Advanced multimodal for Segmentation and Classification of breast cancer with DenseNet and Optimal Attention approach.

Looking for a Needle in a Haystack: The Importance of Having Optimal Display Luminance Level for Breast Cancer Detection on Digital Breast Tomosynthesis

Background Earlier detection of breast cancer through mammography screening has reduced disease-specific mortality; however, confounding issues such as technical challenges, breast density, and tumor size can result in false negatives and ultimately later stage diagnosis. Next generation liquid biopsy has the potential to complement mammography and enable earlier detection for more women. We have previously demonstrated high sensitivity and specificity for early detection of invasive breast cancer (IBC) by utilizing a novel category of cancer-associated small RNAs, termed orphan noncoding RNAs (oncRNAs), through a liquid biopsy platform. Here, we further improve the ability to detect breast cancer in a larger, multi-source cohort through an AI-driven approach and demonstrate potential for detection of ductal carcinoma in-situ (DCIS). Methods We utilized The Cancer Genome Atlas (TCGA) small RNA-seq database to discover a library of 20,538 oncRNAs, through a female-specific analysis, that were significantly enriched among 1,103 breast tumors compared to 349 normal tissue samples spanning multiple tissue sites. The diagnostic performance of these oncRNAs were assessed in an independent cohort of archived serum samples from 663 female individuals, sourced from Indivumed (Hamburg, Germany), Proteogenex (Inglewood, CA), and MT Group (Los Angeles, CA), including 279 breast cancer patients of various stages (221 IBC and 58 DCIS; mean age: 57.0 ± 13.8 years; ever-smoker: 25.8%) and 304 age-matched controls (mean age: 58.5 ± 13.9 years; ever-smoker: 23.4%) without breast cancer. All samples were collected between 2010–2022 at time of diagnosis for breast cancer patients. We sequenced the small RNA content of these samples at an average depth of 25.28 ± 9.37 million 50-bp single-end reads. We detected 18,025 (87.8%) unique breast cancer-specific oncRNA species within at least one sample from the study cohort. We then trained a generative AI model using 5-fold cross-validation to predict cancer status for all samples. Results Our oncRNA-based model achieved an overall AUC of 0.95 (95% CI, 0.93–0.97) for prediction of IBC versus cancer-free controls with a sensitivity of 0.87 (0.82–0.91) at 90% specificity. We observed high sensitivities, also at 90% specificity, across all tumor stages and tumor sizes (Table 1). Sensitivities for the earliest stage and smallest tumor size were 0.87 (0.78–0.93) and 0.81 (0.61–0.93) for Stage I (n=83) and T1a–b ( >1mm to ≤10mm; n=26), respectively. Additionally, in a small single-source cohort, we also saw high model accuracy and sensitivity for DCIS, which we aim to confirm in additional cohorts. While our overall cancer cohort primarily consisted of individuals with luminal breast cancer, our model had high sensitivities across all breast cancer subtypes at 0.90 (0.84–0.94), 0.73 (0.59–0.85), and 0.86 (0.42–1.0) for luminal (n=181), HER2 positive (n=49), and triple negative (n=7), respectively. Conclusions We further demonstrate the potential utility of oncRNAs as a blood-based biomarker using an AI algorithm for sensitive and accurate early detection of breast cancer in a large cohort. Additionally, we have shown that this oncRNA-based assay performs well in detecting small, early-stage invasive breast tumors, with potential to detect precursors of breast cancer. Table 1: Model sensitivity in breast cancer by tumor stage and size For each tumor stage and size, as defined by the AJCC 7th Edition breast cancer staging system, sensitivity and 95% Pearson-Clopper confidence intervals (CI) are reported at 90% specificity for the number of samples (N). Citation Format: Noura Tbeileh, Taylor Cavazos, Mehran Karimzadeh, Jeffrey Wang, Alice Huang, Dung Ngoc Lam, Seda Kilinc, Jieyang Wang, Xuan Zhao, Andy Pohl, Helen Li, Lisa Fish, Kimberly Chau, Marra Francis, Lee Schwartzberg, Patrick Arensdorf, Hani Goodarzi, Fereydoun Hormozdiari, Babak Alipanahi. Cell-free orphan noncoding RNAs and AI enable early detection of invasive breast cancer and ductal carcinoma in-situ [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-13-08.

Objective. Conduct a comprehensive assessment of the indicators of screening programs for early detection of breast and cervical cancer in the Gomel region.Materials and methods. A total of 7,611 first-time detected cases of breast cancer and 1,370 cases of cervical cancer in the Gomel region were analyzed for the period 2012-2022 (according to Belarusian Cancer Registry). The results of examination of 105130 participants of the screening program for early detection of breast cancer and 70258 participants of the screening program for early detection of cervical cancer were analyzed.Results. During the period 2017-2022, due to screening, 12% of cases of breast cancer were detected from the total number of newly diagnosed malignant breast pathologies. From 2018 to 2022, the proportion of newly detected cases of cervical cancer within the screening program increased from 0.83% to 2.61%, which indicates that the introduction of the screening program contributes to an increase in the detection of precancerous conditions of the cervix.Conclusions. The screening program for breast cancer and cervical cancer has shown its effectiveness in detecting new cases of malignant neoplasms of these localizations. An important result of the screening conducted in the Gomel region is the fact that the observed trend towards a decrease in the detection of breast cancer and cervical cancer in the early stages does not give any cause for optimism, as this is evidence of a deterioration in the structure of the detected pathology.

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

Background: Primary prevention and early detection of hereditary breast cancer has been one of the main topics of breast cancer research in recent decades. The knowledge of risk factors for breast cancer has been increasing continuously just like the recommendations for risk management. Pathogenic germline variants (mutations, class 4/5) of risk genes are significant susceptibility factors in healthy individuals. At the same time, germline mutations serve as biomarkers for targeted therapy in breast cancer treatment. Therefore, management of healthy mutation carriers to enable primary prevention is in the focus as much as the consideration of pathogenic germline variants for therapeutic decisions. Since 1996, the German Consortium has provided quality-assured care for counselees and patients with familial burden of breast and ovarian cancer. Summary: Currently, there are 23 university centers with over 100 cooperating DKG-certified breast and gynecological cancer centers. These centers provide standardized, evidence-based, and knowledge-generating care, which includes aspects of primary as well as secondary and tertiary prevention. An important aspect of quality assurance and development was the inclusion of the HBOC centers in the certification system of the German Cancer Society (GCS). Since 2020, the centers have been regularly audited and their quality standards continuously reviewed according to quality indicators adapted to the current state of research. The standard of care at GC-HBOC’ centers involves the evaluation as well as evolution of various aspects of care like inclusion criteria, identification of new risk genes, management of variants of unknown significance (class 3), evaluation of risk-reducing options, intensified surveillance, and communication of risks. Among these, the possibility of intensified surveillance in the GC-HBOC for early detection of breast cancer is an important component of individual risk management for many counselees. As has been shown in recent years, in carriers of pathogenic variants in high-risk genes, this approach enables the detection of breast cancer at very early, more favorable stages although no reduction of mortality has been demonstrated yet. The key component of the intensified surveillance is annual contrast-enhanced breast MRI, supplemented by up to biannual breast ultrasound and mammography usually starting at age 40. Key Messages: Apart from early detection, the central goal of care is the prevention of cancer. By utilizing individualized risk calculation, the optimal timeframe for risk-reducing surgery can be estimated, and counselees can be supported in reaching preference-sensitive decisions.

The goal of the Guideline "Early Detection of Breast Cancer in Germany" is to assist physicians, healthy women, and patients in the decision-making process in favour of appropriate health care regarding early detection and diagnosis of breast cancer. The principle of early detection of breast cancer embraces the detection of non-invasive stages of breast cancer (UICC stage 0, carcinoma in situ), reducing the frequency of invasive breast cancer development, as well as the identification of breast cancer at an early stage (UICC stage I) having a chance of cure of more than 90%, as shown by a large number of trials. The Guideline summarized in the following paper is a precondition to establishing a nation-wide, comprehensive, quality-assurance program for the early detection and diagnosis of breast cancer. The resulting consequence should be a timely mortality reduction of breast cancer. The cure of early stage disease will additionally be achieved by less intensive treatment methods while largely maintaining the quality of life of breast cancer patients. Implementing the Guideline offers the possibility of a significant improvement in women's health care.

BackgroundThe human cytosolic thioredoxin (Trx) contains a redox-active dithiol moiety in its conserved active-site sequence. Activation by a wide variety of stimuli leads to secretion of this cytoplasmic protein. Function of Trx1 has been implicated in regulating cell proliferation, differentiation, and apoptosis. The aim of this study was to assess the clinical significance of serum Trx1 level in patients with breast carcinoma.ResultsTo clarify whether serum levels of Trx1 could be a serum marker for breast carcinoma, we measured the serum levels of Trx1 in patients with various carcinomas (breast, lung, colorectal, and kidney cancers) using an ELISA, and investigated its associations with the tumour grading from I to III. At the cut-off point 33.1725 ng/ml on the receiver operating characteristic curve (ROC) Trx1 could well discriminate breast carcinoma from normal controls with a sensitivity of 89.8%, specificity 78.0%, and area under the ROC (AUC) 0.901 ± 0.0252. The serum level was well correlated with the progress of the breast carcinoma. We also investigated the diagnostic capacity of CEA and CA15-3 for the early detection of metastatic breast cancer comparing that of Trx1. In contrast to the serum CEA and CA15-3 tumour markers, the serum Trx1 levels of the early cancer (grade I) patients were significantly higher than those of normal control subjects, showing a high diagnostic sensitivity and selectivity (89.4% sensitivity, and 72.0% specificity). The serum levels of Trx1 in various patients with lung, colorectal, and kidney carcinomas indicate that the level of Trx1 is significantly higher than those of other cancer patients. Combinational analysis of CEA or CA15-3 with Trx1 for the detection of breast cancer suggest that the diagnostic capacity of CEA or CA15-3 alone for the early detection of breast cancer, especially regarding sensitivity, is significantly improved by its combination with Trx1.ConclusionsTaken together, we conclude that serum Trx1 is useful for the early diagnosis of breast cancer or the early prediction prognosis of breast cancer, and therefore has a valuable use as a diagnostic marker and companion marker to CEA and CA15-3 for breast cancer.

A vision for improved cancer screening in Nigeria.

In Western countries, women have a higher than 1-in-8 chance of developing breast cancer during their lives. Breast cancer represents the most frequently diagnosed cancer in women. The National Cancer Institute of U.S.A. estimates that, based on current rates, 13.2% of women born today will be diagnosed with breast cancer at some time in their lives. In order to reduce mortality, early detection of breast cancer is important, because therapeutic actions are more likely to be successful in the early stages of the disease. For women whose tumors were discovered early by mammography, the five-year survival rate was about 82% as opposed to 60% for the cases where the tumors were not found early. Mammography is currently the best radiological technique available for early detection of nonpalpable breast cancer. However, it is difficult for radiologists to provide both accurate and uniform evaluations for the large number of mammograms that they have to interpret in screening programs where most of the cases are normal; it has been observed that 10-30% of breast lesions are missed during routine screening. The situation is even more challenging since the early malignancies have small size and subtle contrast when compared with normal breast structures. Double reading (as carried out, for example, by two radiologists) helps to reduce the number of false negatives by 5-15%. Digital image-processing techniques represent useful tools for helping radiologists to improve their diagnosis with the aid of computer systems. In this sense, different CAD (computer-aided diagnosis) tools have been developed for improving image quality, identifying malignant signs, enhancing mammographic features, etc. On the average, the reader's sensitivity can be increased by 10% with the assistance of CAD systems. Some works have studied this potential of CAD to improve radiologists' performance in detecting clustered microcalcifications. There are a number of different classes of abnormality that may be observed in mammograms. One of the most significant types of mammographic abnormality is microcalcification. Microcalcifications are tiny granule like deposits of calcium. They are relatively bright (dense) in comparison with the surrounding normal tissue, and are up to about 1 mm in diameter, with an average diameter of 0.3 mm. Microcalcifications are of particular clinical significance when found in clusters of three or more within a square-centimeter region of a mammogram. Lanyi has described microcalcifications as “the most important leading symptom in mammographic detection of preclinical carcinomas.” Sickles noted that more than 50% of nonpalpable cancers had mammographically visible calcifications, and in 36% of nonpalpable cancers, calcifications were the only sign of abnormality. In an important study of cancers missed in screening mammography, it was observed that the presence of microcalcifications was the predominant feature in 18% of the missed cancers.

In the MRISC study, women with an inherited risk for breast cancer were screened by a 6-month clinical breast examination (CBE) and yearly MRI and mammography. We found that the MRISC screening scheme could facilitate early breast cancer diagnosis and that MRI was a more sensitive screening method than mammography, but less specific. In the current study we investigated the contribution of MRI in the early detection of breast cancer in relation to tumor characteristics. From November 1999 to October 2003, 1909 women were included and 50 breast cancers were detected, of which 45 were evaluable and included in the current study. We compared the characteristics of tumors detected by MRI-only with those of all other (non-palpable) screen-detected tumors. Further, we compared the sensitivity of mammography and MRI within subgroups according to different tumor characteristics. Twenty-two (49%) of the 45 breast cancers were detected by MRI and not visible at mammography, of which 20 (44%) were also not palpable (MRI-only detected tumors). MRI-only detected tumors were more often node-negative than other screen-detected cancers (94 vs. 59%; P=0.02) and tended to be more often <or=1 cm (58 vs. 31%; P=0.11). MRI was more sensitive than mammography for a wide spectrum of invasive tumor characteristics i.e., size, nodal status, histology, grade and ER status. Half of the breast cancers detected in this study were visible by MRI only and these tumors were smaller and significantly more often node-negative than other screen-detected tumors, suggesting that MRI makes an important contribution to the early detection of hereditary breast cancer.

Background Breast cancer remains a significant global health concern, with over 7.8 million cases reported in the last five years. Early detection and accurate classification are crucial for reducing mortality rates and improving outcomes. Machine learning (ML) has emerged as a transformative tool in medical imaging, enabling more efficient and accurate diagnostic processes. Objective This study aims to develop a machine learning-based predictive model for early detection and classification of breast cancer using the Wisconsin Breast Cancer Diagnostic dataset. Methods The dataset, comprising 569 samples and 32 features derived from fine needle aspirate biopsy images, was pre-processed through data cleaning, normalization using the Robust Scaler, and feature selection. Five supervised ML algorithms—Logistic Regression, Support Vector Classification (SVC) with linear and radial basis function (RBF) kernels, Decision Tree, and Random Forest—were implemented. Models were evaluated using performance metrics, including accuracy, precision, sensitivity, specificity, and F1 scores. Results The SVC-RBF model demonstrated the highest accuracy (98.68%) and balanced performance across other metrics, making it the most effective classifier for distinguishing between benign and malignant tumors. Key features such as texture mean and area (worst) significantly contributed to classification accuracy. Conclusions This study highlights the potential of ML algorithms, particularly SVC-RBF, to revolutionize breast cancer diagnostics through improved accuracy and efficiency. Future research should validate these findings with diverse datasets and explore their integration into clinical workflows to enhance decision-making and patient care.

Background: Early detection of breast cancer is crucial for optimal patient outcomes but cannot always be accomplished based on symptoms or screening mammography. Biomarker-based screening could aid early detection of breast cancer by improving sensitivity and specificity. Exai Bio has developed a novel liquid biopsy technology that detects and analyzes small non-coding RNAs that are cancer specific, termed orphan non-coding RNAs (oncRNAs). Previous work in patients with diagnosed breast cancer demonstrated that changes in oncRNAs in serum reflected treatment response and event-free survival. In this study, we developed an assay that measures oncRNAs in serum to detect breast cancer across the range of tumor stages and sizes. Methods: Previously, a library of ~260,000 oncRNAs from 32 different cancers was compiled based on smRNA sequences found in tumor tissues and largely absent in tumor-adjacent normal tissues from The Cancer Genome Atlas (TCGA). To refine this library for applications in serum, we sequenced smRNA in 31 control serum samples. These smRNA sequences were filtered from the larger library, reducing its size to 250,332 oncRNAs. The diagnostic performance of these oncRNAs was then assessed in an independent cohort of archived serum samples from 96 female patients with clinically diagnosed, untreated breast cancer and 95 age- and sex-matched individuals with no known history of cancer. We sequenced smRNAs at an average depth of 17.7 million 50-bp single-end reads per sample. Of the 250,332 oncRNAs in our library, 171,981 (68.7%) were detected in our independent study cohort. An ensemble of logistic regression models was trained with 5-fold cross-validation, using only those oncRNAs yielding an odds ratio &amp;gt;1 and observed in &amp;gt;6% of samples within each training set. Results: The cohort of 96 breast cancer patients and 95 matched controls had mean ages of 59.4 and 56.3 years, respectively. Area under the receiver operating characteristic curve (AUC) for detecting breast cancer was 0.94 (95% CI, 0.85–0.96). Sensitivities for detecting breast cancer at 95% specificity ranged from 0.75 to 0.87 among the four breast cancer stages, including a sensitivity of 0.81 for tumor stage I (Table 1); and from 0.67 to 0.87 among the four main TNM T categories (Table 2). Sensitivities at 95% specificity were relatively high for small tumors, at 0.75 (95% CI, 0.40–0.97) for T1b (&amp;gt;5mm to ≤10mm; n = 9) and 0.80 (0.68–0.94) for T1c (&amp;gt;10mm to ≤20mm; n = 37). Conclusions We have demonstrated the potential value of an oncRNA-based liquid biopsy assay by showing that oncRNAs can be used to detect breast cancer in serum samples with high sensitivity, and that detection requires fewer reads than are needed with other platforms. Moreover, we found that this oncRNA-based assay performed well in detecting early-stage breast cancer and small tumors. This suggests that an oncRNA-based liquid biopsy assay may be beneficial for early detection of breast cancer. Table 1. Model sensitivity by tumor stage. For the indicated numbers of cases (N), sensitivity and Pearson-Clopper 95% confidence intervals are reported for tumor detection by the oncRNA-based model at 95% specificity by tumor stage, as defined by the AJCC 7th Edition breast cancer staging system. Table 2. Model sensitivity by tumor size. For the indicated numbers of cases (N), sensitivity and Pearson-Clopper 95% confidence intervals are reported for tumor detection by the oncRNA-based model at 95% specificity by TNM T category, as defined by the AJCC 7th Edition breast cancer staging system. Citation Format: Taylor B. Cavazos, Jeffrey Wang, Oluwadamilare I. Afolabi, Alice Huang, Dung Ngoc Lam, Seda Kilinc, Jieyang Wang, Lisa Fish, Xuan Zhao, Andy Pohl, Helen Li, Kimberly H. Chau, Patrick A. Arensdorf, Fereydoun Hormozdiari, Hani Goodarzi, Babak Alipanahi. Orphan non-coding RNAs for early detection of breast cancer with liquid biopsy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-18.

Breast cancer has now overtaken lung cancer as the world’s most commonly diagnosed cancer, with thousands of new cases per year. Early detection and classification of breast cancer are necessary to overcome the death rate. Recently, many deep learning-based studies have been proposed for automatic diagnosis and classification of this deadly disease, using histopathology images. This study proposed a novel solution for multi-class breast cancer classification from histopathology images using deep learning. For this purpose, a novel 6B-Net deep CNN model, with feature fusion and selection mechanism, was developed for multi-class breast cancer classification. For the evaluation of the proposed method, two large, publicly available datasets, namely, BreaKHis, with eight classes containing 7909 images, and a breast cancer histopathology dataset, containing 3771 images of four classes, were used. The proposed method achieves a multi-class average accuracy of 94.20%, with a classification training time of 226 s in four classes of breast cancer, and a multi-class average accuracy of 90.10%, with a classification training time of 147 s in eight classes of breast cancer. The experimental outcomes show that the proposed method achieves the highest multi-class average accuracy for breast cancer classification, and hence, the proposed method can effectively be applied for early detection and classification of breast cancer to assist the pathologists in early and accurate diagnosis of breast cancer.

Breast cancer is a prevalent cause of death, and is the only form of cancer that is common among women worldwide and mammograms-based computer-aided diagnosis (CAD) program that allows early detection, diagnosis and treatment of breast cancer. But the performance of the current CAD systems is still unsatisfactory. Early recognition of lumps will reduce overall breast cancer mortality. This study investigates a method of breast CAD, focused on feature fusion with deep features of the Convolutional Neural Network (CNN). First, present a scheme of mass detection based on CNN deep features and modified clustering of the Extreme Learning Machine (MRELM). It forecasts load through Recurrent Extreme Learning Machine (RELM) and utilizes Artificial Bee Colony (ABC) to optimize weights and biases. Second, a collection of features is constructed that relays deep features, morphological features, texture features, and density features. Third, MRELM classifier is developed to distinguish benign and malignant breast masses using the fused feature set. Extensive studies show the precision and efficacy of the proposed method of mass diagnosis and classification of breast cancer.

Introduction: According to the National Cancer Institute José Alencar Gomes da Silva (INCA), the estimates for each year of the 2020–2022 triennium are 625,000 new cases of malignant neoplasms, with breast cancer being the most incident in women and responsible for 7% of deaths worldwide. Therefore, understanding the landscape of breast cancer in Santa Catarina will provide valuable information for planning and implementing more effective and targeted health policies. Methodology: The study is based on multiple sources, including reports from INCA, the Global Cancer Observatory, the Ministry of Health, the Brazilian Institute of Geography and Statistics, and the World Health Organization. It also considers the effects of the COVID-19 pandemic on screening and early detection of breast cancer. The role of national health programs, such as the National Breast Cancer Control Program, and the use of information systems like the National Cancer Information System and the Breast Cancer Control Program Information System are emphasized. Conclusion: In summary, this descriptive, ecological, and retrospective study analyzed the landscape of breast cancer in Santa Catarina from 2019 to 2023. The results highlight the importance of mammography, consultations with mastologists, and cytology and histology exams for early detection and proper treatment of breast cancer. Additionally, there is a need for health policies that encourage screening and ensure equitable access to health services across all age groups. It is crucial to adopt measures to mitigate the effects of the COVID-19 pandemic on health service utilization, ensuring that early detection and treatment of breast cancer are not compromised, thereby preventing more women from dying from the disease..