Search Results (14)

Background and Objectives: The accuracy of breast cancer diagnosis depends on the concordance between imaging features and pathological findings. While BI-RADS (Breast Imaging Reporting and Data System) provides standardized risk stratification, its correlation with histologic grade and immunohistochemical markers remains underexplored. This study assessed the diagnostic performance of BI-RADS 3, 4, and 5 classifications and their association with tumor grade and markers such as ER, PR, HER2, and Ki-67. Materials and Methods: In this prospective study, 67 women aged 33–82 years (mean 56.4) underwent both mammography and ultrasound. All lesions were biopsied using ultrasound-guided 14G core needles. Imaging characteristics (e.g., margins, echogenicity, calcifications), histopathological subtype, and immunohistochemical data were collected. Statistical methods included logistic regression, Chi-square tests, and Spearman’s correlation to assess associations between BI-RADS, histology, and immunohistochemical markers. Results: BI-RADS 5 lesions showed a 91% malignancy rate. Evaluated features included spiculated margins, pleomorphic microcalcifications, and hypoechoic masses with posterior shadowing, and were correlated with histological and immunohistochemical results. Invasive tumors typically appeared as irregular, hypoechoic masses with posterior shadowing, while mucinous carcinomas mimicked benign features. Higher BI-RADS scores correlated significantly with increased Ki-67 index (ρ = 0.76, p < 0.001). Logistic regression yielded an AUC of 0.877, with 93.8% sensitivity and 80.0% specificity. Conclusions: BI-RADS scoring effectively predicts malignancy and correlates with tumor proliferative markers. Integrating imaging, histopathology, and molecular profiling enhances diagnostic precision and supports risk-adapted clinical management in breast oncology. Full article

Mammary tumors occur in female and male guinea pigs. However, published data on their histology and sex predispositions are limited. Histologically, we examined proliferative mammary lesions of 69 female and 48 male pet guinea pigs. Lobular hyperplasia was observed only in females (n = 50). Benign tumors included simple adenomas (n = 20), adenolipomas (n = 3) and intraductal papillary adenomas (n = 5). All except two intraductal papillary adenomas occurred in females. Most malignancies were tubulopapillary and solid carcinomas (n = 54), and intraductal papillary carcinomas (n = 13). These were diagnosed more frequently in male (n = 41) than in female (n = 26) guinea pigs. The carcinomas of males had higher mitotic counts than those of females (p = 0.05). Three carcinosarcomas developed in adenolipoma, and one arose in adenoma. Results show that the mammary tumor classification of dogs and cats can be applied to guinea pigs. However, some tumors (adenolipoma, metaplastic carcinoma) are unique to guinea pigs and shared with laboratory rodents and humans, respectively. Benign tumors may undergo malignant progression. Male guinea pigs appear predisposed to ductal-associated and malignant tumors. Data suggest that male guinea pigs represent an animal model for human male breast cancer. Full article

Background and Objectives: Axillary lymph node (ALN) staging is crucial for the management of invasive breast cancer (BC). Although various radiological investigations are available, ultrasound (US) is the preferred tool for evaluating ALNs. Despite its immediacy, widespread use, and good predictive value, US is limited by intra- and inter-operator variability. This study aims to evaluate US and Elastosonography Shear Wave (SW-ES) parameters for ALN staging to develop a predictive model, named the Cassandra score (CS), to improve the interpretation of findings and standardize staging. Materials and Methods: Sixty-three women diagnosed with BC and treated at two Italian hospitals were enrolled in the study. A total of 529 lymph nodes were surgically removed, underwent intraoperative US examination, and were individually sent for a final histological analysis. The study aimed to establish a direct correlation between eight US-SWES features (margins, vascularity, roundness index (RI), loss of hilum fat, cortical thickness, shear-wave elastography hardness (SWEH), peripheral infiltration (PI), and hypoechoic appearance) and the histological outcome (benign vs. malignant). Results: Several statistical models were compared. PI was strongly correlated with malignant ALNs. An ROC analysis for Model A revealed an impressive AUC of 0.978 (S.E. = 0.007, p < 0.001), while in Model B, the cut-offs of SWEH and RI were modified to minimize the risk of false negatives (AUC of 0.973, S.E. = 0.009, p < 0.001). Model C used the same cut-offs as Model B, but excluded SWEH from the formula, to make the Cassandra model usable even if the US machine does not have SW-ES capability (AUC of 0.940, S.E. = 0.015, p < 0.001). A two-tiered model was finally set up, leveraging the strong predictive capabilities of SWEH and RI. In the first tier, only SWES and RI were evaluated: a positive result was predicted if both hardness and roundness were present (SWES > 137 kPa and RI < 1.55), and conversely, a negative result was predicted if both were absent (SWES < 137 kPa and RI > 1.55). In the second tier, if there was a mix of the results (SWES > 137 kPa and RI > 1.55 or SWES < 137 kPa and RI < 1.55), the algorithm in Model B was applied. The model demonstrated an overall prediction accuracy of 90.2% in the training set, 87.5% in the validation set, and 88.9% across the entire dataset. The NPV was notably high at 99.2% in the validation set. This model was named the Cassandra score (CS) and is proposed for the clinical management of BC patients. Conclusion: CS is a simple, non-invasive, fast, and reliable method that showed a PPV of 99.1% in the malignancy prediction of ALNs, potentially being also well suited for young sonographers. Full article

Background: Females exposed prenatally to diethylstilbestrol (DES) have an elevated risk of cervical dysplasia, breast cancer, and clear cell adenocarcinoma (CCA) of the cervix/vagina. Testicular cancer risk is increased in prenatally exposed males. Epigenetic changes may mediate the transmission of DES effects to the next (“third”) generation of offspring. Methods: Using data self-reported by third-generation females, we assessed DES in relation to the risk of cancer and benign breast and reproductive tract conditions. Using data from prenatally DES-exposed and unexposed mothers, we assessed DES in relation to cancer risk in their female and male offspring. Cancer risk was assessed by standardized incidence ratios (SIR) and 95% confidence intervals (CI); the risks of benign and malignant diagnoses were assessed by hazard ratios (HR) and 95% CI. Results: In self-reported data, DES exposure was not associated with an increased risk of overall cancer (HR 0.83; CI 0.36–1.90), breast cancer, or severe cervical dysplasia. No females reported CCA. The risk of borderline ovarian cancer appeared elevated, but the HR was imprecise (3.46; CI 0.37–32.42). Based on mothers’ reports, DES exposure did not increase the risk of overall cancer (HR 0.80; CI 0.49–1.32) or of other cancers in third-generation females. Overall cancer risk in exposed males appeared elevated (HR 1.41; CI 0.70–2.86), but the CI was wide. The risk of testicular cancer was not elevated in exposed males; no cases of prostate cancer were reported. Conclusions: To date, there is little evidence that DES is associated with cancer risk in third-generation females or males, but these individuals are relatively young, and further follow-up is needed. Full article

Breast cancer was the most diagnosed cancer around the world in 2020. Screening programs, based on mammography, aim to achieve early diagnosis which is of extreme importance when it comes to cancer. There are several flaws associated with mammography, with one of the most important being tissue overlapping that can result in both lesion masking and fake-lesion appearance. To overcome this, digital breast tomosynthesis takes images (slices) at different angles that are later reconstructed into a 3D image. Having in mind that the slices are planar images where tissue overlapping does not occur, the goal of the work done here was to develop a deep learning model that could, based on the said slices, classify lesions as benign or malignant. The developed model was based on the work done by Muduli et. al, with a slight change in the fully connected layers and in the regularization done. In total, 77 DBT volumes—39 benign and 38 malignant—were available. From each volume, nine slices were taken, one where the lesion was most visible and four above/below. To increase the quantity and the variability of the data, common data augmentation techniques (rotation, translation, mirroring) were applied to the original images three times. Therefore, 2772 images were used for training. Data augmentation techniques were then applied two more times—one set used for validation and one set used for testing. Our model achieved, on the testing set, an accuracy of 93.2% while the values of sensitivity, specificity, precision, F1-score, and Cohen’s kappa were 92%, 94%, 94%, 94%, and 0.86, respectively. Given these results, the work done here suggests that the use of single-slice DBT can compare to state-of-the-art studies and gives a hint that with more data, better augmentation techniques and the use of transfer learning might overcome the use of mammograms in this type of studies. Full article

Radiologists assess the results of mammography, the key screening tool for the detection of breast cancer, to determine the presence of malignancy. They, routinely, compare recent and prior mammographic views to identify changes between the screenings. In case a new lesion appears in a mammogram, or a region is changing rapidly, it is more likely to be suspicious, compared to a lesion that remains unchanged and it is usually benign. However, visual evaluation of mammograms is challenging even for expert radiologists. For this reason, various Computer-Aided Diagnosis (CAD) algorithms are being developed to assist in the diagnosis of abnormal breast findings using mammograms. Most of the current CAD systems do so using only the most recent mammogram. This paper provides a review of the development of methods to emulate the radiological approach and perform automatic segmentation and/or classification of breast abnormalities using sequential mammogram pairs. It begins with demonstrating the importance of utilizing prior views in mammography, through the review of studies where the performance of expert and less-trained radiologists was compared. Following, image registration techniques and their application to mammography are presented. Subsequently, studies that implemented temporal analysis or subtraction of temporally sequential mammograms are summarized. Finally, a description of the open access mammography datasets is provided. This comprehensive review can serve as a thorough introduction to the use of prior information in breast cancer CAD systems but also provides indicative directions to guide future applications. Full article

Thorough gross examination of breast cancer specimens is critical in order to sample relevant portions for subsequent microscopic examination. This task would benefit from an imaging tool which permits targeted and accurate block selection. Optical coherence tomography (OCT) is a non-destructive imaging technique that visualizes tissue architecture and has the potential to be an adjunct at the gross bench. Our objectives were: (1) to familiarize pathologists with the appearance of breast tissue entities on OCT; and (2) to evaluate the yield and quality of OCT images of unprocessed, formalin-fixed breast specimens for the purpose of learning and establishment of an OCT–histopathology library. Methods: Firstly, 175 samples from 40 formalin-fixed, unprocessed breast specimens with residual tissue after final diagnosis were imaged with OCT and then processed into histology slides. Histology findings were correlated with features on OCT. Results: Residual malignancy was seen in 30% of tissue samples. Corresponding OCT images demonstrated that tumor can be differentiated from fibrous stroma, based on features such as irregular boundary, heterogeneous texture and reduced penetration depth. Ductal carcinoma in situ can be subtle, and it is made more recognizable by the presence of comedo necrosis and calcifications. OCT features of benign and malignant breast entities were compiled in a granular but user-friendly reference tool. Conclusion: OCT images of fixed breast tissue were of sufficient quality to reproduce features of breast entities previously described in fresh tissue specimens. Our findings support the use of readily available unprocessed, fixed breast specimens for the establishment of an OCT–histopathology library. Full article

The aim of this study was to investigate the potential of a machine learning algorithm to classify breast cancer solely by the presence of soft tissue opacities in mammograms, independent of other morphological features, using a deep convolutional neural network (dCNN). Soft tissue opacities were classified based on their radiological appearance using the ACR BI-RADS atlas. We included 1744 mammograms from 438 patients to create 7242 icons by manual labeling. The icons were sorted into three categories: “no opacities” (BI-RADS 1), “probably benign opacities” (BI-RADS 2/3) and “suspicious opacities” (BI-RADS 4/5). A dCNN was trained (70% of data), validated (20%) and finally tested (10%). A sliding window approach was applied to create colored probability maps for visual impression. Diagnostic performance of the dCNN was compared to human readout by experienced radiologists on a “real-world” dataset. The accuracies of the models on the test dataset ranged between 73.8% and 89.8%. Compared to human readout, our dCNN achieved a higher specificity (100%, 95% CI: 85.4–100%; reader 1: 86.2%, 95% CI: 67.4–95.5%; reader 2: 79.3%, 95% CI: 59.7–91.3%), and the sensitivity (84.0%, 95% CI: 63.9–95.5%) was lower than that of human readers (reader 1:88.0%, 95% CI: 67.4–95.4%; reader 2:88.0%, 95% CI: 67.7–96.8%). In conclusion, a dCNN can be used for the automatic detection as well as the standardized and observer-independent classification of soft tissue opacities in mammograms independent of the presence of microcalcifications. Human decision making in accordance with the BI-RADS classification can be mimicked by artificial intelligence. Full article

The purpose of this study is to evaluate the relationship between the pathogenic/likely pathogenic mutations, US features, and histopathologic findings of breast cancer in mutation carriers compared to non-carrier patients. Methods: In this retrospective study, we identified 264 patients with breast cancer and multigene panel testing admitted to our clinic from January 2018 to December 2020. Patient data US findings, US assessment of the axilla, multigene panel tests, histopathology, and immunochemistry reports were reviewed according to the BI-RADS lexicon. Results: The study population was comprised of 40% pathogenic mutation carriers (BRCA1, BRCA2, CHEK2, ATM, PALB, TP 53, NBN, MSH, BRIP 1 genes) and 60% mutation-negative patients. The mean patient age was 43.5 years in the carrier group and 44 years in the negative group. Carrier patients developed breast cancer with benign morphology (acoustic enhancement, soft elastography appearance) compared to non-carriers (p < 0.05). A tendency towards specific US features was observed for each mutation. BRCA1 carriers were associated with BC with microlobulated margins, hyperechoic rim, and soft elastography appearance (p < 0.05). Estrogen receptor (ER)-negative tumors were associated with BRCA1, TP53, and RAD mutations, while BRCA2 and CHEK2 were associated with ER-positive tumors. Conclusions: Patients with pathogenic mutations may exhibit BC with benign US features compared to negative, non-carrier patients. BRCA1, TP53, and RAD carriers account for up to one third of the ER tumors from the carrier group. Axillary US performed worse in depicting involved lymph nodes in carrier patients, compared to negative patients. Full article

Architectural distortion is the third most suspicious appearance on a mammogram representing abnormal regions. Architectural distortion (AD) detection from mammograms is challenging due to its subtle and varying asymmetry on breast mass and small size. Automatic detection of abnormal ADs regions in mammograms using computer algorithms at initial stages could help radiologists and doctors. The architectural distortion star shapes ROIs detection, noise removal, and object location, affecting the classification performance, reducing accuracy. The computer vision-based technique automatically removes the noise and detects the location of objects from varying patterns. The current study investigated the gap to detect architectural distortion ROIs (region of interest) from mammograms using computer vision techniques. Proposed an automated computer-aided diagnostic system based on architectural distortion using computer vision and deep learning to predict breast cancer from digital mammograms. The proposed mammogram classification framework pertains to four steps such as image preprocessing, augmentation and image pixel-wise segmentation. Architectural distortion ROI’s detection, training deep learning, and machine learning networks to classify AD’s ROIs into malignant and benign classes. The proposed method has been evaluated on three databases, the PINUM, the CBIS-DDSM, and the DDSM mammogram images, using computer vision and depth-wise 2D V-net 64 convolutional neural networks and achieved 0.95, 0.97, and 0.98 accuracies, respectively. Experimental results reveal that our proposed method outperforms as compared with the ShuffelNet, MobileNet, SVM, K-NN, RF, and previous studies. Full article

Osteoporosis, characterized by reduced bone mass and increased bone fragility, is a disease prevalent in women. Likewise, breast cancer is a multifactorial disease and considered the major cause of mortality in premenopausal and postmenopausal women worldwide. Our data demonstrated the association of the MYLK gene and PTGS1 gene variants with osteoporosis and benign breast tumor risk and the impact of ovariectomy on osteoporosis in Korean women. We performed a genome-wide association study (GWAS) of women with osteoporosis and benign breast tumors. There were 60 single nucleotide polymorphisms (SNPs) and 12 SNPs in the MYLK and PTGS1 genes, associated with benign breast tumors and osteoporosis. Our study showed that women with homozygous MYLK rs12163585 major alleles had an increased risk of osteoporosis following ovariectomy compared to those with minor alleles. Women carrying the minor PTGS1 rs1213265 allele and not treated via ovariectomy carried a higher risk of osteoporosis than those who underwent ovariectomy with a homozygous genotype at the major alleles. Our results suggest that both the MYLK and PTGS1 genes are genetic factors associated with the phenotypes, and these associations appear to be modulated by ovariectomy. Full article

The aim of this paper is to highlight the role of contrast-enhanced ultrasound in breast cancer in terms of diagnosis, staging and follow-up of the post-treatment response. Contrast-enhanced ultrasound (CEUS) is successfully used to diagnose multiple pathologies and has also clinical relevance in breast cancer. CEUS has high accuracy in differentiating benign from malignant lesions by analyzing the enhancement characteristics and calculating the time-intensity curve’s quantitative parameters. It also has a significant role in axillary staging, especially when the lymph nodes are not suspicious on clinical examination and have a normal appearance on gray-scale ultrasound. The most significant clinical impact consists of predicting the response to neoadjuvant chemotherapy, which offers the possibility of adjusting the therapy by dynamically evaluating the patient. CEUS is a high-performance, feasible, non-irradiating, accessible, easy-to-implement imaging method and has proven to be a valuable addition to breast ultrasound. Full article

(1) Background: Bone metastasis is among diseases that frequently appear in breast, lung and prostate cancer; the most popular imaging method of screening in metastasis is bone scintigraphy and presents very high sensitivity (95%). In the context of image recognition, this work investigates convolutional neural networks (CNNs), which are an efficient type of deep neural networks, to sort out the diagnosis problem of bone metastasis on prostate cancer patients; (2) Methods: As a deep learning model, CNN is able to extract the feature of an image and use this feature to classify images. It is widely applied in medical image classification. This study is devoted to developing a robust CNN model that efficiently and fast classifies bone scintigraphy images of patients suffering from prostate cancer, by determining whether or not they develop metastasis of prostate cancer. The retrospective study included 778 sequential male patients who underwent whole-body bone scans. A nuclear medicine physician classified all the cases into three categories: (a) benign, (b) malignant and (c) degenerative, which were used as gold standard; (3) Results: An efficient and fast CNN architecture was built, based on CNN exploration performance, using whole body scintigraphy images for bone metastasis diagnosis, achieving a high prediction accuracy. The results showed that the method is sufficiently precise when it comes to differentiate a bone metastasis case from other either degenerative changes or normal tissue cases (overall classification accuracy = 91.61% ± 2.46%). The accuracy of prostate patient cases identification regarding normal, malignant and degenerative changes was 91.3%, 94.7% and 88.6%, respectively. To strengthen the outcomes of this study the authors further compared the best performing CNN method to other popular CNN architectures for medical imaging, like ResNet50, VGG16, GoogleNet and MobileNet, as clearly reported in the literature; and (4) Conclusions: The remarkable outcome of this study is the ability of the method for an easier and more precise interpretation of whole-body images, with effects on the diagnosis accuracy and decision making on the treatment to be applied. Full article

Both benign and malignant conditions related to regional or systemic disorders could be included in the differential diagnosis of bilateral breast edema. Some of them are often unilateral, including stromal infiltration and lymphatic obstruction presented in “peau d’ orange”, which is the usual presentation of breast cancer. However, the term “idiopathic” could be included in the spectrum of diagnoses. Here, we present a woman of 78 years old who came into our breast unit with a bilateral, painless edema of the breasts (appeared one month ago). Clinical examination revealed that both breasts were swollen with widespread erythema and the appearance of an orange peel/“peau d’ orange”. On palpation, the breasts were not sensitive, and no tumor was palpable. However, clinically palpable lymph nodes were found in both axillas. Her temperature was normal. The breast edema could not be explained from her medical history nor the medications taken. Breast ultrasound, Mammography and Magnetic Resonance Imaging were non-conclusive (BI-RADS 0) and bilateral core biopsy was negative for cancer. Anti-inflammatory plus antibiotic therapy was prescribed for 10 days and at the end of treatment, regional redness and edema were disappeared and reduced, respectively. Total recovery was found one month after the initial findings. It can be concluded that bilateral breast edema is correlated to regional or systemic conditions or it is presented as an “idiopathic” disorder of unknown etiology. Full article