Impact of screening mammography on mortality from breast cancer before age 60 in women 40 to 49 years of age

Diagnostic value of preoperative examination for evaluating margin status in breast cancer

BACKGROUND

A positive resection margin is a major risk factor for local breast cancer recurrence after breast-conserving surgery (BCS). Preoperative imaging examinations are frequently employed to assess the surgical margin.

AIM

To investigate the role and value of preoperative imaging examinations [magnetic resonance imaging (MRI), molybdenum target, and ultrasound] in evaluating margins for BCS.

METHODS

A retrospective study was conducted on 323 breast cancer patients who met the criteria for BCS and consented to the procedure from January 2014 to July 2021. The study gathered preoperative imaging data (MRI, ultrasound, and molybdenum target examination) and intraoperative and postoperative pathological information. Based on their BCS outcomes, patients were categorized into positive and negative margin groups. Subsequently, the patients were randomly split into a training set (226 patients, approximately 70%) and a validation set (97 patients, approximately 30%). The imaging and pathological information was analyzed and summarized using R software. Non-conditional logistic regression and LASSO regression were conducted in the validation set to identify factors that might influence the failure of BCS. A column chart was generated and applied to the validation set to examine the relationship between pathological margin range and prognosis. This study aims to identify the risk factors associated with failure in BCS.

RESULTS

The multivariate non-conditional logistic regression analysis demonstrated that various factors raise the risk of positive margins following BCS. These factors comprise non-mass enhancement (NME) on dynamic contrast-enhanced MRI, multiple focal vascular signs around the lesion on MRI, tumor size exceeding 2 cm, type III time-signal intensity curve, indistinct margins on molybdenum target examination, unclear margins on ultrasound examination, and estrogen receptor (ER) positivity in immunohistochemistry. LASSO regression was additionally employed in this study to identify four predictive factors for the model: ER, molybdenum target tumor type (MT Xmd Shape), maximum intensity projection imaging feature, and lesion type on MRI. The model constructed with these predictive factors exhibited strong consistency with the real-world scenario in both the training set and validation set. Particularly, the outcomes of the column chart model accurately predicted the likelihood of positive margins in BCS.

CONCLUSION

The proposed column chart model effectively predicts the success of BCS for breast cancer. The model utilizes preoperative ultrasound, molybdenum target, MRI, and core needle biopsy pathology evaluation results, all of which align with the real-world scenario. Hence, our model can offer dependable guidance for clinical decision-making concerning BCS.

Diagnostic value of preoperative examination for evaluating margin status in breast cancer

BACKGROUND

A positive resection margin is a major risk factor for local breast cancer recurrence after breast-conserving surgery (BCS). Preoperative imaging examinations are frequently employed to assess the surgical margin.

AIM

To investigate the role and value of preoperative imaging examinations [magnetic resonance imaging (MRI), molybdenum target, and ultrasound] in evaluating margins for BCS.

METHODS

A retrospective study was conducted on 323 breast cancer patients who met the criteria for BCS and consented to the procedure from January 2014 to July 2021. The study gathered preoperative imaging data (MRI, ultrasound, and molybdenum target examination) and intraoperative and postoperative pathological information. Based on their BCS outcomes, patients were categorized into positive and negative margin groups. Subsequently, the patients were randomly split into a training set (226 patients, approximately 70%) and a validation set (97 patients, approximately 30%). The imaging and pathological information was analyzed and summarized using R software. Non-conditional logistic regression and LASSO regression were conducted in the validation set to identify factors that might influence the failure of BCS. A column chart was generated and applied to the validation set to examine the relationship between pathological margin range and prognosis. This study aims to identify the risk factors associated with failure in BCS.

RESULTS

The multivariate non-conditional logistic regression analysis demonstrated that various factors raise the risk of positive margins following BCS. These factors comprise non-mass enhancement (NME) on dynamic contrast-enhanced MRI, multiple focal vascular signs around the lesion on MRI, tumor size exceeding 2 cm, type III time-signal intensity curve, indistinct margins on molybdenum target examination, unclear margins on ultrasound examination, and estrogen receptor (ER) positivity in immunohistochemistry. LASSO regression was additionally employed in this study to identify four predictive factors for the model: ER, molybdenum target tumor type (MT Xmd Shape), maximum intensity projection imaging feature, and lesion type on MRI. The model constructed with these predictive factors exhibited strong consistency with the real-world scenario in both the training set and validation set. Particularly, the outcomes of the column chart model accurately predicted the likelihood of positive margins in BCS.

CONCLUSION

The proposed column chart model effectively predicts the success of BCS for breast cancer. The model utilizes preoperative ultrasound, molybdenum target, MRI, and core needle biopsy pathology evaluation results, all of which align with the real-world scenario. Hence, our model can offer dependable guidance for clinical decision-making concerning BCS.

Overall Survival in Metastatic Breast Cancer Patients in the Third Millennium: Results of the COSMO Study

INTRODUCTION

Metastatic breast cancer (MBC) is a life-threatening disease, and although some data suggest a trend in survival improvement, it has not yet been unequivocally demonstrated. This study aimed to evaluate the overall survival (OS) of MBC patients, assessing its correlation with prognostic factors.

PATIENTS AND METHODS

COSMO (Checking Overall Survival in a MBC Observational study) is an Italian longitudinal retrospective multicenter study that enrolled patients with MBC diagnosed between 2000 and 2008. The primary objective was to detect a temporal difference in OS; the secondary objective was to identify prognostic factors as causal factors of the temporal variation in OS.

RESULTS

A total of 3721 of 3930 patients from 31 centers were distributed in 3 periods: 886 (23.8%), 1302 (35.0%), and 1533 (41.2%) in 2000-2002, 2003-2005, and 2006-2008, respectively. With a median follow-up of 9.3 years, median OS was 2.8 years (95% confidence interval, 2.6-2.9). No difference in OS was found in the 3 cohorts (P for trend = .563). The worst prognosis was observed for patients with triple-negative MBC (OS, 1.5 years) and for those with central nervous system metastases (1.7 years); the best prognosis was observed in those with bone metastases or nonvisceral disease (3.4 and 3.2 years, respectively) and in patients with a disease-free interval, defined as the time between resection of the primary malignancy and diagnosis of MBC, of > 2 years (3 years).

CONCLUSIONS

The COSMO study found improvement in OS between 2000 and 2008. Molecular subtype remained the strongest prognostic factor, and the role of other prognostic factors was confirmed, in particular disease-free interval, site of metastasis, and age.

A Plan-Do-Study-Act Approach to the Development, Implementation and Evaluation of a Patient Navigation Program to Reduce Breast Cancer Screening Disparities in Un- and Under-Insured, Racially and Ethnically Diverse Urban Women

Introduction: For the over 28 million Americans without health insurance, there is a great need to develop programs that help meet the health needs of the uninsured population. Materials and Methods: We applied the Plan-Do-Study-Act (PDSA) quality improvement framework to the development, implementation, and evaluation of a breast cancer screening navigation program for un- and under-insured women. Results: Six critical steps emerged: (1) obtain program funding; (2) navigator training; (3) establish a referral base network of community partners that serve the un- and under-insured women; (4) implement a process to address the barriers to accessing mammography; (5) develop a language- and culturally-tailored messaging and media campaign; and (6) develop measures and process evaluation to optimize and expand the program's reach. Discussion: A Plan-Do-Study-Act approach allowed identification of the key elements for successful development, implementation and optimization of a breast cancer screening navigation program aimed at reaching and screening un- and underinsured women.

Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development

BACKGROUND

Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women.

OBJECTIVE

This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors.

METHODS

A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance.

RESULTS

The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms.

CONCLUSIONS

The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.

Breast cancer and the black swan

Most current research in cancer is attempting to find ways of preventing patients from dying after metastatic relapse. Driven by data and analysis, this project is an approach to solve the problem upstream, i.e., to prevent relapse. This project started with the unexpected observation of bimodal relapse patterns in breast and a number of other cancers. This was not explainable with the current cancer paradigm that has guided cancer therapy and early detection for many years. After much analysis using computer simulation and input from a number of medical specialties, we eventually came to the conclusion that the surgery to remove the primary tumour produced systemic inflammation for a week after surgery. This systemic inflammation apparently caused exits of cancer cells and micrometastases from dormant states and resulted in relapses in the first 3 years post-surgery. It was determined in a retrospective study that the common inexpensive perioperative non-steroidal anti-inflammatory drug (NSAID) ketorolac could curtail the early relapse events after breast cancer surgery. A second retrospective study strongly confirmed this but an apparently underpowered prospective study showed no advantage. We are analysing these data and are now proposing to test the perioperative NSAID at Beth Israel Deaconess Medical Centre with triple-negative breast cancer (TNBC) patients, the category that could respond best to the perioperative NSAID. If this works as well as we expect, we would then transfer this technology to low- and/or middle-incomes countries (LMICs), starting with Nigeria where early onset type of TNBC is common. There is an unmet need in LMICs, especially in countries like Nigeria (190 million population), for a means to prevent surgery induced relapse that we are attempting to resolve. This work aims, thus, to describe eventual mechanisms, and ways to test a solution addressing an unmet need. But first, we consider the context, including within an historical perspective, important to explain how and why a Kuhnian paradigm shift may be considered.

Countercurrents: The bias of choice

Of the several kinds of bias that might corrupt an epidemiology study, selection bias is the most insidious […]

Prodromes and Preclinical Detection of Brain Diseases: Surveying the Ethical Landscape of Predicting Brain Health

The future of medicine lies not primarily in cures but in disease modification and prevention. While the science of preclinical detection is young, it is moving rapidly. Preclinical interventions offer hope to decrease the severity of a disease or delay the development of a disorder. With such promise, the research and practice of detecting brain disorders at a preclinical stage present unique ethical challenges that must be addressed to ensure the benefit of these technologies. Direct brain interventions have the potential to impact not just what a patient has but who they are and who they could become. Further, receiving an assessment for a preclinical or prodromal state has potential to impact perceptions about capacity, autonomy and personhood and could become entangled with stigma and discrimination. Exploring ethical issues alongside and integrated into the experimental design and research of these technologies is critical. This review will highlight ethical issues attendant to the current and near future states of preclinical detection across the life span, specifically as it relates to autism spectrum disorder (ASD), schizophrenia, and Alzheimer’s disease.

Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm

In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.

Benefits and harms of breast cancer screening with mammography in women aged 40-49 years: A systematic review

Early detection of breast cancer through screening can lower breast cancer mortality rates and reduce the burden of this disease in the population. In most western countries, mammography screening starting from age 50 is recommended. However, there is debate about whether breast cancer screening should be extended to younger women. This systematic review provides an overview of the evidence from RCTs on the benefits and harms of breast cancer screening with mammography in women aged 40-49 years. The quality of the evidence for each outcome was appraised using the GRADE approach. Four articles reporting on two different trials-the Age trial and the Canadian National Breast Screening Study-I (CNBSS-I)-were included. The results showed no significant effect on breast cancer mortality (Age trial: RR 0.93 (95% CI 0.80-1.09); CNBSS-I: HR 1.10 (95% CI 0.86-1.40)) nor on all-cause mortality (RR 0.98, 95% CI 0.93-1.03) in women aged 40-49 years offered screening. Among regularly attending women, the cumulative risk of experiencing a false-positive recall was 20.5%. Over-diagnosis of invasive breast cancer at 5 years post-cessation of screening for women aged 40-49 years was estimated to be 32% and at 20 years post-cessation of screening to be 48%. Including ductal carcinoma in situ, these numbers were 41% and 55%. Based on the current evidence from randomised trials, extending mammography screening to younger age groups cannot be recommended. However, there were limitations including relatively low sensitivity of screening and screening attendance, insufficient power, and contamination, which may explain the nonsignificant results.

Incidence, mortality and receptor status of breast cancer in African Caribbean women: Data from the cancer registry of Guadeloupe

BACKGROUND

Geographical disparities in breast cancer incidence and outcomes are reported worldwide. Women of African descent show lower incidence, higher mortality rates and earlier age of onset. We analyzed data from the cancer registry of Guadeloupe for the period 2008-2013.

METHODS

We describe breast cancer characteristics by molecular subtype, as well as estimated observed and net survival. We used Cox proportional hazard models to determine associations between cancer subtypes and death rate, adjusted for variables of interest.

RESULTS

Overall, 1275 cases were recorded with a mean age at diagnosis of 57(±14) years. World standardized incidence and mortality were respectively 71.9/100,000 and 14.1/100,000 person-years. Age-specific incidence rates were comparable to European and US populations below the age of 45, and higher in Guadeloupean women aged between 45 and 55 years. Overall, 65.1% of patients were hormone receptor (HR)+ and 20.1% were HR-. Triple negative breast cancers (TNBC) accounted for 14% of all cases, and were more frequent in patients under 40 (21.6% vs. 13.4%, p=0.02). Five-year net survival was 84.9% [81.4-88.6]. It was higher for HR+/Her2+ and HR+/Her2- subtypes, and lower for HR-/Her2+ and TNBC patients.

CONCLUSION

We found high age-specific incidence rates of breast cancer in women aged 45 to 55 years, which warrants further investigation in our population. However, this population of mainly African descent had good overall survival rates, and data according to subtypes are consistent with those reported internationally. These results may suggest that poorer survival in other African descent populations may not be an inherent feature of the disease but may be amenable to improvement.

Screening mammography: sparing the emperor's blushes

Differing interpretations about evidence on benefits and harms of screening mammography has led to conflicting recommendations among different jurisdictions that range from intensive screening starting at age 40 to no screening at all. Despite broad attention of scientific and nonscientific media, evidence suggests substantial discrepancy between real and perceived benefits of screening mammography among women. In this commentary, underlying concept of mammographic screening, limitations in primary evidence, results from secondary evidence, and existing misunderstandings are underscored with a critical gaze at available information.

Evaluation of risk communication in a mammography patient decision aid

OBJECTIVES

We characterized patients' comprehension, memory, and impressions of risk communication messages in a patient decision aid (PtDA), Mammopad, and clarified perceived importance of numeric risk information in medical decision making.

METHODS

Participants were 75 women in their forties with average risk factors for breast cancer. We used mixed methods, comprising a risk estimation problem administered within a pretest-posttest design, and semi-structured qualitative interviews with a subsample of 21 women.

RESULTS

Participants' positive predictive value estimates of screening mammography improved after using Mammopad. Although risk information was only briefly memorable, through content analysis, we identified themes describing why participants value quantitative risk information, and obstacles to understanding. We describe ways the most complicated graphic was incompletely comprehended.

CONCLUSIONS

Comprehension of risk information following Mammopad use could be improved. Patients valued receiving numeric statistical information, particularly in pictograph format. Obstacles to understanding risk information, including potential for confusion between statistics, should be identified and mitigated in PtDA design.

PRACTICE IMPLICATIONS

Using simple pictographs accompanied by text, PtDAs may enhance a shared decision-making discussion. PtDA designers and providers should be aware of benefits and limitations of graphical risk presentations. Incorporating comprehension checks could help identify and correct misapprehensions of graphically presented statistics.

Evaluation of risk communication in a mammography patient decision aid

OBJECTIVES

We characterized patients' comprehension, memory, and impressions of risk communication messages in a patient decision aid (PtDA), Mammopad, and clarified perceived importance of numeric risk information in medical decision making.

METHODS

Participants were 75 women in their forties with average risk factors for breast cancer. We used mixed methods, comprising a risk estimation problem administered within a pretest-posttest design, and semi-structured qualitative interviews with a subsample of 21 women.

RESULTS

Participants' positive predictive value estimates of screening mammography improved after using Mammopad. Although risk information was only briefly memorable, through content analysis, we identified themes describing why participants value quantitative risk information, and obstacles to understanding. We describe ways the most complicated graphic was incompletely comprehended.

CONCLUSIONS

Comprehension of risk information following Mammopad use could be improved. Patients valued receiving numeric statistical information, particularly in pictograph format. Obstacles to understanding risk information, including potential for confusion between statistics, should be identified and mitigated in PtDA design.

PRACTICE IMPLICATIONS

Using simple pictographs accompanied by text, PtDAs may enhance a shared decision-making discussion. PtDA designers and providers should be aware of benefits and limitations of graphical risk presentations. Incorporating comprehension checks could help identify and correct misapprehensions of graphically presented statistics.