Assessing breast arterial calcification in mammograms and its implications for atherosclerotic cardiovascular disease risk.

PURPOSE: Breast arterial calcifications (BAC) are incidentally observed on mammograms, yet their implications remain unclear. We investigated lifestyle, reproductive, and cardiovascular determinants of BAC in women undergoing mammography screening. Further, we investigated the relationship between BAC, coronary arterial calcifications (CAC) and estimated 10-year atherosclerotic cardiovascular (ASCVD) risk. METHODS: In this cross-sectional study, we obtained reproductive history and CVD risk factors from 215 women aged 18 or older who underwent mammography and cardiac computed tomographic angiography (CCTA) within a 2-year period between 2007 and 2017 at hospital. BAC was categorized as binary (present/absent) and semi-quantitatively (mild, moderate, severe). CAC was determined using the Agatston method and recorded as binary (present/absent). Adjusted odds ratios (ORs) and 95 % confidence intervals (CIs) were calculated, accounting for age as a confounding factor. ASCVD risk over a 10-year period was calculated using the Pooled Cohort Risk Equations. RESULTS: Older age, systolic and diastolic blood pressures, higher parity, and younger age at first birth (≤28 years) were significantly associated with greater odds of BAC. Women with both BAC and CAC had the highest estimated 10-year risk of ASCVD (13.30 %). Those with only BAC (8.80 %), only CAC (5.80 %), and no BAC or CAC (4.40 %) had lower estimated 10-year risks of ASCVD. No association was detected between presence of BAC and CAC. CONCLUSIONS: These findings support the hypothesis that BAC on a screening mammogram may help to identify women at potentially increased risk of future cardiovascular disease without additional cost and radiation exposure.

Breast cysticercosis - a case report.

SUMMARY: Isolated incidences of human cysticercosis have been reported world-wide, but it remains a major public health concern in endemic areas such as Mexico, Africa, South-East Asia, Eastern Europe, and South America. Cysticercosis most commonly involves the skeletal muscle, subcutaneous tissue, brain, and eyes. The breast is an uncommon site of presentation for cysticercosis. Due to its rare occurrence, breast cysticercosis is often initially mistaken for other common breast lesions such as cysts, abscess, malignant tumours and fibroadenomas. We report a case of breast cysticercosis in a young South African woman.

Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34.

A lot of underdeveloped nations particularly in Africa struggle with cancer-related, deadly diseases. Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (ResNet34) architectures to detect breast cancer. The study examined how breast cancer can be identified in breast mammography pictures using transfer learning from ResNet18 and ResNet34, and developed a demo app for radiologists using the trained models with the best validation accuracy. 1, 200 datasets of breast x-ray mammography images from the National Radiological Society's (NRS) archives were employed in the study. The dataset was categorised as implant cancer negative, implant cancer positive, cancer negative and cancer positive in order to increase the consistency of x-ray mammography images classification and produce better features. For the multi-class classification of the images, the study gave an average accuracy for binary classification of benign or malignant cancer cases of 86.7% validation accuracy for ResNet34 and 92% validation accuracy for ResNet18. A prototype web application showcasing ResNet18 performance has been created. The acquired results show how transfer learning can improve the accuracy of breast cancer detection, providing invaluable assistance to medical professionals, particularly in an African scenario.

Quantitative characterization of breast lesions and normal fibroglandular tissue using compartmentalized diffusion-weighted model: comparison of intravoxel incoherent motion and restriction spectrum imaging.

BACKGROUND: To compare the compartmentalized diffusion-weighted models, intravoxel incoherent motion (IVIM) and restriction spectrum imaging (RSI), in characterizing breast lesions and normal fibroglandular tissue. METHODS: This prospective study enrolled 152 patients with 157 histopathologically verified breast lesions (41 benign and 116 malignant). All patients underwent a full-protocol preoperative breast MRI, including a multi-b-value DWI sequence. The diffusion parameters derived from the mono-exponential model (ADC), IVIM model (Dt, Dp, f), and RSI model (C1, C2, C3, C1C2, F1, F2, F3, F1F2) were quantitatively measured and then compared among malignant lesions, benign lesions and normal fibroglandular tissues using Kruskal-Wallis test. The Mann-Whitney U-test was used for the pairwise comparisons. Diagnostic models were built by logistic regression analysis. The ROC analysis was performed using five-fold cross-validation and the mean AUC values were calculated and compared to evaluate the discriminative ability of each parameter or model. RESULTS: Almost all quantitative diffusion parameters showed significant differences in distinguishing malignant breast lesions from both benign lesions (other than C2) and normal fibroglandular tissue (all parameters) (all P < 0.0167). In terms of the comparisons of benign lesions and normal fibroglandular tissues, the parameters derived from IVIM (Dp, f) and RSI (C1, C2, C1C2, F1, F2, F3) showed significant differences (all P < 0.005). When using individual parameters, RSI-derived parameters-F1, C1C2, and C2 values yielded the highest AUCs for the comparisons of malignant vs. benign, malignant vs. normal tissue and benign vs. normal tissue (AUCs = 0.871, 0.982, and 0.863, respectively). Furthermore, the combined diagnostic model (IVIM + RSI) exhibited the highest diagnostic efficacy for the pairwise discriminations (AUCs = 0.893, 0.991, and 0.928, respectively). CONCLUSIONS: Quantitative parameters derived from the three-compartment RSI model have great promise as imaging indicators for the differential diagnosis of breast lesions compared with the bi-exponential IVIM model. Additionally, the combined model of IVIM and RSI achieves superior diagnostic performance in characterizing breast lesions.

Diagnostic performance of contrast-enhanced mammography for suspicious findings in dense breasts: A systematic review and meta-analysis.

PURPOSE: Contrast-enhanced spectral imaging (CEM) is a new mammography technique, but its diagnostic value in dense breasts is still inconclusive. We did a systematic review and meta-analysis of studies evaluating the diagnostic performance of CEM for suspicious findings in dense breasts. MATERIALS AND METHODS: The PubMed, Embase, and Cochrane Library databases were searched systematically until August 6, 2023. Prospective and retrospective studies were included to evaluate the diagnostic performance of CEM for suspicious findings in dense breasts. The QUADAS-2 tool was used to evaluate the quality and risk of bias of the included studies. STATA V.16.0 and Review Manager V.5.3 were used to meta-analyze the included studies. RESULTS: A total of 10 studies (827 patients, 958 lesions) were included. These 10 studies reported the diagnostic performance of CEM for the workup of suspicious lesions in patients with dense breasts. The summary sensitivity and summary specificity were 0.95 (95% CI, 0.92-0.97) and 0.81 (95% CI, 0.70-0.89), respectively. Enhanced lesions, circumscribed margins, and malignancy were statistically correlated. The relative malignancy OR value of the enhanced lesions was 28.11 (95% CI, 6.84-115.48). The relative malignancy OR value of circumscribed margins was 0.17 (95% CI, 0.07-0.45). CONCLUSION: CEM has high diagnostic performance in the workup of suspicious findings in dense breasts, and when lesions are enhanced and have irregular margins, they are often malignant.

Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection.

BACKGROUND: Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women. METHODS: We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI's performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A-D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories. RESULTS: Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7-77.3] vs. 67.1% [95% CI, 58.8-74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9-93.2] vs. 77.6% [95% CI, 61.7-77.9]), PPV (1.5% [95% CI, 1.2-1.9] vs. 0.5% [95% CI, 0.4-0.6]), recall rate (7.1% [95% CI, 6.9-7.2] vs. 22.5% [95% CI, 22.2-22.7]), and AUC values (0.8 [95% CI, 0.76-0.84] vs. 0.74 [95% CI, 0.7-0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category. CONCLUSIONS: AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue.

Lesion-conditioning of synthetic MRI-derived subtraction-MIPs of the breast using a latent diffusion model.

The purpose of this feasibility study is to investigate if latent diffusion models (LDMs) are capable to generate contrast enhanced (CE) MRI-derived subtraction maximum intensity projections (MIPs) of the breast, which are conditioned by lesions. We trained an LDM with n = 2832 CE-MIPs of breast MRI examinations of n = 1966 patients (median age: 50 years) acquired between the years 2015 and 2020. The LDM was subsequently conditioned with n = 756 segmented lesions from n = 407 examinations, indicating their location and BI-RADS scores. By applying the LDM, synthetic images were generated from the segmentations of an independent validation dataset. Lesions, anatomical correctness, and realistic impression of synthetic and real MIP images were further assessed in a multi-rater study with five independent raters, each evaluating n = 204 MIPs (50% real/50% synthetic images). The detection of synthetic MIPs by the raters was akin to random guessing with an AUC of 0.58. Interrater reliability of the lesion assessment was high both for real (Kendall's W = 0.77) and synthetic images (W = 0.85). A higher AUC was observed for the detection of suspicious lesions (BI-RADS ≥ 4) in synthetic MIPs (0.88 vs. 0.77; p = 0.051). Our results show that LDMs can generate lesion-conditioned MRI-derived CE subtraction MIPs of the breast, however, they also indicate that the LDM tended to generate rather typical or 'textbook representations' of lesions.

Abbreviated Breast MRI: State of the Art.

Abbreviated MRI is an umbrella term, defined as a focused MRI examination tailored to answer a single specific clinical question. For abbreviated breast MRI, this question is: "Is there evidence of breast cancer?" Abbreviated MRI of the breast makes maximum use of the fact that the kinetics of breast cancers and of benign tissue differ most in the very early postcontrast phase; therefore, abbreviated breast MRI focuses on this period. The different published approaches to abbreviated MRI include the following three subtypes: (a) short protocols, consisting of a precontrast and either a single postcontrast acquisition (first postcontrast subtracted [FAST]) or a time-resolved series of postcontrast acquisitions with lower spatial resolution (ultrafast [UF]), obtained during the early postcontrast phase immediately after contrast agent injection; (b) abridged protocols, consisting of FAST or UF acquisitions plus selected additional pulse sequences; and (c) noncontrast protocols, where diffusion-weighted imaging replaces the contrast information. Abbreviated MRI was proposed to increase tolerability of and access to breast MRI as a screening tool. But its widening application now includes follow-up after breast cancer and even diagnostic assessment. This review defines the three subtypes of abbreviated MRI, highlighting the differences between the protocols and their clinical implications and summarizing the respective evidence on diagnostic accuracy and clinical utility.

Towards precision medicine in breast imaging: A novel open mammography database with tailor-made 3D image retrieval for AI and teaching.

This project addresses the global challenge of breast cancer, particularly in low-resource settings, by creating a pioneering mammography database. Breast cancer, identified by the World Health Organization as a leading cause of cancer death among women, often faces diagnostic and treatment resource constraints in low- and middle-income countries. To enhance early diagnosis and address educational setbacks, the project focuses on leveraging artificial intelligence (AI) technologies through a comprehensive database. Developed in collaboration with Ambra Health, a cloud-based medical image management software, the database comprises 941 mammography images from 100 anonymized cases, with 62 % including 3D images. Accessible through http://mamografia.unifesp.br, the platform facilitates a simple registration process and an advanced search system based on 169 clinical and imaging variables. The website, customizable to the user's native language, ensures data security through an automatic anonymization system. By providing high-resolution, 3D digital images and supplementary clinical information, the platform aims to promote education and research in breast cancer diagnosis, representing a significant advancement in resource-constrained healthcare environments.

A multi-task fusion model based on a residual-Multi-layer perceptron network for mammographic breast cancer screening.

BACKGROUND AND OBJECTIVE: Deep learning approaches are being increasingly applied for medical computer-aided diagnosis (CAD). However, these methods generally target only specific image-processing tasks, such as lesion segmentation or benign state prediction. For the breast cancer screening task, single feature extraction models are generally used, which directly extract only those potential features from the input mammogram that are relevant to the target task. This can lead to the neglect of other important morphological features of the lesion as well as other auxiliary information from the internal breast tissue. To obtain more comprehensive and objective diagnostic results, in this study, we developed a multi-task fusion model that combines multiple specific tasks for CAD of mammograms. METHODS: We first trained a set of separate, task-specific models, including a density classification model, a mass segmentation model, and a lesion benignity-malignancy classification model, and then developed a multi-task fusion model that incorporates all of the mammographic features from these different tasks to yield comprehensive and refined prediction results for breast cancer diagnosis. RESULTS: The experimental results showed that our proposed multi-task fusion model outperformed other related state-of-the-art models in both breast cancer screening tasks in the publicly available datasets CBIS-DDSM and INbreast, achieving a competitive screening performance with area-under-the-curve scores of 0.92 and 0.95, respectively. CONCLUSIONS: Our model not only allows an overall assessment of lesion types in mammography but also provides intermediate results related to radiological features and potential cancer risk factors, indicating its potential to offer comprehensive workflow support to radiologists.

In situtumor model for longitudinal in silico imaging trials.

Objective.In this article, we introduce a computational model for simulating the growth of breast cancer lesions accounting for the stiffness of surrounding anatomical structures.Approach.In our model, ligaments are classified as the most rigid structures while the softer parts of the breast are occupied by fat and glandular tissues As a result of these variations in tissue elasticity, the rapidly proliferating tumor cells are met with differential resistance. It is found that these cells are likely to circumvent stiffer terrains such as ligaments, instead electing to proliferate preferentially within the more yielding confines of the breast's soft topography. By manipulating the interstitial tumor pressure in direct proportion to the elastic constants of the tissues surrounding the tumor, this model thus creates the potential for realizing a database of unique lesion morphology sculpted by the distinctive topography of each local anatomical infrastructure. We modeled the growth of simulated lesions within volumes extracted from fatty breast models, developed by Graffet alwith a resolution of 50µm generated with the open-source and readily available Virtual Imaging Clinical Trials for Regulatory Evaluation (VICTRE) imaging pipeline. To visualize and validate the realism of the lesion models, we leveraged the imaging component of the VICTRE pipeline, which replicates the siemens mammomat inspiration mammography system in a digital format. This system was instrumental in generating digital mammogram (DM) images for each breast model containing the simulated lesions.Results.By utilizing the DM images, we were able to effectively illustrate the imaging characteristics of the lesions as they integrated with the anatomical backgrounds. Our research also involved a reader study that compared 25 simulated DM regions of interest (ROIs) with inserted lesions from our models with DM ROIs from the DDSM dataset containing real manifestations of breast cancer. In general the simulation time for the lesions was approximately 2.5 hours, but it varied depending on the lesion's local environment.Significance.The lesion growth model will facilitate and enhance longitudinal in silico trials investigating the progression of breast cancer.

Locoregional staging of breast cancer: contrast-enhanced mammography versus breast magnetic resonance imaging.

PURPOSE: Breast cancer diagnosis often involves assessing the locoregional spread of the disease through MRI, as multicentricity, multifocality and/or bilaterality are increasingly common. Contrast-enhanced mammography (CEM) is emerging as a potential alternative method. This study compares the performance of CEM and MRI in preoperative staging of women with confirmed breast carcinoma. Patients were also asked to fill in a satisfaction questionnaire to rate their comfort level with each investigation. METHODS: From May 1st, 2021 to May 1st, 2022, we enrolled 70 women with confirmed breast carcinoma who were candidates for surgery. For pre-operative locoregional staging, all patients underwent CEM and MRI examination, which two radiologists evaluated blindly. We further investigated all suspicious locations for disease spread, identified by both CEM and MRI, with a second-look ultrasound (US) and eventual histological examination. RESULTS: In our study cohort, MRI and CEM identified 114 and 102 areas of focal contrast enhancement, respectively. A true discrepancy between MRI and CEM occurred in 9 out of 70 patients examined. MRI reported 8 additional lesions that proved to be false positives on second-look US in 6 patients, while it identified 4 lesions that were not detected by CEM and were pathological (true positives) in 3 patients. CONCLUSIONS: CEM showed results comparable to MRI in the staging of breast cancer in our study population, with a high rate of patient acceptability.

Mastitis granulomatosa no caseificante: revisión retrospectiva de 61 casos / Non-caseating granulomatous mastitis: Restrospective review of 61 cases

Objetivo: determinar las características clínicas, de diagnóstico y opciones de tratamiento de la mastitis granulomatosa no caseificante. Métodos: el presente estudio es de tipo descriptivo, transversal, retrospectivo y no experimental de 61 pacientes atendidos en el Hospital Belén de Trujillo, desde enero de 2018 hasta diciembre de 2022. Fue criterio de inclusión que tengan diagnóstico histopatológico de mastitis granulomatosa no caseificante BAAR negativo. Resultados: el 93,4% afectó a mujeres en edad reproductiva (edad promedio 33 ± 7 años). La paridad, el haber dejado de dar de lactar y el uso de anticonceptivos se asociaron en forma estadísticamente significativa con la mastitis (p ≤ 0,05). En su mayoría afectó la mama izquierda. La tumoración varió entre 3 y 11 cm con un tamaño promedio de 5,0 ± 2 cm. En el 49,2% la tumoración se acompañó de una o más fistulas. El 38,5% hizo uso de anticonceptivos hormonales. El cultivo de 25 tejidos mamarios biopsiados fue negativo. El estudio ecográfico fue categorizado en el 91,8% como BIRADS 2 y 3. El 67,2% mejoró con tratamiento antituberculoso, aun cuando no había evidencia de BAAR positivo. El 32,8% mejoró con tratamiento diverso con antibióticos y sin antibióticos. Conclusiones: este tipo de mastitis se presenta en la etapa reproductiva de la mujer, pero después del periodo de lactancia. Los anticonceptivos hormonales podrían predisponer a esta afección. Su manejo es variable, puede hacerse con antituberculosos, antibióticos comunes y en casos extremos resección quirúrgica.(AU)

Objective: To determine the clinical characteristics, diagnosis and treatment options of non-caseating granulomatous mastitis. Methods: The present study is descriptive, cross-sectional, retrospective and non-experimental of 61 patients treated at the Hospital Belen of Trujillo, from January 2018 to December 2022. The inclusion criterion was that they have a histopathological diagnosis of granulomatous mastitis without caseification Acid-Fast Bacilli negative. Results: 93.4% affected women of reproductive age (age average 33 ± 7 years). Parity, having finishing breastfeeding and the use of contraceptives were associated in a statistically significant way with matitis (p ≤ 0.05). It mostly affected the left breast. The tumor varied between 3 and 11 cm with an average size of 5 ± 2 cm. In 49.2%, the tumor was accompanied by one or more fistulas. 38.5% used hormonal contraceptives. The culture of 25 biopsied breast tissues was negative. The ultrasound study was categorized in 91.8% as BIRADS 2 and 3. 67.2% improved with anti-tuberculosis treatment even though there was no evidence of positive AFB. 32.8% improved with diverse treatment, with antibiotics and without antibiotics. Conclusions: This type of mastitis occurs in the reproductive stage of the woman, but after the lactation period. Hormonal contraceptives could predispose to this condition. Its management is variable, it can be done with anti-tuberculosis drugs, common antibiotics and in extreme cases surgical resection.(AU)

Mastitis granulomatosa: experiencia diagnóstica y terapéutica multidisciplinaria del Hospital Argerich de Buenos Aires / Granulomatous mastitis: Multidisciplinary diagnostic and therapeutic experience at Argerich Hospital in Buenos Aires

Introducción: la mastitis granulomatosa es una enfermedad inflamatoria crónica que se presenta como un tumor mamario doloroso, asociado a abscesos y fístulas, que puede simular un carcinoma. Su etiología no es clara y se postula un mecanismo autoinmune modulado por el bacilo Corynebacterium. No existen guías diagnósticas ni algoritmos terapéuticos. En nuestro estudio analizamos las presentaciones clínicas, imágenes, cultivos y hallazgos patológicos junto con los tratamientos, resultados y evolución. Material y métodos: realizamos un estudio descriptivo, retrospectivo y observacional de las pacientes con diagnóstico de mastitis granulomatosa tratadas en nuestro hospital en el periodo 2017-2022. Resultados: se evaluaron 51 pacientes con una edad media de 38,1 años; que habían tenido embarazos y lactancia, 88,2%. Clínica: tumor palpable abscedado y/o fistulizado, 74,5%; tumor palpable doloroso 13,8%; tumor asintomático, 11,7 %; eritema nodoso, 11,7%; imágenes BIRADS 4-5: 82,3%; tamaño promedio: 3,54 cm. Patología: vacuolas quísticas asociadas a Corynebacterium en 24 biopsias (47,1%); bacteriología: cultivos positivos para Corynebacterium en 13 de 47 muestras (27,6%). Tratamiento: antiobioterapia 92,1%; inmunosupresión, 78,4% y tratamiento quirúrgico 60,7 %. Tiempo medio de inmunosupresión con corticoides 7,09 meses y con metotrexato 9,27 meses. Complicaciones: 9,8%; secuelas estéticas: 39,2% tiempo libre de enfermedad: 14,04 meses. Recurrencias: 13,7 %. Conclusiones: la búsqueda bacteriológica y patológica del Corynebacterium junto a un abordaje multidisciplinario es esencial para un tratamiento a medida del paciente en pos de lograr el mayor éxito terapéutico.(AU)

Introduction: Granulomatous mastitis is a chronic inflammatory disease that presents as a painful breast mass, associated with abscesses and fistulas, which can mimic carcinoma. Etiology is still unclear, and an autoimmune response related to Corynebacterium is postulated. There are no diagnostic guidelines or therapeutic algorithms. In our study we analyzed the clinical presentations, images, cultures, and pathological findings together with the treatments, results, and evolution. Material and methods: We carried out a descriptive, retrospective, and observational study of patients diagnosed with granulomatous mastitis treated in our hospital in the period 2017-2022. Results: 51 women. Average age 38.1 years. Pregnancies and Lactation 88.2%. Clinic: Abscessed and/or fistulized palpable mass 74.5%, painful palpable mass 13.8%. Asymptomatic mass 11.7%. Erythema Nodosum 11.7% BIRADS images 4/5: 82.3%. Average size: 3.54 cm. Pathology: Cystic vacuoles associated with Corynebacterium in 24 biopsies (47.1%). Bacteriology: positive cultures for Corynebacterium in 13 of 47 samples (27.6%). Treatment: antibiotic therapy 92.1%, immunosuppression 78.4%, surgical treatment 60.7%. Mean time of immunosuppression with corticosteroids 7.09 months and with methotrexate 9.27 months. Complications: 9.8% Aesthetic sequelae: 39.2% Disease-free time: 14.04 months. Recurrences: 13.7%. Conclusions: The bacteriological and pathological search for Corynebacterium and a patient tailor made multidisciplinary approach is essential to achieve therapeutic success.(AU)

Automated Breast Volume Scanner Is More Valuable Than Hand-Held Ultrasound in Diagnosis of Small Breast cancer: An Analysis of 725 Patients With 912 Lesions Evaluations.

ABSTRACT: This study aimed to evaluate the clinical value of automated breast volume scanner (ABVS) compared with hand-held ultrasound (HHUS). From January 2015 to May 2019, a total of 912 breast lesions in 725 consecutive patients were included in this study. κ statistics were calculated to identify interobserver agreement of ABVS and HHUS. The diagnostic performance for ABVS and HHUS was expressed as the area under the receiver operating characteristic curve, as well as the corresponding 95% confidence interval, sensitivity, and specificity. The sensitivities of ABVS and HHUS were 95.95% and 93.69%, and the specificities were 85.47% and 81.20%, respectively. A difference that nearly reached statistical significance was observed in sensitivities between ABVS and HHUS (P = 0.0525). The specificity of ABVS was significantly higher than that of HHUS (P = 0.006). When lesions were classified according to their maximum diameter, the sensitivity and specificity of ABVS were significantly higher than HHUS for lesions ≤20 mm, while they made no statistical significance between ABVS and HHUS for lesions >20 mm. The interobserver agreement for ABVS was better than that of HHUS. Automated breast volume scanner was more valuable than HHUS in diagnosing breast cancer, especially for lesions ≤20 mm, and it could be a valuable diagnostic tool for breast cancer.

Validation of artificial intelligence contrast mammography in diagnosis of breast cancer: Relationship to histopathological results.

INTRODUCTION: Contrast-enhanced mammography (CEM) is used for characterization of breast lesions with increased diagnostic accuracy compared to digital mammography (DM). Artificial intelligence (AI) approaches are emerging with accuracies equal to an average radiologist. However, most studies trained deep learning (DL) models on DM images and there is a paucity in literature for discovering the application of AI using CEM. OBJECTIVES: To develop and test a DL model that classifies CEM images and produces corresponding highlights of lesions detected. METHODS: Fully annotated 2006 images of 326 females available from the previously published Categorized Digital Database for Contrast Enhanced Mammography images (CDD-CESM) were used for training. We developed a DL multiview contrast mammography model (MVCM) for classification of CEM low energy and recombined images. An external test set of 288 images of 37 females not included in the training was used for validation. Correlation with histopathological results and follow-up was considered the standard reference. The study protocol was approved by the Institutional Review Board and patient informed consent was obtained. RESULTS: Assessment was done on an external test set of 37 females (mean age, 51.31 years ± 11.07 [SD]) with AUC-ROC for AI performance 0.936; (95 % CI: 0.898, 0.973; p < 0.001) and the best cut off value for prediction of malignancy using AI score = 0.28. Findings were then correlated with histopathological results and follow up which revealed a sensitivity of 75 %, specificity 96.3 %, total accuracy of 90.1 %, positive predictive value (PPV) 87.1 %, and negative predictive value (NPV) 92 %, p-value (<0.001). Diagnostic indices of radiologists were sensitivity 88.9 %, specificity 92.6 %, total accuracy 91.7 %, PPV 80 %, and NPV 96.2 %, p-value (<0.001). CONCLUSION: A deep learning multiview CEM model was developed and evaluated in a cohort of female participants and showed promising results in detecting breast cancer. This warrants further studies, external training, and validation.

Predictive value of background parenchymal enhancement on breast magnetic resonance imaging for pathological tumor response to neoadjuvant chemotherapy in breast cancers: a systematic review.

OBJECTIVES: This review aimed to assess the predictive value of background parenchymal enhancement (BPE) on breast magnetic resonance imaging (MRI) as an imaging biomarker for pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT). METHODS: Two reviewers independently performed a systemic literature search using the PubMed, MEDLINE, and Embase databases for studies published up to 11 June 2022. Data from relevant articles were extracted to assess the relationship between BPE and pCR. RESULTS: This systematic review included 13 studies with extensive heterogeneity in population characteristics, MRI follow-up points, MRI protocol, NACT protocol, pCR definition, and BPE assessment. Baseline BPE levels were not associated with pCR, except in 1 study that reported higher baseline BPE of the younger participants (< 55 years) in the pCR group than the non-pCR group. A total of 5 studies qualitatively assessed BPE levels and indicated a correlation between reduced BPE after NACT and pCR; however, among the studies that quantitatively measured BPE, the same association was observed only in the subgroup analysis of 2 articles that assessed the status of hormone receptor and human epidermal growth factor receptor 2. In addition, the predictive ability of early BPE changes for pCR was reported in several articles and remains controversial. CONCLUSIONS: Changes in BPE may be a promising imaging biomarker for predicting pCR in breast cancer. Because current studies remain insufficient, particularly those that quantitatively measure BPE, prospective and multicenter large-sample studies are needed to confirm this relationship.