Stage Breast Cancer Research Articles

Stage Shifting by Modifying the Determinants of Breast Cancer Stage at Diagnosis: A Simulation Study.

Breast cancer is the most common cancer in Canadian women; nearly 25% of women diagnosed with cancer have breast cancer. The early detection of breast cancer is a major challenge because tumours often grow without causing symptom. The diagnosis of breast cancer at an early stage (stages I and II) improves survival outcomes because treatments are more effective and better tolerated. To better inform the prevention of and screening for breast cancer, simulations using modifiable rather than non-modifiable risk factors may be helpful in shifting the stage at diagnosis downward. Breast cancer stages were simulated using the data distributions from Alberta's Tomorrow Project participants who developed breast cancer. Using multivariable partial proportional odds regression models, modifiable lifestyle factors associated with the stage of cancer at diagnosis were evaluated. The proportions or mean levels of these lifestyle factors in the simulated population were systematically changed, then multiplied by their corresponding estimated odds ratios from the real data example. The effects of these changes were evaluated singly as well as cumulatively. Increasing total dietary protein (g/day) intake was the single most important lifestyle factor in shifting the breast cancer stage downwards followed by decreasing total dietary energy intake (kcal/day). Increasing the proportion of women who spend time in the sun between 11 am and 4 pm in the summer months, who have had a mammogram, who have been pregnant or reducing the proportion who are in stressful situations had much smaller effects. The percentage of Stage I diagnoses could be increased by approximately 12% with small modifications of these lifestyle factors. Shifting the breast cancer stage at diagnosis of a population may be achieved through changes to lifestyle factors. This proof of principle study that evaluated multiple factors associated with the stage at diagnosis in a population can be expanded to other cancers as well, providing opportunities for cancer prevention programs to target specific factors and identify populations at higher risk.

Structured screening for sporadic breast cancer

The aim of secondary prevention of breast cancer is to detect the disease at the earliest curable stage and thus to reduce breast cancer-specific mortality. To this end, the nationwide population-based mammography screening program (MSP) was set up in Germany in 2005 in addition to an interdisciplinary prevention project for high-risk groups. Overview of the current state of the MSP, the upcoming age expansion, and potential further developments. Narrative review article with topic-guided literature and data search. Approximately 50% of the 70,500 new cases of breast cancer that occur each year are related to the age group of the MSP. 10years after introduction of the MSP, the incidence of advanced breast cancer stages and breast cancer-related mortality of the screening target group have steadily decreased by about one quarter, while no relevant trends were seen in the neighboring age groups at the population level. The MSP has effectively contributed to areduction of breast cancer mortality. With the expansion of the age groups to 45-75years, more women have access to structured, quality assured screening. With the use of advanced stratifications and diagnostics as well as artificial intelligence, the MSP could be further optimized.

Development and Validation of a Nomogram for Prognosis Prediction in Patients with Synchronous Primary Thyroid and Breast Cancer Based on SEER Database

This study aimed to develop prognostic prediction models for patients diagnosed with synchronous thyroid and breast cancer (TBC). Utilizing the SEER database, key predictive factors were identified, including T stage of thyroid cancer, T stage of breast cancer, M stage of breast cancer, patient age, thyroid cancer surgery type, and isotope therapy. A nomogram predicting 5-year and 10-year survival rates was constructed and validated, exhibiting strong performance (C-statistic: 0.79 in the development cohort (95% CI: 0.74-0.84), and 0.82 in the validation cohort (95% CI: 0.77-0.89)). The area under the Receiver Operator Characteristic (ROC) curve ranged from 0.798 to 0.883 for both cohorts. Calibration and decision curve analyses further affirmed the model’s clinical utility. Stratifying patients into high-risk and low-risk groups using the nomogram revealed significant differences in survival rates (P < 0.0001). The successful development and validation of this nomogram for predicting 5-year and 10-year survival rates in patients with synchronous TBC hold promise for similar patient populations, contributing significantly to cancer research.

Validation of primary and outcome data quality in a Swedish population-based breast cancer quality registry

BackgroundPopulation-based cancer quality registries are of great importance for the improvement of cancer care. However, little is known about the quality of recurrence data in cancer quality registries. The aim of this study was to evaluate data quality in the regional Breast Cancer Quality Registry of Central Sweden, with emphasis on the validity of recorded information on recurrence.MethodsValidation by re-abstraction was performed on a random sample of 800 women with primary invasive breast cancer stage I-III diagnosed between 1993 and 2010, of which 400 had at least one registered recurrence and 400 had no registered recurrence. Registry data were compared with data from medical records. Exact agreement, correlation and kappa values, sensitivity and specificity were calculated.ResultsSeven hundred forty-seven women (93%) were available for analysis. Exact agreement was high for diagnostics, tumor characteristics, surgery, and adjuvant oncological treatment (90% or more for most variables). The registry’s sensitivity was low for regional recurrence (47%), but higher for local and distant recurrence (80% and 75%), whereas specificity was overall high (≥ 95%). Combining all recurrence categories irrespective of localization improved sensitivity to 90% with a specificity of 91%. In 87% of women, the date of first recurrence according to medical records fell within ± 90 days of the date recorded in the registry.ConclusionsWhile the quality of data in the regional Breast Cancer Quality Registry was generally high, data accuracy on recurrences was lower. The overall precision of identifying any recurrence, irrespective of localization, was high. However, the accuracy of classification of recurrences (local, regional or distant) was lower, with evidence of underreporting for each of the recurrence categories. Given the importance of recurrence-related outcomes in the assessment of quality of care, efforts should be made to improve the reporting of recurrences.

Interrogations of single-cell RNA splicing landscapes with SCASL define new cell identities with physiological relevance

RNA splicing shapes the gene regulatory programs that underlie various physiological and disease processes. Here, we present the SCASL (single-cell clustering based on alternative splicing landscapes) method for interrogating the heterogeneity of RNA splicing with single-cell RNA-seq data. SCASL resolves the issue of biased and sparse data coverage on single-cell RNA splicing and provides a new scheme for classifications of cell identities. With previously published datasets as examples, SCASL identifies new cell clusters indicating potentially precancerous and early-tumor stages in triple-negative breast cancer, illustrates cell lineages of embryonic liver development, and provides fine clusters of highly heterogeneous tumor-associated CD4 and CD8 T cells with functional and physiological relevance. Most of these findings are not readily available via conventional cell clustering based on single-cell gene expression data. Our study shows the potential of SCASL in revealing the intrinsic RNA splicing heterogeneity and generating biological insights into the dynamic and functional cell landscapes in complex tissues.

Benefits and limitations of breastfeeding for mothers&#x0D; with malignant tumors

In the modern world breastfeeding (BF) is considered as a significant factor in reducing the risk of developing malignant tumors of the mammary gland and ovaries. However, the problem of maintenance or refusing BF in malignant neoplasms (malignant neoplasms) still remains open. In recent years, there has been a steady increase in breast cancer (BC), especially in developed countries, with more than 10% of all breast malignancies (BC) diagnosed in women under 40 years of age. The presented article highlights aspects of maintaining lactation in different periods of the relevance of BF in relation to the timing of manifestation of cancer, and also presents data from the multicenter study POSITIVE (Pregnancy Outcome and Safety of Interrupting Therapy for women with endocrine responsIVE breast cancer) on the possibilities of implementing reproductive function and BF in women receiving therapy for hormone-receptor positive (HR+) BC stages I–III. BF with cancer detected in the postpartum period depends on the treatment of the underlying disease. In patients undergoing surgical treatment, BF is not contraindicated, but it is not always technically possible. During chemotherapy, BF is not recommended. When carrying out radiotherapy, it is possible to undergo BF using a healthy mammary gland. There is currently no evidence to suggest that BF woman diagnosed with cancer may have any negative effects. In general, it has been shown that preserving and maintaining lactation in women with a history of cancer is safe for both mother and child.

The TRPV6 Calcium Channel and Its Relationship with Cancer.

Transient receptor potential vanilloid-6 (TRPV6) is a cation channel belonging to the TRP superfamily, specifically the vanilloid subfamily, and is the sixth member of this subfamily. Its presence in the body is primarily limited to the skin, ovaries, kidney, testes, and digestive tract epithelium. The body maintains calcium homeostasis using the TRPV6 channel, which has a greater calcium selectivity than the other TRP channels. Several pieces of evidence suggest that it is upregulated in the advanced stages of thyroid, ovarian, breast, colon, and prostate cancers. The function of TRPV6 in regulating calcium signaling in cancer will be covered in this review, along with its potential applications as a cancer treatment target.

Cancer Guard: Early Detection of Breast Cancer

Breast cancer stands as the most prevalent form of cancer among women, globally contributing to the highest number of cancer-related deaths. Timely detection of abnormalities significantly enhances the prospects of successful treatment and reduces mortality rates. Hence an automatic detection will be very useful for medical practitioner. This research introduces a novel framework for enhancing breast cancer detection and stage classification by integrating image processing techniques such as Gray Level Co-occurrence Matrix (GLCM) and Convolutional Neural Network (CNN) techniques. Initially, mammographic images undergo preprocessing to improve quality, followed by GLCM feature extraction for capturing textural information. With the help of GLCM technique, accuracy of the network can be increased by extracting various features. A CNN model is then employed for automatic feature learning and classification. This framework enhances the accuracy of distinguishing between malignant and benign tissues and extends to stage detection, enabling classification into various stages. Experimental results demonstrate the effectiveness of the proposed approach in achieving high precision and recall rates, suggesting potential for clinical integration to improve patient outcomes and streamline healthcare workflows.

Classification of Early Breast Cancer using Convolutional Neural Networks

Breast cancer is a life-threatening disease that impacts millions of people worldwide, necessitating heightened awareness and effective early detection strategies. Current methods for determining the presence and type of breast cancer in a clinical breast exam conducted by healthcare providers may be inaccurate, imprecise, and prone to error. The goal of this research project is to offer a second opinion for physicians to predict whether or not a patient has early onset breast cancer through a machine learning model developed from an image dataset, to significantly reduce the error in breast cancer staging, making the process more efficient in healthcare. Early detection is crucial for successful treatments of breast cancer, as treatment options in late-stage diagnosis of breast cancer can have a much worse prognosis. In the US, 240,000 cases of breast cancer are diagnosed per year in both men and women with a mortality rate of approximately 42,000 per year. [1] To approach this problem, I have tested an MLP Classifier, Logistic Regression, Ridge Classifier, Random Forest Classifier, Decision Tree Classifier, Support Vector Classifier, and trained a Convolutional Neural Network to compare various results and determine the best accuracy. The Convolutional Neural Network yielded impressive outcomes, reaching up to 79% testing accuracy and 98% training accuracy. The final model exhibits the capability to identify early signs of breast cancer and differentiate between malignant and benign tumours with relatively high accuracy showing the potential of AI in combination with medical imaging that can assist medical staff in diagnosis.

Concordance Rate of Radiological Stage With Post-Operative Pathological Stage In Breast Cancer

Objective: To assess the concordance between the radiological and post-operative pathological stages in invasive breast carcinoma. Study Design: Cross-validation study Place and Duration of Study: Department of Medical Oncology, Jinnah Postgraduate Medical Centre, Karachi Pakistan, from Jan to Jul 2020. Methodology: A total of 160 females aged 20-80 years with confirmed diagnosis of breast cancer underwent upfront modified radical mastectomy along with axillary node dissection were included in the study. Demographic and clinic pathological findings of all the patients were reported in pre-designed proforma. Stage concordance was labelled as a patient assigned to the same stage radiologically and pathologically. Results: The mean age of the 160 patients was 47.22±10.51 years. The relationship between radiological and pathological staging was weak (r=0.383) but statistically significant (p=0.001). The tumor stage on radiological and pathological assessment was similar in 60 cases (37.3%). Radiologic assessment overestimated the stage in 75 cases (46.6%) and underestimated it in 26 cases (16.1%). The overall concordance between radiological and pathological staging was 18.8% (p=0.001). Conclusion: Among patients with breast cancer, a high rate of discordance was observed between the radiological and pathological stages. The high odds of discordance are therefore important to plan treatment.

Exploring the Relationship Between Diabetes and Breast Cancer in the United Arab Emirates.

Introduction This study delves into the complex interplay between diabetes and breast cancer within the United Arab Emirates (UAE), a subject of considerable global health concern. Given the increasing incidence of both diseases worldwide, this research investigates explicitly the potential influence of diabetes on the staging of breast cancer. The UAE, mirroring global trends, has experienced a surge in both conditions attributed to a blend of genetic, environmental, and lifestyle factors. The core objective of this investigation is to explore the link between diabetes and the stage at which breast cancer is diagnosed in UAE patients. Material and method To conduct this study, data were extracted from an extensive medical database consisting of anonymized records about breast cancer patients and their comorbid conditions. The research encompassed adult patients of all genders, all of whom had been definitively diagnosed with breast cancer. The data was analyzed using a suite of Python libraries, including Pandas, NumPy, SciPy, Scikit-learn, Matplotlib, and Seaborn. Descriptive and inferential statistical methods were employed, focusing on the Chi-Square test and logistic regression analysis to evaluate the relationship between diabetes and the stages of breast cancer, considering other comorbidities as well. Results The analysis included 131 breast cancer patients, predominantly female (98.47%), with an average age of 54.2 years. Among these patients, 22.14% were diabetic. The prevalence of other comorbidities, such as dyslipidemia, hypertension, and hypothyroidism, was also recorded. The Chi-Square test indicated no significant correlation between diabetes and the stages of breast cancer (χ² = 3.07, p = 0.381). Stage II was the most frequently diagnosed, irrespective of the presence or absence of diabetes. Conclusion In conclusion, this study finds no substantial link between diabetes and the stage of breast cancer diagnosis among patients in the UAE after adjusting for age and other comorbid conditions. These results underscore the need for early breast cancer detection approaches that are not exclusively dependent on the diabetic status of the patients. However, limitations such as the retrospective cohort design and the relatively small sample size highlight the necessity for further comprehensive studies. Such research would deepen the understanding of the relationship between diabetes and breast cancer and contribute to the advancement of breast cancer healthcare in the UAE.

Assessment of Serum Soluble Toll-like Receptor-4 and Interleukin-8 as Biomarkers in Patients with Breast Cancer

Background: Chemokines and toll-like receptors (TLR) have a synergistic role in the initiation and progression of breast cancer. TLR-4 is involved in the invasiveness of cancerous cells. Interleukin-8 (IL-8) has a significant role in boosting the angiogenesis and growth of cancer cells. Objective: To measure the level of soluble TLR-4 and interleukin-8 in patients with breast cancer and to study the possibility of using them as diagnostic and prognostic biomarkers in these patients. Methods: The study enrolled a total of 200 female subjects: 100 patients who were diagnosed with breast cancer and 100 healthy controls. Soluble TLR-4 and IL-8 levels in the blood were evaluated by the enzyme-linked immunosorbent assay. Results: upregulated levels of soluble TLR-4 as well as interleukin-8 in individuals with breast cancer compared to controls (5.79 vs. 1.01 ng/ml for TLR-4 and 29.1 vs. 7.1 pg/ml for IL-8). The concentration of soluble Toll-like receptor-4 besides interleukin-8 is significantly higher in stage IV than in other stages. A substantial increase in these biomarkers in grade 3 was detected compared to other grades. ROC curves show that both biomarkers were useful in differentiating between healthy females and those who have breast cancer. Conclusions: High concentrations of soluble TLR-4 as well as IL-8 have been linked to advanced stages of breast cancer, and these markers were effective in detecting the disease and predicting its prognosis.

Development of an algorithm for biomedical image analysis of the spatial organization of collagen in breast cancer tissue of patients with different clinical status.

Collagen, the main component of the tumor microenvironment, plays a key role in the development of breast cancer (BCa); however, the specific changes in its spatial organization during tumor progression have not been definitively elucidated. The existing and available methods for assessing the morphometric parameters of the stroma's fibrous component are insufficient for a detailed description of the state of collagen fibers and for assessing its changes to evaluate the aggressiveness of the BCa course. The aim of the work was to develop an algorithm for microphoto analysis to assess the spatial organization of collagen in BCa tissue of patients with different clinical statuses. The study was conducted on 60 tissue samples of stage I-II BCa. The processed images were analyzed using the software packages CurveAlign v4.0 and imagej. We established that the increase in BCa stage and the decrease in tumor differentiation grade are associated with decreased length, width, and straightness of collagen fibers, as well as their increased density. The formation of an aggressive basal molecular BCa subtype was accompanied by an increase in tumor-stroma ratio. The obtained results indicate the possibility of practical application of the developed algorithm for evaluating the spatial organization of collagen in BCa tissue to predict the aggressiveness of the disease course.

Cytokine Plasma Levels in Breast Cancer Patients, Before and After Surgery.

Studying the levels of cytokines in the plasma of patients could be valuable in guiding immunotherapy policies. We assessed the plasma levels of 4 major cytokines [interferon (IFN)-β, interleukin-2 (IL-2), tumor necrosis factor alpha (TNF-α), transforming growth factor beta (TGF-β)] collected from 19 patients with ductal breast cancer (BCa), before surgery (BS) and 5 days after surgery (AS). The ratio AS/BS was also calculated and correlated with histopathological variables and tumor-infiltrating lymphocyte (TIL) density. The IFN-β and TNF-α levels were significantly higher in BCa patients, BS and AS, than healthy controls (P < 0.02). High IL-2 levels BS were linked with node involvement (P = 0.02), and marginally with HER2 expression (P = 0.08), while high TNF-α levels were linked with high PgR expression (P = 0.02). Increasing IFN-β, IL-2, and TNF-α levels were noted AS, which was more evident in patients with larger tumors. The TGF-β levels were significantly lower in BCa patients (P < 0.007). Linear regression analysis showed a direct association of IFN-β levels AS (P = 0.02, r = 0.52) and of TNF-α AS/BS-ratio (P = 0.001, r = 0.72) with TIL-density. It is suggested that although effector immune response is evident in the majority of early stage BCa patients, removal of the primary tumor further unblocks such responses.

Sleep quality and psychological disorders in breast cancer female patients receiving radiotherapy at a tertiary oncology center in West Saudi Arabia.

Breast cancer is the most prevalent type of cancer among women worldwide. Many recently diagnosed and treated breast cancer patients complain about sleep disorders, depression, anxiety, stress, and fatigue. This study aimed to evaluate sleep quality among breast cancer patients and to assess its association with psychological disorders, and socio-demographic and clinical characteristics of patients. This cross-sectional study was conducted among women who received radiotherapy for breast cancer (stage 1-3) at King Abdul-Aziz Medical City-Jeddah from January to August 2021. Each participant completed a personal information form, the Pittsburgh Sleep Quality Index (PSQI), the Depression, Anxiety, and Stress Scale-21 (DASS-21), and the International Physical Activity Questionnaire (IPAQ). Clinical data were collected from the medical records to evaluate certain risk factors. Descriptive statistics were used for participant characteristics, and analyses of variance were used to assess associations between the qualitative variables. Fifty-six women with a mean age of 50.1years and an average of 10months after cancer diagnosis were included. Poor sleep quality was present in 58% of respondents. Symptomatic depression, anxiety, and stress were found in 34%, 32%, and 30% of women, respectively. Poor sleep quality was associated with depression (p = 0.031), anxiety (p = 0.03), and stress (p = 0.024) and was independently associated with hot flashes. In multivariate analysis, patients with depression or withcancer for more than 6months were less likely to have good sleep after controlling other variables (21.74 and 14.71 times, respectively). Poor sleep quality was present in 58% of women with breast cancer receiving radiotherapy and was significantly associated with depression, anxiety, stress, and hot flashes. Depression level and cancer duration were significant predictors of sleep quality. Early identification and proper management of psychological and sleep disorders are necessary to improve the quality of life and survival of breast cancer patients.

Ambient air pollutants and breast cancer stage in Tehran, Iran

This study aimed to examine the impacts of single and multiple air pollutants (AP) on the severity of breast cancer (BC). Data of 1148 diagnosed BC cases (2008–2016) were obtained from the Cancer Research Center and private oncologist offices in Tehran, Iran. Ambient PM10, SO2, NO, NO2, NOX, benzene, toluene, ethylbenzene, m-xylene, p-xylene, o-xylene, and BTEX data were obtained from previously developed land use regression models. Associations between pollutants and stage of BC were assessed by multinomial logistic regression models. An increase of 10 μg/m3 in ethylbenzene, o-xylene, m-xylene, and 10 ppb of NO corresponded to 10.41 (95% CI 1.32–82.41), 4.07 (1.46–11.33), 2.89 (1.08–7.73) and 1.08 (1.00–1.15) increase in the odds of stage I versus non-invasive BC, respectively. Benzene (OR, odds ratio = 1.16, 95% CI 1.01–1.33) and o-xylene (OR = 1.18, 1.02–1.38) were associated with increased odds of incidence of BC stages III & IV versus non-invasive stages. BC stage I and stage III&IV in women living in low SES areas was associated with significantly higher levels of benzene, ethylbenzene, o-xylene, and m-xylene. The highest multiple-air-pollutants quartile was associated with a higher odds of stage I BC (OR = 3.16) in patients under 50 years old. This study provides evidence that exposure to AP is associated with increased BC stage at diagnosis, especially under premenopause age.

Impact of the COVID-19 pandemic on breast cancer pathological stage at diagnosis in Tunisian patients.

Breast cancer (BC) patients' diagnosis and management was affected by a global reorganization after the Coronavirus disease 2019 (COVID-19). Our study aimed to assess the impact of the pandemic on the pathological stage of newly diagnosed patients with BC compared to pre-pandemic and to identify predictive factors of tumor advanced stage. Pathological records of all consecutive newly operated BC patients between March 2020 and December 2021 were reviewed retrospectively. Clinical and pathological prognostic factors of BC were collected and compared between pre-pandemic and pandemic periods. Then, predictive factors of tumor advanced stage were identified. Of the 225 cases included in the analysis, 98.7% were females and 1.3% were males. The median time from first histological diagnosis to first surgical treatment was enlarged by 42 days with a significant difference between the two periods (p = 0.002). Newly diagnosed BC patients during the COVID-19 pandemic were operated at a more advanced stage (54.1% vs 36.2%, p = 0.007), had a greater lymphovascular invasion (p = 0.002), lymph node metastasis (p = 0.015) and are more commonly of IBC NST histological type (p = 0.005). Moreover, multivariate analyses showed that the pandemic period (AOR = 2.28; p = 0.016) and the lympho-vascular invasion (p < 0.001) were independently associated with advanced stage of tumors. Our findings proved an increase in alarming rates of advanced stage BC associated with the COVID-19 crisis. These findings support recommendations for a quick restoration of BC screening at full capacity, with adequate prioritization strategies to mitigate harm.

Unmet Social Needs and Breast Cancer Screening Utilization and Stage at Presentation

Unmet social needs in local populations may hinder the development of targeted cancer control interventions aimed at improving screening utilization and early-stage breast cancer diagnosis to ultimately improve breast cancer survival disparities. To evaluate if (1) city-funded screening mammography is associated with utilization of screening mammography, (2) unmet social needs are associated with utilization of screening mammography, and (3) unmet social needs are associated with later-stage disease at diagnosis. This cohort study included patients with stages I-IV invasive ductal or lobular carcinoma treated at an academic medical center (including both an underserved safety-net hospital [SNH] and a National Cancer Institute-designated academic cancer center [ACC]) from 2020 to 2023. Eligible patients were aged 18 years or older and able to consent. Data were analyzed between July 2023 and September 2023. The Health Leads Social Needs Screening Toolkit, a screening tool that gathers information on the most common social need domains affecting patient health. Univariable and multivariable logistic regression was utilized to evaluate the following primary outcomes: (1) routine screening mammography and (2) American Joint Committee on Cancer 8th edition clinical stage at presentation. Of the 322 women who completed the Health Leads Social Needs Screening Toolkit, 201 (62%) self-identified as Hispanic, 63 (19%) as non-Hispanic Black, and 63 (19%) as non-Hispanic White. Two hundred fifty-five (76%) patients with access to city-funded screening mammography completed a screening mammogram. Patients who presented to the SNH were more likely to present with late-stage disease compared with early-stage disease (15 of 48 [31%] vs 50 of 274 [18%]; P = .04). On multivariable logistic regression, not completing a screening mammography was associated with having an increasing number of unmet social needs (OR, 0.74; 95% CI, 0.55-0.99; P = .047) and an increasing age at diagnosis (OR, 0.92; 95% CI, 0.89-0.96; P < .001). Moreover, increasing unmet social needs was significantly associated with late-stage diagnosis above and beyond screening mammography (OR, 1.38; 95% CI, 1.01-1.89; P = .04). In this cohort study, access to screening mammography did not translate to utilization of screening mammography, increasing unmet social needs were significantly associated with lower rates of screening mammography, and those with increasing unmet social needs were more likely to present with late-stage disease. This association transcended recruitment site (SNH vs ACC), indicating that patients in either hospital setting may benefit from unmet social needs screening to overcome access to care barriers associated with late-stage disease at diagnosis.

Current Status of Imaging for Breast Cancer Staging

Current Status of Imaging for Breast Cancer Staging

Stability analysis of fractional order breast cancer model in chemotherapy patients with cardiotoxicity by applying LADM

AbstractBreast cancer is the most common type of cancer in women. Chemotherapy is primarily used for patients with stage 2 to 4 breast cancer. Most chemotherapy drugs are effective at destroying rapidly growing and proliferating cancer cells. However, drugs also damage normal, rapidly growing cells, which can lead to serious side effects. Breast cancer treatment with chemotherapy can affect heart health. Side effects of chemotherapy on the heart are called cardiotoxicity. Therefore, we have constructed a mathematical model from the breast cancer patient population. In this article, we utilize the Caputo–Fabrizio fractional order derivative for mathematical modeling of the breast cancer stages in chemotherapy patients. The use of Caputo–Fabrizio fractional derivative provides a more valuable insight into the complexity of the breast cancer model. The stability of the fractional order model is also proven by the $\mathscr{P}$ P -stable approach of the fixed point theorem. Also, the numerical simulations are performed via Laplace Adomian decomposition method to establish the dependence of the breast cancer dynamics on the order of the fractional derivatives. Based on the geometric results in the figures, we can conclude that the magnitude of the fractional order has a considerable impact on the days, which the maximum or minimum of the system solutions are reached, with a shift in the time at which this happens as the fractional order decreases from 1. However, it is obvious that the solutions of Caputo–Fabrizio fractional model approach the relevant results of the classical integer order system, when the fractional order approaches to 1.