Early-stage Breast Cancer Detection Research Articles

Abstract 2304: Detection of early-disseminated cancer cells with deep learning-enabled holographic imaging

Abstract Recent research increasingly validates the early dissemination of tumor cells in peripheral blood during the nascent stages of cancer, often preceding clinical identification of the primary tumor. This emerging evidence emphasizes the potential of Circulating Tumor Cells (CTCs) as early indicators for cancer. Despite their significance, CTCs are exceptionally rare compared to normal blood cells, which presents a considerable challenge in detection, particularly during early stages of cancer. Traditional detection methods, such as those based on nucleated cell enrichment, antibody labeling and fluorescence imaging, encounter notable limitations when applied in the context of early detection. These limitations arise from the inherent heterogeneity of CTCs, and the multistep process involved in labeling and manual enumeration, compromising cell viability and resulting in cell loss. This loss becomes particularly problematic in the context of early cancer detection scenarios where the tumor burden is extremely low, and any cell loss can significantly impact assay sensitivity. To overcome these challenges, we propose an innovative CTC detection approach employing a deep learning framework combined with holographic imaging. This technique utilizes coherent light to generate holograms from cell samples, capturing detailed 3D morphological and optical properties of individual cells. We developed a custom convolutional neural network designed to facilitate high-throughput cell sorting in real-time. Trained on healthy blood samples and diverse cancer cell lines, the dataset consists of more than 50 million cellular holograms enhanced by proprietary Gaussian annotation and image augmentation strategies. Moreover, by combining the morphological signatures from holography with antigen signatures (e.g. HER2/ER/PR) in real-time, our model demonstrates the ability to detect less than 1 false positive per 1 million nucleated cells, highlighting its potential as a robust tool for targeted early-stage breast cancer detection. Our technology represents highly sensitive alternative to traditional screening methods, with the promise of enhancing patient outcomes. Citation Format: Nicholas Heller, Kevin Mallery, Nathaniel Bristow, Yash Travadi, Eng Hock Lee, Jiarong Hong. Detection of early-disseminated cancer cells with deep learning-enabled holographic imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2304.

Breast Cancer Risk Factors among Women with Solid Breast Lesions.

Breast cancer is the most frequent malignancy in women worldwide and one of the most curable cancers if diagnosed at an early stage. Female patients presenting solid breast lesions are greatly predisposed to breast cancer development, and as such, effective screening of high-risk patients is valuable in early-stage breast cancer detection. The aim of our study was to identify the most relevant demographic, reproductive and lifestyle risk factors for breast cancer among women with solid breast lesions living in western Romania, namely the urban region consisting of Timisoara and the rural surrounding regions. From January 2017 to December 2021, 1161 patients with solid breast lesions, as detected by sonoelastography, were divided into two groups: patients with benign lesions (1019, 87.77%) and patients with malignant nodules (142, 12.23%). The malignancy group was confirmed by a histopathological result. Variables including age, BMI, menarche, menopause, years of exposure to estrogen, number of births, breastfeeding period, use of oral combined contraceptives, smoker status, family medical history and living area (rural-urban) were recorded. It was evidenced by our study that the main risk factors for malignancy were elevated age (OR = 1.07, 95% CI 1.05-1.08), BMI (OR = 1.06, 95% CI 1.02-1.10), living area (rural) (OR = 1.86, 95% CI 1.13-2.85) and family medical history (negative) (OR 3.13, 95% CI 1.43-8.29). The other proposed risk factors were not found to be statistically significant. Age and BMI were observed to be the most significant factors for breast cancer risk increase, followed by living in a rural area. A family history of breast cancer was shown to be inversely correlated with cancer risk increase.

Digital mammogram based robust feature extraction and selection for effective breast cancer classification in earlier stage

Breast cancer poses a significant health risk for women, demanding early detection to mitigate its mortality impact. Leveraging the power of Deep Learning (DL) in medical imaging, this paper introduces a hybrid model that integrates YOLOv7 and Half UNet for feature extraction. YOLOv7 identifies and localizes potential cancerous regions, while Half UNet focuses on extracting pertinent features with its encoder-decoder structure. The fusion of these discriminative features, coupled with feature selection via Coati Optimization, ensures a comprehensive and optimized dataset. The selected features then feed into the CatBoost classification algorithm, refining parameters iteratively for precise predictions and minimizing the loss function. Evaluation metrics, including precision, recall, specificity, and accuracy, demonstrate the model’s superior performance. Notably, the proposed model surpasses existing methods in early-stage breast cancer detection. Beyond numerical metrics, its significance lies in the potential to positively impact patient outcomes and increase survival rates. By amalgamating cutting-edge DL techniques, the model excels in identifying intricate patterns crucial for early cancer detection. The efficient fusion of YOLOv7 and Half UNet, coupled with feature optimization through Coati Optimization, sets this model apart. This research contributes to the evolving landscape of medical imaging and DL applications, emphasizing the potential for enhanced breast cancer diagnosis and improved patient prognoses.

Detection of Early-Stage Breast Cancer using Electromagnetic Sensor with Aid of the Convolution Neural Network

Detection of Early-Stage Breast Cancer using Electromagnetic Sensor with Aid of the Convolution Neural Network

Circulating tumor biomarkers in early-stage breast cancer: characteristics, detection, and clinical developments.

Breast cancer is the most common form of cancer in women, contributing to high rates of morbidity and mortality owing to the ability of these tumors to metastasize via the vascular system even in the early stages of progression. While ultrasonography and mammography have enabled the more reliable detection of early-stage breast cancer, these approaches entail high rates of false positive and false negative results Mammograms also expose patients to radiation, raising clinical concerns. As such, there is substantial interest in the development of more accurate and efficacious approaches to diagnosing breast cancer in its early stages when patients are more likely to benefit from curative treatment efforts. Blood-based biomarkers derived from the tumor microenvironment (TME) have frequently been studied as candidate targets that can enable tumor detection when used for patient screening. Through these efforts, many promising biomarkers including tumor antigens, circulating tumor cell clusters, microRNAs, extracellular vesicles, circulating tumor DNA, metabolites, and lipids have emerged as targets that may enable the detection of breast tumors at various stages of progression. This review provides a systematic overview of the TME characteristics of early breast cancer, together with details on current approaches to detecting blood-based biomarkers in affected patients. The limitations, challenges, and prospects associated with different experimental and clinical platforms employed in this context are also discussed at length.

From bedside to portable and wearable: development of a conformable ultrasound patch for deep breast tissue imaging.

A breakthrough study from Du etal. has developed a wearable, ultrasound imaging patch for standardized and reproducible breast tissue imaging. The technology utilizes a honeycomb patch design to facilitate guided movement of the ultrasound array, enabling comprehensive, multiangle breast imaging. The system was validated in vitro and in vivo with a single human subject and has the potential for early-stage breast cancer detection. This study addressed the current limitations of wearable ultrasound technologies, including imaging over large, curvilinear organs and integration of superior piezoelectric materials for high-performance ultrasound arrays. The transition of ultrasound from the bedside to portable and wearable devices will pave the way for integration with big data collection, such as artificial intelligence (AI)-based diagnosis and personalized ultrasonographic profile generation, for rapid and objective measurements. This advancement is especially important in the context of breast cancer, where early diagnosis and assessment of medical therapy responses are paramount to patient care.

Early-Stage Breast Cancer Detection in Breast Milk.

For the first time, we show that BM obtained from patients with breast cancer carries ctDNA, surpassing plasma-based liquid biopsy for detection and molecular profiling of early-stage breast cancer, even prior to diagnosis by image. See related commentary by Cunningham and Turner, p. 2125. This article is featured in Selected Articles from This Issue, p. 2109.

Implementation research: including breast examinations in a cervical cancer screening programme, Rwanda.

To evaluate whether integrating breast and cervical cancer screening in Rwanda's Women's Cancer Early Detection Program led to early breast cancer diagnoses in asymptomatic women. Launched in three districts in 2018-2019, the early detection programme offered clinical breast examination screening for all women receiving cervical cancer screening, and diagnostic breast examination for women with breast cancer symptoms. Women with abnormal breast examinations were referred to district hospitals and then to referral hospitals if needed. We examined how often clinics were held, patient volumes and number of referrals. We also examined intervals between referrals and visits to the next care level and, among women diagnosed with cancer, their initial reasons for seeking care. Health centres held clinics > 68% of the weeks. Overall, 9763 women received cervical cancer screening and clinical breast examination and 7616 received breast examination alone. Of 585 women referred from health centres, 436 (74.5%) visited the district hospital after a median of 9days (interquartile range, IQR: 3-19). Of 200 women referred to referral hospitals, 179 (89.5%) attended after a median of 11days (IQR: 4-18). Of 29 women diagnosed with breast cancer, 19 were ≥ 50 years and 23 had stage III or stage IV disease. All women with breast cancer whose reasons for seeking care were known (23 women) had experienced breast cancer symptoms. In the short-term, integrating clinical breast examination with cervical cancer screening was not associated with detection of early-stage breast cancer among asymptomatic women. Priority should be given to encouraging women to seek timely care for symptoms.

GBDTMO: as new option for early-stage breast cancer detection and classification using machine learning

Breast cancer is the second leading cause of disease death in women, after lung and bronchus cancer. According to measurements, mammography misses breast cancer in 10% to 15% of cases for women aged 50 to 69 years. In the current study, we used the Wisconsin breast cancer dataset to develop a two-stage model for breast cancer diagnosis. The main goal of this study effort is to effectively carry out feature selection and classification tasks. Gradient Boosting Decision Tree-based Mayfly Optimisation (GBDTMO), an innovative and efficient breast cancer diagnostic machine learning system, is provided. In the second stage, we employ a Mayfly search to determine which subset of traits is the best. Two more well-known datasets on breast cancer, the ICCR and the Cancer Corpus, were also compared for classification accuracy. The accuracy of the suggested GBDTMO model was higher than that of the existing GBDT and Practical Federated Gradient Boosting Decision Tree (PFGBDT), which had accuracy values of 93.25% and 94.25%, respectively. Similarly, the recall, F-measure, and ROC area values were 98.52%, 97.52%, and 96.32%, respectively. Furthermore, it demonstrated a lower RMSE of 0.98 than the existing GBDT and PFGBDT.

Generative adversarial networks with modified wasp swarm algorithm-based early-stage breast cancer detection techniques

Generative adversarial networks with modified wasp swarm algorithm-based early-stage breast cancer detection techniques

Semiannual Breast US or MRI Screening in Patients with a Personal History of Breast Cancer.

Background The wide variability of screening imaging use in patients with a personal history of breast cancer (PHBC) warrants investigation of its comparative clinical effectiveness. While more intensive screening with US or MRI at an interval of less than 1 year could increase early-stage breast cancer detection, its benefit has not been established. Purpose To investigate the outcomes of semiannual multimodality screening in patients with PHBC. Materials and Methods An academic medical center database was retrospectively searched for patients diagnosed with breast cancer between January 2015 and June 2018 who had undergone annual mammography with either semiannual incidence US or MRI screening from July 2019 to December 2019 and three subsequent semiannual screenings over a 2-year period. The primary outcome was second breast cancers diagnosed during follow-up. Examination-level cancer detection and interval cancer rates were calculated. Screening performances were compared with χ2 or Fisher exact tests or a logistic model with generalized estimating equations. Results Our final cohort included 2758 asymptomatic women (median age, 53 years; range, 20-84 years). Among 5615 US and 1807 MRI examinations, 18 breast cancers were detected after negative findings on a prior semiannual incidence US screening examination; 44% (eight of 18) were stage 0 (three detected with MRI; five, with US), and 39% (seven of 18) were stage I (three detected with MRI; four, with US). MRI had a cancer detection rate up to 17.1 per 1000 examinations (eight of 467; 95% CI: 8.7, 33.4), and the overall cancer detection rates of US and MRI were 1.8 (10 of 5615; 95% CI: 1.0, 3.3) and 4.4 (eight of 1807; 95% CI: 2.2, 8.8) per 1000 examinations, respectively (P = .11). Conclusion Supplemental semiannual US or MRI screening depicted second breast cancers after negative findings at prior semiannual incidence US examination in patients with PHBC. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Berg in this issue.

Multimodal analysis of genome-wide methylation, copy number aberrations, and end motif signatures enhances detection of early-stage breast cancer.

Breast cancer causes the most cancer-related death in women and is the costliest cancer in the US regarding medical service and prescription drug expenses. Breast cancer screening is recommended by health authorities in the US, but current screening efforts are often compromised by high false positive rates. Liquid biopsy based on circulating tumor DNA (ctDNA) has emerged as a potential approach to screen for cancer. However, the detection of breast cancer, particularly in early stages, is challenging due to the low amount of ctDNA and heterogeneity of molecular subtypes. Here, we employed a multimodal approach, namely Screen for the Presence of Tumor by DNA Methylation and Size (SPOT-MAS), to simultaneously analyze multiple signatures of cell free DNA (cfDNA) in plasma samples of 239 nonmetastatic breast cancer patients and 278 healthy subjects. We identified distinct profiles of genome-wide methylation changes (GWM), copy number alterations (CNA), and 4-nucleotide oligomer (4-mer) end motifs (EM) in cfDNA of breast cancer patients. We further used all three signatures to construct a multi-featured machine learning model and showed that the combination model outperformed base models built from individual features, achieving an AUC of 0.91 (95% CI: 0.87-0.95), a sensitivity of 65% at 96% specificity. Our findings showed that a multimodal liquid biopsy assay based on analysis of cfDNA methylation, CNA and EM could enhance the accuracy for the detection of early- stage breast cancer.

Clinical validation of the novel CLIA-CA-62 assay efficacy for early-stage breast cancer detection.

Without organized screening programs up to 60-70% of breast cancers are diagnosed at advanced stages that have significantly lower five-year survival rate and poorer outcomes, which is a serious global public health problem. The purpose of the blind clinical study was the assessment of the novel in-vitro diagnostic chemiluminescent CLIA-CA-62 assay for early-stage breast cancer detection. Blind serum samples of 196 BC patients with known TNM staging, 85% with DCIS, Stage I & IIA, and 73 healthy control subjects were analyzed with the CLIA-CA-62 and CA 15-3 ELISA assays. Results were also compared to the pathology findings and to published data from mammography, MRI, ultrasound, and multi-cancer early detection test (MCED). The CLIA-CA-62 overall sensitivity for BC was 92% (100% for DCIS) at 93% specificity and it decreased in invasive stages (Stage I=97%, Stage II=85% and Stage III=83%). For the CA 15-3 assay sensitivity was 27-46% at 80% specificity. Sensitivity for mammography was 63-80% at 60% specificity, depending on the stage and the parenchymal density. These results demonstrate that CLIA-CA-62 immunoassay could prove useful as a supplement to current mammography screening and other imaging methods, thus increasing the diagnostic sensitivity in DCIS and Stage I breast cancer detection.

The Impact of Social Determinants of Health on the Time Between Diagnostic Breast Imaging and Biopsy at a Safety Net Hospital.

Although social determinants of health (SDH) are thought to be associated with health outcomes, there is limited literature on the direct impact of SDH on delays in breast cancer diagnosis via breast imaging. Identifying SDH associated with longer lapses (defined in this study as a time interval between two events) between imaging and biopsy is essential to early-stage detection of breast cancer, which has a significant impact on survival. Previous work demonstrated associations between both housing and food insecurity with longer lapses between diagnostic imaging and biopsy. We aim to expand upon this retrospective analysis with a longer study period, more participants, and improved data cleaning techniques to better understand how SDH may affect the lapse between imaging and biopsy. This retrospective study was institutional review board approved and HIPAA compliant. Informed consent was waived. Patients who underwent screening mammography between January 1, 2015, and January 1, 2020, were assessed for timing of recommended biopsy due to a BI-RADS category 4 or 5. SDH were assessed with the unique Tool for Health & Resilience in Vulnerable Environments screening questionnaire developed at Boston Medical Center. Associations between imaging and biopsy timing and eight explanatory SDH variables (food insecurity, housing insecurity, ability to pay for medications, transportation access, ability to pay for utilities, caretaking needs, employment, and desire for more education) were assessed with multivariate Cox proportional hazard modeling, as well as demographic data. There were 2,885 unique patients who underwent 3,142 unique diagnostic imaging studies and were included in the multivariate analysis. Of those 3,142 imaging studies, 196 (6.2%) had not yet been followed by the recommended biopsy by the end of the study period; 2,271 patients (78.7%) had SDH data in at least one domain; and the individual domains ranged from 962 patients (32.1%) with complete data for education to 2,175 patients (75.4%) with complete data for food insecurity. A positive screen for at least one SDH was associated with a longer lapse between diagnostic imaging and biopsy (P= .048). Furthermore, housing insecurity alone was nearly associated with longer lapses between diagnostic imaging and biopsy (P= .059). Those who desired more education were found to have shorter lapses between diagnostic imaging and biopsy (P= .037). Only a positive screen of the aggregate of all SDH (using a novel tool developed at our safety net hospital) was associated with a statistically significant lengthening of this lapse. Of the eight SDH screened, housing insecurity was the closest to association with longer lapses between diagnostic imaging and biopsy, whereas patients who desired more education were found to have statistically significant shorter lapses; however, this survey domain had the lowest completion rate. Identification of which SDH might affect the time from imaging to biopsy can potentially inform targeted programs to intervene. Government and health system interventions addressing SDH, notably housing insecurity, could allow for shorter time to breast cancer diagnosis and treatment.

Investigation of Early-Stage Breast Cancer Detection using Quantum Neural Network

aided image diagnostics (CAD) have been used in many fields of diagnostic medicine. It relies heavily on classical computer vision and artificial intelligence. Quantum neural network (QNN) has been introduced by many researchers around the world and presented recently by research corporations such as Microsoft, Google, and IBM. In this paper, the investigation of the validity of using the QNN algorithm for machine-based breast cancer detection was performed. To validate the learnability of the QNN, a series of learnability tests were performed alongside with classical convolutional neural network (CCNN). QNN is built using the Cirq library to perform the assimilation of quantum computation on classical computers. Series of investigations were performed to study the learnability characteristics of QNN and CCNN under the same computational conditions. The comparison was performed for real Mammogram data sets. The investigations showed success in terms of recognizing the data and training. Our work shows better performance of QNN in terms of successfully training and producing a valid model for smaller data set compared to CCNN.

Automated Early Phase Breast Cancer Detection using Hybrid Machine Learning Algorithms

Breast cancer is the most common cancer among women. It occurs when few breast cells begin to grow abnormally. The national average for 2022 is 100.4 cases per 1,00,000 people, with a large number of women being diagnosed with breast cancer. The objective is to design a prediction system that can predict breast cancer at early stages using a set of attributes that have been selected from a critical dataset. The Wisconsin Kaggle dataset is used for this experiment. The goal of this work is to predict breast cancer utilizing hybrid machine learning methodologies, such as SVM and PCA. ML algorithms that could help to predict cancer, as the early detection of this disease would help to slow down the progression of other diseases. In our paper, we are implementing Hybrid algorithms like PCA and SVM and optimizing SVM with k-fold cross-validation for predicting Breast cancer at early stages with high accuracy. The goal is to raise the fraction of early-stage breast cancer detection and to reduce mistake rates with maximum precision, which are sustainable.

Muc-1 Family Tumor Markers and Their Role in the Diagnosis of Breast Cancer, Review Article

Background & Objective: Tumor markers are elements produced by tumors or other cells in the body in response to cancer or some benign condition. Although most of these markers are made by normal cells as well as cancer cells, they are produced at much higher levels in cancerous conditions. This study aimed to provide a method for using tumor markers to diagnose cancer and detect the presence of metastasis and recurrence of the disease. Materials & Methods: The present narrative review study was done by selecting the appropriate keywords and searching for research and review studies indexed in Google Scholar, PubMed, Science Direct, and SID databases. Results: These studies often indicate the effective role of tumor markers in the MUC-1 family (especially Cancer antigen15-3 (CA15-3) and CEA (carcinoembryonic antigen) as the most widely used tumor markers in patients with breast cancer. Conclusions: Based on information from studies on tumor markers, the combination of CEA, CA15-3, PRL (prolactin), KL-6 (Kerbs von den Lungen), Thioredoxin 1, and FER (ferritin) tumor markers can increase the sensitivity of early-stage breast cancer detection, and CA15-3 tumor markers can also be used to identify the presence or absence of metastasis to the axillary lymph nodes. The use of ultrasound (especially color Doppler) and its combination with CEA and CA15-3 tumor markers are recommended to improve the accuracy of a breast cancer diagnosis.

Reducing Disparities in Timely Follow-Up After Abnormal Findings on Screening Mammography.

Screening mammography has led to increased detection of early-stage breast cancer and has contributed to decreased breast cancer mortality [1]. However, these benefits of early detection are diminished when patients do not have timely diagnosis and treatment [2]. To facilitate timely treatment of screen-detected breast cancers, it is important to minimize delays to diagnostic imaging after an abnormal screening mammographic result.

A Novel Approach of a Low-Cost UWB Microwave Imaging System with High Resolution Based on SAR and a New Fast Reconstruction Algorithm for Early-Stage Breast Cancer Detection.

In this article, a new efficient and robust approach—the high-resolution microwave imaging system—for early breast cancer diagnosis is presented. The core concept of the proposed approach is to employ a combination of a newly proposed delay-and-sum (DAS) algorithm and the specific absorption rate (SAR) parameter to provide high image quality of breast tumors, along with fast image processing. The new algorithm enhances the tumor response by altering the parameter referring to the distance between the antenna and the tumor in the conventional DAS matrices. This adjustment entails a much clearer reconstructed image with short processing time. To achieve these aims, a high directional Vivaldi antenna is applied around a simulated hemispherical breast model with an embedded tumor. The detection of the tumor is carried out by calculating the maximum value of SAR inside the breast model. Consequently, the antenna position is relocated near the tumor region and is moved to nine positions in a trajectory path, leading to a shorter propagation distance in the image-creation process. At each position, the breast model is illuminated with short pulses of low power waves, and the back-scattered signals are recorded to produce a two-dimensional image of the scanned breast. Several simulations of testing scenarios for reconstruction imaging are investigated. These simulations involve different tumor sizes and materials. The influence of the number of antennas on the reconstructed images is also examined. Compared with the results from the conventional DAS, the proposed technique significantly improves the quality of the reconstructed images, and it detects and localizes the cancer inside the breast with high quality in a fast computing time, employing fewer antennas.

Diagnostic efficacy of the new prospective biomarker's combination CA 15-3 and CA-62 for early-stage breast cancer detection: Results of the blind prospective-retrospective clinical study.

Combination of different cancer markers is often used for predicting tumor growth, for the response to cancer therapy, and for increase in the positive diagnosis ratio in the malignant tumors. Evaluation of the diagnostic efficacy of CA 15-3 and CA-62 cancer markers combination for early stages of breast cancer (BC) detection. This blind study was performed on 2 clinically validated Sets that included serum measurements of CA 15-3 ELISA and CLIA-CA-62 assays in 488 serum samples with TNM classification. A study included 300 BC patients (254 at Stages I and II, 20 with ductal carcinoma in situ (DCIS), and 26 Stages III and IV patients), 47 patients with breast benign diseases, and 141 healthy controls. Sensitivity for DCIS & Stage I breast cancer detection was 75% at 100% Specificity (AUC = 0.895) using a following combination of two antigens: 10 < CA15-3 < 46 U/ml and CA-62 ⩾ 6300 U/ml, which allows eliminating false positive results. The results obtained in a blind study demonstrate that a combination of CA15-3 with CA-62 yields 75% Sensitivity at 100% Specificity for DCIS and Stage I breast cancer detection, which has a potential to be integrated into existing screening programs.