Benign breast disease and breast cancer risk in African women: a case-control study.

PURPOSE: To examine the association between benign breast disease (BBD) and breast cancer (BC) in a heterogeneous population of African women. METHODS: BC cases and controls were enrolled in three sub-Saharan African countries, Nigeria, Cameroon, and Uganda, between 1998 and 2018. Multivariable logistic regression was used to test the association between BBD and BC. Risk factors dually associated with BBD and BC were selected. Using a parametric mediation analysis model, we assessed if selected BC risk factors were mediated by BBD. RESULTS: Of 6,274 participants, 55.6% (3,478) were breast cancer cases. 360 (5.7%) self-reported BBD. Fibroadenoma (46.8%) was the most commonly reported BBD. Women with a self-reported history of BBD had greater odds of developing BC than those without (adjusted odds ratio [aOR] 1.47, 95% CI 1.13-1.91). Biopsy-confirmed BBD was associated with BC (aOR 2.25, 95% CI 1.26-4.02). BBD did not significantly mediate the effects of any of the selected BC risk factors. CONCLUSIONS: In this study, BBD was associated with BC and did not significantly mediate the effects of selected BC risk factors.

Racial/Ethnic Differences in Pathologic Complete Response to Neoadjuvant Chemotherapy and Survival Among Early-Stage, Erb-B2 Receptor Tyrosine Kinase 2-Low Breast Cancer Patients.

Racial/ethnic differences in pathologic complete response (pCR), and in overall survival (OS) by pCR status, among early-stage, erb-b2 receptor tyrosine kinase 2 (ERBB2)-low breast cancer patients after neoadjuvant chemotherapy (NACT) are unknown. Data were from the 2010-2020 National Cancer Database that included Asian/Pacific Islander (API), American Indian/Alaska Native/Other (AIANO), Black, Hispanic, and White patients. pCR and OS were modeled using logistic regression and Cox regression, respectively. Of 25,577 patients, Black patients achieved a 17.4% pCR rate, Hispanic 16.0%, White 14.7%, API 13.5%, and AIANO 10.9%. AIANO patients had lower odds of pCR than White patients (adjusted odds ratio, 0.66; 95% confidence interval [CI], 0.48-0.91). Among patients without pCR, API (adjusted hazard ratio [aHR], 0.62; 95% CI, 0.51-0.76) and Hispanic (aHR, 0.77; 95% CI, 0.67-0.89) patients had lower mortality risks than White patients. Among patients with pCR, similar OS rates were observed between Hispanic (aHR, 1.08; 95% CI, 0.66-1.78), Black (aHR, 0.77; 95% CI, 0.55-1.09), API (aHR, 0.41; 95% CI, 0.15-1.12), or AIANO (aHR, 0.35; 95% CI, 0.05-2.50) and White patients. Post-NACT pCR rates were similar across racial/ethnic groups of early-stage, ERBB2-low breast cancer patients. Among patients without pCR, API and Hispanic patients had better OS; among patients with pCR, there was no differential OS by race/ethnicity. Our findings suggest the need for longitudinal studies of OS differences in this patient population.

Palliative Care Use Trends, Racial/Ethnic Disparities, and Overall Survival Differences Among Patients With Metastatic Breast Cancer.

Background: Palliative care improves cancer patients' quality of life. Limited research has investigated racial/ethnic disparities in palliative care utilization and its associated survival among metastatic breast cancer (MBC) patients. Objectives: To examine racial/ethnic palliative care use disparities and assess racial/ethnic overall survival differences in MBC patients stratified by palliative care use. Design: A retrospective study of MBC patients from the 2004-2020 National Cancer Database. Measurements: Palliative care was defined as noncurative cancer treatment, including surgery, radiotherapy, systemic therapy, and/or pain management; utilization was coded "yes/no." Racial/ethnic groups included Asian, American Indian or Alaska Native (AIAN), Black, Hawaiian or Other Pacific Islander (HPI), Hispanic, and White. Logistic regression was performed to assess palliative care use disparities. Overall survival was modeled using Cox regression. Results: Of 148,931 patients, the mean age was 62 years; 99% were female; 73% identified as White, 17% as Black, 6% as Hispanic, 3% as Asian, 0.3% as AIAN, and 0.3% as HPI; 42% and 39% had Medicare and private insurance, respectively. Overall, 21% used palliative care, with an increasing utilization rate from 2004 to 2020 (3.6% increase per year, p-trend <0.001). Black (adjusted odds ratio [aOR] = 0.89; 95% confidence interval [CI]: 0.84 to 0.94), Asian (aOR = 0.76; 95% CI: 0.68 to 0.86), and Hispanic (aOR = 0.68; 95% CI: 0.62 to 0.74) patients had a lower likelihood of palliative care utilization than White patients. Among palliative care users, compared with White patients, Black (adjusted hazard ratio [aHR] = 1.14, 95% CI: 1.07 to 1.21) patients had a greater mortality risk, while Asian (aHR = 0.83, 95% CI: 0.71 to 0.97) and Hispanic (aHR = 0.77, 95% CI: 0.69 to 0.87) patients had a lower mortality risk. Conclusions: Palliative care utilization among MBC patients significantly increased but remained suboptimal. Racial/ethnic minority patients were less likely to use palliative care, and Black patients had worse survival, than White patients, suggesting the need for improving palliative care access and ameliorating disparities in MBC patients.

Benign breast disease and breast cancer risk in African women: A case-control study.

Purpose: To examine the association between benign breast disease (BBD) and breast cancer (BC) in a heterogeneous population of African women. Methods: BC cases and matched controls were enrolled in three sub-Saharan African countries, Nigeria Cameroon, and Uganda, between 1998-2018. Multivariable logistic regression was used to test the association between BBD and BC. Risk factors dually associated with BBD and BC were selected. Using a parametric mediation analysis model, we assessed if selected BC risk factors were mediated by BBD. Results: Of 6418 participants, 55.7% (3572) were breast cancer cases. 360 (5.7%) self-reported BBD. Fibroadenoma (46.8%) was the most reported BBD. Women with a self-reported history of BBD had greater odds of developing BC than those without (adjusted odds ratio [aOR] = 1.47, 95% CI: 1.13-1.91). Biopsy-confirmed BBD was associated with BC (aOR = 3.11, 95% CI: 1.78-5.44). BBD did not significantly mediate the effects of any of the selected BC risk factors. Conclusions: In this study, BBD was associated with BC and did not significantly mediate the effects of selected BC risk factors.

External Evaluation of a Mammography-based Deep Learning Model for Predicting Breast Cancer in an Ethnically Diverse Population.

Purpose: To externally evaluate a mammography-based deep learning (DL) model (Mirai) in a high-risk racially diverse population and compare its performance with other mammographic measures. Materials and Methods: A total of 6435 screening mammograms in 2096 female patients (median age, 56.4 years ± 11.2 [SD]) enrolled in a hospital-based case-control study from 2006 to 2020 were retrospectively evaluated. Pathologically confirmed breast cancer was the primary outcome. Mirai scores were the primary predictors. Breast density and Breast Imaging Reporting and Data System (BI-RADS) assessment categories were comparative predictors. Performance was evaluated using area under the receiver operating characteristic curve (AUC) and concordance index analyses. Results: Mirai achieved 1- and 5-year AUCs of 0.71 (95% CI: 0.68, 0.74) and 0.65 (95% CI: 0.64, 0.67), respectively. One-year AUCs for nondense versus dense breasts were 0.72 versus 0.58 (P = .10). There was no evidence of a difference in near-term discrimination performance between BI-RADS and Mirai (1-year AUC, 0.73 vs 0.68; P = .34). For longer-term prediction (2-5 years), Mirai outperformed BI-RADS assessment (5-year AUC, 0.63 vs 0.54; P < .001). Using only images of the unaffected breast reduced the discriminatory performance of the DL model (P < .001 at all time points), suggesting that its predictions are likely dependent on the detection of ipsilateral premalignant patterns. Conclusion: A mammography DL model showed good performance in a high-risk external dataset enriched for African American patients, benign breast disease, and BRCA mutation carriers, and study findings suggest that the model performance is likely driven by the detection of precancerous changes.Keywords: Breast, Cancer, Computer Applications, Convolutional Neural Network, Deep Learning Algorithms, Informatics, Epidemiology, Machine Learning, Mammography, Oncology, Radiomics Supplemental material is available for this article. © RSNA, 2023See also commentary by Kontos and Kalpathy-Cramer in this issue.

Corrigendum: Radiology subspecialisation in Africa: A review of the current status.

[This corrects the article DOI: 10.4102/sajr.v25i1.2168.].

Radiology subspecialisation in Africa: A review of the current status.

BACKGROUND: Radiology subspecialisation is well-established in much of Europe, North America, and Australasia. It is a natural evolution of the radiology speciality catalysed by multiple factors. OBJECTIVES: The aim of this article is to analyse and provide an overview of the current status of radiology subspecialisation in African countries. METHODS: We reviewed English-language articles, reports, and other documents on radiology specialisation and subspecialisation in Africa. RESULTS: There are 54 sovereign countries in Africa (discounting disputed territories). Eighteen African countries with well-established radiology residency training were assessed for the availability of formal subspecialisation training locally. Eight (Egypt, Ethiopia, Kenya, Morocco, Nigeria, South Africa, Tanzania, and Tunisia) out of the 18 countries have local subspecialist training programmes. Data and/or information on subspecialisation were unavailable for three (Algeria, Libya, and Senegal) of the 18 countries. Paediatric Radiology (Ethiopia, Nigeria, South Africa, Tunisia) and Interventional Radiology (Egypt, Kenya, South Africa, Tanzania) were the most frequently available subspecialist training programmes. Except Tanzania, all the countries with subspecialisation training programmes have ≥ 100 radiologists in their workforce. CONCLUSION: There is limited availability of subspecialist radiology training programmes in African countries. Alternative models of subspecialist radiology training are suggested to address this deficit.