Breast-Cancer Tumor Size, Overdiagnosis, and Mammography Screening Effectiveness

Genomic Characterization of De Novo Metastatic Breast Cancer

De novo metastatic breast cancer (dnMBC) represents a minority of MBC cases, and as such, its genomics are poorly understood. Characterizing the genomics of dnMBC represents an opportunity to delineate metastatic drivers in the absence of treatment selection. In this review, we first summarize the literature of the genomics of MBC which showed that MBCs have greater mutational burden than early stage, treatment naïve breast cancers. We then turn to recent studies that have sought to focus on dnMBC. We propose that understanding genomic differences between dnMBC and relapsed MBC can inform treatment choices. Finally, we discuss translational strategies to better dissect the genomics of dnMBC.

Outcome of Patients With an Ultralow-Risk 70-Gene Signature in the MINDACT Trial

PURPOSE

Patients with 70-gene signature ultralow-risk breast cancers have shown excellent survival in historic cohorts, including randomized trials. The ultralow-risk subgroup was characterized to help avoid overtreatment. We evaluated outcomes of ultralow-risk patients in the largest cohort to date.

METHODS

Of the 6,693 patients enrolled in the EORTC-10041/BIG-3-04 randomized phase III MINDACT trial, profiling revealed an ultralow-risk 70-gene signature in 1,000 patients (15%). Distant metastasis-free interval (DMFI) and breast cancer-specific survival (BCSS) were assessed in patients stratified by 70-gene signature result (high, low, and ultralow) by Kaplan-Meier analysis and hazard ratios with 95% CI from Cox regression.

RESULTS

Median follow-up was 8.7 years. Of the ultralow-risk patients (n = 1,000), 67% were > 50 years, 81% had tumors ≤ 2 cm, 80% were lymph node-negative, 96% had grade 1 or 2 tumors, and 99% were estrogen receptor (ER)-positive. Systemic therapy was received by 84% of patients (69% endocrine therapy, 14% endocrine therapy plus chemotherapy, 1% other) and 16% received no adjuvant systemic treatment. The 8-year DMFI for ultralow-risk patients was 97.0% (95% CI, 95.8 to 98.1), which was 2.5% higher than for patients with low-risk tumors (n = 3,295, 94.5% [95% CI, 93.6 to 95.3]). The hazard ratio for DMFI was 0.65 (95% CI, 0.45 to 0.94) for ultralow versus low risk, after adjusting for clinical-pathologic and treatment characteristics. The 8-year BCSS for ultralow-risk patients was 99.6% (95% CI, 99.1 to 100).

CONCLUSION

Patients with an ultralow-risk 70-gene signature have the best prognosis, distinctive from low risk, with 8-year BCSS above 99%, and very few patients developed distant metastases with an 8-year DMFI rate of 97%. These patients could be candidates for further de-escalation of treatment, to avoid overtreatment and the risk of side effects.

Diagnostic Performance of AI for Cancers Registered in A Mammography Screening Program: A Retrospective Analysis

Purpose: To evaluate the performance of an artificial intelligence (AI) algorithm in a simulated screening setting and its effectiveness in detecting missed and interval cancers. Methods: Digital mammograms were collected from Bahcesehir Mammographic Screening Program which is the first organized, population-based, 10-year (2009-2019) screening program in Turkey. In total, 211 mammograms were extracted from the archive of the screening program in this retrospective study. One hundred ten of them were diagnosed as breast cancer (74 screen-detected, 27 interval, 9 missed), 101 of them were negative mammograms with a follow-up for at least 24 months. Cancer detection rates of radiologists in the screening program were compared with an AI system. Three different mammography assessment methods were used: (1) 2 radiologists’ assessment at screening center, (2) AI assessment based on the established risk score threshold, (3) a hypothetical radiologist and AI team-up in which AI was considered to be the third reader. Results: Area under curve was 0.853 (95% CI = 0.801-0.905) and the cut-off value for risk score was 34.5% with a sensitivity of 72.8% and a specificity of 88.3% for AI cancer detection in ROC analysis. Cancer detection rates were 67.3% for radiologists, 72.7% for AI, and 83.6% for radiologist and AI team-up. AI detected 72.7% of all cancers on its own, of which 77.5% were screen-detected, 15% were interval cancers, and 7.5% were missed cancers. Conclusion: AI may potentially enhance the capacity of breast cancer screening programs by increasing cancer detection rates and decreasing false-negative evaluations.

Breast Cancer Screening: Is There Room for De-escalation?

Purpose of Review

Breast cancer screening is highly controversial and different agencies have widely varying guidelines. Yet it is currently used extensively in the USA and frequently the thought is "the more, the better." The purpose of this review is to objectively assess the risks and benefits of screening mammography and consider whether there may be areas where it could be de-escalated.

Recent Findings

Over the past few years, there have been several meta-analyses that are concordant, and it is now agreed that the main benefit of screening mammography is about a 20% reduction in breast cancer mortality. This actually benefits about 5% of patients with mammographically detected tumors. We now appreciate that the main harm of screening is overdiagnosis, i.e. detection of a cancer that will not cause the patient any harm and would not have ever been detected without the screening. This currently represents about 20 to 30% of screening detected cancers. Finding extra cancers with more intense screening is not always good, because in this situation, the risk of overdiagnosis increases and the benefit decreases. In some groups, the risk of overdiagnosis approaches 75%.

Summary

Our goal should be not only to find more cancers, but to avoid finding cancers that would never have caused the patient any harm and lead to unnecessary treatment. The authors suggest some situations where it may be reasonable to de-escalate screening.

Social inequalities in the incidence, mortality, and survival of neoplasms in women from a municipality in Southeastern Brazil

This study aims to analyze inequalities in the incidence, mortality, and survival of the main types of cancer in women according to the Social Vulnerability Index (SVI). The study was conducted in Campinas, São Paulo State, Brazil, from 2010 to 2014, and used data from the Population-based Cancer Registry and the Mortality Information System. Incidence and mortality rates standardized by age and 5-year survival estimates were calculated according to the social vulnerability strata (SVS), based on the São Paulo Social Vulnerability Index. Three SVS were delimited, with SVS1 being the lowest level of vulnerability and SVS3 being the highest. Rate ratios and the concentration index were calculated. The significance level was 5%. Women in SVS1 had a higher risk of breast cancer (0.46; 95%CI: 0.41; 0.51), colorectal cancer (0.56; 95%CI: 0.47; 0.68), and thyroid cancer (0.32; 95%CI: 0.26; 0.40), whereas women from SVS3 had a higher risk of cervical cancer (2.32; 95%CI: 1.63; 3.29). Women from SVS1 had higher mortality rates for breast (0.69; 95%CI: 0.53; 0.88) and colorectal cancer (0.69; 95%CI: 0.59; 0.80) and women from SVS3 had higher rates for cervical (2.35; 95%CI: 1.57; 3.52) and stomach cancer (1.43; 95%CI: 1.06; 1.91). Women of highest social vulnerability had lower survival rates for all types of cancer. The observed inequalities differed according to the location of the cancer and the analyzed indicator. Inequalities between incidence, mortality, and survival tend to revert and the latter is always unfavorable to the segment of highest vulnerability, indicating the existence of inequality in access to early diagnosis and timely treatment.

Clinical Course and Predictors of Subsequent Recurrence and Survival of Patients With Ipsilateral Breast Tumor Recurrence

Purpose

To evaluate the clinical course and long-term outcomes of patients with ipsilateral breast tumor recurrence (IBTR) after breast-conserving surgery (BCS) and identify independent prognostic factors for further recurrence.

Methods

In this retrospective study, we reviewed the records of 327 patients who experienced IBTR after undergoing BCS for breast cancer at Asan Medical Center during 1990–2013. Overall survival (OS) after IBTR and cumulative incidence rates of recurrences after IBTR were calculated. The association of clinicopathological factors with survival and the development of further recurrence after IBTR was determined in multivariate analysis.

Results

At a median follow-up of 127.7 months, 97 patients experienced recurrence after IBTR. The 5-year and 10-year cumulative incidence rates of recurrence after IBTR were 32% and 41%, respectively. The 5-year and 10-year OS rates after IBTR were 86.6% and 70.3%, respectively. In multivariate analysis, hormone receptor negativity was associated with decreases in OS after IBTR (hazard ratio [HR] 2.83, 95% confidence interval [CI] 1.18–6.78). Patients with longer disease-free interval (DFI) had decreased risks of second recurrence (HR .99, 95% CI .99–1.00), and second locoregional recurrence (LRR) (HR .98, 95% CI .97–.99). Lymphovascular invasion (LVI) of IBTR was associated with increased recurrence rates (second recurrence-free survival, HR 3.58, 95% CI 2.16–5.94; second LRR free survival, HR 5.21, 95% CI 2.77–9.78; second distant metastasis-free survival, 2.11, 95% CI 1.04–4.30) and lower survival rates (OS after IBTR, HR 4.64, 95% CI 2.23–9.67).

Conclusions

Despite subsequent recurrences during long-term follow-up, the survival rates after IBTR remained high. Patients with hormone receptor-negative tumors, shorter DFI, and tumors that present LVI of IBTR had higher risks for recurrence and poor survival rates after IBTR. The study findings may help in understanding the course and prognosis of IBTR patients and identifying high-risk IBTR to establish management strategies.

Cost-Effectiveness of Screening Mammography Beyond Age 75 Years : A Cost-Effectiveness Analysis

BACKGROUND

The cost-effectiveness of screening mammography beyond age 75 years remains unclear.

OBJECTIVE

To estimate benefits, harms, and cost-effectiveness of extending mammography to age 80, 85, or 90 years according to comorbidity burden.

DESIGN

Markov microsimulation model.

DATA SOURCES

SEER (Surveillance, Epidemiology, and End Results) program and Breast Cancer Surveillance Consortium.

TARGET POPULATION

U.S. women aged 65 to 90 years in groups defined by Charlson comorbidity score (CCS).

TIME HORIZON

Lifetime.

PERSPECTIVE

National health payer.

INTERVENTION

Screening mammography to age 75, 80, 85, or 90 years.

OUTCOME MEASURES

Breast cancer death, survival, and costs.

RESULTS OF BASE-CASE ANALYSIS

Extending biennial mammography from age 75 to 80 years averted 1.7, 1.4, and 1.0 breast cancer deaths and increased days of life gained by 5.8, 4.2, and 2.7 days per 1000 women for comorbidity scores of 0, 1, and 2, respectively. Annual mammography beyond age 75 years was not cost-effective, but extending biennial mammography to age 80 years was ($54 000, $65 000, and $85 000 per quality-adjusted life-year [QALY] gained for women with CCSs of 0, 1, and ≥2, respectively). Overdiagnosis cases were double the number of deaths averted from breast cancer.

RESULTS OF SENSITIVITY ANALYSIS

Costs per QALY gained were sensitive to changes in invasive cancer incidence and shift of breast cancer stage with screening mammography.

LIMITATION

No randomized controlled trials of screening mammography beyond age 75 years are available to provide model parameter inputs.

CONCLUSION

Although annual mammography is not cost-effective, biennial screening mammography to age 80 years is; however, the absolute number of deaths averted is small, especially for women with comorbidities. Women considering screening beyond age 75 years should weigh the potential harms of overdiagnosis versus the potential benefit of averting death from breast cancer.

PRIMARY FUNDING SOURCE

National Cancer Institute and National Institutes of Health.

Genome-Wide DNA Methylation Signatures Predict the Early Asymptomatic Doxorubicin-Induced Cardiotoxicity in Breast Cancer

Chemotherapy with doxorubicin (DOX) may cause unpredictable cardiotoxicity. This study aimed to determine whether the methylation signature of peripheral blood mononuclear cells (PBMCs) prior to and after the first cycle of DOX-based chemotherapy could predict the risk of cardiotoxicity in breast cancer patients. Cardiotoxicity was defined as a decrease in left ventricular ejection fraction (LVEF) by >10%. DNA methylation of PBMCs from 9 patients with abnormal LVEF and 10 patients with normal LVEF were examined using Infinium HumanMethylation450 BeadChip. We have identified 14,883 differentially methylated CpGs at baseline and 18,718 CpGs after the first cycle of chemotherapy, which significantly correlated with LVEF status. Significant differentially methylated regions (DMRs) were found in the promoter and the gene body of SLFN12, IRF6 and RNF39 in patients with abnormal LVEF. The pathway analysis found enrichment for regulation of transcription, mRNA splicing, pathways in cancer and ErbB2/4 signaling. The preliminary results from this study showed that the DNA methylation profile of PBMCs may predict the risk of DOX-induced cardiotoxicity prior to chemotherapy. Further studies with larger cohorts of patients are needed to confirm these findings.

Optimal Breast Density Characterization Using a Three-Dimensional Automated Breast Densitometry System

Dense breasts are a risk factor for breast cancer. Assessment of breast density is important and radiologist-dependent. We objectively measured mammographic density using the three-dimensional automatic mammographic density measurement device Volpara™ and examined the criteria for combined use of ultrasonography (US). Of 1227 patients who underwent primary breast cancer surgery between January 2019 and April 2021 at our hospital, 441 were included. A case series study was conducted based on patient age, diagnostic accuracy, effects of mammography (MMG) combined with US, size of invasion, and calcifications. The mean density of both breasts according to the Volpara Density Grade (VDG) was 0–3.4% in 2 patients, 3.5–7.4% in 55 patients, 7.5–15.4% in 173 patients, and ≥15.5% in 211 patients. Breast density tended to be higher in younger patients. Diagnostic accuracy of MMG tended to decrease with increasing breast density. US detection rates were not associated with VDG on MMG and were favorable at all densities. The risk of a non-detected result was high in patients without malignant suspicious calcifications. Supplementary use of US for patients without suspicious calcifications on MMG and high breast density, particularly ≥25.5%, could improve the breast cancer detection rate.

An Early Breast Cancer Detection System Using Recurrent Neural Network (RNN) with Animal Migration Optimization (AMO) Based Classification Method

Breast cancer can be detected using early signs of it mammograms and digital mammography. For Computer Aided Detection (CAD), algorithms can be developed using this opportunities. Early detection is assisted by self-test and periodical check-ups and it can enhance the survival chance significantly. Due the need of breast cancer’s early detection and false diagnosis impact on patients, made researchers to investigate Deep Learning (DL) techniques for mammograms. So, it requires a non-invasive cancer detection system, which is highly effective, accurate, fast as well as robust. Proposed work has three steps, (i) Pre-processing, (ii) Segmentation, and (iii) Classification. Firstly, preprocessing stage removing noise from images by using mean and median filtering algorithms are used, while keeping its features intact for better understanding and recognition, then edge detection by using canny edge detector. It uses Gaussian filter for smoothening image. Gaussian smoothening is used for enhancing image analysis process quality, result in blurring of fine-scaled image edges. In the next stage, image representation is changed into something, which makes analyses process as a simple one. Foreground and background subtraction is used for accurate breast image detection in segmentation. After completion of segmentation stage, the remove unwanted image in input image dataset. Finally, a novel RNN forclassifying and detecting breast cancer using Auto Encoder (AE) based RNN for feature extraction by integrating Animal Migration Optimization (AMO) for tuning the parameters of RNN model, then softmax classifier use RNN algorithm. Experimental results are conducted using Mini-Mammographic (MIAS) dataset of breast cancer. The classifiers are measured through measures like precision, recall, f-measure and accuracy.

Breast magnetic resonance imaging as a problem solving tool in women recalled at biennial screening mammography: A population-based study in the Netherlands

Purpose

Problem solving magnetic resonance imaging (MRI) is used to exclude malignancy in women with equivocal findings on conventional imaging. However, recommendations on its use for women recalled after screening are lacking. This study evaluates the impact of problem solving MRI on diagnostic workup among women recalled from the Dutch screening program, as well as time trends and inter-hospital variation in its use.

Methods

Women who were recalled at screening mammography in the South of the Netherlands (2008–2017) were included. Two-year follow-up data were collected. Diagnostic-workup and accuracy of problem solving MRI were evaluated and time trends and inter-hospital variation in its use were examined.

Results

In the study period 16,175 women were recalled, of whom 906 underwent problem solving MRI. Almost half of the women (45.4%) who underwent problem solving MRI were referred back to the screening program without further workup. The sensitivity, specificity, and positive and negative predictive values of problem solving MRI were 98.2%, 70.0%, 31.1%, and 99.6%, respectively. The percentage of recalled women receiving problem solving MRI fluctuated over time (4.7%–7.2%) and significantly varied among hospitals (2.2%–7.0%).

Conclusion

The use of problem solving MRI may exclude malignancy in recalled women. The use of problem solving MRI varied over time and among hospitals, which indicates the need for guidelines on problem solving MRI.

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Problem solving MRI did correctly refer back women to the screening program.

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The sensitivity and specificity of problem solving MRI were 98.2% and 70.0%.

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Positive and negative predictive values of problem solving MRI were 31.1% and 99.6%.

•

By excluding malignancy, problem solving MRI may reduce invasive diagnostic workup.

The Role of Trastuzumab in Patients with HER2 Positive Small (pT1mi/a) Breast Cancers, a Multicenter Retrospective Study

Simple Summary

Treatment of small HER2+ breast cancers with adjuvant Trastuzumab is still controversial. This study aims to measure the effect of Trastuzumab in early stages of breast cancer (pT1mic/a pN0/1mi) in terms of disease recurrence and to identify which factors most affect the prognosis of small HER2+ tumors. We retrospectively selected and reviewed 100 HER2+ pT1mic-pT1a breast cancer patients with a median follow-up of 86 months. In our study the primary outcome was the disease recurrence rate, which appeared to be significantly lower among patients who received adjuvant Trastuzumab. Among the patients who did not receive adjuvant Trastuzumab, HR− HER2+ tumors showed a risk seven times higher of relapse. The results of our study indicate that adjuvant Trastuzumab reduces the risk of developing a disease recurrence even in small HER2+ tumors. Adjuvant targeted therapy should be considered in patients with HR− HER2+ tumors, the category with the highest risk of recurrence.

Abstract

The treatment with adjuvant Trastuzumab in patients diagnosed with HER2+ small breast cancers is controversial: limited prospective data from randomized trials is available. This study aims to measure the effect of Trastuzumab in the early stages of breast cancer (pT1mic/a pN0/1mi) in terms of disease recurrence and to identify which are the factors that most affect the prognosis of small HER2+ tumors. One hundred HER2+ pT1mic-pT1a breast cancer patients who were treated in three Turin Breast Units between January 1998 and December 2018 were retrospectively selected and reviewed. Trastuzumab was administered to 23 patients. Clinicopathological features and disease-free survival (DFS) were compared between different subgroups. The primary outcome was the disease recurrence rate. Median follow-up time was 86 months. Compared to pT1a tumors, pT1mic lesions had a higher tumor grade (84% of pT1mic vs. 55% of pT1a; p = 0.003), a higher Ki-67 index (81% vs. 46%; p = 0.007) and were more frequently hormone receptor (HR) negative (69% vs. 36%, p = 0.001). Disease recurrence rate was significantly lower among patients who received adjuvant Trastuzumab (p = 0.02), with this therapy conferring an 85% reduction in the risk of relapse (HR 0.15; p = 0.02). Among the patients who did not receive adjuvant Trastuzumab, the only factor significantly associated with an increased risk of developing a recurrence was the immunohistochemical (IHC) subtype: in fact, HR− HER2+ tumors showed a risk seven times higher of relapsing (HR 7.29; p = 0.003). Adjuvant Trastuzumab appears to reduce the risk of disease recurrence even in small HER2+ tumors. The adjuvant targeted therapy should be considered in patients with HR− HER2+ tumors since they have the highest risk of recurrence, independently from size and grade.

The Role of the Innate Immune System in Cancer Dormancy and Relapse

Metastatic spread and recurrence are intimately linked to therapy failure, which remains an overarching clinical challenge for patients with cancer. Cancer cells often disseminate early in the disease process and can remain dormant for years or decades before re-emerging as metastatic disease, often after successful treatment. The interactions of dormant cancer cells and their metastatic niche, comprised of various stromal and immune cells, can determine the length of time that cancer cells remain dormant, as well as when they reactivate. New studies are defining how innate immune cells in the primary tumor may be corrupted to help facilitate many aspects of dissemination and re-emergence from a dormant state. Although the scientific literature has partially shed light on the drivers of immune escape in cancer, the specific mechanisms regulating metastasis and dormancy in the context of anti-tumor immunity are still mostly unknown. This review follows the journey of metastatic cells from dissemination to dormancy and the onset of metastatic outgrowth and recurrent tumor development, with emphasis on the role of the innate immune system. To this end, further research identifying how immune cells interact with cancer cells at each step of cancer progression will pave the way for new therapies that target the reactivation of dormant cancer cells into recurrent, metastatic cancers.

Cost effectiveness of breast cancer screening and prevention: a systematic review with a focus on risk-adapted strategies

OBJECTIVES

Benefit and cost effectiveness of breast cancer screening are still matters of controversy. Risk-adapted strategies are proposed to improve its benefit-harm and cost-benefit relations. Our objective was to perform a systematic review on economic breast cancer models evaluating primary and secondary prevention strategies in the European health care setting, with specific focus on model results, model characteristics, and risk-adapted strategies.

METHODS

Literature databases were systematically searched for economic breast cancer models evaluating the cost effectiveness of breast cancer screening and prevention strategies in the European health care context. Characteristics, methodological details and results of the identified studies are reported in evidence tables. Economic model outputs are standardized to achieve comparable cost-effectiveness ratios.

RESULTS

Thirty-two economic evaluations of breast cancer screening and seven evaluations of primary breast cancer prevention were included. Five screening studies and none of the prevention studies considered risk-adapted strategies. Studies differed in methodologic features. Only about half of the screening studies modeled overdiagnosis-related harms, most often indirectly and without reporting their magnitude. All models predict gains in life expectancy and/or quality-adjusted life expectancy at acceptable costs. However, risk-adapted screening was shown to be more effective and efficient than conventional screening.

CONCLUSIONS

Economic models suggest that breast cancer screening and prevention are cost effective in the European setting. All screening models predict gains in life expectancy, which has not yet been confirmed by trials. European models evaluating risk-adapted screening strategies are rare, but suggest that risk-adapted screening is more effective and efficient than conventional screening.

Trends in adjuvant therapy after breast-conserving surgery for ductal carcinoma in situ of breast: a retrospective cohort study using the National Breast Cancer Registry of Japan

PURPOSE

Radiotherapy (RT) and endocrine therapy (ET) are standard treatment options after breast-conserving surgery (BCS) for ductal carcinoma in situ (DCIS). We investigated the national patterns of adjuvant therapy use after BCS for DCIS in Japan.

METHODS

We obtained relevant data of patients diagnosed with DCIS undergoing surgery and treated with BCS between 2014 and 2016 from the Japanese Breast Cancer Registry database. The relationship between the clinicopathologic, institutional, and regional factors, and adjuvant treatment was examined using multivariable analyses.

RESULTS

We identified 9516 patients who underwent BCS for DCIS. Overall, 23% received no adjuvant treatment, 71% received RT, 32% received ET, and 26% received combination therapy. The percentages of patients who received ET and combination therapy in 2016 were significantly lower [odds ratio (OR): 0.71, 0.77, respectively] than in 2014. The proportion of RT was low among young or elderly patients (OR: 0.75, 0.44, respectively) and in non-certified facilities (OR: 0.56). The proportion of ET was high in non-certified facilities (OR: 1.58) and among patients with positive margins (OR: 1.62). Combination therapy was higher among patients with positive margins (OR: 1.53).

CONCLUSIONS

Our study found a distinct adjuvant treatment pattern after BCS for DCIS depending on clinicopathologic factors, year, age, which indicate that physicians provide individualized treatment according to the background of the patients and the biology of DCIS. The facilities and regions remain significant factors of influencing adjuvant treatment pattern.

A Novel Seven Gene Signature-Based Prognostic Model to Predict Distant Metastasis of Lymph Node-Negative Triple-Negative Breast Cancer

Background

The prognosis of lymph node-negative triple-negative breast cancer (TNBC) is still worse than that of other subtypes despite adjuvant chemotherapy. Reliable prognostic biomarkers are required to identify lymph node-negative TNBC patients at a high risk of distant metastasis and optimize individual treatment.

Methods

We analyzed the RNA sequencing data of primary tumor tissue and the clinicopathological data of 202 lymph node-negative TNBC patients. The cohort was randomly divided into training and validation sets. Least absolute shrinkage and selection operator Cox regression and multivariate Cox regression were used to construct the prognostic model.

Results

A clinical prognostic model, seven-gene signature, and combined model were constructed using the training set and validated using the validation set. The seven-gene signature was established based on the genomic variables associated with distant metastasis after shrinkage correction. The difference in the risk of distant metastasis between the low- and high-risk groups was statistically significant using the seven-gene signature (training set: P < 0.001; validation set: P = 0.039). The combined model showed significance in the training set (P < 0.001) and trended toward significance in the validation set (P = 0.071). The seven-gene signature showed improved prognostic accuracy relative to the clinical signature in the training data (AUC value of 4-year ROC, 0.879 vs. 0.699, P = 0.046). Moreover, the composite clinical and gene signature also showed improved prognostic accuracy relative to the clinical signature (AUC value of 4-year ROC: 0.888 vs. 0.699, P = 0.029; AUC value of 5-year ROC: 0.882 vs. 0.693, P = 0.038). A nomogram model was constructed with the seven-gene signature, patient age, and tumor size.

Conclusions

The proposed signature may improve the risk stratification of lymph node-negative TNBC patients. High-risk lymph node-negative TNBC patients may benefit from treatment escalation.

Role of mammogram and ultrasound imaging in predicting breast cancer subtypes in screening and symptomatic patients

BACKGROUND

Breast cancer (BC) radiogenomics, or correlation analysis of imaging features and BC molecular subtypes, can complement genetic analysis with less resource-intensive diagnostic methods to provide an early and accurate triage of BC. This is pertinent because BC is the most prevalent cancer amongst adult women, resulting in rising demands on public health resources.

AIM

To find combinations of mammogram and ultrasound imaging features that predict BC molecular subtypes in a sample of screening and symptomatic patients.

METHODS

This retrospective study evaluated 328 consecutive patients in 2017-2018 with histologically confirmed BC, of which 237 (72%) presented with symptoms and 91 (28%) were detected via a screening program. All the patients underwent mammography and ultrasound imaging prior to biopsy. The images were retrospectively read by two breast-imaging radiologists with 5-10 years of experience with no knowledge of the histology results to ensure statistical independence. To test the hypothesis that imaging features are correlated with tumor subtypes, univariate binomial and multinomial logistic regression models were performed. Our study also used the multivariate logistic regression (with and without interaction terms) to identify combinations of mammogram and ultrasound (US) imaging characteristics predictive of molecular subtypes.

RESULTS

The presence of circumscribed margins, posterior enhancement, and large size is correlated with triple-negative BC (TNBC), while high-risk microcalcifications and microlobulated margins is predictive of HER2-enriched cancers. Ductal carcinoma in situ is characterized by small size on ultrasound, absence of posterior acoustic features, and architectural distortion on mammogram, while luminal subtypes tend to be small, with spiculated margins and posterior acoustic shadowing (Luminal A type). These results are broadly consistent with findings from prior studies. In addition, we also find that US size signals a higher odds ratio for TNBC if presented during screening. As TNBC tends to display sonographic features such as circumscribed margins and posterior enhancement, resulting in visual similarity with benign common lesions, at the screening stage, size may be a useful factor in deciding whether to recommend a biopsy.

CONCLUSION

Several imaging features were shown to be independent variables predicting molecular subtypes of BC. Knowledge of such correlations could help clinicians stratify BC patients, possibly enabling earlier treatment or aiding in therapeutic decisions in countries where receptor testing is not readily available.

Breast Cancer Screening Trials: Endpoints and Overdiagnosis

Screening mammography was assessed in 9 randomized trials initiated between 1963 and 1990, with breast cancer-specific mortality as the primary endpoint. In contrast, breast cancer detection has been the primary endpoint in most screening trials initiated during the past decade. These trials have evaluated digital breast tomosynthesis, magnetic resonance imaging, and ultrasound, and novel screening strategies have been recommended solely on the basis of improvements in breast cancer detection rates. Yet, the assumption that increases in tumor detection produce reductions in cancer mortality has not been validated, and tumor-detection endpoints may exacerbate the problem of overdiagnosis. Indeed, the detection of greater numbers of early stage breast cancers in the absence of a subsequent decline in rates of metastatic cancers and cancer-related mortality is the hallmark of overdiagnosis. There is now evidence to suggest that both ductal carcinoma in situ and invasive cancers are overdiagnosed as a consequence of screening. For each patient who is overdiagnosed with breast cancer, the adverse consequences include unnecessary anxiety, financial hardships, and a small risk of morbidity and mortality from unnecessary treatments. Moreover, the overtreatment of breast cancer, as a consequence of overdiagnosis, is costly and contributes to waste in health-care spending. In this article, we argue that there is a need to establish better endpoints in breast cancer screening trials, including quality of life and composite endpoints. Tumor-detection endpoints should be abandoned, because they may lead to the implementation of screening strategies that increase the risk of overdiagnosis.

How do people understand overtesting and overdiagnosis? Systematic review and meta-synthesis of qualitative research

RATIONALE

The public should be informed about overtesting and overdiagnosis. Diverse qualitative studies have examined public understandings of this information. A synthesis was needed to systematise the body of evidence and yield new, generalisable insights.

AIM

Synthesise data from qualitative studies exploring patient and public understanding of overtesting and overdiagnosis.

METHODS

We searched Scopus, CINAHL, Ovid MEDLINE and PsycINFO databases from inception to March 18, 2020. We included published English-language primary studies exploring the perspectives of patients/the public about overtesting/overdiagnosis from any setting, year and relating to any condition. Only qualitative parts of mixed-methods studies were synthesised. We excluded studies that only examined overtreatment or sampled people with specialised medical knowledge. Two authors independently selected studies, extracted data, assessed the methodological quality of included studies using the CASP tool, and assessed confidence in the synthesis findings using the GRADE-CERQual approach. Data was analysed using thematic meta-synthesis, utilising descriptive and interpretive methods.

RESULTS

We synthesised data from 21 studies, comprising 1638 participants, from 2754 unique records identified. We identified six descriptive themes, all graded as moderate confidence (indicating they are likely to reasonably represent the available evidence): i) high confidence in screening and testing; ii) difficulty in understanding overuse; iii) acceptance that overuse can be harmful; iv) rejection or problematisation of overuse; v) limited impacts of overuse information on intended test and screening uptake; vi) desire for information and shared decision-making regarding overuse. The descriptive themes were underpinned by two analytic themes: i) perceived intrinsic value of information and information gathering, and; ii) differences in comprehension and acceptance of overuse concepts.

CONCLUSIONS

This study identified novel and important insights about how lay people interpret overuse concepts. It will guide the development of more effective public messages about overuse, highlighting the importance of interpretative frameworks in these communications.

Clinical correlates of circulating cell-free DNA tumor fraction

BACKGROUND

Oncology applications of cell-free DNA analysis are often limited by the amount of circulating tumor DNA and the fraction of cell-free DNA derived from tumor cells in a blood sample. This circulating tumor fraction varies widely between individuals and cancer types. Clinical factors that influence tumor fraction have not been completely elucidated.

METHODS AND FINDINGS

Circulating tumor fraction was determined for breast, lung, and colorectal cancer participant samples in the first substudy of the Circulating Cell-free Genome Atlas study (CCGA; NCT02889978; multi-cancer early detection test development) and was related to tumor and patient characteristics. Linear models were created to determine the influence of tumor size combined with mitotic or metabolic activity (as tumor mitotic volume or excessive lesion glycolysis, respectively), histologic type, histologic grade, and lymph node status on tumor fraction. For breast and lung cancer, tumor mitotic volume and excessive lesion glycolysis (primary lesion volume scaled by percentage positive for Ki-67 or PET standardized uptake value minus 1.0, respectively) were the only statistically significant covariates. For colorectal cancer, the surface area of tumors invading beyond the subserosa was the only significant covariate. The models were validated with cases from the second CCGA substudy and show that these clinical correlates of circulating tumor fraction can predict and explain the performance of a multi-cancer early detection test.

CONCLUSIONS

Prognostic clinical variables, including mitotic or metabolic activity and depth of invasion, were identified as correlates of circulating tumor DNA by linear models that relate clinical covariates to tumor fraction. The identified correlates indicate that faster growing tumors have higher tumor fractions. Early cancer detection from assays that analyze cell-free DNA is determined by circulating tumor fraction. Results support that early detection is particularly sensitive for faster growing, aggressive tumors with high mortality, many of which have no available screening today.

Breast Cancer Mortality in the Americas and Australasia over the Period 1980-2017 with Predictions for 2025

Simple Summary

Globally, breast cancer is the most common neoplasm and the leading cause of cancer death in women. It is also the common cancer for which the largest advancements have been made in terms of screening, early diagnosis, management and treatment over the last decades. These advances have had an impact on breast cancer mortality, which therefore depends on many aspects, including countries income and the health care system, leading to inequalities across the world. Breast cancer mortality has been substantially decreasing in high income countries of North America and Australia, but trends have been less consistent in Latin America and Asia, indicating the scope for further global advancemets in screening and management of breast cancer.

Abstract

Substantial progress has been made in the diagnosis, management, and treatment of breast cancer over the last decades. This has affected mortality rates but has also led to inequality in epidemiological trends between different regions of the world. We extracted death certification data for breast cancer from the World Health Organization database. We analyzed trends in breast cancer mortality in selected countries from America, Asia, and Oceania over the 1980–2017 period and predicted numbers of deaths and rates for 2025. In North America, we observed decreased breast cancer mortality, reaching a rate of about 13/100,000 women in 2017. In Latin American countries, breast cancer mortality rates did not consistently decrease. The highest decreases in mortality were observed in Australia. Mortality trends in Asian countries remained among the lowest globally. We have predicted decreased mortality from breast cancer in 2025 for most of the analyzed countries. The epidemiological situation regarding breast cancer mortality is expected to change in the coming years. Advancements in diagnosis and treatment of breast cancer must be extended in various areas of the world to obtain global control of breast cancer mortality.

A Multi-million Mammography Image Dataset and Population-Based Screening Cohort for the Training and Evaluation of Deep Neural Networks-the Cohort of Screen-Aged Women (CSAW)

For AI researchers, access to a large and well-curated dataset is crucial. Working in the field of breast radiology, our aim was to develop a high-quality platform that can be used for evaluation of networks aiming to predict breast cancer risk, estimate mammographic sensitivity, and detect tumors. Our dataset, Cohort of Screen-Aged Women (CSAW), is a population-based cohort of all women 40 to 74 years of age invited to screening in the Stockholm region, Sweden, between 2008 and 2015. All women were invited to mammography screening every 18 to 24 months free of charge. Images were collected from the PACS of the three breast centers that completely cover the region. DICOM metadata were collected together with the images. Screening decisions and clinical outcome data were collected by linkage to the regional cancer center registers. Incident cancer cases, from one center, were pixel-level annotated by a radiologist. A separate subset for efficient evaluation of external networks was defined for the uptake area of one center. The collection and use of the dataset for the purpose of AI research has been approved by the Ethical Review Board. CSAW included 499,807 women invited to screening between 2008 and 2015 with a total of 1,182,733 completed screening examinations. Around 2 million mammography images have currently been collected, including all images for women who developed breast cancer. There were 10,582 women diagnosed with breast cancer; for 8463, it was their first breast cancer. Clinical data include biopsy-verified breast cancer diagnoses, histological origin, tumor size, lymph node status, Elston grade, and receptor status. One thousand eight hundred ninety-one images of 898 women had tumors pixel level annotated including any tumor signs in the prior negative screening mammogram. Our dataset has already been used for evaluation by several research groups. We have defined a high-volume platform for training and evaluation of deep neural networks in the domain of mammographic imaging.

Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

Breast cancer originates from the ducts or lobules of the breast and is the second leading cause of death after cervical cancer. Therefore, early breast cancer screening is required, one of which is mammography. Mammography images can be automatically identified using Computer-Aided Diagnosis by leveraging machine learning classifications. This study analyzes the Support Vector Machine (SVM) in classifying breast cancer. It compares the performance of three features extraction methods used in SVM, namely Histogram of Oriented Gradient (HOG), GLCM, and shape feature extraction. The dataset consists of 320 mammogram image data from MIAS containing 203 normal images and 117 abnormal images. Each extraction method used three kernels, namely Linear, Gaussian, and Polynomial. The shape feature extraction-SVM using Linear kernel shows the best performance with an accuracy of 98.44 %, sensitivity of 100 %, and specificity of 97.50 %.

Artificial Intelligence in Medical Imaging of the Breast

Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.

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.

Combining method of detection and 70-gene signature for enhanced prognostication of breast cancer

PURPOSE

Studies have shown that screen detection by national screening programs is independently associated with better prognosis of breast cancer. The aim of this study is to evaluate the association between tumor biology according to the 70-gene signature (70-GS) and survival of patients with screen-detected and interval breast cancers.

METHODS

All Dutch breast cancer patients enrolled in the MINDACT trial (EORTC-10041/BIG3-04) accrued 2007-2011, who participated in the national screening program (biennial screening, ages 50-75) were included (n = 1102). Distant Metastasis-Free Interval (DMFI) was evaluated according to the 70-GS for patients with screen-detected (n = 754) and interval cancers (n = 348).

RESULTS

Patients with screen-detected cancers had 8-year DMFI rates of 98.2% for 70-GS ultralow-, 94.6% for low-, and 93.8% for high-risk tumors (p = 0.4). For interval cancers, there was a significantly lower 8-year DMFI rate for patients with 70-GS high-risk tumors (85.2%) compared to low- (92.2%) and ultralow-risk tumors (97.4%, p = 0.0023). Among patients with 70-GS high-risk tumors, a significant difference in 8-year DMFI rate was observed between interval (85.2%, n = 166) versus screen-detected cancers (93.8%, n = 238; p = 0.002) with a HR of 2.3 (95%CI 1.2-4.4, p = 0.010) adjusted for clinical-pathological characteristics and adjuvant systemic treatment.

CONCLUSION

Among patients with 70-GS high-risk tumors, a significant difference in DMFI was observed between screen-detected and interval cancers, suggesting that method of detection is an additional prognostic factor in this subgroup and should be taken into account when deciding on adjuvant treatment strategies.

Association of Medicaid Expansion Under the Affordable Care Act With Breast Cancer Stage at Diagnosis

Importance

The expansion of Medicaid sought to fill gaps in insurance coverage among low-income Americans. Although coverage has improved, little is known about the relationship between Medicaid expansion and breast cancer stage at diagnosis.

Objective

To review the association of Medicaid expansion with breast cancer stage at diagnosis and the disparities associated with insurance status, age, and race/ethnicity.

Design, Setting, and Participants

This cohort study used data from the National Cancer Database to characterize the relationship between breast cancer stage and race/ethnicity, age, and insurance status. Data from 2007 to 2016 were obtained, and breast cancer stage trends were assessed. Additionally, preexpansion years (2012-2013) were compared with postexpansion years (2015-2016) to assess Medicaid expansion in 2014. Data were analyzed from August 12, 2019, to January 19, 2020. The cohort included a total of 1 796 902 patients with primary breast cancer who had private insurance, Medicare, or Medicaid or were uninsured across 45 states.

Main Outcomes and Measures

Percent change of uninsured patients with breast cancer and stage at diagnosis, stratified by insurance status, race/ethnicity, age, and state.

Results

This study included a total of 1 796 902 women. Between 2012 and 2016, 71 235 (4.0%) were uninsured or had Medicaid. Among all races/ethnicities, in expansion states, there was a reduction in uninsured patients from 22.6% (4771 of 21 127) to 13.5% (2999 of 22 150) (P < .001), and in nonexpansion states, there was a reduction from 36.5% (5431 of 14 870) to 35.6% (4663 of 13 088) (P = .12). Across all races, there was a reduction in advanced-stage disease from 21.8% (4603 of 21 127) to 19.3% (4280 of 22 150) (P < .001) in expansion states compared with 24.2% (3604 of 14 870) to 23.5% (3072 of 13 088) (P = .14) in nonexpansion states. In African American patients, incidence of advanced disease decreased from 24.6% (1017 of 4136) to 21.6% (920 of 4259) (P < .001) in expansion states and remained at approximately 27% (27.4% [1220 of 4453] to 27.5% [1078 of 3924]; P = .94) in nonexpansion states. Further analysis suggested that the improvement was associated with a reduction in stage 3 diagnoses.

Conclusions and Relevance

In this cohort study, expansion of Medicaid was associated with a reduced number of uninsured patients and a reduced incidence of advanced-stage breast cancer. African American patients and patients younger than 50 years experienced particular benefit. These data suggest that increasing access to health care resources may alter the distribution of breast cancer stage at diagnosis.

HER2 Isoforms Uniquely Program Intratumor Heterogeneity and Predetermine Breast Cancer Trajectories During the Occult Tumorigenic Phase

HER2-positive breast cancers are among the most heterogeneous breast cancer subtypes. The early amplification of HER2 and its known oncogenic isoforms provide a plausible mechanism in which distinct programs of tumor heterogeneity could be traced to the initial oncogenic event. Here a Cancer rainbow mouse simultaneously expressing fluorescently barcoded wildtype (^WTHER2), exon-16 null (^d16HER2), and N-terminally truncated (^p95HER2) HER2 isoforms is used to trace tumorigenesis from initiation to invasion. Tumorigenesis was visualized using whole-gland fluorescent lineage tracing and single-cell molecular pathology. We demonstrate that within weeks of expression, morphologic aberrations were already present and unique to each HER2 isoform. Although ^WTHER2 cells were abundant throughout the mammary ducts, detectable lesions were exceptionally rare. In contrast, ^d16HER2 and ^p95HER2 induced rapid tumor development. ^d16HER2 incited homogenous and proliferative luminal-like lesions which infrequently progressed to invasive phenotypes whereas ^p95HER2 lesions were heterogenous and invasive at the smallest detectable stage. Distinct cancer trajectories were observed for ^d16HER2 and ^p95HER2 tumors as evidenced by oncogene-dependent changes in epithelial specification and the tumor microenvironment. These data provide direct experimental evidence that intratumor heterogeneity programs begin very early and well in advance of screen or clinically detectable breast cancer. IMPLICATIONS: Although all HER2 breast cancers are treated equally, we show a mechanism by which clinically undetected HER2 isoforms program heterogenous cancer phenotypes through biased epithelial specification and adaptations within the tumor microenvironment.

Tumour volume distribution can yield information on tumour growth and tumour control

BACKGROUND

It is shown that tumour volume distributions can yield information on two aspects of cancer research: tumour induction and tumour control.

MATERIALS AND METHODS

From the hypothesis that the intrinsic distribution of breast cancer volumes follows an exponential distribution, firstly the probability density function of tumour growth time was deduced via a mathematical transformation of the probability density functions of tumour volumes. In a second step, the distribution of tumour volumes was used to model the variation of the clonogenic cell number between patients in order to determine tumour control probabilities for radiotherapy patients.

RESULTS

Distribution of lag times, i.e. the time from the appearance of the first fully malignant cell until a clinically observable cancer, can be used to deduce the probability of tumour induction as a function of patient age. The integration of the volume variation with a Poisson-TCP model results in a logistic function which explains population-averaged survival data of radiotherapy patients.

CONCLUSIONS

The inclusion of tumour volume distributions into the TCP formalism enables a direct link to be deduced between a cohort TCP model (logistic) and a TCP model for individual patients (Poisson). The TCP model can be applied to non-uniform tumour dose distributions.

A Deep-Learning Diagnostic Support System for the Detection of COVID-19 Using Chest Radiographs: A Multireader Validation Study

MATERIALS AND METHODS

Five publicly available databases comprising normal CXR, confirmed COVID-19 pneumonia cases, and other pneumonias were used. After the harmonization of the data, the training set included 7966 normal cases, 5451 with other pneumonia, and 258 CXRs with COVID-19 pneumonia, whereas in the testing data set, each category was represented by 100 cases. Eleven blinded radiologists with various levels of expertise independently read the testing data set. The data were analyzed separately with the newly proposed artificial intelligence-based system and by consultant radiologists and residents, with respect to positive predictive value (PPV), sensitivity, and F-score (harmonic mean for PPV and sensitivity). The χ2 test was used to compare the sensitivity, specificity, accuracy, PPV, and F-scores of the readers and the system.

RESULTS

The proposed system achieved higher overall diagnostic accuracy (94.3%) than the radiologists (61.4% ± 5.3%). The radiologists reached average sensitivities for normal CXR, other type of pneumonia, and COVID-19 pneumonia of 85.0% ± 12.8%, 60.1% ± 12.2%, and 53.2% ± 11.2%, respectively, which were significantly lower than the results achieved by the algorithm (98.0%, 88.0%, and 97.0%; P < 0.00032). The mean PPVs for all 11 radiologists for the 3 categories were 82.4%, 59.0%, and 59.0% for the healthy, other pneumonia, and COVID-19 pneumonia, respectively, resulting in an F-score of 65.5% ± 12.4%, which was significantly lower than the F-score of the algorithm (94.3% ± 2.0%, P < 0.00001). When other pneumonia and COVID-19 pneumonia cases were pooled, the proposed system reached an accuracy of 95.7% for any pathology and the radiologists, 88.8%. The overall accuracy of consultants did not vary significantly compared with residents (65.0% ± 5.8% vs 67.4% ± 4.2%); however, consultants detected significantly more COVID-19 pneumonia cases (P = 0.008) and less healthy cases (P < 0.00001).

CONCLUSIONS

The system showed robust accuracy for COVID-19 pneumonia detection on CXR and surpassed radiologists at various training levels.

Journalists' views on media coverage of medical tests and overdiagnosis: a qualitative study

OBJECTIVE

Promotional media coverage of early detection tests is an important driver of overdiagnosis. Following research evidence that global media coverage presents the benefits of testing healthy people far more frequently than harms, and gives little coverage to overdiagnosis, we sought to examine journalists' views on media reporting of tests, overdiagnosis, and strategies to improve critical reporting on tests.

DESIGN

Qualitative study using semistructured telephone interviews. Interviews were conducted between February and March 2020 and were audiorecorded and transcribed verbatim. Framework thematic analysis was used to analyse the data.

PARTICIPANTS AND SETTING

Twenty-two journalists (mainly specialising in health reporting, average 14.5 years' experience) based in Australia.

RESULTS

This sample of journalists acknowledged the potential harms of medical tests but felt that knowledge of harms was low among journalists and the public at large. Most were aware of the term overdiagnosis, but commonly felt that it is challenging to both understand and communicate in light of strong beliefs in the benefits of early detection. Journalists felt that newsworthiness in the form of major public health impact was the key ingredient for stories about medical tests. The journalists acknowledged that factors, like the press release and 'click bait culture' in particular, can influence the framing of coverage about tests. Lack of knowledge and training, as well as time pressures, were perceived to be the main barriers to critical reporting on tests. Journalists felt that training and better access to information about potential harms would enable more critical reporting.

CONCLUSIONS

Effectively communicating overdiagnosis is a challenge in light of common beliefs about the benefits of testing and the culture of current journalism practices. Providing journalists with training, support and better access to information about potential harms of tests could aid critical reporting of tests.

Adjuvant chemotherapy for small, lymph node-negative, triple-negative breast cancer: A single-center study and a meta-analysis of the published literature

BACKGROUND

Current guidelines recommend adjuvant chemotherapy for patients with small, lymph node-negative, triple-negative breast cancer (TNBC) measuring >5 mm (T1b disease), but clinical evidence to support this recommendation is lacking. Thus, the current study aimed to evaluate the survival benefit of adjuvant chemotherapy in patients with T1N0M0 (measuring ≤2 cm) TNBC with different tumor sizes.

METHODS

The authors retrospectively evaluated consecutive patients with pT1N0M0 TNBC who were diagnosed between 2000 and 2016 at Sun Yat-Sen University Cancer Center. For the meta-analysis, electronic medical databases were searched for all relevant studies regarding the effect of adjuvant chemotherapy on the target population.

RESULTS

Of the 351 enrolled patients, 309 (88%) received adjuvant chemotherapy and 42 patients (12%) did not. The distribution by T classification was T1a in 19 patients (5.4%), T1b in 67 patients (19.1%), and T1c in 265 patients (75.5%). Adjuvant chemotherapy significantly improved recurrence-free survival (RFS) in the patients with T1c disease, but not those with T1b and T1a disease. Meanwhile, there was no difference in RFS noted according to the chemotherapy regimen among patients with T1c disease. Seven eligible studies comprising 1525 patients with T1N0M0 (941 with T1a/bN0M0) were included in the meta-analysis. The meta-analysis demonstrated that adjuvant chemotherapy significantly reduced the rate of disease recurrence for patients with T1a/b disease as a group, but the population driving that was only patients with T1b disease, not those with T1a disease.

CONCLUSIONS

Although the retrospective analysis demonstrated a survival benefit of adjuvant chemotherapy only for patients with T1cN0 TNBC, the meta-analysis showed it also is beneficial for individuals with T1bN0 TNBC. For patients with T1cN0M0 TNBC, less intensive chemotherapy regimens achieve an excellent survival outcome similar to that of intensive anthracycline and taxane combination chemotherapy.

Breast cancer screening in women with and without implants: retrospective study comparing digital mammography to digital mammography combined with digital breast tomosynthesis

OBJECTIVES

Compare four groups being screened: women without breast implants undergoing digital mammography (DM), women without breast implants undergoing DM with digital breast tomosynthesis (DM/DBT), women with implants undergoing DM, and women with implants undergoing DM/DBT.

METHODS

Mammograms from February 2011 to March 2017 were retrospectively reviewed after 13,201 were excluded for a unilateral implant or prior breast cancer. Patients had been allowed to choose between DM and DM/DBT screening. Mammography performance metrics were compared using chi-square tests.

RESULTS

Six thousand forty-one women with implants and 91,550 women without implants were included. In mammograms without implants, DM (n = 113,973) and DM/DBT (n = 61,896) yielded recall rates (RRs) of 8.53% and 6.79% (9726/113,973 and 4204/61,896, respectively, p < .001), cancer detection rates per 1000 exams (CDRs) of 3.96 and 5.12 (451/113,973 and 317/61,896, respectively, p = .003), and positive predictive values for recall (PPV1s) of 4.64% and 7.54% (451/9726 and 317/4204, respectively, p < .001), respectively. In mammograms with implants, DM (n = 6815) and DM/DBT (n = 5138) yielded RRs of 5.81% and 4.87% (396/6815 and 250/5138, respectively, p = .158), CDRs of 2.49 and 2.92 (17/6815 and 15/5138, respectively, p > 0.999), and PPV1s of 4.29% and 6.0% (17/396 and 15/250, respectively, p > 0.999), respectively.

CONCLUSIONS

DM/DBT significantly improved recall rates, cancer detection rates, and positive predictive values for recall compared to DM alone in women without implants. DM/DBT performance in women with implants trended towards similar improvements, though no metric was statistically significant.

KEY POINTS

• Digital mammography with tomosynthesis improved recall rates, cancer detection rates, and positive predictive values for recall compared to digital mammography alone for women without implants. • Digital mammography with tomosynthesis trended towards improving recall rates, cancer detection rates, and positive predictive values for recall compared to digital mammography alone for women with implants, but these trends were not statistically significant - likely related to sample size.

Incorporating Baseline Breast Density When Screening Women at Average Risk for Breast Cancer : A Cost-Effectiveness Analysis

BACKGROUND

Breast density classification is largely determined by mammography, making the timing of the first screening mammogram clinically important.

OBJECTIVE

To evaluate the cost-effectiveness of breast cancer screening strategies that are stratified by breast density.

DESIGN

Microsimulation model to generate the natural history of breast cancer for women with and those without dense breasts and assessment of the cost-effectiveness of strategies tailored to breast density and nontailored strategies.

DATA SOURCES

Model parameters from the literature; statistical modeling; and analysis of Surveillance, Epidemiology, and End Results-Medicare data.

TARGET POPULATION

Women aged 40 years or older.

TIME HORIZON

Lifetime.

PERSPECTIVE

Societal.

INTERVENTION

No screening; biennial or triennial mammography from age 50 to 75 years; annual mammography from age 50 to 75 years for women with dense breasts at age 50 years and biennial or triennial mammography from age 50 to 75 years for those without dense breasts at age 50 years; and annual mammography at age 40 to 75 years for women with dense breasts at age 40 years and biennial or triennial mammography at age 50 to 75 years for those without dense breasts at age 40 years.

OUTCOME MEASURES

Lifetime costs and quality-adjusted life-years (QALYs), discounted at 3% annually.

RESULTS OF BASE-CASE ANALYSIS

Baseline screening at age 40 years followed by annual screening at age 40 to 75 years for women with dense breasts and biennial screening at age 50 to 75 years for women without dense breasts was effective and cost-effective, yielding an incremental cost-effectiveness ratio of $36 200 per QALY versus the biennial strategy at age 50 to 75 years.

RESULTS OF SENSITIVITY ANALYSIS

At a societal willingness-to-pay threshold of $100 000 per QALY, the probability that the density-stratified strategy at age 40 years was optimal was 56% compared with 6 other strategies.

LIMITATION

Findings may not be generalizable outside the United States.

CONCLUSION

The study findings advocate for breast density-stratified screening with baseline mammography at age 40 years.

PRIMARY FUNDING SOURCE

National Cancer Institute.

Surgery for Good Prognosis Breast Cancers

Purpose of Review

The introduction of mammographic screening programmes has resulted increasing numbers of women with small breast cancers with biologically favourable characteristics. Many of these cancers may represent overdiagnosis, with a resulting treatment burden for women and healthcare costs for providers. Here, current surgical approaches to the treatment of such tumours are reviewed, together with alternative approaches to their management.

Recent Findings

The surgical treatment of small, screen-detected breast cancers with biologically favourable characteristics has been extrapolated from the management of symptomatic breast cancers. There is no prospective randomised evidence for conventional open surgery compared with other approaches in this setting. A number of minimally invasive techniques, most notable vacuum-assisted excision, have been described for the management of these tumours, but at present, there is a lack of high-quality evidence to support their routine use. There are currently ongoing randomised trials evaluating risk-adapted surgical and minimally invasive approaches to the management of good prognosis disease.

Summary

It is possible that the surgical treatment of good prognosis screen-detected breast cancers may be de-escalated. However, high-quality evidence from ongoing prospective randomised trials will be required in order to change clinical practice.

Carnitine palmitoyl transferase 1A is a novel diagnostic and predictive biomarker for breast cancer

BACKGROUND

Carnitine palmitoyl transferase 1A (CPT1A), the key regulator of fatty acid oxidation, contributes to tumor metastasis and therapeutic resistance. We aimed to identify its clinical significance as a biomarker for the diagnosis and prediction of breast cancer.

METHODS

Western blot, ELISA and in silico analysis were used to confirm CPT1A levels in breast cancer cell lines, cell culture medium and breast cancer tissues. Four hundred thirty breast cancer patients, 200 patients with benign breast disease, and 400 healthy controls were enrolled and randomly divided into a training set and a test set with a 7:3 ratio. Training set was used to build diagnostic models and 10-fold cross validation was used to demonstrate the performance of the models. Then test set was aimed to validate the effectiveness of the diagnostic models. ELISA was conducted to detect individual serum CPT1A levels. Receiver operating characteristic (ROC) curves were generated, and binary logistic regression analyses were performed to evaluate the effectiveness of CPT1A as a biomarker in breast cancer diagnosis. CPT1A levels between post-operative and pre-operative samples were also compared.

RESULTS

CPT1A was overexpressed in breast cancer tissues, cell lines and cell culture medium. Serum CPT1A levels were higher in breast cancer patients than in controls and were significantly associated with metastasis, TNM stage, histological grading and molecular subtype. CPT1A levels were decreased in post-operative samples compared with paired pre-operative samples. Moreover, CPT1A exhibited a higher efficacy in differentiating breast cancer patients from healthy controls (training set: area under the curve, AUC, 0.892, 95% CI, 0.872-0.920; test set, AUC, 0.904, 95% CI, 0.869-0.939) than did CA15-3, CEA, or CA125.

CONCLUSION

CPT1A is overexpressed in breast cancer and can be secreted out of breast cancer cell. Serum CPT1A is positively associated with breast cancer progression and could serve as an indicator for disease monitoring. Serum CPT1A displayed a remarkably high diagnostic efficiency for breast cancer and could be a novel biomarker for the diagnosis of breast cancer.

Modifying a Mammography Decision Aid for Older Adult Women with Risk Factors for Low Health Literacy

Background:

Guidelines recommend that before being offered mammography screening, women age 75 years and older be informed of the uncertainty of benefit and potential for harm (e.g., being diagnosed with a breast cancer that would otherwise never have shown up in one's lifetime); however, few older women are informed of the risks of mammography screening and most overestimate its benefits.

Objective:

The aim of this study was to learn from women older than age 75 years who have predisposing risk factors for low health literacy (LHL) how they make decisions about mammography screening, whether an existing decision aid (DA) on mammography screening for them was acceptable and helpful, and suggestions for improving the DA.

Methods:

We conducted semi-structured interviews with 18 women who were between ages 75 and 89 years and had predisposing risk factors for LHL (i.e., answered somewhat to not at all confident to the health literacy screening question “How confident are you filling out medical forms by yourself?” and/or had an education level of some college or less).

Key Results:

Findings indicate that women in this study lacked knowledge and understanding that one can decide on mammography screening based on their personal values. Women were enthusiastic about screening based on an interest in taking care of themselves but rely on their providers for health care decisions. Overall, most women found the DA helpful and would recommend the use of the DA.

Conclusions:

Findings from this study provide formative data to test the efficacy of the modified DA in practice. Failing to consider the informational needs of adults with LHL in design of DAs could inadvertently exacerbate existing inequalities in health. It is essential that DAs consider older women's diverse backgrounds and educational levels to support their decision-making. [HLRP: Health Literacy Research and Practice. 2021;5(2):e78–e90.]

Plain Language Summary:

The goal of this research was to understand how women older than age 75 years with risk factors for low health literacy made decisions about getting mammograms, whether an educational pamphlet was helpful, and suggestions for improving it. This research helps in understanding how to involve this population in the process of designing patient-related materials for mammogram decision-making.

A Heart-Breast Cancer-on-a-Chip Platform for Disease Modeling and Monitoring of Cardiotoxicity Induced by Cancer Chemotherapy

Cardiotoxicity is one of the most serious side effects of cancer chemotherapy. Current approaches to monitoring of chemotherapy-induced cardiotoxicity (CIC) as well as model systems that develop in vivo or in vitro CIC platforms fail to notice early signs of CIC. Moreover, breast cancer (BC) patients with preexisting cardiac dysfunctions may lead to different incident levels of CIC. Here, a model is presented for investigating CIC where not only induced pluripotent stem cell (iPSC)-derived cardiac tissues are interacted with BC tissues on a dual-organ platform, but electrochemical immuno-aptasensors can also monitor cell-secreted multiple biomarkers. Fibrotic stages of iPSC-derived cardiac tissues are promoted with a supplement of transforming growth factor-β 1 to assess the differential functionality in healthy and fibrotic cardiac tissues after treatment with doxorubicin (DOX). The production trend of biomarkers evaluated by using the immuno-aptasensors well-matches the outcomes from conventional enzyme-linked immunosorbent assay, demonstrating the accuracy of the authors' sensing platform with much higher sensitivity and lower detection limits for early monitoring of CIC and BC progression. Furthermore, the versatility of this platform is demonstrated by applying a nanoparticle-based DOX-delivery system. The proposed platform would potentially help allow early detection and prediction of CIC in individual patients in the future.

Breast cancer in women under age 40: A decade of trend analysis at a single institution

BACKGROUND

Women should be evaluated for breast cancer risk by age 30 to assess for screening need. Recent trends in breast cancer in this population may further inform recommendations.

OBJECTIVE

The aim of this study was to analyze trends over time in the rate of breast cancer, tumor characteristics and treatment in women under age 40.

METHODS

Retrospective cohort study of women under age 40 at our institution diagnosed with breast cancer from January 2007 to April 2018 was conducted. Patient demographics, tumor characteristics and treatment outcomes were collected. Descriptive statistics and the Mann-Kendell Trend test were calculated. Two-proportion z-tests were used to compare proportions of stage, pathology and treatment between 2007-2013 and 2014-2018.

RESULTS

197 women under age 40 were treated for a new diagnosis of breast cancer at our institution. A higher proportion of women were diagnosed with invasive carcinoma in 2013-2018 (91%) compared to 2007-2012 (78%), p = 0.008. A higher proportion of women were diagnosed with advanced stage disease (stage III-IV) in 2013-2018 (24%) compared to 2007-2012 (2%), p = 0.001. No statistically significant evidence for an increasing trend of overall rate of breast cancer over the last 11 years (p = 0.419) was observed.

CONCLUSIONS

While no statistically significant increase in overall rate of breast cancer was noted, an increase in invasive and later staged breast cancers was observed.

CLINICAL IMPACT

Rise in more aggressive cancers in a population that is largely not screened may have implications both on the individual young woman's morbidity as well as on a public health level.

Implementing Multilabeling, ADASYN, and ReliefF Techniques for Classification of Breast Cancer Diagnostic through Machine Learning: Efficient Computer-Aided Diagnostic System

Multilabel recognition of morphological images and detection of cancerous areas are difficult to locate in the scenario of the image redundancy and less resolution. Cancerous tissues are incredibly tiny in various scenarios. Therefore, for automatic classification, the characteristics of cancer patches in the X-ray image are of critical importance. Due to the slight variation between the textures, using just one feature or using a few features contributes to inaccurate classification outcomes. The present study focuses on five different algorithms for extracting features that can extract further different features. The algorithms are GLCM, LBGLCM, LBP, GLRLM, and SFTA from 8 image groups, and then, the extracted feature spaces are combined. The dataset used for classification is most probably imbalanced. Additionally, another focal point is to eradicate the unbalanced data problem by creating more samples using the ADASYN algorithm so that the error rate is minimized and the accuracy is increased. By using the ReliefF algorithm, it skips less contributing features that relieve the burden on the process. Finally, the feedforward neural network is used for the classification of data. The proposed method showed 99.5% micro, 99.5% macro, 0.5% misclassification, 99.5% recall rats, specificity 99.4%, precision 99.5%, and accuracy 99.5%, showing its robustness in these results. To assess the feasibility of the new system, the INbreast database was used.

The impact of mammographic screening on the subsequent breast cancer risk associated with biopsy-proven benign breast disease

Data on the risk of breast cancer following a benign breast disease (BBD) diagnosis were derived predominantly from populations of women biopsied before the widespread use of mammographic screening and in whom these lesions were mostly incidental findings. Whether or not similar risk associations are seen when these lesions are detected in mammographically screened populations is unknown. To address this, we examined the variation in BBD and breast cancer risk associations by the calendar time of BBD diagnosis (pre- vs. post-mammography era [before vs. 1985 and after]) in a nested case–control study within the Nurses’ Health Study (NHS) and NHSII BBD subcohort (488 cases; 1908 controls). We performed logistic regression analysis, adjusting for matching factors and potential confounders, to estimate odds ratio (ORs) and 95% confidence interval (CI) for the association between BBD subtype (non-proliferative, proliferative without atypia, proliferative with atypical hyperplasia (AH)) and subsequent breast cancer risk. When compared with non-proliferative lesions, both proliferative lesions without atypia (PWA) and AHs were associated with similar levels of risk in the pre-mammographic (pre) and post-mammographic (post) time periods (PWA: OR [95% CI] = 1.73 [1.27, 2.36] pre vs. 1.12 [0.73, 1.74] post; AH: 4.41 [2.90, 6.70] pre vs. 3.69 [2.21, 6.15] post). The interaction by mammography era was not statistically significant (p-interaction = 0.47). These results suggest that the risk associations reported for BBD subtypes in the pre-mammography era remain valid for BBD detected after the widespread implementation of mammographic screening.

Breast cancer detection by dedicated breast positron emission tomography according to the World Health Organization classification of breast tumors

OBJECTIVE

Considering the difficulty in detecting primary breast cancers using whole-body positron emission tomography (WBPET) owing to its limited spatial resolution, we aimed to evaluate the detectability of breast cancer by ring-type dedicated breast PET (DbPET) on the World Health Organization (WHO) histological classification in comparison with WBPET.

METHODS

A total of 938 patients with breast cancer underwent WBPET and ring-type DbPET, and 1021 lesions were histologically assessed based on the WHO classification of tumors of the breast. The findings of WBPET and DbPET were retrospectively evaluated and compared.

RESULTS

The size-related sensitivity of DbPET was superior to that of WBPET for subcentimetric tumors (81.9% vs. 52.4%, P < 0.001). The histological distribution was as follows: 11 lobular carcinoma in situ, 158 ductal carcinoma in situ, 738 infiltrating duct carcinoma not otherwise specified (NOS), 12 lobular carcinoma NOS, 40 mucinous adenocarcinoma, 13 tubular carcinoma, 36 invasive breast carcinoma others, and 13 papillary neoplasms. WBPET had low sensitivity for lobular carcinoma in situ, ductal carcinoma in situ, lobular carcinoma NOS, mucinous adenocarcinoma, and tubular carcinoma. DbPET showed improved sensitivity for all the above except lobular and tubular carcinoma. The maximum standardized uptake values (SUVmax) of DbPET were significantly higher than those of WBPET for histological types, excluding lobular carcinoma in situ. The SUVmax of papillary neoplasms was high regardless of low-grade histology and Ki-67 labeling index.

CONCLUSIONS

DBPET was found to have high sensitivity and SUVmax values for all histologic types that showed low sensitivity of detection on WBPET, except lobular carcinoma in situ.