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Life (Basel, Switzerland) Jun 2024Breast cancer is the most common malignancy diagnosed in the female population worldwide and the leading cause of death among perimenopausal women. Screening is... (Review)
Review
Breast cancer is the most common malignancy diagnosed in the female population worldwide and the leading cause of death among perimenopausal women. Screening is essential, since earlier detection in combination with improvements in breast cancer treatment can reduce the associated mortality. The aim of this study was to review and compare the recommendations from published guidelines on breast cancer screening. A total of 14 guidelines on breast cancer screening issued between 2014 and 2022 were identified. A descriptive review of relevant guidelines by the World Health Organization (WHO), the U.S. Preventive Services Task Force (USPSTF), the American Cancer Society (ACS), the National Comprehensive Cancer Network (NCCN), the American College of Obstetricians and Gynecologists (ACOG), the American Society of Breast Surgeons (ASBrS), the American College of Radiology (ACR), the Task Force on Preventive Health Care (CTFPHC), the European Commission Initiative on Breast Cancer (ECIBC), the European Society for Medical Oncology (ESMO), the Royal Australian College of General Practitioners (RACGP) and the Japanese Journal of Clinical Oncology (JJCO) for women both at average and high-risk was carried out. There is a consensus among all the reviewed guidelines that mammography is the gold standard screening modality for average-risk women. For this risk group, most of the guidelines suggest annual or biennial mammographic screening at 40-74 years, while screening should particularly focus at 50-69 years. Most of the guidelines suggest that the age limit to stop screening should be determined based on the women's health status and life expectancy. For women at high-risk, most guidelines recommend the use of annual mammography or magnetic resonance imaging, while the starting age should be earlier than the average-risk group, depending on the risk factor. There is discrepancy among the recommendations regarding the age at onset of screening in the various high-risk categories. The development of consistent international practice protocols for the most appropriate breast cancer screening programs seems of major importance to reduce mortality rates and safely guide everyday clinical practice.
PubMed: 38929759
DOI: 10.3390/life14060777 -
Diagnostics (Basel, Switzerland) Jun 2024The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the...
The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1-5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%). To determine the feasibility of the AI prediction model, mammograms and detailed clinical information were collected. C-indices and area under the receiver operating characteristic curves (AUCs) for 1-5-year outcomes were obtained. We compared the AUCs of our AI prediction model, Mirai, and clinical statistical risk models, including the Tyrer-Cuzick (TC) model and Gail model, using DeLong's test. A total of 16,894 mammograms were independently collected for external validation, of which 4002 were followed by a cancer diagnosis within 5 years. Our AI prediction model obtained a C-index of 0.76, with AUCs of 0.90, 0.84, 0.81, 0.78, and 0.81, to predict the 1-5-year risks. Our AI prediction model showed significantly higher AUCs than those of the TC model (AUC: 0.57; < 0.001) and Gail model (AUC: 0.52; < 0.001), and achieved similar performance to Mirai. The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction.
PubMed: 38928628
DOI: 10.3390/diagnostics14121212 -
Biomedicines Jun 2024Breast cancer remains a leading cause of mortality among women, with molecular subtypes significantly influencing prognosis and treatment strategies. Currently,...
Breast cancer remains a leading cause of mortality among women, with molecular subtypes significantly influencing prognosis and treatment strategies. Currently, identifying the molecular subtype of cancer requires a biopsy-a specialized, expensive, and time-consuming procedure, often yielding to results that must be supported with additional biopsies due to technique errors or tumor heterogeneity. This study introduces a novel approach for predicting breast cancer molecular subtypes using mammography images and advanced artificial intelligence (AI) methodologies. Using the OPTIMAM imaging database, 1397 images from 660 patients were selected. The pretrained deep learning model ResNet-101 was employed to classify tumors into five subtypes: Luminal A, Luminal B1, Luminal B2, HER2, and Triple Negative. Various classification strategies were studied: binary classifications (one vs. all others, specific combinations) and multi-class classification (evaluating all subtypes simultaneously). To address imbalanced data, strategies like oversampling, undersampling, and data augmentation were explored. Performance was evaluated using accuracy and area under the receiver operating characteristic curve (AUC). Binary classification results showed a maximum average accuracy and AUC of 79.02% and 64.69%, respectively, while multi-class classification achieved an average AUC of 60.62% with oversampling and data augmentation. The most notable binary classification was HER2 vs. non-HER2, with an accuracy of 89.79% and an AUC of 73.31%. Binary classification for specific combinations of subtypes revealed an accuracy of 76.42% for HER2 vs. Luminal A and an AUC of 73.04% for HER2 vs. Luminal B1. These findings highlight the potential of mammography-based AI for non-invasive breast cancer subtype prediction, offering a promising alternative to biopsies and paving the way for personalized treatment plans.
PubMed: 38927578
DOI: 10.3390/biomedicines12061371 -
Tomography (Ann Arbor, Mich.) Jun 2024Computer-aided diagnosis systems play a crucial role in the diagnosis and early detection of breast cancer. However, most current methods focus primarily on the...
Computer-aided diagnosis systems play a crucial role in the diagnosis and early detection of breast cancer. However, most current methods focus primarily on the dual-view analysis of a single breast, thereby neglecting the potentially valuable information between bilateral mammograms. In this paper, we propose a Four-View Correlation and Contrastive Joint Learning Network (FV-Net) for the classification of bilateral mammogram images. Specifically, FV-Net focuses on extracting and matching features across the four views of bilateral mammograms while maximizing both their similarities and dissimilarities. Through the Cross-Mammogram Dual-Pathway Attention Module, feature matching between bilateral mammogram views is achieved, capturing the consistency and complementary features across mammograms and effectively reducing feature misalignment. In the reconstituted feature maps derived from bilateral mammograms, the Bilateral-Mammogram Contrastive Joint Learning module performs associative contrastive learning on positive and negative sample pairs within each local region. This aims to maximize the correlation between similar local features and enhance the differentiation between dissimilar features across the bilateral mammogram representations. Our experimental results on a test set comprising 20% of the combined Mini-DDSM and Vindr-mamo datasets, as well as on the INbreast dataset, show that our model exhibits superior performance in breast cancer classification compared to competing methods.
Topics: Humans; Breast Neoplasms; Mammography; Female; Radiographic Image Interpretation, Computer-Assisted; Breast; Diagnosis, Computer-Assisted; Machine Learning; Algorithms
PubMed: 38921942
DOI: 10.3390/tomography10060065 -
Journal of Imaging Jun 2024After breast conserving surgery (BCS), surgical clips indicate the tumor bed and, thereby, the most probable area for tumor relapse. The aim of this study was to...
BACKGROUND
After breast conserving surgery (BCS), surgical clips indicate the tumor bed and, thereby, the most probable area for tumor relapse. The aim of this study was to investigate whether a U-Net-based deep convolutional neural network (dCNN) may be used to detect surgical clips in follow-up mammograms after BCS.
METHODS
884 mammograms and 517 tomosynthetic images depicting surgical clips and calcifications were manually segmented and classified. A U-Net-based segmentation network was trained with 922 images and validated with 394 images. An external test dataset consisting of 39 images was annotated by two radiologists with up to 7 years of experience in breast imaging. The network's performance was compared to that of human readers using accuracy and interrater agreement (Cohen's Kappa).
RESULTS
The overall classification accuracy on the validation set after 45 epochs ranged between 88.2% and 92.6%, indicating that the model's performance is comparable to the decisions of a human reader. In 17.4% of cases, calcifications have been misclassified as post-operative clips. The interrater reliability of the model compared to the radiologists showed substantial agreement (κ = 0.72, κ = 0.78) while the readers compared to each other revealed a Cohen's Kappa of 0.84, thus showing near-perfect agreement.
CONCLUSIONS
With this study, we show that surgery clips can adequately be identified by an AI technique. A potential application of the proposed technique is patient triage as well as the automatic exclusion of post-operative cases from PGMI (Perfect, Good, Moderate, Inadequate) evaluation, thus improving the quality management workflow.
PubMed: 38921624
DOI: 10.3390/jimaging10060147 -
The Indian Journal of Radiology &... Jul 2024Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely... (Review)
Review
Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely variable. To evaluate published literature on breast cancer detection in mammography for reproducibility and to ascertain best practices for model design. The PubMed and Scopus databases were searched to identify records that described the use of deep learning to detect lesions or classify images into cancer or noncancer. A modification of Quality Assessment of Diagnostic Accuracy Studies (mQUADAS-2) tool was developed for this review and was applied to the included studies. Results of reported studies (area under curve [AUC] of receiver operator curve [ROC] curve, sensitivity, specificity) were recorded. A total of 12,123 records were screened, of which 107 fit the inclusion criteria. Training and test datasets, key idea behind model architecture, and results were recorded for these studies. Based on mQUADAS-2 assessment, 103 studies had high risk of bias due to nonrepresentative patient selection. Four studies were of adequate quality, of which three trained their own model, and one used a commercial network. Ensemble models were used in two of these. Common strategies used for model training included patch classifiers, image classification networks (ResNet in 67%), and object detection networks (RetinaNet in 67%). The highest reported AUC was 0.927 ± 0.008 on a screening dataset, while it reached 0.945 (0.919-0.968) on an enriched subset. Higher values of AUC (0.955) and specificity (98.5%) were reached when combined radiologist and Artificial Intelligence readings were used than either of them alone. None of the studies provided explainability beyond localization accuracy. None of the studies have studied interaction between AI and radiologist in a real world setting. While deep learning holds much promise in mammography interpretation, evaluation in a reproducible clinical setting and explainable networks are the need of the hour.
PubMed: 38912238
DOI: 10.1055/s-0043-1775737 -
Open Life Sciences 2024Richter transformation (RT) represents the development of intrusive lymphoma in individuals previously or concurrently diagnosed with chronic lymphocytic leukemia (CLL)...
Richter transformation (RT) represents the development of intrusive lymphoma in individuals previously or concurrently diagnosed with chronic lymphocytic leukemia (CLL) and is characterized by lymph node enlargement. However, cases involving extra-nodal organ involvement as the first symptom are rare. There are no reports of RT with breast lesions as the first symptom. Nonspecific and atypical clinical manifestations represent key challenges in the accurate diagnosis and appropriate treatment of RT. This case report describes an elderly female patient who presented with breast lesions as the first RT symptom. The patient was admitted with a painless mass in the left breast. Examination revealed multiple lymphadenopathies and abnormally high white blood cell levels. The patient was diagnosed with CLL after hematological tests, assessments of bone marrow morphology, and tissue biopsy. Mammography and B-ultrasonography showed solid space-occupying lesions (BI-RADS category 5) in the left breast. Initially, the patient declined a breast biopsy and was therefore prescribed ibrupotinib treatment, which showed limited efficacy. A needle biopsy of the affected breast indicated the presence of diffuse large B-cell lymphoma. Based on auxiliary and pathological examinations and medical history, the final diagnosis was RT with breast involvement. Zanubrutinib with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone treatment provided initial control; however, the treatment strategy required adjustment because of the patient's fluctuating condition. The current status of the patient is marked as stable, showing an overall achievement of partial alleviation. The patient is in the process of receiving follow-up treatment. We also performed a comprehensive literature review on RT, with particular emphasis on its biological paradigm, prognosis implications, existing therapeutic approaches, and emerging directions in treatment modalities.
PubMed: 38911930
DOI: 10.1515/biol-2022-0889 -
BMC Women's Health Jun 2024Breast imaging clinics in the United States (U.S.) are increasingly implementing breast cancer risk assessment (BCRA) to align with evolving guideline recommendations...
BACKGROUND
Breast imaging clinics in the United States (U.S.) are increasingly implementing breast cancer risk assessment (BCRA) to align with evolving guideline recommendations but with limited uptake of risk-reduction care. Effectively communicating risk information to women is central to implementation efforts, but remains understudied in the U.S. This study aims to characterize, and identify factors associated with women's interest in and preferences for breast cancer risk communication.
METHODS
This is a cross-sectional survey study of U.S. women presenting for a mammogram between January and March of 2021 at a large, tertiary breast imaging clinic. Survey items assessed women's interest in knowing their risk and preferences for risk communication if considered to be at high risk in hypothetical situations. Multivariable logistic regression modeling assessed factors associated with women's interest in knowing their personal risk and preferences for details around exact risk estimates.
RESULTS
Among 1119 women, 72.7% were interested in knowing their breast cancer risk. If at high risk, 77% preferred to receive their exact risk estimate and preferred verbal (52.9% phone/47% in-person) vs. written (26.5% online/19.5% letter) communications. Adjusted regression analyses found that those with a primary family history of breast cancer were significantly more interested in knowing their risk (OR 1.5, 95% CI 1.0, 2.1, p = 0.04), while those categorized as "more than one race or other" were significantly less interested in knowing their risk (OR 0.4, 95% CI 0.2, 0.9, p = 0.02). Women 60 + years of age were significantly less likely to prefer exact estimates of their risk (OR 0.6, 95% CI 0.5, 0.98, p < 0.01), while women with greater than a high school education were significantly more likely to prefer exact risk estimates (OR 2.5, 95% CI 1.5, 4.2, p < 0.001).
CONCLUSION
U.S. women in this study expressed strong interest in knowing their risk and preferred to receive exact risk estimates verbally if found to be at high risk. Sociodemographic and family history influenced women's interest and preferences for risk communication. Breast imaging centers implementing risk assessment should consider strategies tailored to women's preferences to increase interest in risk estimates and improve risk communication.
Topics: Humans; Female; Breast Neoplasms; Cross-Sectional Studies; Middle Aged; Patient Preference; United States; Adult; Mammography; Risk Assessment; Aged; Communication; Surveys and Questionnaires; Tertiary Care Centers; Health Knowledge, Attitudes, Practice
PubMed: 38907193
DOI: 10.1186/s12905-024-03197-7 -
RoFo : Fortschritte Auf Dem Gebiete Der... Jun 2024Axillary lymphadenopathy (LA) after COVID-19 vaccination is now known to be a common side effect. In these cases, malignancy cannot always be excluded on the basis of...
Axillary lymphadenopathy (LA) after COVID-19 vaccination is now known to be a common side effect. In these cases, malignancy cannot always be excluded on the basis of morphological imaging criteria.Narrative review for decision-making regarding control and follow-up intervals for axillary LA according to currently published research. This article provides a practical overview of the management of vaccine-associated LA using image examples and a flowchart and provides recommendations for follow-up intervals. A particular focus is on patients presenting for diagnostic breast imaging. The diagnostic criteria for pathological lymph nodes (LN) are explained.Axillary LA is a common adverse effect after COVID-19 vaccination (0.3-53%). The average duration of LA is more than 100 days. LA is also known to occur after other vaccinations, such as the seasonal influenza vaccine. Systematic studies on this topic are missing. Other causes of LA after vaccination (infections, autoimmune diseases, malignancies) should be considered for the differential diagnosis. If the LA persists for more than 3 months after COVID-19 vaccination, a primarily sonographic follow-up examination is recommended after another 3 months. A minimally invasive biopsy of the LA is recommended if a clinically suspicious LN persists or progresses. In the case of histologically confirmed breast cancer, a core biopsy without a follow-up interval is recommended regardless of the vaccination, as treatment appropriate to the stage should not be influenced by follow-up intervals. For follow-up after breast cancer, the procedure depends on the duration of the LA and the woman's individual risk of recurrence.Vaccination history should be well documented and taken into account when evaluating suspicious LN. Biopsy of abnormal, persistent, or progressive LNs is recommended. Preoperative staging of breast cancer should not be delayed by follow-up. The risk of false-positive findings is accepted, and the suspicious LNs are histologically examined in a minimally invasive procedure. · The vaccination history must be documented (vaccine, date, place of application).. · If axillary LA persists for more than 3 months after vaccination, a sonographic follow-up examination is recommended after 3 months.. · Enlarged LNs that are persistent, progressive in size, or are suspicious on control sonography should be biopsied.. · Suspicious LNs should be clarified before starting oncological therapy, irrespective of the vaccination status, according to the guidelines and without delaying therapy.. · Wilpert C, Wenkel E, Baltzer PA et al. Vaccine-associated axillary lymphadenopathy with a focus on COVID-19 vaccines. Fortschr Röntgenstr 2024; DOI 10.1055/a-2328-7536.
PubMed: 38906159
DOI: 10.1055/a-2328-7536 -
Cureus May 2024Breast cancer represents a significant global health challenge, with Saudi Arabia experiencing high incidence rates, particularly among females. Early detection...
BACKGROUND
Breast cancer represents a significant global health challenge, with Saudi Arabia experiencing high incidence rates, particularly among females. Early detection through screening methods such as mammography and breast self-examination offers promise in reducing mortality rates. However, participation in screening remains suboptimal, posing a barrier to effective cancer control. In regions like Jazan, situated in southwestern Saudi Arabia, comprehensive studies on breast cancer awareness and screening practices are lacking.
METHODS
This cross-sectional study conducted in Jazan, Saudi Arabia, aimed to comprehensively assess breast cancer awareness, perceptions, and screening practices among the local population. An online survey platform was utilized to reach individuals aged 18 years or older residing in Jazan. Recruitment efforts utilized social media platforms, community networks, and local organizations to ensure diverse representation across socioeconomic backgrounds, education levels, and geographical locations. A meticulously designed questionnaire captured demographic information, breast cancer awareness, knowledge, health-seeking behaviors, screening practices, and barriers to mammogram screening. Participants provided electronic informed consent before self-administering the questionnaire.
RESULTS
The study conducted in Jazan, Saudi Arabia, encompassed 533 participants, predominantly young to middle-aged individuals. Most participants were Saudi nationals (97.6%), employed in the government sector (55.7%), and resided in urban areas (61.0%). Awareness of breast cancer was high, with 98.1% having heard of the disease. However, perceptions of age of onset and prevalence varied. While participants showed varied awareness of breast cancer warning signs and risk factors, family history was a commonly agreed-upon risk factor (54.4%). Health-seeking behavior for breast cancer symptoms varied, with nipple changes prompting the most immediate medical attention (36.4%). Although most participants were aware of self-breast examination (84.6%) and mammograms (56.7%), utilization rates were suboptimal, with barriers including fear (79.7%) and embarrassment (71.5%) hindering mammogram screening uptake.
CONCLUSION
This study provides insights into breast cancer awareness and screening practices among participants in Saudi Arabia. While awareness of breast self-examination and mammography is high, disparities in screening service access persist due to barriers like fear and embarrassment. Addressing these barriers through culturally sensitive interventions and collaborative efforts is crucial for enhancing screening uptake and promoting health equity.
PubMed: 38903297
DOI: 10.7759/cureus.60759