Breast Cancer Diagnosis Using Optimized Attribute Division in Modular Neural Networks

We describe in this paper the evolution of modular neural networks using hierarchical genetic algorithms for pattern recognition. Modular neural networks (MNN) have shown significant learning improvement over single neural networks (NN). For this reason, the use of MNN for pattern recognition is well justified. However, network topology design of MNN is at least an order of magnitude more difficult than for classical NNs. We describe in this paper the use of a hierarchical genetic algorithm (HGA) for optimizing the topology of each of the neural network modules of the MNN. The HGA is clearly needed due to the fact that topology optimization requires that we are able to manage both the layer and node information for each of the MNN modules. Simulation results prove the feasibility and advantages of the proposed approach.

Background: Our team has shown that younger versus older women, and both Blacks and Hispanics breast cancer (BC) survivors versus White women have a higher mortality after a second cancer. We have also shown that county-level neighborhood poverty is associated with increased cancer mortality after a BC diagnosis. In this study, we assess to what extent neighborhood-level socioeconomic status (SES) contributes to high BC mortality after second cancers and compare the association to BC mortality after a first cancer. We hypothesize that social determinants are critical to address in women who develop a second cancer despite now being part of the health system. Methods: Women diagnosed with a first BC and/or second cancer after first BC between 2007 and 2019 were identified from the Maryland (MD) Cancer Registry. Census tract-level neighborhood SES environments for poverty and median income were determined using local spatial autocorrelation analysis with Local Moran’s I of MD’s five-year census-tract estimates from the American Community Survey. MD census tract estimates above or below the state mean were identified as high (H)- or low (L)- tracts, respectively. We defined four neighborhood environments for each SES indicator as H-tracts among H-tracts (HH-tracts); L-tracts among L-tracts (LL-tracts); H-tracts among LL-tracts (HL-tracts); L-tracts among HH-tracts (LH-tracts). Women at least 20 years of age diagnosed with a local or regional primary BC, received surgery, and survived more than 365 days were assigned a tract-level neighborhood SES environment for each SES indicator associated with their first and second BC diagnosis. Time-to-event analyses were used to compare BC deaths across each tract-level SES indicator neighborhood environment separately. The data was analyzed using Multivariate Cox proportional hazard regression models adjusted for age at diagnosis, tract median income, cancer stage, treatment, and Estrogen Receptor status. Models were also stratified by race/ethnicity (Non-Hispanic White, Non-Hispanic Black), age at diagnosis (<50, 50-69, =>70), and treatment (hormone therapy and chemotherapy). Results: The analytical population included 15,149 MD women diagnosed with early-stage BC, of which 2,436 developed a second cancer, and BC deaths after first BC and second cancer diagnosis was 2039 and 315, respectively. We detected a 66% (HR=1.66, 95%CI=1.09, 2.53), 14% (HR=1.14, 95%CI=0.66,1.97), and 15% (HR=1.15, 95%CI=0.73,1.81) increase in BC death for women in LL, LH, and HL median income census tracts when compared to HH median income census tracts in the neighborhood income model after second cancer diagnosis. A 30% (HR=1.30, 95%CI=0.89, 1.90), 31% (HR=1.31, 95%CI=0.90, 1.93), and 20% (HR=1.20 95%CI=0.80, 1.79) increase in BC deaths for women in HH, LH, and HL poverty census tracts when compared to LL poverty census tracts was observed in the neighborhood poverty model after second cancer diagnosis. Notably, we observed increased BC death for women in LL, LH, and HL income neighborhood environments as well as in HH and HL poverty neighborhood environments at first BC diagnosis though not as robust as the second cancer neighborhood environment observations. Finally, neighborhood-level SES environments at first BC diagnosis were not statistically associated with BC death upon second diagnosis. Conclusion: Overall, our findings highlight the importance of neighborhood-level SES, such as poverty and median income, at the time of second cancer diagnosis as a strong indicator of BC mortality after second cancer, possibly even more than after a first BC. These findings provide initial evidence of the need for continued surveillance of neighborhood-level SES factors after a first BC diagnosis, and early interventions to improve survival among this group of high-risk women. Citation Format: Ifeoma Nwigwe, Michael Desjardins, Kala Visvanathan. The impact of neighborhood-level social determinants on breast cancer mortality after a second primary breast cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P5-04-03.

Breast cancer (BC) and genital organ cancers (GOC) are known to have a major impact on the quality of life of patients. The aim of this study was to analyze the risk of depression, anxiety, and adjustment disorders in women in Germany with a suspected but unconfirmed diagnosis of BC or GOC in their medical history. This study included women who received a suspected diagnosis of BC or GOC and were followed between 2007 and 2015 (index date). These women were matched (1:1:1) by age to women with a confirmed diagnosis of BC or GOC and women without a cancer diagnosis. The main outcome measure of the study was the rate of depression, anxiety, and adjustment disorder diagnoses within 3years of the index date. The present analysis included a total of 4,842 patients (mean age=49.3years). Within 3years of the index date, 23.5% of women with a confirmed diagnosis of BC or GOC, 14.1% of those with a suspected diagnosis of BC or GOC, and 10.5% of those without a cancer diagnosis developed depression, anxiety, or an adjustment disorder (log-rank p value <0.001). Women with a suspected diagnosis of cancer were at a higher risk for these psychiatric conditions than those without a cancer diagnosis (BC and GOC: HR 1.32; BC: HR 1.21; GOC: HR 1.50). A suspected diagnosis of BC or GOC in a woman's medical history is associated with an increased risk of developing depression, anxiety, and adjustment disorders.

In present days, real time navigation depends on Kalman filter to fuse data from Global Positioning System (GPS) and Inertial Navigation System (INS). But there exist drawbacks like long design time, requirement of prior knowledge in fusing data from GPS and INS using Kalman Filter. In order to overcome the drawbacks of Kalman filter, GPS and INS integration are done using Artificial Neural Networks (ANN) like FFNNs. But still there exists certain drawbacks like less accuracy in FFNNs for GPS and INS data integration. Therefore, this paper introduces Higher Order Neural Networks (HONNs) approach for INS and GPS data integration to overcome the drawback. A higher order feed forward neural network architecture with optimum number of nodes is used. In the given architectures, the replacement of summation at each node by multiplication results in more powerful mapping, because of its capability of processing higher-order information from training data. Performance comparison of HONNs with FFNNs shows that the proposed architecture provides satisfactory results in terms of error rate and number of epochs. The HONNs like Multiplicative Neural Network (MNN) module and Sigma-Pi-ANN module and Traditional Feed Forward Neural Networks(FFNNs) like Radial Basis Function Neural Network (RBF) module and Back propagation Neural Network (BPN) module are trained to predict the INS position error and provide accurate position of the moving aircrafts. Sigma-Pi ANN is found to be the best in terms of accuracy, number of epochs and execution time among the two HONNs.

The association between cardiovascular disease and breast and gynecologic cancers among black female patients

We described in this paper the use of Modular Neural Networks (MNN) for pattern recognition in parallel using a cluster of computers with a master-slave topology. In this paper, we are proposing the use of MNN to face recognition with large databases to validate this approach. Also, a parallel genetic algorithm to optimization architecture was used.

BACKGROUND: Germline mutations in PALB2, BRCA2 and STK11 are well established as increasing risk of both breast and pancreatic cancer. More recently, ATM and BRCA1 mutations have also been associated with risk, but literature is limited. We investigated the prevalence of pathogenic mutations and likely pathogenic variants (“mutations”) in BRCA1/2, PALB2, STK11 and ATM, comparing mutation occurrence in individuals with diagnoses of breast cancer alone to those with both breast and pancreatic cancer primaries. Prevalence of CDKN2A (p16) mutations was also evaluated in the breast–pancreatic cohort because of its contribution to hereditary pancreatic cancer. METHODS: Clinical histories and test results were reviewed for patients undergoing multi-gene panel testing at one clinical laboratory between April 2012 and June 2015. The study population was limited to women with breast cancer only (n=27,573) and women with both breast and pancreatic cancer (n=97) without other primaries. Patients underwent comprehensive analysis of 5-49 genes, depending on the panel ordered. Demographic and clinical information was provided by clinicians on test requisition forms and pedigrees/clinic notes if provided. Gene-specific mutation frequencies were compared between women with breast cancer only and women with breast and pancreatic cancer using Fisher's exact test. RESULTS: Mutations were identified in BRCA1, BRCA2, PALB2 or ATM in 13 of the 97 breast - pancreatic cancer probands (13.4%) and 1,255 of the 27,573 breast cancer probands (4.6%). Gene-specific mutation frequencies and statistical comparisons may be found in Table 1. ATM mutations were significantly more likely to be identified in women with breast and pancreatic cancer compared to breast cancer alone (Table 1). Interestingly, no CDKN2A or STK11 mutations were identified in the breast plus pancreatic cohort, although this may have been limited by the small number of individuals tested for this gene. Of those 13 women with breast and pancreatic cancers who had identified mutations, 11 (85%) had diagnoses of breast cancer over age 50. Table 1. Mutation FrequenciesGeneMutation Frequence n/N (%)Mutation Frequence n/N (%)pOR (95% PI) Breast &amp; PancreaticBreast Only ATM6/89 (6.74%)209/17,570 (1.19%)0.000766.00(2.12,13.85)BRCA12/90 (2.22 %)429/26,336 (1.63%)0.661.37(0.16,5.14)BRCA24/90 (4.44%)442/26,336 (1.68%)0.0662.72 (0.72,7.28)CDKN2A0/54 (0.00%)13/3,965 (0.33%)10.00 (0, 24.70)PALB21/891.12% 175/17,570 (1%)0.591.13(0.03, 6.54)STK110/860.00% 0/16,931 (0.00)1Inf (0, Inf) CONCLUSION: This exploratory study substantiates the association of deleterious germline ATM mutations with predisposition to both breast and pancreatic cancers. These results also suggest that mutations in ATM may account for a larger portion of inherited breast and pancreatic cancer kindreds than mutations in other well-described genes such as BRCA2, PALB2 and STK11. A personal history of breast and pancreatic cancer may warrant the expansion of current NCCN testing criteria as a single indicator for germline testing, and that pancreatic screening consortia (CAPS) consider inclusion of ATM mutations in screening recommendations. Citation Format: Gordon OK, Childers K, McFarland R, Laduca H. ATM mutations contribution to hereditary breast-pancreatic cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P3-08-07.

Several studies have demonstrated an increased frequency of cancer in patients with systemic sclerosis (SSc), specially lung and breast cancers. The pathogenesis of the association between SSc and cancer is not fully established. The aim of this study was to describe new cases of the association between SSc and breast cancer and to perform a review of the literature. We retrospectively studied the medical files of eight patients followed in our institution for SSc and breast cancer. We analyzed them with data available in the literature for a total of 46 patients. Cutaneous extension of SSc was clearly mentioned in 17 cases: the SSc was limited in 10 cases and diffuse in 7 cases The median age at the diagnosis of cancer was 54 years (range: 40-71). The median duration between SSc onset and breast cancer diagnosis was 11.5 months (range: 0-288). The duration between SSc onset and breast cancer diagnosis was < or = 12 months in 27 of 44 patients (61.4%), and in 11 (25%) of them the diagnosis of both diseases was made simultaneously. It was clearly mentioned for 35 patients whether the diagnosis of breast cancer was made before or after the onset of SSc. The diagnosis of breast cancer was made before SSc onset in 17 of 35 patients (48.6%) and after SSc onset in 18 of 35 patients (51.4%). For 33 patients, the follow-up was available: 18 (54.5%) died, 11 (33.3%) of them within the 1st year after the diagnosis of the cancer. For none of the patients did the anticancer treatment improve the SSc. The close temporal relationship between SSc onset and breast cancer diagnosis is highly suggestive of a pathophysiological link. SSc is probably not a paraneoplastic disease since the anticancer treatment has no influence on the evolution of SSc. However, it can be suggested that SSc could be a disease facilitating breast cancer and/or metastases development.

One of the fatal diseases that occurs in women is breast cancer and is associated with late diagnosis and poor access to medical care according to the patient's needs, therefore neural networks play a relevant role in detection of breast cancer and aims to be a support to guarantee its accuracy and reliability in cancer results. Therefore, the aim of the present systematic review is to learn how neural networks help to improve accuracy in breast cancer diagnosis through image recognition. For this, the formula generated with the PICO methodology was used; Likewise, the first result was 203 investigations related to the topic and based on the established inclusion and exclusion criteria, 20 final free access scientific articles were selected from the Scopus database. In relation to the results, it was found that the use of neural networks in the diagnosis of breast cancer, especially convolutional neural networks (CNN), has proven to be a promising tool to improve the accuracy and early detection of the disease, reaching achieve an accuracy of 98 % in the recognition of clinical images, which means a big difference compared to traditional methods. On the other hand, although there are challenges such as the limited availability of high-quality data sets and bias in training data, it is suggested to investigate the development of methods that integrate multiple sources of information and the use of deep learning techniques.

BackgroundWomen’s return to work after diagnosis of breast cancer (BC) is becoming more prevalent. However, register-based national investigation on sickness absence (SA) and disability pension (DP) in BC women is lacking. The aim of the study was to explore SA and DP before and after a first BC diagnosis and the possibility to predict new cancer-related SA by using disease-related and sociodemographic factors.MethodsA longitudinal register study of the 3536 women in Sweden aged 19–64 with a first BC diagnosis in 2010 was conducted by linkage of five nationwide registers. Particularly, detailed information on SA and DP was obtained from the National Social Insurance Agency. Descriptive statistics on SA and DP 2 years before through 3 years after the BC diagnosis were performed. The risk of having a new SA spell due to BC or BC-related diagnoses was modeled using logistic regression.ResultsThe proportion of women with SA increased during the year following the BC diagnosis date and declined over the next 2 years to proportions before diagnosis. At the time of BC diagnosis, half of the women began a new SA spell > 14 days with cancer, cancer-related, or mental diagnosis. Disease-related and sociodemographic factors including occupational sector, living area, age, cancer stage, educational level, and number of previous SA days showed statistical significance (p < 0.05) in predicting a new SA around BC diagnosis. By using these factors, it was possible to correctly predict 67% of the new SA spell.ConclusionsSA among women with BC was elevated mainly in the first year after diagnosis. New SA following BC diagnosis can accurately be predicted.

Among patients with breast cancer, tumors that contain estrogen receptors (ER) are associated with improved survival and better response to hormone therapy than those not expressing these receptors. The purpose of these case comparison studies was to examine the relationship between carotenoids, vitamin A, and the tumor ER status in women at diagnosis of primary breast cancer. The focus of the first study was the relationship between dietary intake and ER status, and the focus of the second study was the relationship between ER status and the plasma carotenoid, retinol, and tocopherol concentrations. We evaluated tumor ER status and self-reported dietary intake in 142 women and plasma concentrations of carotenoids, retinol, and tocopherols in 149 women, at diagnosis of breast cancer, before any medical or surgical treatment. In the first study the overall odds of ER-positive status were increased in relation to number of mammograms in the past five years, number of breast-fed babies, dietary carotenoid intake, and more frequent intake of yellow and green vegetables. Overall odds of ER-positive status were decreased in relation to years of oral contraceptive use and preformed vitamin A intake. In the second study older women, women with higher plasma lutein concentration, and women not using beta-carotene supplements were more likely to be ER positive, when data were adjusted for body mass index and factors that may influence breast cancer risk or hormonal status. Significant independent relationships between plasma retinol or tocopherol concentrations and ER status were not observed. The strong and independent relationships between carotenoid intake, plasma lutein concentration, and ER status may relate to observations linking a carotenoid-rich diet with improved prognosis after diagnosis of breast cancer.

eP061: Genetic risk for breast and ovarian cancer in a diverse and unselected population

This study was to analyze the clinical application value of magnetic resonance imaging (MRI) image features based on intelligent algorithms in the diagnosis and treatment of breast cancer and to provide an effective reference assessment for breast cancer diagnosis. The MRI diagnosis model (ACO-MRI) based on the ant colony algorithm (ACO) was proposed, which was compared with the diagnosis methods based on support vector machine (SVM) and proximity (KNN) algorithm, and the proposed algorithm was applied to MRI images to diagnose breast cancer. The results showed that the accuracy, sensitivity, and specificity of the ACO-MRI model were greater than those of the KNN and SVM algorithm. Moreover, the specificity was statistically considerable compared with the two algorithms of KNN and SVM ( P &lt; 0.05 ). By comparing 1/5 number of ants and the average gray path of the ACO-MRI model under 1/8 number of ants, it was found that the average gray path value of 1/8 number of ants was greatly higher than the average gray path value of 1/5 number of ants ( P &lt; 0.05 ). The differences in the overall distribution of breast MRI imaging features among Luminal A, Luminal B, HER-2 overexpression, and TN were compared. There were considerable differences in the overall distribution of the three breast MRI imaging features of the boundaries, morphology, and enhancement methods among the four groups ( P &lt; 0.05 ). In short, MRI image based on the intelligent algorithm ACO-MRI diagnosis model can effectively improve the diagnosis effect of breast cancer. Its image feature boundaries, morphology, and enhancement methods had good imaging features in the diagnosis of breast cancer.

Combined anxiety and depressive symptoms before diagnosis of breast cancer

e22521 Background: Endeavor Health implemented a family-health-history screening tool called the Genetic Wellness Assessment (GWA) in routine primary care annual visits to improve access to inherited cancer risk assessment, prevention, and tailored treatment strategies through population-based genetic testing (PBGT). Our study aimed to assess the clinical stages of breast cancer (BC) diagnosis in patients who were identified as high-risk for inherited cancer on the GWA, received PBGT, and were diagnosed with BC post completion of the PBGT. Methods: Data was extracted from the electronic health record and the internal Endeavor Health cancer registry (CR) to identify patients who were screened as high-risk for inherited cancer using the GWA, underwent PBGT, and were subsequently diagnosed with BC between 2018 and 2024 (GWA patients). The stages of BC diagnosis in these patients were compared to the larger BC patient population in the internal CR during the same timeframe. Stage of breast cancer was defined as ductal carcinoma in situ (DCIS), localized (confined to primary site), regional (spread to regional lymph nodes), and distant (cancer has metastasized). Descriptive statistics was used for data analysis. Results: Thirty-three GWA patients were diagnosed with BC. Nine (27%) with DCIS, 22 (67%) with localized disease, and 2 (6%) with regional disease. Four (12%) of these patients had a pathogenic variant in a BC risk gene (2 BRCA2 , 1 CHEK2 , and 1 TP53 ). During this same time period, 1447 patients were available in CR data, for which the stage breakdown at diagnosis was: DCIS 268 (19%), 1045 (72%) localized, 115 (8%) regional, and 19 (1%) distant. Conclusions: A higher proportion of the general Endeavor Health BC population had regional or distant disease compared to the GWA population. Notably, nearly one third of GWA patients were diagnosed with DCIS and there were no cases of newly diagnosed metastatic BC in this population. Further research is warranted to understand how GWA participation may influence patient screening and prevention practices and result in earlier BC diagnosis. Breast cancer diagnosis stages in GWA patients and endeavor health cancer registry patients from 2018 to 2024. BC Diagnosis Stages in GWA Patients BC Diagnosis Stages in Cancer Registry Patients Count Percentage Count Percentage DCIS 9 27% 268 19% Localized 22 67% 1045 72% Regional 2 6% 115 8% Distant 0 0% 19 1% Total 33 1447