Association between socioeconomic factors at diagnosis and survival in breast cancer: A population-based study

Racial and survival disparities in inflammatory breast cancer (IBC) and non-IBC: a population-based study focused on Native Hawaiians and other Pacific Islanders

Background

It is well known that race is an independent predictor of breast cancer mortality and advanced stage at diagnosis. Inflammatory breast cancer (IBC) is the most aggressive type of breast cancer and has distinct clinical and biological features. Previous studies have shown that Blacks have a higher incidence of IBC than Whites. However, the proportion of IBC and the role of race on prognosis in Native Hawaiian and other Pacific Islander (NH/PI) populations with breast cancer are poorly understood. In this study, we aimed to examine the proportion of IBC to non-IBC in NH/PIs and to identify the clinicopathological, biological, and socioeconomic factors associated with the overall survival of NH/PIs compared to other races.

Methods

Utilizing a comprehensive cancer registry from the largest hospital in Hawaii, newly diagnosed primary invasive breast cancer patients diagnosed between 2000 and 2018 were identified. Univariate and multivariate Cox proportional hazards models were used to test the association between race and clinical outcomes. Variables with P-values <0.05 in the univariate analysis and race (variable of interest) were included in a multivariate analysis.

Results

The cohort included 3691 patients, 60 of whom had IBC. NH/PI race had the highest proportion of IBC compared to other races (3.44%) but was not found to be an independent poor prognostic factor in IBC (HR 1.17 [95%CI 0.26-5.22]). Conversely, NH/PI race was associated with worse survival outcomes in patients with non-IBC (HR 1.65 [95%CI, 1.14-2.39]) along with other factors such as lack of insurance, underinsured status, triple-negative breast cancer (TNBC) subtype, age, and advanced clinical stage.

Conclusions

The findings of this study highlight that NH/PIs had higher rates of IBC and inferior survival in non-IBC compared to other races but not in IBC. It is essential to disaggregate NH/PI race from Asians in future population-based research studies. Further research is needed to understand the factors contributing to higher rates of IBC and poor survival outcomes in NH/PIs with non-IBC as well as targeted interventions to improve breast cancer outcomes in this population to ultimately help improve survival rates and reduce health inequities in NH/PIs with breast cancer.

Establishment of prediction models to predict survival among patients with cervical cancer based on socioeconomic factors: a retrospective cohort study based on the SEER Database

OBJECTIVE

To construct and validate predictive models based on socioeconomic factors for predicting overall survival (OS) in cervical cancer and compare them with the American Joint Council on Cancer (AJCC) staging system.

DESIGN

Retrospective cohort study.

SETTING AND PARTICIPANTS

We extracted data from 5954 patients who were diagnosed with cervical cancer between 2007 and 2011 from the Surveillance, Epidemiology, and End Results Database. This database holds data related to cancer incidence from 18 population-based cancer registries in the USA.

OUTCOME MEASURES

1-year and 5-year OS.

RESULTS

Of the total 5954 patients, 5820 patients had 1-year mortality and 5460 patients had 5-year mortality. Lower local education level [Hazard ratios (HR): 1.15, 95% confidence interval (CI): 1.04 to 1.27, p= 0.005] and being widowed (HR 1.28, 95% CI 1.06 to 1.55, p=0.009) were associated with a worse OS for patients with cervical cancer. Having insurance (HR 0.75, 95% CI 0.62 to 0.90, p=0.002), earning a local median annual income of ≥US$56 270 (HR 0.83, 95% CI 0.75 to 0.92, p<0.001) and being married (HR 0.79, 95% CI 0.69 to 0.89, p<0.001) were related to better OS in patients with cervical cancer. The predictive models based on socioeconomic factors and the AJCC staging system had a favourable performance for predicting OS in cervical cancer compared with the AJCC staging system alone.

CONCLUSION

Our proposed predictive models exhibit superior predictive performance, which may highlight the potential clinical application of incorporating socioeconomic factors in predicting OS in cervical cancer.

The Association Between Socioeconomic Factors at Diagnosis and Survival in Medulloblastoma: A Propensity Score-Matched Analysis and Population-Based Study

BACKGROUND

Medulloblastoma (MB) is the most common malignant brain tumor of childhood. The associations between socioeconomic statuses (SES) and survival outcomes of medulloblastoma remain unclear. The aim of this study was to develop a nomogram to predict medulloblastoma specific death (MBSD) and overall survival (OS) in patients with medulloblastoma, taking into account socioeconomic factors in patients with medulloblastoma.

METHODS

We included patients diagnosed with MB between 1975 and 2016 from the Surveillance, Epidemiology, and End Results database. Propensity Score Matching (PSM) was performed to reduce selection bias. Multivariate cox proportional hazards model was used to assess SES impact and clinically relevant variables of medulloblastoma specific death and overall survival. Independent prognostic factors determined by multivariate analysis were used to construct nomograms.

RESULTS

A total of 2660 patients were enrolled after matching. Study showed unemployed rate (MBSD, high level vs. low level, P = 0.020) (OS, high level vs. low level, P = 0.017), and marital status (OS, married vs unmarried/unknown, P = 0.029) were important factors affecting prognosis of medulloblastoma in male. Meanwhile, median household income (MBSD, quartile 1 vs. quartile 3, P = 0.047) (OS, quartile 1 vs. quartile 2, P = 0.017) (OS, quartile 1 vs. quartile 3, P = 0.014), residence (MBSD, urban vs. rural, P = 0.041), and insurance status (MBSD, insured vs. uninsured/unknown, P = 0.002)(OS, insured vs. uninsured/unknown, P = 0.001) were significant factors affecting prognosis of medulloblastoma in female. Through the calibration plot and C-index test, our nomogram was also of predictive significance.

CONCLUSIONS

The unique features of MB have provided a scenario for analysis of the impact of racial, ethnic, gender, and socioeconomic factors. The current findings have important public health implications for achieving the goal of a healthy population. Given the known morbidity rates, long-term psychological, financial and medical burdens that these children and their families must bear, it is critical to identify and address these gaps.

hist2RNA: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images

Gene expression can be used to subtype breast cancer with improved prediction of risk of recurrence and treatment responsiveness over that obtained using routine immunohistochemistry (IHC). However, in the clinic, molecular profiling is primarily used for ER+ breast cancer, which is costly, tissue destructive, requires specialised platforms, and takes several weeks to obtain a result. Deep learning algorithms can effectively extract morphological patterns in digital histopathology images to predict molecular phenotypes quickly and cost-effectively. We propose a new, computationally efficient approach called hist2RNA inspired by bulk RNA sequencing techniques to predict the expression of 138 genes (incorporated from 6 commercially available molecular profiling tests), including luminal PAM50 subtype, from hematoxylin and eosin (H&E)-stained whole slide images (WSIs). The training phase involves the aggregation of extracted features for each patient from a pretrained model to predict gene expression at the patient level using annotated H&E images from The Cancer Genome Atlas (TCGA, n = 335). We demonstrate successful gene prediction on a held-out test set (n = 160, corr = 0.82 across patients, corr = 0.29 across genes) and perform exploratory analysis on an external tissue microarray (TMA) dataset (n = 498) with known IHC and survival information. Our model is able to predict gene expression and luminal PAM50 subtype (Luminal A versus Luminal B) on the TMA dataset with prognostic significance for overall survival in univariate analysis (c-index = 0.56, hazard ratio = 2.16 (95% CI 1.12-3.06), p < 5 × 10^-3), and independent significance in multivariate analysis incorporating standard clinicopathological variables (c-index = 0.65, hazard ratio = 1.87 (95% CI 1.30-2.68), p < 5 × 10^-3). The proposed strategy achieves superior performance while requiring less training time, resulting in less energy consumption and computational cost compared to patch-based models. Additionally, hist2RNA predicts gene expression that has potential to determine luminal molecular subtypes which correlates with overall survival, without the need for expensive molecular testing.

Development and validation of nomograms for predicting survival outcomes in patients with T1-2N1 breast cancer to identify those who could not benefit from postmastectomy radiotherapy

Purpose

In this study, we aimed to develop and validate nomograms for predicting the survival outcomes in patients with T1-2N1 breast cancer to identify the patients who could not benefit from postmastectomy radiotherapy (PMRT).

Methods

Data from 10191 patients with T1-2N1 breast cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Of them, 6542 patients who had not received PMRT formed the training set. Concurrently, we retrospectively enrolled 419 patients from the Affiliated Hospital of North Sichuan Medical College (NSMC), and 286 patients who did not undergo PMRT formed the external validation set. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were used for selecting prognostic factors in the training set. Using the selected factors, two prognostic nomograms were constructed. The nomograms’ performance was assessed using the concordance index (C-index), calibration curves, decision curve analysis (DCA), and risk subgroup classification. The stabilized inverse probability of treatment weights (IPTWs) was used to balance the baseline characteristics of the different risk groups. Finally, the survival outcomes and effectiveness of PMRT after IPTW adjustment were evaluated using adjusted Kaplan–Meier curves and Cox regression models.

Results

The 8-year overall survival (OS) and breast cancer-specific survival (BCSS) rates for the SEER cohort were 84.3% and 90.1%, with a median follow-up time of 76 months, while those for the NSMC cohort were 84.1% and 86.9%, with a median follow-up time of 73 months. Moreover, significant differences were observed in the survival curves for the different risk subgroups (P &lt; 0.001) in both SEER and NSMC cohorts. The subgroup analysis after adjustment by IPTW revealed that PMRT was significantly associated with improved OS and BCSS in the intermediate- (hazard ratio [HR] = 0.72, 95% confidence interval [CI]: 0.59–0.88, P=0.001; HR = 0.77, 95% CI: 0.62–0.95, P = 0.015) and high- (HR=0.66, 95% CI: 0.52–0.83, P&lt;0.001; HR=0.74, 95% CI: 0.56–0.99, P=0.039) risk groups. However, PMRT had no significant effects on patients in the low-risk groups.

Conclusion

According to the prognostic nomogram, we performed risk subgroup classification and found that patients in the low-risk group did not benefit from PMRT.

Preoperative and postoperative clinical factors in predicting the early recurrence risk of intermittent exotropia after surgery

PURPOSE

Preoperative and postoperative identification of early recurrence risk factors in intermittent exotropia (IXT) patients after surgery.

DESIGN

Prospective clinical cohort study.

METHODS

We included 210 basic-type IXT patients who underwent either the bilateral rectus recession or unilateral recession and resection procedure and had complete follow-up until recurrence or for more than 24 months postoperatively. The primary outcome was early recurrence, defined as postoperative exodeviation over 11 prism diopters at any time beyond postoperative 1 month and within 24 months. Survival was estimated by the Kaplan-Meier method. Preoperative and postoperative clinical characteristics were collected from patients, and preoperative and postoperative Cox proportional hazards regression analyses were performed. Preoperative model was fit with nine preoperative clinical factors (sex, onset age of exotropia, duration of disease, spherical equivalent of the more myopic eye, preoperative distant exodeviation, near stereoacuity, distant stereoacuity, near control and distant control). Postoperative model was fit by adding two factors relevant to surgery (surgery type and immediate postoperative deviation). Corresponding nomograms were constructed and evaluated using the concordance indexes (C-indexes) and calibration curves. Decision curve analysis (DCA) was used to determine the clinical utility.

RESULTS

The recurrence rate was 8.10% for 6 months, 11.90% for 12 months, 17.14% for 18 months, and 27.14% for 24 months after surgery. Younger onset age, larger preoperative angle and less immediate postoperative overcorrection were found to increase the risk for recurrence. Though onset age and age at surgery were strongly correlated in this study, age at surgery was not significantly associated with IXT recurrence. The C-indexes for the preoperative and postoperative nomograms were 0.66 (95% confidence interval [CI]: 0.60-0.73) and 0.74 (95% CI: 0.68, 0.79), respectively. Calibration plots between predicted and actual observed 6-, 12-, 18-, and 24-month overall survival using the two nomograms revealed high consistency. The DCA indicated that both models yielded great clinical benefits.

CONCLUSIONS

By relatively accurate weighing of each risk factor, the nomograms offer good prediction for early recurrence in IXT patients and may help clinicians and individualized patients make appropriate intervention plans.

Pharmacogenomics and health disparities, are we helping?

Pharmacogenomics has been at the forefront of precision medicine during the last few decades. Precision medicine carries the potential of improving health outcomes at both the individual as well as population levels. To harness the benefits of its initiatives, careful dissection of existing health disparities as they relate to precision medicine is of paramount importance. Attempting to address the existing disparities at the early stages of design and implementation of these efforts is the only guarantee of a successful just outcome. In this review, we glance at a few determinants of existing health disparities as they intersect with pharmacogenomics research and implementation. In our opinion, highlighting these disparities is imperative for the purpose of researching meaningful solutions. Failing to identify, and hence address, these disparities in the context of the current and future precision medicine initiatives would leave an already strained health system, even more inundated with inequality.

Racial, ethnic and socioeconomic disparities in diagnosis, treatment, and survival of patients with breast cancer

BACKGROUND

The objective of this study was to determine the influence of race/ethnicity and socioeconomic status (SES) on breast cancer outcomes.

METHODS

A retrospective analysis was performed of Non-Hispanic Black (NHB), Non-Hispanic White (NHW), and Hispanic patients with non-metastatic breast cancer in the SEER cancer registry between 2007 and 2016.

RESULTS

A total of 382,975 patients were identified. On multivariate analysis, NHB (OR 1.18, 95%CI: 1.15-1.20) and Hispanic (OR 1.20, 95%CI: 1.17-1.22) patients were more likely to present with higher stage disease than NHW patients. There was an increased likelihood of not undergoing breast-reconstruction for NHB (OR 1.07, 95%CI: 1.03-1.11) and Hispanic patients (OR 1.60, 95%CI 1.54-1.66). NHB patients had increased hazard for all-cause mortality (HR: 1.13, 95%CI 1.10-1.16). All-cause mortality increased across SES categories (lower SES: HR 1.33, 95%CI 1.30-1.37, middle SES: HR 1.20, 95%CI 1.17-1.23).

CONCLUSIONS

This population-based analysis confirms worse disease presentation, access to surgical therapy, and survival across racial, ethnic, and socioeconomic factors. These disparities were compounded across worsening SES and insurance coverage.

Development of Predictive Models for Survival among Women with Breast Cancer in Malaysia

Prediction of survival probabilities based on models developed by other countries has shown inconsistent findings among Malaysian patients. This study aimed to develop predictive models for survival among women with breast cancer in Malaysia. A retrospective cohort study was conducted involving patients who were diagnosed between 2012 and 2016 in seven breast cancer centres, where their survival status was followed until 31 December 2021. A total of 13 predictors were selected to model five-year survival probabilities by applying Cox proportional hazards (PH), artificial neural networks (ANN), and decision tree (DT) classification analysis. The random-split dataset strategy was used to develop and measure the models' performance. Among 1006 patients, the majority were Malay, with ductal carcinoma, hormone-sensitive, HER2-negative, at T2-, N1-stage, without metastasis, received surgery and chemotherapy. The estimated five-year survival rate was 60.5% (95% CI: 57.6, 63.6). For Cox PH, the c-index was 0.82 for model derivation and 0.81 for validation. The model was well-calibrated. The Cox PH model outperformed the DT and ANN models in most performance indices, with the Cox PH model having the highest accuracy of 0.841. The accuracies of the DT and ANN models were 0.811 and 0.821, respectively. The Cox PH model is more useful for survival prediction in this study's setting.

Sexual Dysfunction in Women with Cancer: A Systematic Review of Longitudinal Studies

BACKGROUND

Several factors affect sexual function, including cancer development and treatment. This study summarized the risk of women with cancer of developing sexual dysfunctions.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We searched the EMBASE, PubMed, LILACS, SciELO, CINAHL, Scopus, and Web of Science databases using the descriptors cancer, neoplasms, sexual dysfunction, sexual function, and women. The Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies assessed the quality of studies.

RESULTS

Sixteen studies were included in this review. Women with cancer presented sexual dysfunctions in 14 out of 16 included studies. The incidence of sexual dysfunctions ranged from 30% to 80%, while the risk of developing sexual dysfunction increased 2.7- and 3.5-fold in women with cervical and breast cancer, respectively.

CONCLUSION

Different cancer treatments increase the risk of developing sexual dysfunction in women, especially desire, arousal, and orgasm, leading to biopsychosocial changes in the health of this population.

Spatial autocorrelation and stratified heterogeneity in the evaluation of breast cancer risk inequity and socioeconomic factors analysis in China: Evidence from Nanchang, Jiangxi Province

Study of socioeconomic factors can play an important role in the spatial distribution of breast cancer by leading to a better understanding of its spatial pattern and assist breast cancer screening and early diagnosis. Taking Nanchang, a major city in central China, as an example, spatial autocorrelation and stratified heterogeneity were applied using a 10 10 km grid division to analyse breast cancer risk and socioeconomic factors. The research results showed that the median incidence rate of female breast cancer in Nanchang from 2016 to 2018 was 6.6/100,000 with a standard deviation of 12.3/100,000. Areas with higher incidence rates were mainly located in the central urban area and the major county towns. Spatial regression analysis showed that there was a statistically significant correlation between the spatial patterns of breast cancer incidence on the one hand, and on the other socioeconomic factors, such as total gross domestic product (GDP), per capita GDP and density of places of social and economic activities, i.e. points of interest. In addition, the normalized difference vegetation index also played a part in this respect. This research could serve as a reference for regional public health policy formulation and breast cancer screening.

Development and Validation of Prognostic Nomogram for Elderly Breast Cancer: A Large-Cohort Retrospective Study

Purpose

Our research aims to study the bone metastatic patterns and prognostic outcomes in elderly breast cancer (BC) and to develop elder-specific nomograms.

Methods

We downloaded the data of BC patients between 2010 and 2016 from the Surveillance, Epidemiology, and End Results database. The differences in clinical features and prognosis between young (age < 65) and elderly (age ≥ 65) BC patients were compared. The univariate and multivariate Cox analyses were used to determine the overall survival (OS)- and cancer-specific survival (CSS)-related variables and establish two nomograms of BC patients with bone metastasis (BCBM). The receiver operating characteristic (ROC) curve with area under the curve (AUC), calibration curve, decision curve analysis (DCA), and Kaplan–Meier survival curve were selected to evaluate nomograms.

Results

A total of 230,177 BC patients were enrolled in our research, including 142,025 young and 88,152 elderly patients. The prognosis of elderly BCBM patients was significantly worse than young patients. Age, race, breast subtype, tumor size, tumor grade, brain metastasis, liver metastasis, surgery, and chemotherapy were independent prognostic variables for elderly BCBM patients, including OS and CSS. The AUC values at 12, 18, and 24 months were 0.750, 0.751, and 0.739 for OS nomogram and 0.759, 0.762, and 0.752 for CSS nomogram in the training cohort, which were higher than the AUC values of all single independent prognostic variables. The survival curve showed a distinct prognosis between low-, median- and high-risk groups (p < 0.001). Finally, calibration curves and DCA indicated that both nomograms have favorable performance.

Conclusion

Elderly and young patients presented with different bone metastatic frequencies, clinical features, and prognostic outcomes. Two elder-specific nomograms incorporating nine clinical variables were established and validated to be a valuable predictor for elderly BCBM patients.

Socioeconomic Status and Inflammation in Women with Early-stage Breast Cancer: Mediation by Body Mass Index

BACKGROUND

Breast cancer is the most common cancer among women in the US, and women of low socioeconomic status (SES) show markedly poorer outcomes than those of high SES. SES may influence health through inflammation, although links between SES and inflammatory biomarkers have not been investigated in women with breast cancer. This study tested the hypothesis that breast cancer patients of lower SES would show higher levels of inflammation than those of higher SES. BMI was examined as a mediator of this association.

METHODS

Women recently diagnosed with early-stage breast cancer (N = 194) were recruited before neoadjuvant or adjuvant therapy. Participants completed questionnaires and provided blood samples for immune assessment. SES was indexed by participants' self-reported education and annual household income, BMI was determined by height and weight measurements, and blood was assayed for inflammatory biomarkers linked with cancer outcomes: IL-6, CRP, TNF-α, and sTNF-RII. General linear models tested associations between SES and inflammation, and mediation models examined indirect effects through BMI.

RESULTS

Consistent with hypotheses, education status was associated with CRP, (F(2,185) = 4.72, p = 0.001), and sTNF-RII, (F(2,185) = 4.19, p = 0.02), such that lower education was associated with higher levels of both biomarkers. Further, BMI mediated the associations between education and CRP, (95% CIs [-0.62, -0.11; -0.76, -0.21]), sTNF-RII, (95% CIs [-0.09, -0.01; -0.10, -0.02]), and IL-6, (95% CIs [-0.32, -0.05; -0.38, -0.09]). Annual household income was not significantly associated with inflammation (ps > 0.25), and indirect effects on inflammation through BMI were not significant.

CONCLUSIONS

Lower education was associated with higher levels of inflammation in this sample, which may presage poor breast cancer-related and clinical outcomes. SES should inform the development of interventions targeting BMI and inflammation in breast cancer.

Prognostic Score for De Novo Metastatic Breast Cancer With Liver Metastasis and Its Predictive Value of Locoregional Treatment Benefit

Background

There is a significant survival difference and lack of effective treatment among breast cancer patients with liver metastasis. This present study aimed to construct a novel prognostic score for predicting the prognosis and locoregional treatment benefit of de novo metastatic breast cancer with liver metastasis (BCLM).

Methods

In total, 2,398 eligible patients between 2010 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. They were assigned to the training set including 1,662 patients (2010–2014) and validation set comprising 736 patients (2015–2016) depending on the time of diagnosis. The prognostic score was based on regression coefficients in the multivariate Cox regression analysis. And then, patients were stratified into low-, intermediate-, and high-risk groups by the prognostic score. The discrimination and calibration of prognostic score were evaluated using time-dependent receiver operating characteristic (ROC) curves analysis and calibration curves, respectively. Subgroup analysis was performed to evaluate locoregional surgery and chemotherapy benefit in different risk groups.

Results

Age, race, insurance and marital status, T stage, pathological grade, molecular subtypes, and extrahepatic metastasis were identified as independent prognostic variables in the prognostic score. The prognostic score showed high discrimination power with an area under the curve (AUC) of 0.77 and 0.72 and excellent agreement suggested by calibration plots in the training and validation sets, respectively. Intermediate-risk [hazard ratio (HR) 2.39, 95% confidence interval (CI) 2.09–2.73, P<0.001] and high-risk groups (HR 4.88; 95% CI 4.13–5.76; P<0.001) had significantly worse prognosis in comparison with the low-risk group. The median overall survival (OS) in three prognostic groups were 44, 18, and 7 months, with a 3-year survival rate of 56, 23, and 7%, respectively. Apart from the high-risk group (HR 0.79; 95% CI 0.56–1.10; P=0.157), the low-risk (HR 0.64; 95% CI 0.49–0.84; P=0.001) and intermediate-risk groups (HR 0.68; 95% CI 0.55–0.85; P=0.001) could benefit from the surgery of primary site, while chemotherapy improved prognosis in all risk groups.

Conclusions

A prognostic score was developed to accurately predict the prognosis of de novo BCLM patients. Moreover, it may be useful for further subdividing them into different risk groups and helping guide clinicians in treatment decisions.

Association of Breast Tumour Expression of Cannabinoid Receptors CBR1 and CBR2 with Prognostic Factors and Survival in Breast Cancer Patients

Cannabinoid receptors (CBR) are potential therapeutic targets for breast cancer. However, the role of CBR in breast cancer survival remains poorly understood. Data from a prospective cohort of 522 women diagnosed with invasive breast cancer between 2010 and 2012 were analysed. Clinical and pathological features were retrieved from electronic medical records. CBR expression was measured by immunohistochemistry. Adjusted partial Spearman correlations and multivariate Cox models were used to estimate associations with breast cancer prognostic factors and survival, respectively. The median follow-up was 92.0 months (range 7.0-114.0). CBR expression was heterogenous in tumours. Cytoplasmic expression of CBR1 was positively correlated with lymph node invasion (r_s = 0.110; p = 0.0155) and positive status of the human epidermal growth factor receptor 2 (HER2) (r_s = 0.168; p = 0.0002), while nuclear CBR2 was negatively correlated with grade (r_s = -0.171; p = 0.0002) and positively correlated with oestrogen receptor and progesterone receptor-positive status (r_s = 0.173; p = 0.0002 and r_s = 0.121; p = 0.0084, respectively). High cytoplasmic expression of CBR2 was associated, with 13% higher locoregional and distant recurrences (HR = 1.13 [0.97-1.33]), though this association did not reach statistical significance. Although the few events occurring during follow-up may have limited the detection of significant associations, these results indicate that CBR expression in breast cancer deserves further investigation.

Association between socioeconomic status and survival in patients with hepatocellular carcinoma

BACKGROUND

The effect of socioeconomic status (SES) on hepatocellular carcinoma (HCC) is still unclear, and there is no nomogram integrated SES and clinicopathological factors to predict the prognosis of HCC. This research aims to confirm the effects of SES on predicting patients' survival and to establish a nomogram to predict the prognosis of HCC.

METHODS

The data of HCC patients were collected from the Surveillance, Epidemiology, and Final Results (SEER) database from 2011 to 2015. SES (age at diagnosis, race and sex, median family income, education level, insurance status, marital status, residence, cost of living index, poverty rate) and clinicopathological factors were included in univariate and multivariate Cox regression analysis. Nomograms for predicting 1-, 3-, and 5-year cancer-specific survival (CSS) and overall survival (OS) were established and evaluated by the concordance index (C-index), the receiver operating characteristic curve (ROC), the calibration plot, the integrated discrimination improvement (IDI), and the net reclassification improvement (NRI).

RESULTS

A total of 33,670 diagnosed HCC patients were involved, and nomograms consisting of 19 variables were established. The C-indexes of the nomograms are higher than TNM staging system, which predicts the CSS (0.789 vs. 0.692, p < 0.01) and OS (0.777 vs. 0.675, p < 0.01). The ROC curve, calibration diagram, IDI, and NRI showed the improved prognostic value in 1-, 3-, and 5-year survival rates.

CONCLUSION

SES plays an important role in the prognosis of HCC patients. Therefore, policymakers can make more precise and socially approved policies to improve HCC patients' CSS and OS.

Screen-detected breast cancer is associated with better prognosis and survival compared to self-detected/symptomatic cases in a Chilean cohort of female patients

PURPOSE

The implementation of national breast cancer (BC) screening programs in Latin America has been rather inconsistent. Instead, most countries have opted for "opportunistic" mammogram screenings on the population at risk. Our study assessed and compared epidemiological, clinical factors, and survival rates associated with BC detected by screening (SDBC) or self-detected/symptomatic (non-SDBC) in Chilean female patients.

METHODS

Registry-based cohort study that included non-metastatic BC (stage I/II/III) patients diagnosed between 1993 and 2020, from a public hospital (PH) and a private university cancer center (PC). Epidemiological and clinical data were obtained from medical records.

RESULTS

A total of 4559 patients were included. Most patients (55%; n = 2507) came from PH and were diagnosed by signs/symptoms (non-SDBC; n = 3132, 68.6%); these patients displayed poorer overall (OS) and invasive disease-free survival (iDFS) compared to SDBC. Importantly, the proportion of stage I and "luminal" BC (HR + /HER2 -) were significantly higher in SDBC vs. non-SDBC. Finally, using a stage/subset-stratified age/insurance-adjusted model, we found that non-SDBC cases are at a higher risk of death (HR:1.75; p < 0.001). In contrast, patients with PC health insurance have a lower risk of death (HR: 0.60; p < 0.001).

CONCLUSION

We confirm previous studies that report better prognosis/survival on SDBC patients. This is probably due to a higher proportion of stage I and luminal-A cases versus non-SDBC. In turn, the survival benefit observed in patients with PC health insurance might be attributed to a larger proportion of SDBC. Our data support the implementation of a systematic BC screening program in Chile to improve patient prognosis and survival rates.

A Prognostic Risk Stratification Model to Identify Potential Population Benefiting From Postmastectomy Radiotherapy in T1-2 Breast Cancer With 1-3 Positive Axillary Lymph Nodes

Background and Objectives

To establish a prognostic stratification nomogram for T1–2 breast cancer with 1–3 positive lymph nodes to determine which patients can benefit from postmastectomy radiotherapy (PMRT).

Methods

A population-based study was conducted utilizing data collected from the Surveillance, Epidemiology, and End Results database. Chi-square test or Fisher exact test was used to compare the distribution of characteristics. Cox analysis identified significant prognostic factors for survival. A prognostic stratification model was constructed by R software. Propensity score matching was applied to balance characteristics between PMRT cohort and control cohort. Kaplan-Meier method was performed to evaluate the performance of stratification and the benefits of PMRT in the total population and three risk groups.

Results

The overall performance of the nomogram was good (3-year, 5-year, 10-year AUC were 0.75, 0.72 and 0.67, respectively). The nomogram was performed to excellently distinguish low-risk, moderate-risk, and high-risk groups with 10-year overall survival (OS) of 86.9%, 73.7%, and 62.7%, respectively (P<0.001). In the high-risk group, PMRT can significantly better OS with 10-year all-cause mortality reduced by 6.7% (P = 0.027). However, there was no significant survival difference between PMRT cohort and control cohort in low-risk (P=0.49) and moderate-risk groups (P = 0.35).

Conclusion

The current study developed the first prognostic stratification nomogram for T1–2 breast cancer with 1–3 positive axillary lymph nodes and found that patients in the high-risk group may be easier to benefit from PMRT.

A New Electrochemical Platform for Dasatinib Anticancer Drug Sensing Using Fe3O4-SWCNTs/Ionic Liquid Paste Sensor

A new electrochemical platform was suggested for the sensing of the dasatinib (DA) anticancer drug based on paste electrode modification (PE) amplified with Fe₃O₄-SWCNTs nanocomposite and 1-hexyl-3-methylimidazolium tetrafluoroborate (mim-BF₄⁻). The new platform showed a linear dynamic range from 0.001–220 µM with a detection limit of 0.7 nM to determine DA at optimal condition. Electrochemical investigation showed that the redox reaction of DA is relative to changing the pH of solution. Moreover, Fe₃O₄-SWCNTs/mim-BF₄⁻/PE has improved the oxidation current of DA about 5.58 times which reduced its oxidation potential by about 120 mV at optimal condition. In the final step, Fe₃O₄-SWCNTs/mim-BF₄⁻/PE was used as an analytical platform to determine the DA in tablets and a dextrose saline spike sample, and the results showed recovery data 99.58–103.6% which confirm the powerful ability of the sensor as an analytical tool to determine the DA in real samples.

VDAC1 Conversely Correlates with Cytc Expression and Predicts Poor Prognosis in Human Breast Cancer Patients

Aim

The main objective of this article was to evaluate the association of voltage-dependent anion channel 1 (VDAC1) with Cytochrome C (Cytc) expression, various clinicopathological features, and prognosis in breast cancer (BC) patients. Meanwhile, the correlation of Cytc expression with various clinical features and 5-year disease-free survival (5-DFS) of BC was also investigated.

Methods

In vivo, expression of VDAC1 and Cytc was examined in 219 BC tissues and 100 benign breast lesions by immunohistochemical (IHC) analysis. In vitro, MTT and wound healing migration assay were performed to detect the effect of VDAC1 on BC cells.

Results

Expression of VDAC1 is conversely associated with Cytc in BC (P = 0.011), especially in triple-negative breast cancer (TNBC) (P = 0.004). Knockdown of VDAC1 inhibited proliferation (P < 0.001) and migration (P < 0.05) of MCF-7 cells. High expression of VDAC1 and low expression of Cytc had a significant association with multiple clinicopathological parameters (P < 0.05) and poor 5-DFS (P < 0.001) in BC.

Conclusion

VDAC1 was elevated in BC tissues and conversely associated with Cytc. Detection of VDAC1 may provide guidance for the poor prognosis of BC, especially TNBC.

Development and validation of a risk stratification nomogram for predicting prognosis in bone metastatic breast cancer: A population-based study

ABSTRACT

Bone metastasis seriously affects the survival of breast cancer. Therefore, the study aimed to explore the independent prognostic factors in bone metastatic breast cancer (BMBC) and to construct a prognostic nomogram that can accurately predict the survival of BMBC and strictly divide the patients into different risk stratification.Four thousand three hundred seventy six patients with BMBC from the surveillance, epidemiology, and end results database in 2010 to 2015 were collected and randomly divided into training and validation cohort. Multivariate Cox regression identified the independent prognostic factors of BMBC. A nomogram for predicting cancer-specific survival (CSS) in BMBC was created using R software. The predictive performance of the nomogram was evaluated by plotting receiver operating characteristic (ROC) curves and calibration curves.Marital status, race, age, T stage, tumor grade, estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, brain metastasis, liver metastasis, lung metastasis, chemotherapy, and breast surgery were identified as independent prognostic factors for CSS of BMBC. The area under the ROC curve at 1-, 3-, and 5-year of the nomogram were 0.775, 0.756, and 0.717 in the internal validation and 0.785, 0.737, and 0.735 in the external validation, respectively. Calibration curves further confirmed the unbiased prediction of the model. Kaplan-Meier analysis verified the excellent risk stratification of our model.The first prognostic nomogram for BMBC constructed in our study can accurately predict the survival of BMBC, which may provide a practical tool to help clinicians evaluate prognosis and stratify the prognostic risk for BMBC, thereby determining which patients should be given intensive treatment and optimizing individual treatment strategies for BMBC.

Spatial distribution of advanced stage diagnosis and mortality of breast cancer: Socioeconomic and health service offer inequalities in Brazil

Breast cancer presents high incidence and mortality rates, being considered an important public health issue. Analyze the spatial distribution pattern of late stage diagnosis and mortality for breast cancer and its correlation with socioeconomic and health service offer-related population indicators. Ecological study, developed with 161 Intermediate Region of Urban Articulation (IRUA). Mortality data were collected from the Mortality Information System (MIS). Tumor staging data were extracted from the Hospital Cancer Registry (HCR). Socioeconomic variables were obtained from the Atlas of Human Development in Brazil; data on medical density and health services were collected from the National Registry of Health Institutions (NRHI) and Supplementary National Health Agency. Global Moran's Index and Local Indicator of Spatial Association (LISA) were utilized to verify the existence of territorial clusters. Multivariate analysis used models with global spatial effects. The proportion of late stage diagnosis of breast cancer was 39.7% (IC 39.4–40.0). The mean mortality rate for breast cancer, adjusted by the standard world population was 10.65 per 100,000 women (± 3.12). The proportion of late stage diagnosis presented positive spatial correlation with Gini’s Index (p = 0.001) and negative with the density of gynecologist doctors (p = 0.009). The adjusted mortality rates presented a positive spatial correlation with the Human Development Index (p<0.001) and density of gynecologist doctors (p<0.001). Socioeconomic and health service offer-related inequalities of the Brazilian territory are determinants of the spatial pattern of breast cancer morbimortality in Brazil.

A multilevel assessment of the social determinants associated with the late stage diagnosis of breast cancer

The advanced-stage diagnosis of breast cancer reveals the inequalities associated with socioeconomic conditions and the offer of health services. This study analyzes the prevalence of advanced breast cancer and its relationship with individual and contextual socioeconomic indicators and offer of health service. A cross-sectional study is presented herein, on the assessment of malignant breast neoplasms in women diagnosed between 2006 and 2015 (n = 195,201). Data were collected from the Hospital Cancer Registry (HCR), Atlas of Human Development in Brazil, and from the National Registry of Health Institutions (NRHI). A multilevel Poisson Regression was carried out with random intercept. The prevalence of advanced breast cancer diagnosis was 40.0%. Advanced staging was associated with younger age groups (PR 1.41), race/nonwhite (PR 1.13), lower education levels (PR 1.38), and public access to health services (PR 1.25). There was also an association with a low density of mammographic equipment (PR 1.08), and with low indices of local social inequality (PR 1.33) and human development (PR 0.80). This study maps and highlights the causes related to inequalities in the diagnosis of advanced breast cancer in Brazil, and presents essential data to reorient public policies and health-related actions to strengthen the control of breast cancer in Brazil.

Establishment and Validation of Novel Clinical Prognosis Nomograms for Luminal A Breast Cancer Patients with Bone Metastasis

PURPOSE

Overall survival (OS) and cancer-specific survival (CSS) of luminal A breast cancer (BC) patients with bone metastasis remain poor and vary dramatically from person to person. Our goal was to build two universally applicable nomograms to accurately predict OS and CSS for luminal A patients with bone metastasis.

METHODS

The data were collected from the Surveillance, Epidemiology, and End Results (SEER) database for luminal A BC patients with bone metastasis between 2010 and 2015. Univariate and multivariate Cox regression analyses were to assess and identify independent risk factors of OS and CSS. Integrating all significant predictors, nomograms and risk group stratification model was developed. The performance of the nomogram was validated with concordance index (C-index), calibration plots, and decision curve analyses (DCA) for discriminative ability, calibration, and clinical utility, respectively.

RESULTS

3171 luminal A BC patients with bone metastasis were included. Through univariate and multivariate Cox regression analyses, 12 variables were identified as both independent OS- and CSS-related factors, including age, race, primary site, histology grade, tumor size, surgery, brain metastasis, liver metastasis, lung metastasis, estrogen receptor status, progesterone receptor status, and insurance. Our nomograms for 1-, 3-, and 5-year survival were based on those significant prognostic factors to develop. The C-indexes of OS- and CSS-nomograms in the training cohort were 0.701 and 0.704, respectively. Similar results were obtained in the validation cohort. The calibration curves and DCA presented satisfactory calibration and clinical utility.

CONCLUSION

Two nomograms have good discrimination, calibration, and clinical utility, can accurately and effectively predict the prognosis of patients, and may benefit for clinical decision-making. In high-risk patients, more aggressive therapy and closer surveillance should be considered.

Public health insurance and cancer-specific mortality risk among patients with breast cancer: A prospective cohort study in China

Little is known about how health insurance policies, particularly in developing countries, influence breast cancer prognosis. Here, we examined the association between individual health insurance and breast cancer-specific mortality in China. We included 7436 women diagnosed with invasive breast cancer between 2009 and 2016, at West China Hospital, Sichuan University. The health insurance plan of patient was classified as either urban or rural schemes and was also categorized as reimbursement rate (ie, the covered/total charge) below or above the median. Breast cancer-specific mortality was the primary outcome. Using Cox proportional hazards models, we calculated hazard ratios (HRs) for cancer-specific mortality, contrasting rates among patients with a rural insurance scheme or low reimbursement rate to that of those with an urban insurance scheme or high reimbursement rate, respectively. During a median follow-up of 3.1 years, we identified 326 deaths due to breast cancer. Compared to patients covered by urban insurance schemes, patients covered by rural insurance schemes had a 29% increased cancer-specific mortality (95% CI 0%-65%) after adjusting for demographics, tumor characteristics and treatment modes. Reimbursement rate below the median was associated with a 42% increased rate of cancer-specific mortality (95% CI 11%-82%). Every 10% increase in the reimbursement rate is associated with a 7% (95% CI 2%-12%) reduction in cancer-specific mortality risk, particularly in patients covered by rural insurance schemes (26%, 95% CI 9%-39%). Our findings suggest that underinsured patients face a higher risk of breast cancer-specific mortality in developing countries.

Association between socioeconomic factors at diagnosis and survival in breast cancer: A population-based study

Background

The associations between socioeconomic statuses and survival outcomes of breast cancer remain unclear. No model has included both histological and socioeconomic factors to predict the survival of breast cancer. This study was designed to develop nomograms to predict breast cancer–specific survival (BCSS) and overall survival (OS) with consideration of socioeconomic factors for breast cancer patients.

Materials and methods

We included a total of 207 749 female patients, diagnosed with malignant breast cancer between 2007 and 2012 from the Surveillance, Epidemiology, and End Results database. BCSS and OS were evaluated with Gray's test and log‐rank tests, respectively. Marital statuses, insurance statuses, residence, median household income, poverty rate, unemployment rate, and education level were included as socioeconomic factors in univariate and multivariate Cox regression analyses. Clinicopathological factors and socioeconomic factors were integrated to construct nomograms. Calibration plots and concordance indexes (C‐indexes) were used to evaluate the accuracy and discrimination of the models.

Results

Four and three socioeconomic factors were involved in constructing the nomograms for 3‐, 5‐, and 7‐year BCSS and OS, respectively. The C‐indexes of the final nomograms were higher than those of the TNM staging system for predicting BCSS (0.835 vs 0.782; P < .001) and OS (0.773 vs 0.676; P < .001). The performance of the nomograms for predicting OS was significantly lower when excluding socioeconomic factors (P < .001).

Conclusion

These findings may highlight the importance of developing health‐related policies and the necessity of targeted social support‐based interventions for high‐risk patients.