast CT data sets acquired at our institution, and parameters related to calcification size, calcification contrast, cluster diameter, cluster density, and image display method (i.e., single slices, slice averaging, and maximum-intensity projections) were evaluated for their influence on microcalcification detectability. PURPOSE: To investigate the individual effects and the interplay of parameters affecting microcalcification detectability in breast CT. METHODS: Spherical microcalcifications of varying diameters (0.04, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40 mm) and native intensities were computer simulated to portray the partial volume effects of the imaging system. Calcifications were mathematically embedded into 109 patient breast CT volume data sets as individual calcifications or as clusters of calcifications. Six numbers of calcifications (1, 3, 5, 7, 10, 15) distributed within six cluster diameters (1, 3, 5, 6, 8, 10 mm) were simulated to study the effect of cluster density. To study the role of image display method, 2D regions of interest (ROIs) and 3D volumes of interest (VOIs) were generated using single slice extraction, slice averaging, and maximum-intensity projection (MIP). 2D and 3D CNNs were trained on the ROIs and VOIs, and receiver operating characteristic (ROC) curve analysis was used to evaluate detection performance. The area under the ROC curve (AUC) was used as the primary performance metric. RESULTS: Detection performance decreased with increasing section thickness, and peak detection performance occurred using the native section thickness (0.2 mm) and MIP display. The MIP display method, despite using a single slice, yielded comparable performance to the native section thickness, which employed 50 slices. Reduction in slices did not sacrifice detection accuracy and provided significant computational advantages over multi-slice image volumes. Larger cluster diameters resulted in reduced overall detectability, while smaller cluster diameters led to increased detectability. Additionally, we observed that the presence of more calcifications within a cluster improved the overall detectability, while fewer calcifications decreased it. CONCLUSIONS: As breast CT is still a relatively new breast imaging modality, there is an ongoing need to identify optimal imaging protocols. This work demonstrated the utility of MIP presentation for displaying image volumes containing microcalcification clusters. It is likely that human observers may also benefit from viewing MIPs compared to individual slices. The results of this investigation begin to elucidate how model observers interact with microcalcification clusters in a 3D volume, and will be useful for future studies investigating a broader set of parameters related to breast CT.