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Clinical Proteomics Jun 2024The development of breast cancer has been mainly reported in women who have reached the post-menopausal stage; therefore, it is the primary factor responsible for death... (Review)
Review
The development of breast cancer has been mainly reported in women who have reached the post-menopausal stage; therefore, it is the primary factor responsible for death amongst postmenopausal women. However, if treated on time it has shown a survival rate of 20 years in about two-thirds of women. Cases of breast cancer have also been reported in younger women and the leading cause in them is their lifestyle pattern or they may be carriers of high penetrance mutated genes. Premenopausal women who have breast cancer have been diagnosed with aggressive build-up of tumors and are therefore at more risk of loss of life. Mammography is an effective way to test for breast cancer in women after menopause but is not so effective for premenopausal women or younger females. Imaging techniques like contrast-enhanced MRI can up to some extent indicate the presence of a tumor but it cannot adequately differentiate between benign and malignant tumors. Although the 'omics' strategies continuing for the last 20 years have been helpful at the molecular level in enabling the characteristics and proper understanding of such tumors over long-term longitudinal monitoring. Classification, diagnosis, and prediction of the outcomes have been made through tissue and serum biomarkers but these also fail to diagnose the disease at an early stage. Considerably there is no adequate detection technique present globally that can help early detection and provide adequate specificity, safety, sensitivity, and convenience for the younger and premenopausal women, thereby it becomes necessary to take early measures and build efficient tools and techniques for the same. Through biopsies of nipple aspirate fluid (NAF) biomarker profiling can be performed. It is a naturally secreted fluid from the cells of epithelium found in the breast. Nowadays, home-based liquid biopsy collection kits are also available through which a routine check on breast health can be performed with the help of NAF. Herein, we will review the biomarker screening liquid biopsy, and the new emerging technologies for the examination of cancer at an early stage, especially in premenopausal women.
PubMed: 38943056
DOI: 10.1186/s12014-024-09495-4 -
Korean Journal of Radiology Jul 2024Evaluating the performance of a binary diagnostic test, including artificial intelligence classification algorithms, involves measuring sensitivity, specificity,... (Review)
Review
Evaluating the performance of a binary diagnostic test, including artificial intelligence classification algorithms, involves measuring sensitivity, specificity, positive predictive value, and negative predictive value. Particularly when comparing the performance of two diagnostic tests applied on the same set of patients, these metrics are crucial for identifying the more accurate test. However, comparing predictive values presents statistical challenges because their denominators depend on the test outcomes, unlike the comparison of sensitivities and specificities. This paper reviews existing methods for comparing predictive values and proposes using the permutation test. The permutation test is an intuitive, non-parametric method suitable for datasets with small sample sizes. We demonstrate each method using a dataset from MRI and combined modality of mammography and ultrasound in diagnosing breast cancer.
Topics: Humans; Breast Neoplasms; Female; Predictive Value of Tests; Magnetic Resonance Imaging; Mammography; Sensitivity and Specificity; Algorithms; Ultrasonography, Mammary
PubMed: 38942459
DOI: 10.3348/kjr.2024.0049 -
Korean Journal of Radiology Jul 2024In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known...
OBJECTIVE
In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR).
MATERIALS AND METHODS
An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs.
RESULTS
Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs.
CONCLUSION
The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.
Topics: Humans; Republic of Korea; Artificial Intelligence; Surveys and Questionnaires; Societies, Medical; Radiology; Software
PubMed: 38942455
DOI: 10.3348/kjr.2023.1246 -
Polish Journal of Radiology 2024Breast lesions that remain elusive in traditional imaging techniques such as ultrasound and mammography pose a diagnostic challenge. In such cases, magnetic resonance...
PURPOSE
Breast lesions that remain elusive in traditional imaging techniques such as ultrasound and mammography pose a diagnostic challenge. In such cases, magnetic resonance (MR)-guided breast biopsy emerges as a crucial tool for accurate histopathological verification. This article presents a comparative study conducted at 2 centres, exploring the results of MR-guided breast biopsies performed by experienced radiologists, based on inside and external referrals.
MATERIAL AND METHODS
The study involved 228 patients, 120 of whom underwent biopsies at Centre 1, where the same radiologist performed both the qualification and biopsy. The remaining 108 patients were biopsied at Centre 2, based on referrals from different institutions. Uniform examination protocols were adopted at both centres, and all biopsies underwent histopathological verification.
RESULTS
The distribution of lesion types was found to be independent of the apparatus used for biopsies ( = 0.759). Interestingly, Centre 1 exhibited a higher prevalence of infiltrating carcinomas compared to Centre 2 ( = 0.12). Furthermore, the analysis demonstrated a significant variance in the nature of the lesions in relation to breast structure and biopsy centre ( < 0.001).
CONCLUSIONS
MR-guided breast biopsy serves as a remarkable tool for verifying lesions that evade detection through conventional imaging methods and physical examinations. The study findings underscore the crucial role of radiologist experience in determining the efficacy of MR-guided breast biopsies.
PubMed: 38938661
DOI: 10.5114/pjr/186862 -
Polish Journal of Radiology 2024To assess the effectiveness of contrast-enhanced mammography (CEM) recombinant images in detecting malignant lesions in patients with extremely dense breasts compared to...
Comparison of the effectiveness of contrast-enhanced mammography in detecting malignant lesions in patients with extremely dense breasts compared to the all-densities population.
PURPOSE
To assess the effectiveness of contrast-enhanced mammography (CEM) recombinant images in detecting malignant lesions in patients with extremely dense breasts compared to the all-densities population.
MATERIAL AND METHODS
792 patients with 808 breast lesions, in whom the final decision on core-needle biopsy was made based on CEM, and who received the result of histopathological examination, were qualified for a single-centre, retrospective study. Patient electronic records and imaging examinations were reviewed to establish demographics, clinical and imaging findings, and histopathology results. The CEM images were reassessed and assigned to the appropriate American College of Radiology (ACR) density categories.
RESULTS
Extremely dense breasts were present in 86 (10.9%) patients. Histopathological examination confirmed the presence of malignant lesions in 52.6% of cases in the entire group of patients and 43% in the group of extremely dense breasts. CEM incorrectly classified the lesion as false negative in 16/425 (3.8%) cases for the whole group, and in 1/37 (2.7%) cases for extremely dense breasts. The sensitivity of CEM for the group of all patients was 96.2%, specificity - 60%, positive predictive values (PPV) - 72.8%, and negative predictive values (NPV) - 93.5%. In the group of patients with extremely dense breasts, the sensitivity of the method was 97.3%, specificity - 59.2%, PPV - 64.3%, and NPV - 96.7%.
CONCLUSIONS
CEM is characterised by high sensitivity and NPV in detecting malignant lesions regardless of the type of breast density. In patients with extremely dense breasts, CEM could serve as a complementary or additional examination in the absence or low availability of MRI.
PubMed: 38938658
DOI: 10.5114/pjr/186180 -
Journal of the American College of... Jun 2024The Supplemental Nutrition Assistance Program (SNAP) addresses food insecurity for low-income households, which is associated with access to care. Many US states...
PURPOSE
The Supplemental Nutrition Assistance Program (SNAP) addresses food insecurity for low-income households, which is associated with access to care. Many US states expanded SNAP access through policies eliminating the asset test (ie, restrictions based on SNAP applicant assets) and/or broadening income eligibility. The objective of this study was to determine whether state SNAP policies were associated with the use of mammography among women eligible for breast cancer screening.
METHODS
Data for income-eligible women 40 to 79 years of age were obtained from the 2006 to 2019 Behavioral Risk Factor Surveillance System. Difference-in-differences analyses were conducted to compare changes in the percentage of mammography in the past year from pre- to post-SNAP policy adoption (asset test elimination or income eligibility increase) between states that and did not adopt policies expanding SNAP eligibility.
RESULTS
In total, 171,684 and 294,647 income-eligible female respondents were included for the asset test elimination policy and income eligibility increase policy analyses, respectively. Mammography within 1 year was reported by 58.4%. Twenty-eight and 22 states adopted SNAP asset test elimination and income increase policies, respectively. Adoption of asset test elimination policies was associated with a 2.11 (95% confidence interval [CI], 0.07-4.15; P = .043) percentage point increase in mammography received within 1 year, particularly for nonmetropolitan residents (4.14 percentage points; 95% CI, 1.07-7.21 percentage points; P = .008), those with household incomes <$25,000 (2.82 percentage points; 95% CI, 0.68-4.97 percentage points; P = .01), and those residing in states in the South (3.08 percentage points; 95% CI, 0.17-5.99 percentage points; P = .038) or that did not expand Medicaid under the Patient Protection and Affordable Care Act (3.35 percentage points; 95% CI, 0.36-6.34; P = .028). There was no significant association between mammography and state-level policies broadening of SNAP income eligibility.
CONCLUSIONS
State policies eliminating asset test requirements for SNAP eligibility were associated with increased mammography among low-income women eligible for breast cancer screening, particularly for those in the lowest income bracket or residing in nonmetropolitan areas or Medicaid nonexpansion states.
PubMed: 38935002
DOI: 10.1016/j.jacr.2024.04.028 -
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