Breast Cancer Lesions Research Articles

BRCANet: A deep hybrid network in predicting BRCA1/2 gene mutation of breast cancer with dynamic contrast-enhanced breast MRI.

e13576 Background: BRCA1/BRCA2, representing as an important genetic biomarker of breast cancer (BCA), can provide clinically significant implications for personalized risk assessment, effective treatment option, and prognostic prediction. Methods: We here proposed the BRCANet, a novel end-to-end convolutional neural network for noninvasively determining BRCA1/BRCA2 mutation by integrating clinical, radiomics and deep learning of dynamic contrast-enhanced (DCE) MRI. BRCANet accepts different forms of medical data including clinicopathologic identifications, high throughput radiomics and deep imaging features of breast MRI using a deep hybrid neural network for data/feature integration. Model training and cross-validation was performed in 132 case-controlled BCA patients from two in two tertiary care hospitals, in which clinicopathologic, genomic and image data of BCA lesions were available and center-standardized for study analysis. Results: Results show that a BRCANet-Plus model, embedded with clinicopathologic, radiomics and deep MRI features achieves an arear under curve of (0.783; 95% confidence intervals [CIs], 0.704 - 0.848) for predicting BRCA1/2 mutation, outperforming the compared state-of-the-art methods, i.e., BRCANet derived from image-only data (0.743; 95% CIs, 0.659 - 0.815; p = 0.037), and BRCARad derived from radiomics-only data (0.734; 95% CIs, 0.649-0.807; p = 0.031). After net benefit evaluation, the proposed BRCANet-Plus shows promise to improve diagnostic performance against conventional clinical or image approaches. Conclusions: Therefore, we concluded the presented deep hybrid approach by integrating multimodal clinical-imaging data, especially breast MRI, have a great potential to predict BRCA1/2 mutational status of BCA. This proof-of-concept strategy can be utilized for studying similar clinical questions.

Analysis of Gut Microbiota in Patients with Breast Cancer and Benign Breast Lesions.

Breast cancer (BC) and benign breast lesions (BBLs) are common diseases in women worldwide. The gut microbiota plays a vital role in regulating breast diseases’ formation, progression, and therapy response. Hence, we explored the structure and function of gut microflora in patients with BC and BBLs. A cohort of 66 subjects was enrolled in the study. Twenty-six subjects had BC, 20 subjects had BBLs, and 20 matched healthy controls. High throughput 16S ribosomal RNA (16S rRNA) gene sequencing technology was used to determine the microbial community structure. Compared with healthy individuals, BC patients had significantly lower alpha diversity indices (Sobs index, p = 0.019; Chao1 index, p = 0.033). Sobs and Chao1 indices were also lower in patients with BBLs than healthy individuals, without statistical significance (p = 0.279, p = 0.314, respectively). Both unweighted and weighted UniFrac analysis showed that beta diversity differed significantly among the three groups (p = 3.376e–14, p < 0.001, respectively). Compared with healthy individuals, the levels of Porphyromonas and Peptoniphilus were higher in BC patients (p = 0.004, p = 0.007, respectively), whereas Escherichia and Lactobacillus were more enriched in the benign breast lesion group (p < 0.001, p = 0.011, respectively). Our study indicates that patients with BC and BBLs may undergo significant changes in intestinal microbiota. These findings can help elucidate the role of intestinal flora in BC and BBLs patients.

Biogenic amine sensing and cancer detection in human urine based on a light-activated photocatalytic and pH-controlled probe

Development of novel biomarker and achievement of simple, sensitive, rapid and economical detection of cancer in clinic have become more urgent and indispensable. In this paper, a light-activated sensor, PMI-B, was developed. The selective sensing of BAs both in solution and in the PA membrane was achieved, especially for spermine. PMI-B exhibited selective sensing of BAs by a synergistic mechanism based on an autoacting photocatalytic mechanism and a pH-controlled deprotonation mechanism. Furthermore, the detection of cancer based on PMI-B with twenty-six human urine samples was investigated by UV–vis and fluorescence spectra. The ratio differences of A741/A561 and A741/A491 can effectively be used to classify breast cancer and other female cancer and lesion patients. Compared with the established tumor markers, the specificity of the A741/A516 value (the raw median value = 0.27) for PMI-B with various human urine samples was a potential malignant tumor marker for breast cancer patients.

Abstract A033: Identification of cancer-specific alterations in expression of nuclear matrix proteins through proteomic and RNAsequencing analyses of ductal carcinoma in situ

Abstract Background and Objectives: In 2021 breast cancer (BC) was the most common cancer affecting women and the second deadliest cancer in the U.S. Ductal Carcinoma In Situ (DCIS) is the earliest identifiable pre-invasive breast cancer lesion. Approximately 20-25% of diagnosed breast cancer cases are ductal carcinoma in situ (DCIS). Estimates show that 14 to 50% of DCIS cases progress to invasive breast cancer. We used a novel tissue-engineering system established in the Latimer laboratory to generate model systems representative of different molecular and histological subtypes of all stages of BC, including DCIS. Using a mass spectrometry (MS)-based relative quantitative methodology, we analyzed one novel DCIS cell line, the matching isogenic contralateral cell line and the non-tumor adjacent cell line, as well as two non-diseased breast reduction mammoplasty cell lines and the established stage IV BC cell lines MCF7 and MDA MB231. RNA sequencing analysis included additional cell lines from stages I, II and III. In order to limit the number of proteins for proteomic assessment, nuclear matrix proteins (NMPs) were targeted for analysis. NMPs have previously been linked with cancers of the prostate, colon, and breast. Maldi-TOF/TOF-MS analysis identified 270 NMPs with high confidence. Our objective was to identify NMPs with specifically altered expression in DCIS and later stages of BC compared to non-diseased breast and contralateral breast. Such markers may help to accurately identify invasive DCIS and improve early detection of BC. Results: We identified two nuclear matrix proteins that are significantly downregulated in DCIS and all other stages of BC in our collection. This downregulation was also apparent through mRNA expression analysis. These proteins are upregulated in non-diseased breast reduction tissue and contra-lateral DCIS derived from the same patient. We also identified four NMPs that were significantly upregulated in DCIS and all other BC stages compared to non-diseased breast reduction tissue and contralateral DCIS. Gene expression analyses showed that these four genes were upregulated with BC progression. Finally, we identified nine NMPS with significant differences in expression between DCIS and all stages of BC versus non-diseased breast reduction tissue and contralateral DCIS where gene expression did not follow the same trend as proteomic analysis. Conclusion: Our findings suggest that the changes that occur in the nuclear matrix of DCIS vs. normal breast epithelium could provide a basis for the development of prognostic biomarkers for DCIS and allow for targeted prevention of progression to invasive disease. This work was supported by grant W81XWH-0701-0660 from the DOD CDMRP BC program. Citation Format: Ali Almutairy, Abdullah Alhamed, Stephen G. Grant, Miranda Sarachine Falso, Billy W. Day, Jean J. Latimer. Identification of cancer-specific alterations in expression of nuclear matrix proteins through proteomic and RNAsequencing analyses of ductal carcinoma in situ [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A033.

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. Results: 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. Conclusions: 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.

143P Demographics of breast cancer and non-malignant breast lesions in Western India: A study of 8000 breast pathologies

Breast cancer accounted for the highest percentage amongst all new cases of cancer in 2020 (11.7%). Different pathologic subtypes have varied survival rates. Benign breast diseases have a reported incidence of 76% in a series in southern India. But in western India, dearth of proper data makes it difficult to predict the distribution. This study intended at creating data on epidemiological distribution of breast cancer subtypes and benign breast lesions in eastern Rajasthan, India.

Clinical significance of pathologically detected lesions in reduction mammoplasty

Background/Aim: Breast reduction surgery is performed for various reasons, especially macromastia. Although the mammoplasty material obtained after surgery is generally accepted as normal breast tissue, incidental breast cancer or precursor lesions of breast cancer are also observed in these materials. Detection of these lesions will provide important information both for the risk developing breast cancer in the normal population and for the early treatment of these lesions. Our study aimed to investigate the rate of occurrence of high-risk lesions, such as atypical ductal hyperplasia (ADH), atypical lobular hyperplasia (ALH), and carcinoma in situ (DCIS, LCIS) and the relationship of these lesions with patient age and weight of the resected material. Methods: This study was a retrospective cohort study examining incidentally detected pathological lesions after examining reduction mammoplasty materials after breast reduction in our pathology department between 2011 and 2021. None of the cases had been previously diagnosed with breast cancer. The cases were pathologically classified as benign lesions, high-risk lesions with atypia, and in situ carcinoma. The association of atypical high-risk lesions with age (> 40 and ≤ 40 years) and resection weight was evaluated. Results: The surgical materials of 288 breasts were evaluated in a total of 144 women. The mean age of the patients was 38.9 years. Atypical high-risk lesions, such as ADH, ALH, and DCIS were detected in seven patients (4.8%). İnvasive cancer was not observed. The mean age of the patients with risky lesions was 45.98 years, and the mean weight of these lesions was 2,045 grams. Cases with high-risk lesions were older (P = 0.041) and had a higher resection weight (P = 0.003). Conclusion: The findings in our study reveal the necessity for mandatory histopathological examination of reduction mammoplasty materials. This examination is even more important in terms of high-risk lesion detection, especially in patients over 40 years and those with a high resection weight. In these cases, taking more tissue samples than used for the other situation for examination is recommended.

Human Papillomavirus, Cytomegalovirus Infection and P16 Staining in Breast Tumors from Peruvian Women.

Objective:To evaluate the frequency distribution of viral infections in Peruvian Breast Cancer (BC) lesions and its association with clinicopathological features. Additionally, a prospective evaluation of p16 and Tumor-infiltrating lymphocytes (TIL) levels were performed for developing a comprehensive analysis.Methods:Detection of high risk- human papillomavirus (HR- HPV) through qPCR was performed in 447 BC and 79 non-cancer frozen samples. Paired paraffin samples from 238 BC were stained with Human cytomegalovirus (HCMV) and p16 immunohistochemistry. TIL was calculated in 397 BC cases. Results:HCMV was positive in 72.5%. HR- HPV was detected in 2.9% of BC and 1.3% of non-malignant samples. P16+ was found in 28.15% and median TIL percentage was 30. HR- HPV infection was associated with non-ductal histology (p=0.003) and p16+ (p=0.017). Positive P16+ was associated with higher T stage (p=0.022), grade (p=0.009), TIL level (p=0.002), and triple-negative phenotype (p=0.021). Conclusion:HCMV is frequent, but HR- HPV infection is unusual in Peruvian BC. P16+ is associated with HR- PVH infection, high TIL and aggressive features.

Joint Dense Residual and Recurrent Attention Network for DCE-MRI Breast Tumor Segmentation.

Breast cancer detection largely relies on imaging characteristics and the ability of clinicians to easily and quickly identify potential lesions. Magnetic resonance imaging (MRI) of breast tumors has recently shown great promise for enabling the automatic identification of breast tumors. Nevertheless, state-of-the-art MRI-based algorithms utilizing deep learning techniques are still limited in their ability to accurately separate tumor and healthy tissue. Therefore, in the current work, we propose an automatic and accurate two-stage U-Net-based segmentation framework for breast tumor detection using dynamic contrast-enhanced MRI (DCE-MRI). This framework was evaluated using T2-weighted MRI data from 160 breast tumor cases, and its performance was compared with that of the standard U-Net model. In the first stage of the proposed framework, a refined U-Net model was utilized to automatically delineate a breast region of interest (ROI) from the surrounding healthy tissue. Importantly, this automatic segmentation step reduced the impact of the background chest tissue on breast tumors' identification. For the second stage, we employed an improved U-Net model that combined a dense residual module based on dilated convolution with a recurrent attention module. This model was used to accurately and automatically segment the tumor tissue from healthy tissue in the breast ROI derived in the previous step. Overall, compared to the U-Net model, the proposed technique exhibited increases in the Dice similarity coefficient, Jaccard similarity, positive predictive value, sensitivity, and Hausdorff distance of 3%, 3%, 3%, 2%, and 16.2, respectively. The proposed model may in the future aid in the clinical diagnosis of breast cancer lesions and help guide individualized patient treatment.

Evaluation of Strain and Shear Wave Ultrasound Elastography in Predicting Malignancy in BI-RADS Categories 4A and 4B Breast Lesions

Objective: To evaluate elasticity (E) and optimal cut-off values in differentiating benign and malignant tumors in patients with breast imaging–reporting and data system (BI-RADS) 4A/4B lesions.Material and Methods: Patients aged above 18 years with BI-RADS 4A/4B lesions who had tissue biopsies November 2019 - January 2021 were enrolled. B-mode ultrasound BI-RADS classification, E/B distance ratio, and mean elasticity value in kPa and shear wave velocity (SWV) in m/s were recorded pre-biopsy.Results: A total of 148 lesions in 141 patients were included, 129 benign (87.2%), 3 high-risk (2.0%), and 16 malignant (10.8 %). Patients with breast cancer or high-risk lesions were approximately ten years older than those with benign lesions (58.9±10.5 years vs 48±10.6 years of age) (p-value&lt;0.001). The elasticity values were significantly different between the benign and malignant groups with median E/B distance ratios 0.9 and 1.2 (p-value&lt;0.001), median kPameans 4.2 and 7.2, and SWVmeans 1.2 m/s and 1.6 m/s, respectively. Setting SWVmean &gt;1.28 m/s as a cut-off value showed the highest sensitivity (68.0%) among the three single elasticity values, and kPamean &gt;7 the highest specificity (82.0%). The negative predictive value was very high, 91.0-93.0%, for all methods. When using a combined E/B ratio &gt;1 and SWVmean &gt;1.28 as a cut-off value, specificity increased to 93.0% with a high NPV but lower sensitivity.Conclusion: Using a combined E/B ratio &gt;1 and SWVmean &gt;1.28 as a cut-off value can help to differentiate between benign and malignant BI-RADS 4A and 4B lesions and avoid unnecessary biopsies.

Breast-conserving surgery and sentinel lymph node biopsy for breast cancer and their correlation with the expression of polyligand proteoglycan-1.

BACKGROUNDBreast cancer is a malignant tumor with an unclear etiology and is the most common malignant tumor in women. Surgery is the main clinical treatment for breast cancer. Although traditional total mastectomy combined with axillary lymph node dissection is effective, it can result in shoulder dysfunction, especially in middle-aged and elderly patients with breast cancer with weak constitution and other underlying diseases. Furthermore, the postoperative quality of life is poor.AIMTo assess breast-conserving surgery and sentinel lymph node biopsy for breast cancer treatment and their correlation with polyligand proteoglycan-1. METHODSOverall, 80 patients with breast cancer treated in our hospital from January 2021 to July 2021 were retrospectively selected and divided into an observation group (n = 44) and control group (n = 36) according to the treatment plan. The observation group was treated with breast-conserving surgery and sentinel lymph node biopsy, and the control group was treated with total breast resection. Simultaneously, immunohistochemical staining was used to detect the expression of syndecan-1 (SDC-1) in the lesions, and its relationship with clinicopathological findings was analyzed. RESULTSIntraoperative blood loss, operation time, and hospital stay in the observation group were 65.51 ± 9.94 mL, 65.59 ± 9.40 min, and 14.80 ± 3.03 d, respectively, which were significantly lower than those in the control group (P < 0.05). The incidence of postoperative complications in the observation group was 11.36%, which was significantly lower than that in the control group (P < 0.05). The positive expression rate of SDC-1 in the observation group was 25.00%, and there was no significant difference between the groups (P > 0.05). The positive expression rate of SDC-1 in patients with American Joint Committee on Cancer (AJCC) stage II was 14.29%, which was significantly lower than that in patients with AJCC stage I (P < 0.05). The positive expression of SDC-1 had no significant relationship with age, course of disease, site, tissue type, and treatment plan (P > 0.05).CONCLUSIONBreast preservation surgery and sentinel lymph node biopsy for breast cancer treatment have fewer complications and quicker recovery than those treated with total breast resection. Low SDC-1 expression in breast cancer lesions is related to AJCC staging.

Relationship between the changes of positivity rate of HER2 expression and the diameter of invasive lesions in early breast cancer and its clinical significance

BackgroundThe expression status of HER2 is an important factor in evaluating the prognosis of breast cancer. We found that the positivity rate of HER2 varied with the diameter of invasive lesions in early breast cancer.We aimed to explore the relationship between the change of HER2 positivity rate of early breast cancer and the diameter of the invasive foci and its clinical significance. MethodsA total of 217 patients with microinvasive breast cancer and T1a stage breast cancer were enrolled in this study. Machine learning algorithm was used to extract morphological features of invasive lesions in early breast cancer. Using Spearman to analysis the clinicopathological and morphological features related to HER2 expression McNemar test was used to analyze the consistency of HER2 expression between the carcinoma in situ area and the invasive cancer area. ResultsIn early breast cancer, the diameter of the invasive foci was strongly negatively correlated with the expression status of HER2 (rho=−0.468, p < 0.001). As the diameter of the invasive foci increases, the HER2 positivity rate gradually decreases. When the diameter of the invasive foci> 2 mm, the positivity rate of HER2 was significantly reduced (from 52.6% to 16.1%, p < 0.001), which was close to the positivity rate of HER2 in ordinary invasive breast cancer. Moreover, most of the clinicopathological characteristics of breast cancer with an invasive lesion diameter of 1–2 mm and DCIS-MI were not significantly different (p > 0.05). Among 217 patients, the consistency rate of HER2 expression in carcinoma in situ and invasive foci areas was 97.7% (212/217), and there was no significant difference in HER2 expression status (p = 0.25 and p = 0.50, respectively). ConclusionsWe recommend that breast cancer with an invasive lesion diameter of 1–2 mm should be classified as microinvasive breast cancer. In early breast cancer,the expression status of HER2 in the invasive foci area can refer to the HER2 expression status in carcinoma in situ area. However, it needs further support from a large amount of data and follow-up results.

Consistency Analysis of CTLM Imaging and Mammography in the Diagnosis of Breast Tumor Lesions.

To analyze the consistency of preoperative CTLM imaging in the diagnosis of breast cancer lesions and postoperative pathological examination. The clinical data of 225 patients with breast tumor in our breast surgery department were collected. All patients underwent mammography, CTLM, and pathological examination. To analyze the image characteristics of breast CTLM imaging, calculate the diagnostic efficacy of CTLM imaging for breast tumors, and compare the image characteristics of CTLM imaging for benign and malignant tumors. (1) Postoperative pathological examination showed that 136 cases (60.44%) of lesions were benign tumors, and 89 cases (39.56%) were malignant tumors. (2) The "spokes distribution" of normal breast CTLM images was interrupted. In the 3D reconstructed images, the morphology of the abnormal angiogenesis area is mostly irregular nonbanded structure, which is manifested as slab structure, spindle structure, spherical structure, diverticulum structure, inverted conical structure, rings structure, branched structure, and dumbbell structure. (3) The detection rate of breast tumor by CTLM imaging was 84.44%. The specificity and coincidence rate of CTLM imaging were higher than that of mammography (P < 0.05). (4) The features of CTLM imaging images of breast malignant tumors are mostly bright white locally, with irregular edges and obvious attenuation of laser signal, and the reconstructed shape of 3D images is mostly like a slab structure. CTLM imaging can provide related information of neovascularization in breast cancer lesions, which is basically consistent with pathologically confirmed lesions.

A Review and Computational Analysis of Breast Cancer Using Different Machine Learning Techniques

Breast cancer is found to be prime cause of women death in the current era. It is mainly due to the late detection of the disease. Artificial intelligence and machine learning plays an important role in breast cancer image classification and lesion detection. This paper focuses on the study and analysis of breast image classification, feature extraction and feature selection. It also covers the role of Convolution Neural Network (CNN) architecture with the impact of transfer learning. Furthermore, this survey also discusses different database repositories which are publicly available for breast cancer with their features. Overall, this study and analysis helps the researcher working in this area for the further investigation. Keywords— Breast cancer, Artificial intelligence, Machine learning, CNN

Is 9-G DBT-Guided Vacuum-Assisted Breast Biopsy Sufficient to Completely Remove T1 Breast Cancers (below 20 mm)? Analysis of 146 Patients with Histology as Reference Standard

Objectives: Vacuum-assisted breast biopsy (VABB) is a safe procedure comparable to surgical biopsy for the characterization of distortions, microcalcifications, and mass lesions. Vacuum-assisted excision of T1 breast tumors could be on potential management in alternative to surgery. The primary objective of this work was to assess the therapeutic success of the stereotaxic vacuum breast biopsy in small breast cancer (T1N0M0) lesions excision. Methods: From our electronic database, all the vacuum breast biopsies performed from January 1, 2015, to December 1, 2019, have been retrospectively reevaluated. N = 2,200 cases were identified and n = 145 ensured “mammographic complete removal” at the end of vacuum-assisted excision treatment and were considered for analysis. Surgical gold standard was used. Results: N = 143 procedures were successfully completed with complete removal of mammographic calcifications. The mean size of the lesions completely excised with VABB was 8.9 ± 3.6 mm (range, 3–23 mm). Lesions below 10 mm were n = 118 and lesion with diameter >10 mm were n = 28. N = 3/146 cases (4.4%), relapses were observed in follow-up (at 12–24 up to a maximum of 60 months): the mean size of relapsed lesions completely excised was 3.6 ± 2.1 mm (range, 2–6 mm). No relapse before 12 months were observed. The mean size of the lesions in these patients with relapse at the time of the first VABB procedures was 13 ± 6.5 mm (range, 7–12 mm). N = 117/118 (99%) lesions excised using VABB without relapse after 1 year of follow-up had a diameter below 10 mm. Conclusions: Vacuum breast biopsy could safely remove small breast cancers (T1N0M0) with few relapses.

Pre-operative Localization of Non-Palpable Lesions in Breast Cancer by Charcoal Suspension

Pre-operative Localization of Non-Palpable Lesions in Breast Cancer by Charcoal Suspension

Cancer yield and imaging features of probably benign calcifications at digital magnification view.

To investigate the malignancy rate of probably benign calcifications assessed by digital magnification view and imaging and clinical features associated with malignancy. This retrospective study included consecutive women with digital magnification views assessed as probably benign for calcifications without other associated mammographic findings from March 2009 to January 2014. Initial studies rendering a probably benign assessment were analyzed, with biopsy or 4-year imaging follow-up. Fisher's exact test and univariable logistic regression were performed. Cancer yields were calculated. A total of 458 lesions in 422 patients were finally included. The overall cancer yield was 2.2% (10 of 458, invasive cancer [n = 4] and DCIS [n = 6]). Calcification distribution (OR = 23.80, p = .041), calcification morphology (OR = 10.84, p = .005), increased calcifications (OR = 29.40, p = .001), and having a concurrent newly diagnosed breast cancer or high-risk lesion (OR = 10.24, p = .001) were associated with malignancy. Cancer yields did not significantly differ between grouped punctate calcifications vs. calcifications with other features (1.2% [2 of 162] vs. 2.7% [8 of 296], p = .506). The cancer yield was 1.6% (7 of 437) in women without newly diagnosed breast cancer or high-risk lesions. The cancer yield of probably benign calcifications assessed by digital magnification view was below the 2% threshold for grouped punctate calcifications and for women without newly diagnosed breast cancer or high-risk lesions. Calcification distribution, morphology, increase in calcifications, and the presence of newly diagnosed breast cancer/high-risk lesion were associated with malignancy. • Among 458 probably benign calcifications assessed by digital magnification view, the overall cancer yield was 2.2% (10 of 458). • The cancer yield was below the 2% threshold for grouped punctate calcifications (1.2%, 2 of 162) and in women without newly diagnosed breast cancer or high-risk lesions (1.6%, 7 of 437). • Calcification distribution, morphology, increase in calcifications, and the presence of newly diagnosed breast cancer/high-risk lesion were associated with malignancy (all p < .05).

Contrast Media-Enhanced Breast Computed Tomography With a Photon-Counting Detector: Initial Experiences on In Vivo Image Quality and Correlation to Histology.

The aim of this study was to investigate the feasibility, the image quality, and the correlation with histology of dedicated spiral breast computed tomography (B-CT) equipped with a photon-counting detector in patients with suspicious breast lesions after application of iodinated contrast media. The local ethics committee approved this prospective study. Twelve women with suspicious breast lesions found in mammography or B-CT underwent contrast-enhanced spiral B-CT and supplementary ultrasound. For all lesions, biopsy-proven diagnosis and histological workup after surgical resection were obtained including the size of cancer/ductal carcinoma in situ, which were correlated to sizes measured in B-CT. Signal-to-noise ratio and contrast-to-noise ratio were evaluated for tumor, glandular tissue, and fatty tissue. Of the 12 patients, 15 suspicious lesions were found, 14 were malignant, and 1 benign lesion corresponded to a chronic inflammation. All lesions showed strong contrast media uptake with a signal-to-noise ratio of 119.7 ± 52.5 with a contrast-to-noise ratio between glandular tissue and breast cancer lesion of 12.6 ± 5.9. The correlation of the size of invasive tumors measured in B-CT compared with histological size was significant and strong R = 0.77 ( P < 0.05), whereas the correlation with the size of the peritumoral ductal carcinoma in situ was not significant R = 0.80 ( P = 0.11). Contrast-enhanced B-CT shows high contrast between breast cancer and surrounding glandular tissue; therefore, it is a promising technique for cancer detection and staging depicting both soft tissue lesions and microcalcifications, which might be a substantial advantage over breast MRI.

Indocyanine Green Fluorescence-Guided Lumpectomy of Nonpalpable Breast Cancer Versus Ultrasound-Guided Excision.

The purpose of this study was to evaluate the use of indocyanine green-guided nonpalpable breast cancer lesion localization (INBCL) and to compare it with ultrasound localization (US). We retrospectively reviewed 78 consecutive patients undergoing breast-conserving surgery for nonpalpable breast cancer (NBC). Of all 78 excisions, 42 cases were guided by INBCL and 36 by US. The rate of clear margins was 90.5% (38/42) in the INBCL group compared to the 83.3% (30/36) in the US (P = .548). A comparison of the margins at first excision for both INBCL and US, in the INBCL series, 92.9% (39/42) of cases had a margin less than 5mm, whereas for US series, it was 72.2% (26/36) (P = .033). When results of the excised tissue are taken into account, the mean specimen volume for INBCL was 58cm3, but for US it was 73cm3, with difference in mean volume being 15cm3 (P = .062). INBCL for NBCs was more accurate than US because a smaller volume of the tissue may be excised by using the technique, without compromising margin status in nonpalpable lesions.

Assessment of Suspected Breast Lesions in Early-Stage Triple-Negative Breast Cancer during Follow-Up after Breast-Conserving Surgery Using Multiparametric MRI.

Background The local recurrence rate of triple-negative breast cancer (TNBC) can be as high as 12%.The standard treatment for early-stage TNBC is breast-conserving surgery (BCS), followed by postoperative radiotherapy with or without chemotherapy. However, detection of the local recurrence of the disease after radiotherapy is a major issue. Objective The aim of this study was at investigating the role of dynamic and functional magnetic resonance imaging (MRI) during follow-up after BCS and radiotherapy with/without chemotherapy to differentiate between locoregional recurrence and postoperative fibrosis. Patients and Methods. This prospective study was conducted at the oncology, radiology, and pathology departments, Tanta University. It involved 50 patients with early-stage TNBC who were treated with BCS, followed by radiotherapy with/without chemotherapy. The suspected lesions were evaluated during the follow-up period by sonomammography. All patients were subjected to MRI, including conventional sequences, diffusion-weighted imaging (DWI), and dynamic postcontrast study. Results Ten cases were confirmed as recurrent malignant lesions. After contrast administration, they all exhibited irregular T1 hypodense lesions of variable morphology with diffusion restriction and positive enhancement. Eight cases displayed a type III curve, while two showed a type II curve. Histopathological assessment was consistent with the MRI findings in all eight cases. The combination of the data produced by DWI-MRI and dynamic contrast-enhanced (DCE) MRI resulted in 100%sensitivity, 92.5% specificity, 90.9% positive predictive value, 100% negative predictive value, and 98% accuracy. Conclusion Combination of DWI-MRI and DCE-MRI could have high diagnostic value for evaluating postoperative changes in patients with TNBC after BCS, followed by radiotherapy with/without chemotherapy. Trial Registrations. No trial to be registered.