Which strategies reduce breast cancer mortality most? Collaborative modeling of optimal screening, treatment, and obesity prevention

Head-to-head comparison of cone-beam breast computed tomography and mammography in the diagnosis of primary breast cancer: A systematic review and meta-analysis

INTRODUCTION

To compare the diagnostic performance of cone-beam breast computed tomography (CBBCT) and mammography (MG) in primary breast cancer.

METHODS

PubMed, Embase, Web of Science, China National Knowledge Infrastructure, WanFang DATA, and China Science and Technology Journal databases were searched comprehensively from inception to March 2023. Sensitivity and specificity were calculated using bivariate random-effects models, and a summary receiver operating characteristic (SROC) curve was constructed. Bivariate I2 statistics and meta-regression analyses were also performed. The differences in diagnostic performance between CBBCT and MG were analysed using Z-test statistics. Clinical utility was explored using Fagan's nomogram, and quality assessment was conducted utilising the Quality Assessment of Diagnostic Accuracy Studies-2 checklist.

RESULTS

The summary sensitivity and specificity for CBBCT in diagnosing primary breast cancer were 0.92 (95 % CI: 0.87-0.94) and 0.79 (95 % CI: 0.71-0.85), respectively, and the area under the curve (AUC) of the SROC was 0.93 (95 % CI: 0.90-0.95). For MG, the summary sensitivity and specificity were 0.77 (95 % CI: 0.69-0.83) and 0.75 (95 % CI: 0.66-0.82), respectively, with an AUC of 0.83 (95 % CI: 0.80-0.86). The Z-test revealed that the summary sensitivity of CBBCT was significantly higher than that of MG (P < 0.001). Additionally, the summary AUC of CBBCT was significantly higher than that of MG (P < 0.001).

CONCLUSION

The diagnostic performance of CBBCT for primary breast cancer was better than that of MG. However, the results of both the CBBCT and MG are based on studies with small sample sizes. Further studies with larger sample sizes and more comprehensive designs are required to address this issue.

Drivers of racial, regional, and socioeconomic disparities in late-stage breast cancer mortality

BACKGROUND

The authors identified tumor, treatment, and patient characteristics that may contribute to differences in breast cancer (BC) mortality by race, rurality, and area-level socioeconomic status (SES) among women diagnosed with stage IIIB-IV BC in Georgia.

METHODS

Using the Georgia Cancer Registry, 3084 patients with stage IIIB-IV primary BC (2013-2017) were identified. Cox proportional hazards regression was used to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) comparing mortality among non-Hispanic Black (NHB) versus non-Hispanic White (NHW), residents of rural versus urban neighborhoods, and residents of low- versus high-SES neighborhoods by tumor, treatment, and patient characteristics. The mediating effects of specific characteristics on the association between race and BC mortality were estimated.

RESULTS

Among the study population, 41% were NHB, 21% resided in rural counties, and 72% resided in low SES neighborhoods. The authors observed mortality disparities by race (HR, 1.27; 95% CI, 1.13, 1.41) and rurality (HR, 1.14; 95% CI, 1.00, 1.30), but not by SES (HR, 1.04; 95% CI, 0.91, 1.19). In the stratified analyses, racial disparities were the most pronounced among women with HER2 overexpressing tumors (HR, 2.30; 95% CI, 1.53, 3.45). Residing in a rural county was associated with increased mortality among uninsured women (HR, 2.25; 95% CI, 1.31, 3.86), and the most pronounced SES disparities were among younger women (<40 years: HR, 1.46; 95% CI, 0.88, 2.42).

CONCLUSIONS

There is considerable variation in racial, regional, and socioeconomic disparities in late-stage BC mortality by tumor, treatment, and patient characteristics.

Recently approved treatment options for patients with metastatic triple-negative and HER2-neu-positive breast cancer

Breast cancer (BC) is the most common cancer affecting women worldwide. In 2021, the estimated number of new breast cancer cases was 281 550 and about 43 500 women died from metastatic breast cancer (mBC). For women aged 20-59 years, mBC remains the leading cause of cancer death and is, therefore, an important public health concern. Only 5% of women initially present with metastatic disease. Approximately 20% of patients presenting with local or locoregional disease progress to mBC despite adjuvant therapy. Inspite of all the medicosurgical advancements, the overall prognosis for patients diagnosed with mBC remains poor, with median overall survival of approximately 31 months, although this varies based on tumor biology. In recent years, there has been significant progress in developing immunotargeted therapies such as antihuman epidermal growth factor receptor 2 (anti-HER2) or check point inhibitors that confirmed to have dramatically improve the prognosis of mBC, a historically unfavorable disease subset. Even with the major progress that has been made in understanding the biology of BC, challenges such as resistance frequency to therapies, unknown efficacy, concerns for safety of drug combination and toxicities still remain high. Therefore, a new targeted and more selective treatment approaches are the need of the hour. In this review, we aim to outline the most recently approved medications in treatment of Her2-positive and triple-negative breast cancers.

Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts

Since 2000, the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.

Since 2000, our research teams have used computer models of breast cancer to address important clinical and policy-relevant questions as part of the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET). Our 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to represent the burden of breast cancer. We have used our models to investigate questions related to breast cancer biology, compare strategies to improve the balance of benefits and harms of screening mammography, and support insights into the delivery of care by modeling outcomes following clinical decisions about breast cancer treatment. Moving forward, our research will continue to use systems modeling to address issues related to reducing the burden of breast cancer including modeling structural inequities affecting racial disparities. Our future work will also leverage lessons from engaging multidisciplinary scientific teams, expand efforts to share modeling resources with other researchers, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.

Simulating the Impact of Risk-Based Screening and Treatment on Breast Cancer Outcomes with MISCAN-Fadia

The MISCAN-Fadia microsimulation model uses continuous tumor growth to simulate the natural history of breast cancer and has been used extensively to estimate the impact of screening and adjuvant treatment on breast cancer incidence and mortality trends. The model simulates individual life histories from birth to death, with and without breast cancer, in the presence and in the absence of screening and treatment. Life histories are simulated according to discrete events such as birth, tumor inception, the tumor's clinical diagnosis diameter in the absence of screening, and death from breast cancer or death from other causes. MISCAN-Fadia consists of 4 main components: demography, natural history of breast cancer, screening, and treatment. Screening impact on the natural history of breast cancer is assessed by simulating continuous tumor growth and the "fatal diameter" concept. This concept implies that tumors diagnosed at a size that is between the screen detection threshold and the fatal diameter are cured, while tumors diagnosed at a diameter larger than the fatal tumor diameter metastasize and lead to breast cancer death. MISCAN-Fadia has been extended by including a different natural history for molecular subtypes based on a tumor's estrogen receptor (ER) status and human epidermal growth factor receptor 2 (HER2) status. In addition, personalized screening strategies that target women based on their risk such as breast density have been incorporated into the model. This personalized approach to screening will continue to develop in light of potential polygenic risk stratification possibilities and new screening modalities.

Contribution of Breast Cancer to Overall Mortality for US Women

OBJECTIVE

Breast cancer simulation models must take changing mortality rates into account to evaluate the potential impact of cancer control interventions. We estimated mortality rates due to breast cancer and all other causes combined to determine their impact on overall mortality by year, age, and birth cohort.

METHODS

Based on mortality rates from publicly available datasets, an age-period-cohort model was used to estimate the proportion of deaths due to breast cancer for US women aged 0 to 119 years, with birth years 1900 to 2000. Breast cancer mortality was calculated as all-cause mortality multiplied by the proportion of deaths due to breast cancer; other-cause mortality was the difference between all-cause and breast cancer mortality.

RESULTS

Breast cancer and other-cause mortality rates were higher for older ages and birth cohorts. The percent of deaths due to breast cancer increased across birth cohorts from 1900 to 1940 then decreased. Among 50-year-old women, in the 1920 birth cohort, 52 (9.9%) of 100,000 deaths (95% CI, 9.8% to 10.1%) were attributed to breast cancer whereas 476 of 100,000 were due to other causes; in the 1960 birth cohort, 22 (8.5%) of 100,000 deaths (95% CI, 8.3% to 8.7%) were attributed to breast cancer with 242 of 100,000 deaths due to other causes. The percentage of all deaths due to breast cancer was highest (4.1% to 12.9%) for women in their 40s and 50s for all birth cohorts.

CONCLUSIONS

This study offers evidence that advances in breast cancer screening and treatment have reduced breast cancer mortality for women across the age spectrum, and provides estimates of age-, year- and birth cohort-specific competing mortality rates for simulation models. Other-cause mortality estimates are important in these models because most women die from causes other than breast cancer.

Evaluating Parameter Uncertainty in a Simulation Model of Cancer Using Emulators

Background. Microsimulation models have been extensively used in the field of cancer modeling. However, there is substantial uncertainty regarding estimates from these models, for example, overdiagnosis in prostate cancer. This is usually not thoroughly examined due to the high computational effort required. Objective. To quantify uncertainty in model outcomes due to uncertainty in model parameters, using a computationally efficient emulator (Gaussian process regression) instead of the model. Methods. We use a microsimulation model of prostate cancer (microsimulation screening analysis [MISCAN]) to simulate individual life histories. We analyze the effect of parametric uncertainty on overdiagnosis with probabilistic sensitivity analyses (ProbSAs). To minimize the number of MISCAN runs needed for ProbSAs, we emulate MISCAN, using data pairs of parameter values and outcomes to fit a Gaussian process regression model. We evaluate to what extent the emulator accurately reproduces MISCAN by computing its prediction error. Results. Using an emulator instead of MISCAN, we may reduce the computation time necessary to run a ProbSA by more than 85%. The average relative prediction error of the emulator for overdiagnosis equaled 1.7%. We predicted that 42% of screen-detected men are overdiagnosed, with an associated empirical confidence interval between 38% and 48%. Sensitivity analyses show that the accuracy of the emulator is sensitive to which model parameters are included in the training runs. Conclusions. For a computationally expensive simulation model with a large number of parameters, we show it is possible to conduct a ProbSA, within a reasonable computation time, by using a Gaussian process regression emulator instead of the original simulation model.

Evidence of Benefit from Mammography Screening of Average-Risk Women Ages 40-49 Years: Science, Metrics, and Value Judgments

The majority of randomized control trials and service-based screening studies of women ages 40-49 years demonstrate reductions in mortality of 29%-48% when long-term outcome is assessed. Annual screening is preferable in these younger women due to faster tumor-doubling times. Advances in mammography technique and breast ultrasound may allow even better results in the future.

The breast lesion excision system procedure: An optimal solution for the management of indeterminate BI-RADS category 3 breast lesions in women with severe anxiety

Objectives:

To evaluate the efficacy of the Breast lesion excision system (BLES) procedure as a primary excisional biopsy for the management of breast imaging-reporting and data system (BI-RADS) category 3, small, and solid breast lesions in women having severe breast cancer anxiety.

Methods:

A retospective study was conducted on 68 patients who underwent a BLES procedure. The study protocol was approved by the local ethical committee of Yeni Yuzyıl University in Istanbul, Turkey. The study was carried out according to the principles of the Helsinki Declaration. Small breast lesions removed using a (12, 15 or 20 mm) wand from September 2011 to November 2014. These were category 3 lesions as determined by ultrasound (US) imaging according to BI-RADS. The radiological and pathological sizes of these lesions, the complete excision rates, the procedure durations, the pathological diagnosis, the complications, and the imaging findings before and after the procedure were all recorded.

Results:

All the patients had a benign pathology. The mean duration of procedure was 12 (range=8-22) minutes. There was no major complication during the procedure and in the following period. Only some small hematomas were determined in 3 (4.2%) patients, and no additional surgical intervention was performed.

Conclusion:

The BLES procedure is an optimal solution for the management of indeterminate BI-RADS category 3 breast lesions in women with severe anxiety.

An assessment of progress in cancer control

This article summarizes cancer mortality trends and disparities based on data from the National Center for Health Statistics. It is the first in a series of articles that will describe the American Cancer Society's vision for how cancer prevention, early detection, and treatment can be transformed to lower the cancer burden in the United States, and sets the stage for a national cancer control plan, or blueprint, for the American Cancer Society goals for reducing cancer mortality by the year 2035. Although steady progress in reducing cancer mortality has been made over the past few decades, it is clear that much more could, and should, be done to save lives through the comprehensive application of currently available evidence-based public health and clinical interventions to all segments of the population. CA Cancer J Clin 2018;000:000-000. © 2018 American Cancer Society.

Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models

The Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Working Group is a consortium of National Cancer Institute-sponsored investigators who use statistical and simulation modeling to evaluate the impact of cancer control interventions on long-term population-level breast cancer outcomes such as incidence and mortality and to determine the impact of different breast cancer control strategies. The CISNET breast cancer models have been continuously funded since 2000. The models have gone through several updates since their inception to reflect advances in the understanding of the molecular basis of breast cancer, changes in the prevalence of common risk factors, and improvements in therapy and early detection technology. This article provides an overview and history of the CISNET breast cancer models, provides an overview of the major changes in the model inputs over time, and presents examples for how CISNET breast cancer models have been used for policy evaluation.

Structure, Function, and Applications of the Georgetown-Einstein (GE) Breast Cancer Simulation Model

BACKGROUND

The Georgetown University-Albert Einstein College of Medicine breast cancer simulation model (Model GE) has evolved over time in structure and function to reflect advances in knowledge about breast cancer, improvements in early detection and treatment technology, and progress in computing resources. This article describes the model and provides examples of model applications.

METHODS

The model is a discrete events microsimulation of single-life histories of women from multiple birth cohorts. Events are simulated in the absence of screening and treatment, and interventions are then applied to assess their impact on population breast cancer trends. The model accommodates differences in natural history associated with estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) biomarkers, as well as conventional breast cancer risk factors. The approach for simulating breast cancer natural history is phenomenological, relying on dates, stage, and age of clinical and screen detection for a tumor molecular subtype without explicitly modeling tumor growth. The inputs to the model are regularly updated to reflect current practice. Numerous technical modifications, including the use of object-oriented programming (C++), and more efficient algorithms, along with hardware advances, have increased program efficiency permitting simulations of large samples.

RESULTS

The model results consistently match key temporal trends in US breast cancer incidence and mortality.

CONCLUSION

The model has been used in collaboration with other CISNET models to assess cancer control policies and will be applied to evaluate clinical trial design, recurrence risk, and polygenic risk-based screening.

The utility of breast cone-beam computed tomography, ultrasound, and digital mammography for detecting malignant breast tumors: A prospective study with 212 patients

PURPOSE

Breast cone-beam computed tomography (BCBCT) is a flat-panel detector (FPD)-based X-ray imaging system that provides high-quality images of the breast. The purpose of this study was to investigate the ability to detect breast abnormalities using non-contrast BCBCT and contrast-enhanced BCBCT (BCBCT and CE-BCBCT) compared to ultrasound (US) and digital mammography (MG).

MATERIALS AND METHODS

A prospective study was performed from May 2012 to August 2014. Ninety-two patients (172 lesions) underwent BCBCT alone, and 120 patients (270 lesions) underwent BCBCT and CE-BCBCT, all the patients underwent US and MG.

RESULTS

Cancer diagnosis was confirmed pathologically in 102 patients (110 lesions). BCBCT identified 97 of 110 malignant lesions, whereas 93 malignant lesions were identified using MG and US. The areas under the receiver operating curves (AUCs) for breast cancer diagnosis were 0.861 (BCBCT), 0.856 (US), and 0.829 (MG). CE-BCBCT improved cancer diagnostic sensitivity by 20.3% (78.4-98.7%). The AUC values were 0.869 (CE-BCBCT), 0.846 (BCBCT), 0.834 (US), and 0.782 (MG).

CONCLUSION

In this preliminary study, BCBCT was found to accurately identify malignant breast lesions in a diagnostic setting. CE-BCBCT provided additional information and improved cancer diagnosis in style c or d breasts compared to the use of BCBCT, US, or MG alone.

Comparison of the diagnostic workup of women referred at non-blinded or blinded double reading in a population-based screening mammography programme in the south of the Netherlands

Background:

To determine whether referred women experience differences in diagnostic workup at non-blinded or blinded double reading of screening mammograms.

Methods:

We included a consecutive series of respectively 42.996 and 44.491 screens, double read either in a non-blinded or blinded manner between 2009 and 2011. This reading strategy was alternated on a monthly basis.

Results:

The overall ultrasound-guided core needle biopsy (CNB) rate and stereotactic CNB (SCNB) rate per 1000 screens were higher at blinded than at non-blinded reading (7.5 vs 6.0, P=0.008 and 8.1 vs 6.6, P=0.009). Among women with benign workup, these rates were higher at blinded reading (2.6 vs 1.4, P<0.001 and 5.9 vs 4.7, P=0.013). The benign biopsy rates were higher at blinded double reading (P<0.001), whereas the positive predictive value of biopsy did not differ (P=0.103).

Conclusions:

Blinded double-reading results in higher overall CNB and SCNB rates than non-blinded double reading, as well as a higher benign biopsy rate.

Simulation models in population breast cancer screening: A systematic review

The aim of this review was to critically evaluate published simulation models for breast cancer screening of the general population and provide a direction for future modeling. A systematic literature search was performed to identify simulation models with more than one application. A framework for qualitative assessment which incorporated model type; input parameters; modeling approach, transparency of input data sources/assumptions, sensitivity analyses and risk of bias; validation, and outcomes was developed. Predicted mortality reduction (MR) and cost-effectiveness (CE) were compared to estimates from meta-analyses of randomized control trials (RCTs) and acceptability thresholds. Seven original simulation models were distinguished, all sharing common input parameters. The modeling approach was based on tumor progression (except one model) with internal and cross validation of the resulting models, but without any external validation. Differences in lead times for invasive or non-invasive tumors, and the option for cancers not to progress were not explicitly modeled. The models tended to overestimate the MR (11-24%) due to screening as compared to optimal RCTs 10% (95% CI - 2-21%) MR. Only recently, potential harms due to regular breast cancer screening were reported. Most scenarios resulted in acceptable cost-effectiveness estimates given current thresholds. The selected models have been repeatedly applied in various settings to inform decision making and the critical analysis revealed high risk of bias in their outcomes. Given the importance of the models, there is a need for externally validated models which use systematical evidence for input data to allow for more critical evaluation of breast cancer screening.

Effect of treatment and mammography detection on breast cancer survival over time: 1990-2007

BACKGROUND

The extent to which improvements over time in breast cancer survival are related to earlier detection by mammography or to more effective treatments is not known.

METHODS

At a comprehensive cancer care center, the authors conducted a retrospective cohort study of women ages 50 to 69 years who were diagnosed with invasive breast cancer (stages I through III) and were followed over 3 periods (1990-1994, 1995-1999, and 2000-2007). Data were abstracted from patient charts and included detection method, diagnosis, treatment, and follow-up for vital status in the institutional breast cancer registry (n = 2998). The method of detection was categorized as patient or physician detected or mammography detected. Cox proportional hazards models were used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for 5-year disease-specific survival in relation to detection method and treatment factors, and differences in survival were analyzed using the Kaplan-Meier method.

RESULTS

Fifty-eight percent of breast cancers were mammography detected, and 42% were patient or physician detected; 56% of tumors were stage I, 31% were stage II, and 13% were stage III. The average length of follow-up was 10.71 years. The combined 5-year disease-specific survival rate was 89% from 1990 to 1994, 94% from 1995 to 1999, and 96% from 2000 to 2007 (P < .001). In an adjusted model, mammography detection (HR, 0.43; 95% CI, 0.27-0.70), hormone therapy (HR, 0.47; 95% CI, 0.30-0.75), and taxane-containing chemotherapy (HR, 0.61; 95% CI, 0.37-0.99) were significantly associated with a decreased risk of disease-specific mortality.

CONCLUSIONS

Better breast cancer survival over time was related to mammography detection, hormone therapy, and taxane-containing chemotherapy. Treatment improvements alone are not sufficient to explain the observed survival improvements over time.

The contribution of mammography screening to breast cancer incidence trends in the United States: an updated age-period-cohort model

BACKGROUND

The impact of screening mammography on breast cancer incidence is difficult to disentangle from cohort- and age-related effects on incidence.

METHODS

We developed an age-period-cohort model of ductal carcinoma in situ (DCIS) and invasive breast cancer incidence in U.S. females using cancer registry data. Five functions were included in the model to estimate stage-specific effects for age, premenopausal birth cohorts, postmenopausal birth cohorts, period (for all years of diagnosis), and a mammography period effect limited to women ages ≥ 40 years after 1982. Incidence with and without the mammography period effect was calculated.

RESULTS

More recent birth cohorts have elevated underlying risk compared with earlier cohorts for both pre- and postmenopausal women. Comparing models with and without the mammography period effect showed that overall breast cancer incidence would have been 23.1% lower in the absence of mammography in 2010 (95% confidence intervals, 18.8-27.4), including 14.7% (9.5-19.3) lower for invasive breast cancer and 54.5% (47.4-59.6) lower for DCIS. Incidence of distant-staged breast cancer in 2010 would have been 29.0% (13.1-48.1) greater in the absence of mammography screening.

CONCLUSIONS

Mammography contributes to markedly elevated rates of DCIS and early-stage invasive cancers, but also contributes to substantial reductions in the incidence of metastatic breast cancer.

IMPACT

Mammography is an important tool for reducing the burden of breast cancer, but future work is needed to identify risk factors accounting for increasing underlying incidence and to distinguish between indolent and potentially lethal early-stage breast cancers that are detected via mammography.

Confronting confirmation bias about breast cancer screening with the four Cs

The tendency to resist evidence that doesn't support our own beliefs is a type of confirmation bias. Recent changes to recommendations for breast cancer screening underscore the need for nurses to maintain awareness of most recent and reliable evidence, evaluate women's family histories and encourage women to mitigate modifiable risks and make well-informed decisions. When advising women about breast cancer screening, nurses can employ an approach based on the four Cs: (1) commit to staying up-to-date with evidence; (2) convey understanding; (3) communicate the evidence and answer questions; and (4) collaborate with women on their plan for breast cancer screening.

Qualitative assessment of breast cancer early detection services provided through well woman clinics in the district of Gampaha in Sri Lanka

BACKGROUND

Breast cancer is the most common cancer diagnosed among women in Sri Lanka. Early detection can lead to reduction in morbidity and mortality. The objective here was to identify perceptions of public health midwives (PHMs) on the importance of early detection of breast cancer and deficiencies of and suggestions on improving existing breast cancer early detection services provided through Well Woman Clinics.

MATERIALS AND METHODS

A qualitative study using four focus group discussions (FGDs) were conducted among 38 PHMs in the Gampaha district in Sri Lanka and the meetings were audio-recorded, transcribed and analyzed using constant comparison and identifying themes and categories.

RESULTS

All the PHMs had a firm realization on the need of breast cancer early detection. The four FGDs among PHMs revealed non-availability of guidelines, inadequacy of training, lack of skills and material to provide health education, inability to provide privacy during clinical examination, shortage of stationery, lack of community awareness and motivation. The suggestions for the improvements of the programme identified in FGDs were capacity building of PHMs, making availability of guidelines, rescheduling clinics, improving the supervision, strengthening the monitoring, improving coordination between clinical and preventive sectors, and improving community awareness.

CONCLUSIONS

Results of the FGDs can provide useful information on components to be improved in breast cancer early detection services. Study recommendations were training programmes at basic and post basic levels on a regular basis and supervision for the sustainance of the breast cancer early detection program.