Protocol for evaluating the fitness for purpose of an artificial intelligence product for radiology reporting in the BreastScreen New South Wales breast cancer screening programme.

INTRODUCTION: Radiologist shortages threaten the sustainability of breast cancer screening programmes. Artificial intelligence (AI) products that can interpret mammograms could mitigate this risk. While previous studies have suggested this technology has accuracy comparable to radiologists most have been limited by using 'enriched' datasets and/or not considering the interaction between the algorithm and human readers. This study will address these limitations by comparing the accuracy of a workflow using AI alongside radiologists on a large consecutive cohort of examinations from a breast cancer screening programme. The study will combine the strengths of a large retrospective design with the benefit of prospective data collection. It will test this technology without risk to screening programme participants nor the need to wait for follow-up data. With a sample of 2 years of consecutive screening examinations, it is likely the largest test of this technology to date. The study will help determine whether this technology can safely be introduced into the BreastScreen New South Wales (NSW) population-based screening programme to address radiology workforce risks without compromising cancer detection rates or increasing false-positive recalls. METHODS AND ANALYSIS: A retrospective, consecutive cohort of digital mammography screens from 658 207 examinations from BreastScreen NSW will be reinterpreted by the Lunit Insight MMG AI product. The cohort includes 4383 screen-detected and 1171 interval cancers. The results will be compared with radiologist single reading and the AI results will also be used to replace the second reader in a double-reading model. New adjudication reading will be performed where the AI disagrees with the first reader. Recall rates and cancer detection rates of combined AI-radiologist reading will be compared with the rates obtained at the time of screening. ETHICS AND DISSEMINATION: This study has ethical approval from the NSW Health Population Health Services Research Ethics Committee (2022/ETH02397). Findings will be published in peer-reviewed journals and presented at conferences. The findings of this evaluation will be provided to programme managers, governance bodies and other stakeholders in Australian breast cancer screening programmes.

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

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

Optimising the diagnostic accuracy of First post-contrAst SubtracTed breast MRI (FAST MRI) through interpretation-training: a multicentre e-learning study, mapping the learning curve of NHS Breast Screening Programme (NHSBSP) mammogram readers using an enriched dataset.

BACKGROUND: Abbreviated breast MRI (FAST MRI) is being introduced into clinical practice to screen women with mammographically dense breasts or with a personal history of breast cancer. This study aimed to optimise diagnostic accuracy through the adaptation of interpretation-training. METHODS: A FAST MRI interpretation-training programme (short presentations and guided hands-on workstation teaching) was adapted to provide additional training during the assessment task (interpretation of an enriched dataset of 125 FAST MRI scans) by giving readers feedback about the true outcome of each scan immediately after each scan was interpreted (formative assessment). Reader interaction with the FAST MRI scans used developed software (RiViewer) that recorded reader opinions and reading times for each scan. The training programme was additionally adapted for remote e-learning delivery. STUDY DESIGN: Prospective, blinded interpretation of an enriched dataset by multiple readers. RESULTS: 43 mammogram readers completed the training, 22 who interpreted breast MRI in their clinical role (Group 1) and 21 who did not (Group 2). Overall sensitivity was 83% (95%CI 81-84%; 1994/2408), specificity 94% (95%CI 93-94%; 7806/8338), readers' agreement with the true outcome kappa = 0.75 (95%CI 0.74-0.77) and diagnostic odds ratio = 70.67 (95%CI 61.59-81.09). Group 1 readers showed similar sensitivity (84%) to Group 2 (82% p = 0.14), but slightly higher specificity (94% v. 93%, p = 0.001). Concordance with the ground truth increased significantly with the number of FAST MRI scans read through the formative assessment task (p = 0.002) but by differing amounts depending on whether or not a reader had previously attended FAST MRI training (interaction p = 0.02). Concordance with the ground truth was significantly associated with reading batch size (p = 0.02), tending to worsen when more than 50 scans were read per batch. Group 1 took a median of 56 seconds (range 8-47,466) to interpret each FAST MRI scan compared with 78 (14-22,830, p < 0.0001) for Group 2. CONCLUSIONS: Provision of immediate feedback to mammogram readers during the assessment test set reading task increased specificity for FAST MRI interpretation and achieved high diagnostic accuracy. Optimal reading-batch size for FAST MRI was 50 reads per batch. Trial registration (25/09/2019): ISRCTN16624917.

Tumor Doubling Time and Screening Interval.

The goal of screening is to detect breast cancers when still curable to decrease breast cancer-specific mortality. Breast cancer screening in the United States is routinely performed with digital mammography and digital breast tomosynthesis. This article reviews breast cancer doubling time by tumor subtype and examines the impact of doubling time on breast cancer screening intervals. By the article's end, the reader will be better equipped to have informed discussions with patients and medical professionals regarding the benefits and disadvantages of the currently recommended screening mammography intervals.

Artificial Intelligence for Breast Cancer Risk Assessment.

Breast cancer risk prediction models based on common clinical risk factors are used to identify women eligible for high-risk screening and prevention. Unfortunately, these models have only modest discriminatory accuracy with disparities in performance in underrepresented race and ethnicity groups. The field of artificial intelligence (AI) and deep learning are rapidly advancing the field of breast cancer risk prediction with the development of mammography-based AI breast cancer risk models. Early studies suggest mammography-based AI risk models may perform better than traditional risk factor-based models with more equitable performance.

Interval Cancers in Understanding Screening Outcomes.

Interval breast cancers are not detected at routine screening and are diagnosed in the interval between screening examinations. A variety of factors contribute to interval cancers, including patient and tumor characteristics as well as the screening technique and frequency. The interval cancer rate is an important metric by which the effectiveness of screening may be assessed and may serve as a surrogate for mortality benefit.

Breast Density: Where Are We Now?

Breast density refers to the amount of fibroglandular tissue relative to fat on mammography and is determined either qualitatively through visual assessment or quantitatively. It is a heritable and dynamic trait associated with age, race/ethnicity, body mass index, and hormonal factors. Increased breast density has important clinical implications including the potential to mask malignancy and as an independent risk factor for the development of breast cancer. Breast density has been incorporated into breast cancer risk models. Given the impact of dense breasts on the interpretation of mammography, supplemental screening may be indicated.

Fibrocystic Change.

Fibrocystic changes are commonly seen in clinically symptomatic patients and during imaging workup of screening-detected findings. The term "fibrocystic changes" encompasses a broad spectrum of specific benign pathologic entities. Recognition of classically benign findings of fibrocystic changes, including cysts and layering calcifications, can prevent unnecessary follow-ups and biopsies. Imaging findings such as solid masses, nonlayering calcifications, and architectural distortion may require core needle biopsy for diagnosis. In these cases, understanding the varied appearances of fibrocystic change aids determination of radiologic-pathologic concordance. Management of fibrocystic change is typically conservative.

Clinical Integration of Artificial Intelligence for Breast Imaging.

This article describes an approach to planning and implementing artificial intelligence products in a breast screening service. It highlights the importance of an in-depth understanding of the end-to-end workflow and effective project planning by a multidisciplinary team. It discusses the need for monitoring to ensure that performance is stable and meets expectations, as well as focusing on the potential for inadvertantly generating inequality. New cross-discipline roles and expertise will be needed to enhance service delivery.

Contrast-enhanced Mammography versus MR Imaging of the Breast.

Breast MR imaging and contrast-enhanced mammography (CEM) are both techniques that employ intravenously injected contrast agent to assess breast lesions. This approach is associated with a very high sensitivity for malignant lesions that typically exhibit rapid enhancement due to the leakiness of neovasculature. CEM may be readily available at the breast imaging department and can be performed on the spot. Breast MR imaging provides stronger enhancement than the x-ray-based techniques and offers higher sensitivity. From a patient perspective, both modalities have their benefits and downsides; thus, patient preference could also play a role in the selection of the imaging technique.

Patient Communication Innovations in Breast Imaging.

Effective patient communication is paramount in breast radiology, where standardized reporting and patient-centered care practices have long been established. This communication profoundly affects patient experience, well-being, and adherence to medical advice. Breast radiologists play a pivotal role in conveying diagnostic findings and addressing patient concerns, particularly in the context of cancer diagnoses. Technological advances in radiology reporting, patient access to electronic medical records, and the demand for immediate information access have reshaped radiologists' communication practices. Innovative approaches, including image-rich reports, visual timelines, and video radiology reports, have been used in various institutions to enhance patient comprehension and engagement.

Nonsurgical Management of High-Risk Lesions.

This article highlights the recent publications and changing trends in practice regarding management of high-risk lesions of the breast. Traditional management has always been a surgical operation but this is recognized as overtreatment. It is recognized that overdiagnosis is inevitable but what we can control is overtreatment. Vacuum-assisted excision is now established as an alternative technique to surgery for further sampling of these high-risk lesions in the United Kingdom. Guidelines from the United Kingdom and Europe now recognize this alternative pathway, and data are available showing that vacuum-assisted excision is a safe alternative to surgery.

Sonographic Evaluation of Nigerian Women with Mammographic Focal Asymmetric Density.

BACKGROUND AND OBJECTIVE: Focal asymmetric breast densities (FABD) present a diagnostic challenge concerning the need for a further histologic workup to rule out malignancy. We therefore aim to correlate ultrasonography and mammographic findings in women with FABD and evaluate the use of ultrasonography as a workup tool. METHODOLOGY: This is a retrospective study of women who underwent targeted breast sonography due to FABD with a mammogram in a private diagnostic centre in Abuja over three years (2016-2018). Demographic details, clinical indication, mammographic and ultrasonography features were documented and statistical analysis was done using SAS software version 9.3 with the statistical level of significance set at 0.05. RESULT: The age range of 44 patients was 32-69 years with a majority (79.5%) presenting for screening mammography. The predominant breast density pattern in those <60 years was heterogeneous (ACR C). FABD in mammography was noted mostly in the upper outer quadrant and retro-areolar regions (34.1 and 38.6%). Ultrasonography findings were normal breast tissue (56.8%), 4 simple cysts, 1 abscess, 4 solid masses, 2 focal fibrocystic changes, and 4 cases of duct ectasia. Twenty-nine (65.9%) of the abnormal cases were on the same side as the mammogram, while all the incongruent cases were recorded in heterogeneously dense breasts (ACR C). Final BIRADS Scores on USS showed that 41(93.2%) of mammographic FABD had normal and benign findings while only 2(4.6%) had sonographic features of malignancy. CONCLUSION: Breast ultrasonography allows for optimal lesion characterization and is a veritable tool in the workup of patients with focal asymmetric breast densities with the majority presenting as normal breast tissue and benign pathologies.

CONTEXTE ET OBJECTIF: Les densités asymétriques mammographiques focales mammographiques, FABD présentent un défi diagnostique en ce qui concerne la nécessité d'un examen histologique supplémentaire pour exclure une tumeur maligne. Nous visons donc à corréler les résultats échographiques et mammographiques chez les femmes ayant une densité mammaire focale asymétrique et à établir la nécessité d'un bilan plus approfondi. METHODOLOGIE: Une étude rétrospective de 44 femmes ayant subi une échographie ciblée du sein en raison de FABD à la mammographie dans un centre de diagnostic privé à Abuja sur trois ans (2016-2018) Les détails démographiques, les présentations cliniques, les caractéristiques mammographiques et échographiques ont été documentés et analysés statistiquement fait à l'aide du logiciel SAS version 9.3 avec un niveau de signification statistique fixé à 0,05. RESULTAT: La tranche d'âge des patients était de 32 à 69 ans (SD 1), la majorité (79,5%) se présentant pour une mammographie de dépistage. Le schéma de densité mammaire prédominant chez les moins de 60 ans était hétérogène (ACR C). FABD en mammographie a presque la même distribution dans le quadrant externe supérieur et les régions rétroaréolaires (38,4 vs 36,8%). Les résultats échographiques étaient: tissu mammaire normal (65,9%), 4 kystes simples, 1 kyste complexe, 4 masses solides, 2 fibrokystiques focales et 4 cas d'ectasie canalaire.29 (65,9%) des cas anormaux étaient du même côté que la mammographie, alors que tous les cas incongruents ont été enregistrés dans des seins denses de manière hétérogène (ACR C). Les scores finaux BIRADS sur USS ont montré que 41 (93,2%) des FABD mammographiques avaient des résultats normaux et bénins, tandis que seulement 2 (4,6%) avaient des caractéristiques échographiques de malignité. CONCLUSION: L'échographie mammaire permet une caractérisation optimale des lésions et constitue un véritable outil dans le bilan des patientes présentant des densités mammaires asymétriques focales dont la majorité se présente comme un tissu mammaire normal et des pathologies bénignes. MOTS CLES: Sein, Asymétrie focale, Échographie, Mammographie.

Breast Mass Detection and Classification Using Machine Learning Approaches on Two-Dimensional Mammogram: A Review.

Breast cancer is a leading cause of mortality among women, both in India and globally. The prevalence of breast masses is notably common in women aged 20 to 60. These breast masses are classified, according to the breast imaging-reporting and data systems (BI-RADS) standard, into categories such as fibroadenoma, breast cysts, benign, and malignant masses. To aid in the diagnosis of breast disorders, imaging plays a vital role, with mammography being the most widely used modality for detecting breast abnormalities over the years. However, the process of identifying breast diseases through mammograms can be time-consuming, requiring experienced radiologists to review a significant volume of images. Early detection of breast masses is crucial for effective disease management, ultimately reducing mortality rates. To address this challenge, advancements in image processing techniques, specifically utilizing artificial intelligence (AI) and machine learning (ML), have tiled the way for the development of decision support systems. These systems assist radiologists in the accurate identification and classification of breast disorders. This paper presents a review of various studies where diverse machine learning approaches have been applied to digital mammograms. These approaches aim to identify breast masses and classify them into distinct subclasses such as normal, benign and malignant. Additionally, the paper highlights both the advantages and limitations of existing techniques, offering valuable insights for the benefit of future research endeavors in this critical area of medical imaging and breast health.

Breast Cancer Screening: Can We Justify Deescalation?

Novel breast cancer screening methods that detect greater numbers of occult (nonpalpable) tumors have been rapidly incorporated into clinical practice, with the aim of reducing mortality. Yet, tumor detection has never been validated as a proper surrogate outcome measure for breast cancer mortality. Moreover, the detection of greater numbers of occult cancers increases the risk of overdiagnosis, which refers to detection of tumors that pose no threat to life and would never have been detected in the absence of screening. With recent advances in breast cancer therapy, many cancers that were previously curable only if detected as occult tumors with mammography screening are perhaps now curable even when detected as small palpable tumors, thereby giving us an opportunity to deescalate screening and mitigate the risk of overdiagnosis. Thus, a randomized trial comparing screening mammography versus screening clinical breast examination (CBE), with breast cancer mortality as the endpoint, is now warranted. In such a trial, hand-held ultrasound might aid in the interpretation of screening CBE findings. In conclusion, recent improvements in breast cancer therapy provide the justification to assess the deescalation of breast cancer screening. See related article by Farber et al., p. 671.

Incidentally diagnosed superior vena cava narrowing prompted by screening mammography.

An adult woman with a prior history of treated non-Hodgkin's lymphoma presented for screening mammography, which incidentally demonstrated dilated veins throughout the bilateral breasts. Concern for a superior vena cava stenosis or obstruction was raised despite the patient being asymptomatic; the patient underwent further imaging with chest CT, which revealed focal stenosis of the superior vena cava, attributed to fibrosis secondary to prior radiation therapy. Superior vena cava syndrome (SVCS), the spectrum of disease caused by superior vena cava narrowing or obstruction, requires prompt investigation given its association with intrathoracic malignancy, primary lung cancer and poor outcomes. This report explores the benign and malignant causes, signs and symptoms, preferred investigations, and treatment of SVCS. This case highlights the potential importance of screening mammography in revealing unexpected ancillary diagnoses, especially in high-risk patients.