Early Detection Of Breast Cancer Research Articles

Unlocking the complete blood count as a risk stratification tool for breast cancer using machine learning: a large scale retrospective study

Optimizing early breast cancer (BC) detection requires effective risk assessment tools. This retrospective study from Brazil showcases the efficacy of machine learning in discerning complex patterns within routine blood tests, presenting a globally accessible and cost-effective approach for risk evaluation. We analyzed complete blood count (CBC) tests from 396,848 women aged 40–70, who underwent breast imaging or biopsies within six months after their CBC test. Of these, 2861 (0.72%) were identified as cases: 1882 with BC confirmed by anatomopathological tests, and 979 with highly suspicious imaging (BI-RADS 5). The remaining 393,987 participants (99.28%), with BI-RADS 1 or 2 results, were classified as controls. The database was divided into modeling (including training and validation) and testing sets based on diagnostic certainty. The testing set comprised cases confirmed by anatomopathology and controls cancer-free for 4.5–6.5 years post-CBC. Our ridge regression model, incorporating neutrophil–lymphocyte ratio, red blood cells, and age, achieved an AUC of 0.64 (95% CI 0.64–0.65). We also demonstrate that these results are slightly better than those from a boosting machine learning model, LightGBM, plus having the benefit of being fully interpretable. Using the probabilistic output from this model, we divided the study population into four risk groups: high, moderate, average, and low risk, which obtained relative ratios of BC of 1.99, 1.32, 1.02, and 0.42, respectively. The aim of this stratification was to streamline prioritization, potentially improving the early detection of breast cancer, particularly in resource-limited environments. As a risk stratification tool, this model offers the potential for personalized breast cancer screening by prioritizing women based on their individual risk, thereby indicating a shift from a broad population strategy.

Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization

A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.

Implementation of a clinical breast exam and referral program in a rural district of Pakistan

BackgroundThe role of clinical breast examination (CBE) for early detection of breast cancer is extremely important in lower-middle-income countries (LMICs) where access to breast imaging is limited. Our study aimed to describe the outcomes of a community outreach breast education, home CBE and referral program for early recognition of breast abnormalities and improvement of breast cancer awareness in a rural district of Pakistan.MethodsEight health care workers (HCW) and a gynecologist were educated on basic breast cancer knowledge and trained to create breast cancer awareness and conduct CBE in the community. They were then deployed in the Dadu district of Pakistan where they carried out home visits to perform CBE in the community. Breast cancer awareness was assessed in the community using a standardized questionnaire and standard educational intervention was performed. Clinically detectable breast lesions were identified during home CBE and women were referred to the study gynecologist to confirm the presence of clinical abnormalities. Those confirmed to have clinical abnormalities were referred for imaging. Follow-up home visits were carried out to assess reasons for non-compliance in patients who did not follow-through with the gynecologist appointment or prescribed imaging and re-enforce the need for follow-up.ResultsBasic breast cancer knowledge of HCWs and study gynecologist improved post-intervention. HCWs conducted home CBE in 8757 women. Of these, 149 were warranted a CBE by a physician (to avoid missing an abnormality), while 20 were found to have a definitive lump by HCWs, all were referred to the study gynecologist (CBE checkpoint). Only 50% (10/20) of those with a suspected lump complied with the referral to the gynecologist, where 90% concordance was found between their CBEs. Follow-up home visits were conducted in 119/169 non-compliant patients. Major reasons for non-compliance were a lack of understanding of the risks and financial constraints. A significant improvement was observed in the community’s breast cancer knowledge at the follow-up visits using the standardized post-test.ConclusionsBasic and focused education of HCWs can increase their knowledge and dispel myths. Hand-on structured training can enable HCWs to perform CBE. Community awareness is essential for patient compliance and for early-detection, diagnosis, and treatment.

Improved PAA algorithm for breast mass detection in mammograms

Mammography screening is instrumental in the early detection and diagnosis of breast cancer by identifying masses in mammograms. With the rapid development of deep learning, numerous deep learning-based object detection algorithms have been explored for mass detection studies. However, these methods often yield a high false positive rate per image (FPPI) while achieving a high true positive rate (TPR). To maintain a higher TPR while also ensuring lower FPPI, we improved the Probability Anchor Assignment (PAA) algorithm to enhance the detection capability for mammographic characteristics with our previous work. We considered three dimensions: the backbone network, feature fusion module, and dense detection heads. The final experiment showed the effectiveness of the proposed method, and the TPR/FPPI values of the final improved PAA algorithm were 0.96/0.56 on the INbreast datasets. Compared to other methods, our method stands distinguished with its effectiveness in addressing the imbalance between positive and negative classes in cases of single lesion detection.

Abstract PO2-13-08: Cell-free orphan noncoding RNAs and AI enable early detection of invasive breast cancer and ductal carcinoma in-situ

Abstract 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.

Abstract PO5-11-11: Risk Assessment, Prevention and Early Detection of Breast Cancer Related Lymphedema – Objective Measurements and Patient Reported Outcomes

Abstract Background Breast cancer-related lymphedema (BCRL) affects the quality of life, but there is no consensus regarding early detection and monitoring. Patient-reported outcomes (PROs) are important in assessing cancer survivor outcomes, and different questionnaires have been developed. LYMPHA (Lymphatic Microsurgical Preventive Healing Approach) and S-LYMPHA (Simplified LYMPHA) have reduced BCRL rates. This study aims to identify the most reliable PRO in a South Florida multi-ethnic population and to study correlations between PROs and objective measurements in the first 6 months after axillary surgery. Methods Patients undergoing axillary lymph node dissection (ALND) or axillary radiation were included. L-Dex score (Bioimpedance Spectroscopy) and three validated questionnaires (LyQLI, Lymphedema Quality of Life Inventory; LyQOL, Lymphedema Quality of Life and FACT-B4+ Functional Assessment of Cancer Therapy – Lymphedema) were recorded at baseline, and 6 months post-surgery to assess the agreement between patient-reported symptoms and BCRL. L-Dex score outside the normal range or a 10 unit increase above baseline was considered lymphedema. Additional variables such as demographics, tumor characteristics, and treatment modalities were recorded. Results Out of 40 recruited patients, 39 were analyzed (excluding one deceased patient). Patient and treatment characteristics are described in Table 1. Patients were divided into three cohorts (Figure 1) for analysis: ALND with no axillary radiation (7.69%; n=33), Sentinel Lymph Node Biopsy (SLNB) with axillary radiation (7.69 %; n=3), and ALND with axillary radiation (85%; n=3). In the first cohort, LYMPHA or S-LYMPHA were associated with lower rates of lymphedema (19% vs, 50%) (p=0.116). No patients in the second or third cohort developed lymphedema. There were 4 out of 20 (20%) patients with lymphedema with scores above the median for FACT B4+ (p= 0.480) and a lower score (higher quality of life) correlated with lower L-Dex (p=0.76). The higher value in the physical domain of LyQLI showed higher L-Dex (p=0.826). There was a significant inter-domain correlation in LyQLI and LyQOL (p= < 0.001). Conclusion These results validate the use of PROs alongside objective measurements to assess BCRL. In addition, LYMPHA and S-LYMPHA significantly reduce lymphedema rates. Larger numbers will be necessary to reach statistical significance and to identify the best PRO. Figure 1 Division of patient population into three cohorts Table 1 Demographic and Treatment Summary Citation Format: Anshumi Desai, Orli-Friedman Eldar, Gili Halfteck, Mecker Moller, Susan Kesmodel, Dido Franceschi, Neha Goel, Jessica Crystal, Laura Huang, Jennifer Hu, Koru-Sengal Tulay, Wei Zhao, Alexis Narvaez-Rojas, Eli Avisar. Risk Assessment, Prevention and Early Detection of Breast Cancer Related Lymphedema – Objective Measurements and Patient Reported Outcomes [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 PO5-11-11.

Abstract PO5-13-03: Early Detection of Breast Cancer using Targeted Plasma Metabolomic Profiling

Abstract Background: Breast cancer (BC) is the second leading cause of cancer death among women. Accordingly, early diagnosis is key to the successful treatment, management, and care of BC. Recent studies confirmed that plasma metabolites could be reliable cancer biomarkers, allowing for the development of a minimally invasive routine blood test that can be used for screening, as well as for monitoring disease evolution in patients. The exact metabolic pathways involved in early BC development remain unclear. Metabolomic profiling of women with BC may help to identify new biomarkers to predict breast cancer long before symptoms appear. The purpose of this study was to validate a plasma metabolomic biomarker panel for an improved risk assessment for early detection BC in 241 patients, and to understand the potential role and the relationship between BC subtypes and hormone receptor status. Methods: Our study included a total of 185 plasma samples from women with biopsy-confirmed BC and 56 plasma samples from healthy controls. A targeted, quantitative mass spectrometry (MS)-based metabolomics approach was used to analyze 138 metabolites in plasma samples using a combination of direct injection (DI) MS and reverse-phase high performance liquid chromatography (HPLC) tandem mass spectrometry (MS/MS). The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and hormones receptor status were used to determine optimal biomarker sets. The same biomarkers and regression models were used and assessed on the validation models. The area under the receiver operator characteristic curves (AUROC), sensitivities and specificities at selected cut off points were calculated for each subgroup. Results: A large proportion of BC patients were at an early stage, with 98 at stage I (53.0 %), 70 at stage II (37.8%), and 17 at stage III (9.2%). The BC patients were of all subtypes: 138 luminal A (76,2%), 23 luminal B (12.7%), 5 hormone receptor-negative/HER2-positive (2.8%), and 15 triples negative (8.3%). A feature selection was performed on the training set using Partial Least-Squares Discriminant Analysis (PLS-DA), and the top performing metabolites were identified as the most important in discriminating BC from healthy subjects. The impact of age was also investigated. Features with >80% missing values were removed. To predict BC, the best signature comprised 9 variables implicated in fatty acid metabolism, amino acid metabolism, polyamine biosynthesis and related signaling pathways We further developed and validated a logistic regression with AUROC > 0.9 using these metabolites and other clinical data for detecting different stages and subtypes of BC. Conclusion: This study identified and validated a simple, high-performing, metabolite-based test for the early detection of BC. After confirmation in other independent cohort studies, our findings could provide the foundations for the development of a blood-based routine test for women at the highest risk for BC that is cost-effective, accurate and reliable. In addition, this approach could be used to complement other modalities especially for BC patients with dense breasts. Citation Format: Jean-Francois Haince, Lun Zhang, Rashid Ahmed Bux, Paramjit S. Tappia, Bram Ramjiawan, David Wishart, Andrew Maksymiuk. Early Detection of Breast Cancer using Targeted Plasma Metabolomic Profiling [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 PO5-13-03.

Abstract PO5-07-08: A Serum Biomarker for the Early Detection of Breast cancer

Abstract Background: After decades of use, clinicians and medical societies are still debating on the clinical utility of mammography, the standard of care for breast cancer (BC) screening, because of its limitations such as subjective visual interpretation, high rate of false-positive and false-negative (in dense breast) and concerns over radiation exposure. There are 55 million women, and about 45% of them considered dense breast, in need of an accurate, easy to use test for the screening of BC regardless of breast tissue complexities to differentiate between BC and benign findings such as calcification and fibrosis. To this date there is no blood protein biomarker (BM) in clinical use for the early detection of BC. We have developed a novel BC specific protein biomarker -based blood test to detect patients with early-stage BC and established herein the diagnostic performance of our BM in discriminating BC patients from healthy individuals and benign breast cases. Methods: A total of 433 serum samples (318 primary BC, 32 benign breast and 83 normal breast) were obtained from the Cooperative Human Tissue Network and from the IRCCS San Raffaele Pisana Research Center (blind samples). 247 BC samples were provided as blind samples and were used to validate the screening utility. Serum samples were tested with our BC specific BM by sandwich ELISA. Receiver operating characteristic (ROC) curves were calculated to evaluate the diagnostic performance, by determining sensitivity (SE), specificity (SP), area under the curve (AUC), and confidence interval (CI). Statistical analysis, ROC curves and scatter plots were performed with GraphPad Prism 7.0. Results: BM elevation in early cancer cases (stage I-II, Ductal Carcinoma) was found to be statistically significant (p< 0.0001) as compared to healthy and benign cases. Our test discriminated early BC patients from non-cancer cases with 87.3% SE at 85.2% SP and was further validated in a blind set including 247 BC (Ductal & Lobular Carcinoma, stage I-III), 30 benign and 83 normal breast cases (88%SE, 86%SP). The sensitivity for early-stage (I-II) and late stage (III) BC was 86.8% and 94.3%, respectively. While lobular carcinoma is particularly not well detected by mammography with a sensitivity as low as 57% and much lower for women with dense breast, it was detected with high sensitivity (90%) in our test. 80% of fibrocystic changes (FCC), the most common form of benign breast disease in women over 50 years of age were negative with our test. Although it is a benign condition, FCC may be misdiagnosed as a malignant lesion by physical examination and imaging. Conclusion: Our results support the utility of our blood-based test for the early detection of breast cancer which presents improved features over mammography such as higher lobular cancer sensitivity, lower detection rate of common form of benign condition often misdiagnosed by imaging, absence of radiation and expected high compliance. Further testing is needed to confirm accuracy of our test in the dense breast population. Citation Format: Christine Chavany, Jeffrey Dea, Monica Guevara, Rafael Hernandez-Gonzalez, Patrizia Ferroni, Fiorella Guadagni, Rosaura Valle, Moncef Jendoubi. A Serum Biomarker for the Early Detection of Breast cancer [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 PO5-07-08.

Abstract PO2-13-12: Serum Thioredoxin 1 Enhances Breast Cancer Detection in Mammography and Breast Ultrasound, Including Dense Breasts

Abstract Background: Breast cancer (BC), when detected in its early stages, generally carries a more favorable prognosis than other types of cancer. Currently, the screening and early detection of breast cancer rely predominantly on image-oriented diagnostic methods. However, these methods inherently fall short in capturing the dynamic nature of cancer cells. Mammography has known limitations in clinical sensitivity, particularly in cases with high breast density. While breast ultrasound can mitigate some of these limitations, there remain unmet needs in this field. Previously, we reported the promising clinical performance (Sensitivity: 96.43%, Specificity: 97.32%) of serum thioredoxin 1 (Trx1) as a potential novel means for detecting breast cancer. In this study, we aim to evaluate the extent to which serum Trx1 can enhance the accuracy of breast cancer diagnosis, including in dense breasts, within the imaging-based diagnostic system powered by mammography and ultrasonography. Methods: We have generated monoclonal antibodies against Trx1 and developed an ELISA kit (DxMe® BC Kit, E&S Healthcare, Korea) that quantifies Trx1 in serum. A total of 308 biopsy-confirmed breast cancer patients, who had undergone mammography prior to their final diagnosis, were recruited. The clinical information of these 308 patients was retrospectively examined and collected. The data from 254 individuals, who also had results from breast ultrasound, and 261 individuals, who had records of breast density, were analyzed collectively. Freshly collected blood was prepared as previously indicated, and then tested with the kit to determine the level of Trx1. All serum samples were measured twice, and statistical analyses were conducted using ROC analysis, one-way ANOVA, and unpaired t-tests. Results: Clinical sensitivity for BC detection was analyzed using the Trx1 levels of the 254 subjects who underwent both mammography and breast ultrasound. The respective sensitivities of mammography, breast ultrasound, and the DxMe® BC Kit were 72.83%, 92.91%, and 96.46%. With combined analysis of mammography, breast ultrasound, and Trx1, the sensitivity increased to 98.43% for mammography + breast ultrasound, and 98.82% for mammography + Trx1. When breast ultrasound + Trx1 was considered, the sensitivity rose to 99.61%. Likewise, the sensitivity of mammography + breast ultrasound + Trx1 was also measured at 99.61%. Among the 261 breast cancer patients with available information about breast density, 186 patients (71.3%) had dense breasts. The clinical sensitivity of mammography and Trx1 was specifically analyzed for these dense breast cases. Mammography demonstrated a sensitivity of 64.3%, while the DxMe® BC kit exhibited a sensitivity of 97.2%. When mammography and Trx1 were analyzed in parallel, sensitivity increased to 99.7%. Conclusion: This study demonstrated that the DxMe® BC kit, which quantifies serum Trx1 levels, can enhance the accuracy of conventional image-based diagnostics for breast cancer screening and early detection by more than 25%. This improvement in sensitivity was observed when mammography and breast ultrasound were each used in conjunction with Trx1, with no significant difference noted between these two combinations. Notably, in the case of dense breasts, the Trx1 test improved sensitivity by more than 30% compared to mammography. Given that sensitivity increases when mammography and Trx1 tests are used together, it showed that incorporating the Trx1 test alongside traditional mammography could significantly enhance diagnostic performance in dense breasts up to 35%. This approach has the potential to streamline the breast cancer diagnostic process, reduce time and cost, and improve the reliability of current breast cancer screening and early detection methods, regardless of breast density. Citation Format: Young Kim, Hyemi Ko, Yeri Lee, Keunkoo Shin, Jongam Song, Jeryoung Kim, Kyounghoon Suh, Jinsun Lee. Serum Thioredoxin 1 Enhances Breast Cancer Detection in Mammography and Breast Ultrasound, Including Dense Breasts [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 PO2-13-12.

Abstract PO5-07-12: Feasibility of organized population-based mass-level mammography screening (0.2 million women) –Report from LMIC-Uzbekistan

Abstract Background. Breast cancer is one of the leading diseases worldwide. The early detection of breast cancer is the best way to improve survival. Mammography is included in the standard guidelines of all the leading organizations. The procedure is technically demanding and cost sensitive. Despite being a standard, it is not practical in many countries, and more so in LMIC. The state of Uzbekistan embarked on a massive, unique, organized population-based program of mass-level mammography screening, starting from one region of the country and expanding to others. Here, we report the process of such establishment and the feasibility of this innovative model. Methods. Realizing the importance of breast cancer screening, the Republic of Uzbekistan initiated a pilot project in the Bukhara Region. The area is divided into 12 districts. Every district was equipped with a stationary mammography machine in a regional polyclinic and one mobile van. The personnel were appropriately trained and a selection process was established to invite women for screening. The invitation was sent using the local area's existing municipality residential directory, which contains the complete demographic details of every individual living in the area. The state has 100 % coverage in the registry. The study targeted every woman aged 45-65 starting May 2021. All mammographs were digitally recorded and sent in real time to a centralized server at the National Republican Cancer Center in Tashkent, Uzbekistan. Reporting is performed at a centralized reading center on the information system platform. The program envisages a complete care pathway, from screening to diagnosis and treatment. Results. Over a period of two years, 140506 women in the age range–45-65 underwent mammography screening. Depending on the size of the target group of the district, an average of 30-60 mammograms were performed per day. A reported preliminary analysis shows the presence of BIRADS 0 in 4495, BIRADS 1 IN 78634, BIRADS 2 in 64345, BIRADS 4 in 4998, BIRADS 5 in 893. All women with BIRADS 4 and 5 were referred to the regional comprehensive cancer center for further evaluation. A total of 7570 patients underwent cytology and 408 underwent core biopsy. Depending on the reports, the patients were appropriately managed. A total of 1885 patients were recalled for additional screening owing to their inappropriate quality. After a year of function, the project was expanded with “Mobile Vans” to other regions of the country. An additional 13 mobile mammography vans were added after 1.5 years. From January 2023, approximately 50000 mammograms were additionally performed, taking the total number of screened women to 190506. Conclusion. Thus, it is feasible to plan and successfully implement state-owned mass-level mammography screenings. This project has the potential for duplication in any part of the world, and other organizations can learn from this extensive, wide program. Citation Format: Mirzagaleb Tillyashaykhov, Aysulu Seytmuratova, Alexander Ososkov, Saide Djanklich, Yakhyo Ziyaev, Dinesh Pendharlkar. Feasibility of organized population-based mass-level mammography screening (0.2 million women) –Report from LMIC-Uzbekistan [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 PO5-07-12.

Abstract PO4-07-02: Development of an Artificial Intelligence-based breast cancer detection model using Plasma Lipidomic Signature

Abstract Background: Mammography is the current diagnostic standard for breast cancer screening and monitoring. However, accessibility challenges, accuracy issues and patient discomfort all contribute to reduced patient compliance and utilization, resulting in a need for more effective diagnostic tools. An artificial intelligence (AI)-based lipidomic blood test may add significant value to early breast cancer detection rate and improve outcomes for patients. We have previously reported a series of lipidomic studies (n=793) and derived a lipid signature from plasma-enriched extracellular vesicles (EVs) that effectively distinguished people with localized breast cancer from cancer-free controls. Here we report the development of a breast cancer detection AI model from lipidomic data assessed directly using plasma samples. Methods: Lipids in both EVs and plasma collected from fasted breast cancer and control blood samples (n=256) were extracted and analysed by liquid chromatography-high resolution mass spectrometry (LC-HRAM-MS). Over 400 manually curated lipids were quantified. A bootstrapped analysis using Boruta, a robust and statistically rigorous feature selection algorithm based on random forest feature importance, was employed to identify cancer discriminatory lipid signatures in EV and plasma lipidomes consistently selected across 2000 bootstrap samples. The resulting lipid signature was then used to train an ensemble of 18 distinct machine learning models for cancer status prediction using a majority vote to aggregate the individual predictions. Model performance and variability were assessed over 2000 iterations of leave-group-out cross-validation (LGOCV) using an 80/20 train-test split. Average patient-level predictions across LGOCV iterations were recorded for both EV-and plasma-derived models and the two modalities were compared using an exact paired samples test (McNemar’s test). Results: Both the EV- and plasma-derived lipid signatures performed well in distinguishing breast cancer samples from controls. The development of a bioinformatics AI pipeline enabled the creation of a robust ensemble model achieving an F1 score of 0.89 in plasma with LGOCV. The final plasma ensemble predictive performance of 86.1% (±4.5%) in accuracy, 91.4% (±5.4%) in sensitivity, and 78.7% (±8.6%) specificity was achieved, which is comparable to that of EV (accuracy: 86.1±4.4%, sensitivity: 90.4±5.3%, specificity: 80.2±8.7%). Paired samples analysis using McNemar’s test indicated no significant differences between models trained on EV- and plasma-derived lipid signatures in either the sensitivity (p=0.65), specificity (p=0.49), or accuracy (p=0.42). Conclusion: The initial study demonstrated the high performance of a plasma-enriched extracellular vesicle-derived lipid biomarker signature for early breast cancer detection. Direct assessment of the lipidomic signature from plasma showed promise in simplifying the test. Assessing plasma directly offered advantages in terms of scalability, higher throughput, and ease of implementation. Further verification of the lipid signature in an upcoming study involving 500 plasma samples is planned. Ongoing studies will further optimize the plasma lipidomic signature and strengthen our AI pipeline. These findings support the potential clinical application of AI-based lipidomic profiling as a blood-based screening tool for breast cancer detection. Citation Format: Ameline Lim, Cheka Kehelpannala, Fatemeh Vafaee, Forrest Koch, Dana Pascovici, Desmond Li, Kerry Heffernan, Gillian Lamoury, Amani Batarseh, Bruce Mann. Development of an Artificial Intelligence-based breast cancer detection model using Plasma Lipidomic Signature [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 PO4-07-02.

Abstract PO1-07-03: An Accurate Breast Cancer Diagnosis Method Using a Multi-Feature Fusion Neural Network on Contrast-Enhanced Spectral Mammography

Abstract Objectives: Our study aims to develop an accurate and comprehensive deep neural network capable of classifying CESM images to aid in the early detection and diagnosis of breast cancer in clinical settings. Methods: We enrolled diagnostic CESM examinations conducted between January 1, 2019 and January 17, 2021. We developed and tested the performance of a multi-feature fusion network for breast lesion classification by combining low-energy (LE) and dual-energy subtracted (DES) images, dual-view, and bilateral information using a large and diverse CESM dataset. We have evaluated the ability of the proposed network to generalize on external datasets and the results were reported using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results: In the study period 1973CESMs were included. Mean age was 53 years ± 12 (standard deviation). In the internal test dataset, the model with CESM (LE +DES) inputs combined not only bilateral information (left and right breasts) but also dualview information (CC and MLO), achieved best diagnosis performance with AUC of 0.96 [95% confidence interval (CI): 0.95–0.97], accuracy of 0.9 [95% confidence interval (CI): 0.88–0.92], sensitivity of 0.84 [95% confidence interval (CI): 0.80–0.88] and specificity of 0.93 [95% confidence interval (CI): 0.91–0.95], while the model with LE inputs only got an AUC of 0.94 [95% (CI): 0.93–0.95], accuracy of 0.88 [95% confidence interval (CI): 0.86–0.90], sensitivity of 0.79 [95% confidence interval (CI): 0.74–0.83] and specificity of 0.93 [95% confidence interval (CI): 0.91–0.95] (external dataset: an AUC of 0.90 [95% (CI): 0.85–0.94], accuracy of 0.84 [95% confidence interval (CI): 0.78–0.89], sensitivity of 0.77 [95% confidence interval (CI): 0.61–0.88] and specificity of 0.87 [95% confidence interval (CI):0.80–0.92]). Conclusion: CESM is a promising technique in breast cancer diagnosis due to its high feasibility and potential. The results demonstrate that left-right breast fusion and dual-view fusion are helpful for accurate diagnosis on CESM images. Specifically, dual-energy subtracted (DES) images generated by CESM improve diagnostic accuracy when compared to low-energy (LE) images alone. Keywords: Contrast-enhanced spectral mammography; Deep neural network; Breast cancer diagnosis; Multi-feature fusion Citation Format: Yipeng Song, Wei Jiang. An Accurate Breast Cancer Diagnosis Method Using a Multi-Feature Fusion Neural Network on Contrast-Enhanced Spectral Mammography [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 PO1-07-03.

Abstract PO5-12-01: Characteristics of Patients Served by Participants in a Survivorship Navigator Learning Collaborative

Abstract Patient navigation is demonstrated to be a successful model for improving breast cancer screening and early detection, particularly for women in under-resourced communities. The effectiveness of this model has not yet been explored in cancer survivorship. Our research assesses the feasibility of community-based patient navigation after a breast cancer diagnosis. This analysis focuses on the role of patient navigators in providing community-based peer support for women living with breast cancer. We conducted an analysis of data from the Breast Cancer Survivorship Patient Navigator Virtual Learning Collaborative (VLC), a case-based, iterative, virtual training program designed to increase capacity of community-based patient navigators to support women diagnosed with breast cancer. Navigators participated in a 14-week practice-based curriculum focused on improving community support for breast cancer survivors. Each session included a brief lecture followed by a case presentation by a participant. Lecture topics include Breast Cancer 101, Psychosocial support, Treatment options, Clinical trials, Sexual Health, Palliative Care, Genetic Testing, and Trauma. All participants were asked to present at least one de-identified case for discussion, using an 55-item case presentation template. The template included data on patient demographics, diagnosis, treatment, and support. To encourage active learning, navigators were encouraged to present challenging cases for discussion focused on identifying solutions and providing peer support. This analysis focuses on the characteristics of patients presented by community navigators from January 2021 to May 2023. A total of 92 de-identified patient cases were presented by 71 patient navigators and community health workers. (Table 1) The majority of patients were diagnosed within 12 months (62%), with early stage (Stages I-III) disease (78%), and were in active treatment or had not yet started treatment (67%). More than half of the cases had tumors that were HR+ (54%) and nearly half were HER2- (43%); 9% of cases were triple negative and 9% were triple positive. Tumor characteristics were not reported for 27% of cases. Individuals diagnosed with metastatic breast cancer (MBC) compared with those diagnosed with early stage disease were more likely to be Black (59% v 39%), single (56% v 31%), not currently employed (100% v 46%), younger (average age 52 v 55 years old) and insured through Medicaid (56% v 17%) Individuals living with MBC relied more heavily on professional support compared to those diagnosed with early stage disease. The majority of MBC patients relied on the clinical care team (67%) and friends and family (67%) for support, with a large number also receiving support from faith-based organizations (56%), compared to those with early stage disease who relied on friends and family (90%) and the clinical care team (54%). Both groups relied on navigators (33% v 35%) and social workers (44% v 34%), while those with MBC relied more frequently on paid caregivers (44% v 3%), disability services (22% v 6%). The data indicates that individuals with MBC had more unmet needs, including practical support (55% v 33%) and disability (55% v 14%). Supportive care needs vary considerably based on stage of diagnosis, while community-based supportive services are often designed to meet the needs of those diagnosed with early stage disease. Community-based survivorship navigators more readily engage with patients with early stage disease who are newly diagnosed or in active treatment. Individuals with MBC have fewer community-based resources and are less likely to connect with a community-based navigator. Data from our Breast Cancer Survivorship Patient Navigator VLC points to the potential to identify gaps in community-based survivorship care. Future research should focus on expanding the reach of community navigators and ensuring community-based support for patients across all stages. Table 1 Demographics of Patients Presented at VLC Citation Format: Tamara Hamlish, Talia Holzman Castetllands. Characteristics of Patients Served by Participants in a Survivorship Navigator Learning Collaborative [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 PO5-12-01.

Investigating alterations in amino acid levels and immune cell population as potential biomarkers for non-invasive early detection of breast cancer: Insights from a study in north India

Investigating alterations in amino acid levels and immune cell population as potential biomarkers for non-invasive early detection of breast cancer: Insights from a study in north India

Circulating MiR-1185¸ MiR-3183, and MiR-584 Levels in Plasma as Diagnostic Non-invasive Biomarkers for Early Detection of Breast Cancer

Introduction: MicroRNAs (miRNAs) are non-coding RNA molecules with short sequences that function as main post-transcriptional gene regulators of different biological pathways via negative regulation of gene expression, thereby leading to either mRNA degradation or translational blockade. Dysregulated expression of these miRNAs has been related etiologically to many human diseases, including breast cancer. Various cellular processes of breast cancer progression, including cell proliferation, apoptosis, metastasis, recurrence and chemodrug resistance, are modulated by oncogenic miRNA (oncomiR). Objective: The objective of this investigation was to study the expression level and potential diagnostic/ prognostic roles of circulating microRNAs (miR-3183, miR-1185, and miR-584) as novel breast cancer biomarkers. Method: The current study was conducted on 99 breast cancer (BC) female patients, aged between 20-63 years old, as the case group and 50 age-matched healthy females as control (HC). After microRNA extraction from the serum samples, real-time PCR was carried out for relative expression quantification of miR-1185, miR-3183a, and miR-584. The ROC curve analysis was performed to investigate the diagnostic value of miRNAs. Result: It was demonstrated that miRNA-1185, miRNA-584, and miRNA-3183 were significantly up-regulated (p-values <0.0001) in female BC cases compared to the control group. Besides, based on the ROC analysis for BC versus HC, it was revealed that the AUC for miRNA-584 was 0.844 (95% confidence interval (CI) and could be proposed as a diagnostic biomarker for breast cancer screening and follow-up. Conclusion: MiRNAs expression profiling using blood-based samples demonstrated their upregulation in the serum and plasma and revealed the concept that circulating miRNAs have high potential as novel noninvasive biomarkers for cancer diagnosis and screening.

Convolutional neural network with parallel convolution scale attention module and ResCBAM for breast histology image classification

Breast cancer is the most common cause of female morbidity and death worldwide. Compared with other cancers, early detection of breast cancer is more helpful to improve the prognosis of patients. In order to achieve early diagnosis and treatment, clinical treatment requires rapid and accurate diagnosis. Therefore, the development of an automatic detection system for breast cancer suitable for patient imaging is of great significance for assisting clinical treatment. Accurate classification of pathological images plays a key role in computer-aided medical diagnosis and prognosis. However, in the automatic recognition and classification methods of breast cancer pathological images, the scale information, the loss of image information caused by insufficient feature fusion, and the enormous structure of the model may lead to inaccurate or inefficient classification. To minimize the impact, we proposed a lightweight PCSAM-ResCBAM model based on two-stage convolutional neural network. The model included a Parallel Convolution Scale Attention Module network (PCSAM-Net) and a Residual Convolutional Block Attention Module network (ResCBAM-Net). The first-level convolutional network was built through a 4-layer PCSAM module to achieve prediction and classification of patches extracted from images. To optimize the network's ability to represent global features of images, we proposed a tiled feature fusion method to fuse patch features from the same image, and proposed a residual convolutional attention module. Based on the above, the second-level convolutional network was constructed to achieve predictive classification of images. We evaluated the performance of our proposed model on the ICIAR2018 dataset and the BreakHis dataset, respectively. Furthermore, through model ablation studies, we found that scale attention and dilated convolution play an important role in improving model performance. Our proposed model outperforms the existing state-of-the-art models on 200 × and 400 × magnification datasets with a maximum accuracy of 98.74 %.

Knowledge and Practice Regarding Breast Self-Examination (BSE) among Nursing Students Working in A Tertiary Care Hospital Lahore, Pakistan: An Observational Descriptive Cross Sectional Study

Background: Breast self-examination (BSE) is a simple, quick, and cost-free method crucial for the early detection of breast cancer, which significantly reduces morbidity and mortality. Health Sciences students, particularly those in nursing, are ideally positioned to serve as role models and educators on this practice. Objective: The objective of this study was to assess the knowledge and practice of BSE among nursing students at a tertiary care hospital in Lahore, with a view to identifying gaps that could be addressed through targeted educational programs. Methods: This observational descriptive cross-sectional study engaged nursing students from various academic levels at a tertiary care hospital in Lahore. Participants were selected through convenience sampling. Adherence to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement was ensured. Data were collected via a self-administered structured questionnaire and analyzed using SPSS software version 26, focusing on frequencies and percentages of categorical variables. Results: Of the participants, 63.76% demonstrated a high level of knowledge about BSE, scoring ≥75%. In contrast, 36.23% scored below 75%, indicating a low level of knowledge. Practice of BSE was limited, with only 47% (n=65) of the participants engaging in the practice, and the majority, 53% (n=73), not practicing at all. Conclusion: Although nursing students show a good understanding of BSE, a significant gap exists between their knowledge and actual practice. This discrepancy highlights the need for enhanced educational interventions to encourage regular BSE practice, which is essential for the effectiveness of breast cancer screening and early detection efforts.

Radiographic features and molecular subtypes association of breast cancer in women younger than 40 years old

Background: Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death among females, particularly in women under the age of 40 years. However, early detection of breast cancer in this population remains challenging and it tends to present at a later stage with poorer prognosis.Objective: To review mammographic and ultrasonographic findings, pathological features and molecular subtypes of breast cancer in younger than 40-year-old patients diagnosed in King Chulalongkorn Memorial Hospital and to determine which radiological characteristics are associated with molecular subtypes. Materials and Methods: The study included 278 patients aged under 40 years who were diagnosed with breast cancer and underwent mammographic and ultrasonographic studies between January 2009 and December 2019. A retrospective review of mammographic and ultrasonographic findings, histopathological reports as well as biological markers were made. The association of radiological characteristics and molecular subtypes was analyzed by SPSS. Results: In the 278 patients, the most common clinical presentation was palpable mass (268, 96.4%). The common mammographic findings were irregular shape mass (196, 77.8%) with hyperdensity (114, 45.2%) and an obscured margin (99, 39.3%). Presenting of microcalcification is not frequent (122, 48.4%). We found 27 patients with normal mammograms which were later detected in ultrasounds as 25 masses, 1 intraductal lesion and 1 focal duct change. The predominant ultrasonographic features were irregular shape mass (257, 91.5%), an angular margin (89, 31.7%), hypoechogenicity (198, 70.5%), no posterior feature (210, 74.7%) and internal vascularity (170, 60.5%). These radiological characteristics were classified as BI-RADS 5 in 194 lesions (69%). The most common histopathological type was mixed-type carcinoma (143, 50.9%), followed by invasive ductal carcinoma (114, 40.6%). Luminal B was the mostly found in this study (86, 30.6%). The patients frequently presented with stage IIA (91, 32.7%) while 15 patients were detected with an advanced stage at the first presentation. We found that triple negative, HER 2 overexpression and luminal B subtypes were associated with an obscured mass on mammography (p 0.048). Luminal B and HER 2 overexpression subtypes were also associated with the presence of fine pleomorphic microcalcification (p <0.001). Conclusion: In this study, we found an association of the mass margin and suspicious calcification morphology on mammography with molecular subtypes. It would be helpful for further clinical management in young patients. The knowledge can be used for planning appropriate treatments according to molecular subtypes which are associated with these characteristics. However, the precision of cancer treatment is still based on the tissue diagnosis.

Breast Cancer Tumour Masks Segmentation Using Modified U-Net with Channel Attention

Abstract: A modified U-Net for biomedical image segmentation with channel attention is developed for breast cancer image segmentation. A unique step in biomedical image analysis is segmentation because it determines the accuracy of image analysis algorithms. Algorithms for accurate and early breast cancer detection in women using mammograms depend highly on the accuracy of the segmentation stage. In this thesis, in order to improve on the segmentation ability of the traditional UNet architecture, it was modified by varying the fixed kernel sizes that it was originally designed for. In this model, progressively increasing kernel sizes of 3x3, 5x5, 7x7, 9x9 and 11x11 were used, which is different from the architecture of UNet that depended on fixed 3x3 kernel sizes for all convolutional layers. The first Convolutional block of the proposed model contains two Conv2D layers with 3x3 kernel sizes, ReLU (Rectified Linear Unit) activation function, and 16 channel dimensions. A maxpooling2D layer with pool size of (2x2) was placed to downsample the image size before feeding to the next convolutional block which used the same activation function, with 32 channel dimension and 5x5 kernel size. The next convolutional block used 64 channel dimensions with 7x7 kernel size, then a 9x9 kernel size Conv2D block with 128 channel dimensions was placed before the Conv2D with upsampling. The squeeze and excitation block was placed after this convolutional layer and in-between the two convolutional blocks with 11x11 kernel sizes, to reallocate the weight of the essential features to ensure that more discriminate features are learnt from the breast cancer images to improve the overall representational strength of the network through the performance of dynamic feature recalibration of the image channels. This significantly improved the performance of the segmentation by explicitly modeling the interdependencies between channels in the convolutional layers. The increasing kernel sizes enables the model to learn more discriminate features from the images. Jaccard similarity index, F1 score, Recall, Precision, F2 score, and accuracy results were used to compare the result of the proposed model with other models such as U-Net model and Baseline model. The results shows a better performance of 0.88 for Jaccard similarity index, 0.94 for F1 score, 0.93 for recall, 0.92 for precision, 0.92 for F2 score, and an accuracy score of 0.98%. The model is good for biomedical image segmentation.

Diagnostic Potential Role of CXCL3 and Leptin Levels in Breast Cancer

The risk of breast cancer development is believed to be attributed to the alterations of a number of key biological components. Within this context, elevated levels of some chemokines that act as growth factors and can promote cancer development. The current study was designed to evaluate CXCL3 (a chemokine C-X-C Motif Ligand 3) and leptin (a peptide hormone synthesized by adipose tissue with cytokine activity) serum of Iraqi breast cancer patients in comparison to healthy controls. A total of 90 participants consisted of 60 patients diagnosed with breast cancer and 30 healthy women as control group were enrolled into this case-control study. Venous blood samples were collected from all participants to evaluate CXCL3 and leptin serum levels using ELISA. The results demonstrated significantly (P≤0.001) higher mean levels of CXCL3 and leptin in breast cancer patients (1.19±0.10 ng/mL and 130.44± 3.72pg/mL) compared to those of their healthy counterparts (0.430± 0.02\ng/mL and 57.1± 3.2pg/mL respectively). Interestingly, vast majority of the assessed breast cancer cases (up to 95-98%) showed to have elevated serum levels of both of the assessed potential biomarkers (CXCL3 and leptin). The present study results suggest an association of both CXCL3 and leptin in breast cancer pathogenicity. This supports the possibility of utilizing these potential biomarkers for breast cancer early detection and diagnosis.