Screening for breast cancer in 2018-what should we be doing today?

The association between religiosity, spirituality, and breast cancer screening: A cross-sectional analysis of Alberta’s Tomorrow Project

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Religion and spirituality provide a foundation for regulating health behaviors such as cancer screening.

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However, religion and spirituality were not associated with mammography in a population-level sample of women from Alberta, Canada.

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Religion and spirituality may be associated with mammography in population subgroups.

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Future research should employ longitudinal analyses.

Breast cancer is the leading cause of cancer-related mortality among women. Screening permits the early detection and treatment of malignancies, thereby reducing mortality. A woman’s religiosity and spirituality (R/S) may facilitate screening through encouragement of healthy behaviors. Population-level data from Alberta’s Tomorrow Project (ATP) were used to explore the cross-sectional association between R/S and breast cancer screening among women aged 50 to 69 years who did not have a history of breast cancer. Two variables were used to measure R/S: (1) R/S Salience was defined as the importance of religion and spirituality in one’s life; (2) R/S Attendance was defined as the frequency of attendance at religious or spiritual services. We regressed breast cancer screening (mammogram: yes/no) on each R/S variable in separate multivariable logistic regression models. At baseline (n = 2569), 94% of women reported receiving a mammogram. Greater R/S Salience was not associated with receipt of mammogram: the adjusted odds ratio (aOR) was 1.04 (95% confidence interval [CI]: 0.71–1.51. R/S Attendance also showed no association with mammogram: attending at least once monthly versus never attending (aOR: 1.10; 95% CI: 0.71–1.69); attending one to four times yearly versus never attending (aOR: 0.95, 95% CI: 0.57–1.58). Further research could examine specific subgroups of the population, e.g., whether use of R/S to promote breast cancer screening may be more effective among females with strong pre-existing connections to faith.

A Critique of the Theory of Planned Behavior in the Cancer Screening Domain

The theory of planned behavior (TPB) has been widely used to guide research in cancer screening-related behavior, but no critique of the TPB's application in this domain has been published. We used Fawcett and DeSanto-Madeya's 2013 framework to analyze and evaluate the theory. The theory was developed on the basis of antecedent knowledge regarding human behavior, and its concepts and propositions are comprehensively delineated. The TPB shows adequate internal consistency and impressive social and theoretical significance. Methodologically sound studies are called for to further test some theory propositions and to evaluate its pragmatic adequacy for promoting cancer screening in nursing practice.

YOLOv4-Based CNN Model versus Nested Contours Algorithm in the Suspicious Lesion Detection on the Mammography Image: A Direct Comparison in the Real Clinical Settings

BACKGROUND

We directly compared the mammography image processing results obtained with the help of the YOLOv4 convolutional neural network (CNN) model versus those obtained with the help of the NCA-based nested contours algorithm model.

METHOD

We used 1080 images to train the YOLOv4, plus 100 images with proven breast cancer (BC) and 100 images with proven absence of BC to test both models.

RESULTS

the rates of true-positive, false-positive and false-negative outcomes were 60, 10 and 40, respectively, for YOLOv4, and 93, 63 and 7, respectively, for NCA. The sensitivities for the YOLOv4 and the NCA were comparable to each other for star-like lesions, masses with unclear borders, round- or oval-shaped masses with clear borders and partly visualized masses. On the contrary, the NCA was superior to the YOLOv4 in the case of asymmetric density and of changes invisible on the dense parenchyma background. Radiologists changed their earlier decisions in six cases per 100 for NCA. YOLOv4 outputs did not influence the radiologists' decisions.

CONCLUSIONS

in our set, NCA clinically significantly surpasses YOLOv4.

Spatially tracked whole-breast three-dimensional ultrasound system toward point-of-care breast cancer screening in high-risk women with dense breasts

BACKGROUND

Mammographic screening has reduced mortality in women through the early detection of breast cancer. However, the sensitivity for breast cancer detection is significantly reduced in women with dense breasts, in addition to being an independent risk factor. Ultrasound (US) has been proven effective in detecting small, early-stage, and invasive cancers in women with dense breasts.

PURPOSE

To develop an alternative, versatile, and cost-effective spatially tracked three-dimensional (3D) US system for whole-breast imaging. This paper describes the design, development, and validation of the spatially tracked 3DUS system, including its components for spatial tracking, multi-image registration and fusion, feasibility for whole-breast 3DUS imaging and multi-planar visualization in tissue-mimicking phantoms, and a proof-of-concept healthy volunteer study.

METHODS

The spatially tracked 3DUS system contains (a) a six-axis manipulator and counterbalanced stabilizer, (b) an in-house quick-release 3DUS scanner, adaptable to any commercially available US system, and removable, allowing for handheld 3DUS acquisition and two-dimensional US imaging, and (c) custom software for 3D tracking, 3DUS reconstruction, visualization, and spatial-based multi-image registration and fusion of 3DUS images for whole-breast imaging. Spatial tracking of the 3D position and orientation of the system and its joints (J_1-6 ) were evaluated in a clinically accessible workspace for bedside point-of-care (POC) imaging. Multi-image registration and fusion of acquired 3DUS images were assessed with a quadrants-based protocol in tissue-mimicking phantoms and the target registration error (TRE) was quantified. Whole-breast 3DUS imaging and multi-planar visualization were evaluated with a tissue-mimicking breast phantom. Feasibility for spatially tracked whole-breast 3DUS imaging was assessed in a proof-of-concept healthy male and female volunteer study.

RESULTS

Mean tracking errors were 0.87 ± 0.52, 0.70 ± 0.46, 0.53 ± 0.48, 0.34 ± 0.32, 0.43 ± 0.28, and 0.78 ± 0.54 mm for joints J_1-6 , respectively. Lookup table (LUT) corrections minimized the error in joints J₁ , J₂ , and J₅ . Compound motions exercising all joints simultaneously resulted in a mean tracking error of 1.08 ± 0.88 mm (N = 20) within the overall workspace for bedside 3DUS imaging. Multi-image registration and fusion of two acquired 3DUS images resulted in a mean TRE of 1.28 ± 0.10 mm. Whole-breast 3DUS imaging and multi-planar visualization in axial, sagittal, and coronal views were demonstrated with the tissue-mimicking breast phantom. The feasibility of the whole-breast 3DUS approach was demonstrated in healthy male and female volunteers. In the male volunteer, the high-resolution whole-breast 3DUS acquisition protocol was optimized without the added complexities of curvature and tissue deformations. With small post-acquisition corrections for motion, whole-breast 3DUS imaging was performed on the healthy female volunteer showing relevant anatomical structures and details.

CONCLUSIONS

Our spatially tracked 3DUS system shows potential utility as an alternative, accurate, and feasible whole-breast approach with the capability for bedside POC imaging. Future work is focused on reducing misregistration errors due to motion and tissue deformations, to develop a robust spatially tracked whole-breast 3DUS acquisition protocol, then exploring its clinical utility for screening high-risk women with dense breasts.

Ultrasonic Omics Based on Intelligent Classification Algorithm in Hormone Receptor Expression and Efficacy Evaluation of Breast Cancer

The changes of hormone expression and efficacy of breast cancer (BC) were investigated under the VGG19FCN algorithm and ultrasound omics. 120 patients with BC were selected, of which 90 were positive for hormone receptor and 30 were negative. The VGG19FCN model algorithm and classifier were selected to classify the features of ultrasound breast map, and reliable ultrasound feature data were obtained. The evaluation and analysis of BC hormone receptor expression and clinical efficacy in patients with BC were realized by using ultrasonic omics. The evaluation of the results of the VGG19FCN algorithm was $\text{DSC}\left(\text{Dice}\text{similarity}\text{coefficient}\right)=0.9626$ , $\text{MPA}\left(\text{mean}\text{pixel}\text{accuracy}\right)=0.9676$ , and $\text{IOU}\left(\text{intersection}\text{over}\text{union}\right)=0.9155$ . When the classifier was used to classify the lesion features of BC image, the sensitivity of classification was improved to a certain extent. Compared with the classification of radiologists, when classifying whether patients had BC lesions, the sensitivity increased by 22.7%, the accuracy increased from 71.9% to 79.7%, and the specific evaluation index increased by 0.8%. No substantial difference was indicated between RT (arrive time), WIS (wash in slope), and TTP (time to peak) before and after chemotherapy, $P>0.05$ . After chemotherapy, the AUC (area under curve) and PI (peak intensity) of ultrasonographic examination were substantially lower than those before chemotherapy, and there were substantial differences in statistics ( $P<0.05$ ). In summary, the VGG19FCN algorithm effectively reduces the subjectivity of traditional ultrasound images and can effectively improve the value of ultrasound image features in the accurate diagnosis of BC. It provides a theoretical basis for the subsequent treatment of BC and the prediction of biological behavior. The VGG19FCN algorithm had a good performance in ultrasound image processing of BC patients, and hormone receptor expression changed substantially after chemotherapy treatment.

Association between novel dietary and lifestyle inflammation indices with risk of breast cancer (BrCa): a case-control study

Background

Pro-inflammatory diet and lifestyle factors lead to diseases related to chronically systemic inflammation. We examined the novel dietary/lifestyle indicators related to inflammation such dietary inflammation score (DIS), lifestyle inflammation score (LIS), empirical dietary inflammatory index (EDII) and, risk of Breast Cancer (BrCa) in Iranian woman.

Methods

In this hospital-based case–control study, 253 patients with BrCa and 267 non-BrCa controls were enrolled. Food consumption was recorded to calculate the DIS, LIS and EDII using a semi-quantitative Food Frequency Questionnaire (FFQ). We estimated odds ratios (ORs) and, 95% confidence intervals for the association of the inflammatory potential with risk of these cancers using binary logistic regression models modified for the case–control design.

Results

Mean ± SD of age and BMI of the study participants were 47.92 ± 10.33 years and 29.43 ± 5.51 kg/m², respectively. After adjustment for confounders, individuals in highest compared to lowest quartile of DIS and EDII had significantly higher risk of BrCa (DIS: 2.13 (1.15 – 3.92), p-trends: 0.012), EDII: 2.17 (1.12 – 4.22), p-trends: 0.024). However, no significant association was observed for LIS (P-trends: 0.374).

Conclusion

Findings of this study suggested that higher DIS and EDI increased the risk of BrCa, but concerning LIS, further investigation is needed.

Patchless Multi-Stage Transfer Learning for Improved Mammographic Breast Mass Classification

Simple Summary

In this study, we propose a novel deep-learning method based on multi-stage transfer learning (MSTL) from ImageNet and cancer cell line image pre-trained models to classify mammographic masses as either benign or malignant. The proposed method alleviates the challenge of obtaining large amounts of labeled mammogram training data by utilizing a large number of cancer cell line microscopic images as an intermediate domain of learning between the natural domain (ImageNet) and medical domain (mammography). Moreover, our method does not utilize patch separation (to segment the region of interest before classification), which renders it computationally simple and fast compared to previous studies. The findings of this study are of crucial importance in the early diagnosis of breast cancer in young women with dense breasts because mammography does not provide reliable diagnosis in such cases.

Abstract

Despite great achievements in classifying mammographic breast-mass images via deep-learning (DL), obtaining large amounts of training data and ensuring generalizations across different datasets with robust and well-optimized algorithms remain a challenge. ImageNet-based transfer learning (TL) and patch classifiers have been utilized to address these challenges. However, researchers have been unable to achieve the desired performance for DL to be used as a standalone tool. In this study, we propose a novel multi-stage TL from ImageNet and cancer cell line image pre-trained models to classify mammographic breast masses as either benign or malignant. We trained our model on three public datasets: Digital Database for Screening Mammography (DDSM), INbreast, and Mammographic Image Analysis Society (MIAS). In addition, a mixed dataset of the images from these three datasets was used to train the model. We obtained an average five-fold cross validation AUC of 1, 0.9994, 0.9993, and 0.9998 for DDSM, INbreast, MIAS, and mixed datasets, respectively. Moreover, the observed performance improvement using our method against the patch-based method was statistically significant, with a p-value of 0.0029. Furthermore, our patchless approach performed better than patch- and whole image-based methods, improving test accuracy by 8% (91.41% vs. 99.34%), tested on the INbreast dataset. The proposed method is of significant importance in solving the need for a large training dataset as well as reducing the computational burden in training and implementing the mammography-based deep-learning models for early diagnosis of breast cancer.

Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review

Simple Summary

Breast cancer is one of the most common cancers among women globally. Early and accurate screening of breast tumours can improve survival. Ultrasound elastography is a non-invasive and non-ionizing imaging approach to characterize lesions for breast cancer screening, while machine learning techniques could improve the accuracy and reliability of computer-aided diagnosis. This review focuses on the state-of-the-art development and application of the machine learning model in breast tumour classification.

Abstract

Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ≥80%, while only half of them attained acceptable specificity ≥95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.

Leveraging Multi-Task Learning to Cope With Poor and Missing Labels of Mammograms

In breast cancer screening, binary classification of mammograms is a common task aiming to determine whether a case is malignant or benign. A Computer-Aided Diagnosis (CADx) system based on a trainable classifier requires clean data and labels coming from a confirmed diagnosis. Unfortunately, such labels are not easy to obtain in clinical practice, since the histopathological reports of biopsy may not be available alongside mammograms, while normal cases may not have an explicit follow-up confirmation. Such ambiguities result either in reducing the number of samples eligible for training or in a label uncertainty that may decrease the performances. In this work, we maximize the number of samples for training relying on multi-task learning. We design a deep-neural-network-based classifier yielding multiple outputs in one forward pass. The predicted classes include binary malignancy, cancer probability estimation, breast density, and image laterality. Since few samples have all classes available and confirmed, we propose to introduce the uncertainty related to the classes as a per-sample weight during training. Such weighting prevents updating the network's parameters when training on uncertain or missing labels. We evaluate our approach on the public INBreast and private datasets, showing statistically significant improvements compared to baseline and independent state-of-the-art approaches. Moreover, we use mammograms from Susan G. Komen Tissue Bank for fine-tuning, further demonstrating the ability to improve the performances in our multi-task learning setup from raw clinical data. We achieved the binary classification performance of AUC = 80.46 on our private dataset and AUC = 85.23 on the INBreast dataset.

A Novel Multistage Transfer Learning for Ultrasound Breast Cancer Image Classification

Breast cancer diagnosis is one of the many areas that has taken advantage of artificial intelligence to achieve better performance, despite the fact that the availability of a large medical image dataset remains a challenge. Transfer learning (TL) is a phenomenon that enables deep learning algorithms to overcome the issue of shortage of training data in constructing an efficient model by transferring knowledge from a given source task to a target task. However, in most cases, ImageNet (natural images) pre-trained models that do not include medical images, are utilized for transfer learning to medical images. Considering the utilization of microscopic cancer cell line images that can be acquired in large amount, we argue that learning from both natural and medical datasets improves performance in ultrasound breast cancer image classification. The proposed multistage transfer learning (MSTL) algorithm was implemented using three pre-trained models: EfficientNetB2, InceptionV3, and ResNet50 with three optimizers: Adam, Adagrad, and stochastic gradient de-scent (SGD). Dataset sizes of 20,400 cancer cell images, 200 ultrasound images from Mendeley and 400 ultrasound images from the MT-Small-Dataset were used. ResNet50-Adagrad-based MSTL achieved a test accuracy of 99 ± 0.612% on the Mendeley dataset and 98.7 ± 1.1% on the MT-Small-Dataset, averaging over 5-fold cross validation. A p-value of 0.01191 was achieved when comparing MSTL against ImageNet based TL for the Mendeley dataset. The result is a significant improvement in the performance of artificial intelligence methods for ultrasound breast cancer classification compared to state-of-the-art methods and could remarkably improve the early diagnosis of breast cancer in young women.

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.

Adherence to Lifelines Diet Score (LLDS) Is Associated with a Reduced Risk of Breast Cancer (BrCa): A Case-Control Study

BACKGROUND

Previous evidence suggests a link between diet quality and breast cancer (BrCa); however, the link between the Lifelines Diet Score (LLDS)-a fully food-based score that uses the 2015 Dutch Dietary Guidelines-and risk of BrCa has not yet been evaluated. Therefore, the aim of this study was to observe the relationship between adherence to an LLDS and risk of BrCa in Iranian adults.

METHODS

In the hospital-based case-control study, 253 patients with BrCa and 267 non-BrCa controls were enrolled. Individual's food consumption was recorded to calculate LLDS using a semiquantitative food frequency questionnaire. In adjusted models, the association between the inflammatory potential of the diet and the risk of BrCa was estimated by using binary logistic regression.

RESULTS

Compared with control individuals, BrCa patients significantly had higher waist circumference (WC), first pregnancy age, abortion history, and number of children. In addition, the mean intake of vitamin D supplements and anti-inflammatory drugs in the case group was significantly lower than the control group. Furthermore, after adjusted potential confounders, individuals in the highest vs. lowest quartiles of LLDS showed statistically significant lower risk of BrCa in overall population (OR: 0.21; 95% CI: 0.11-0.43; P trend <0.001), premenopausal (OR: 0.26; 95% CI: 0.10-0.68; P trend = 0.003), and post-menopausal women (OR: 0.20; 95% CI: 0.06-0.60; P trend = 0.015).

CONCLUSION

Findings of this study reflected that higher LLDS decreased risk of BrCa, but need further investigation in later studies.

Association Between Dense Breast Legislation and Cancer Stage at Diagnosis

INTRODUCTION

Many states have mandated breast density notification and insurance coverage for additional screening; yet, the association between such legislation and stage of diagnosis for breast cancer is unclear. This study investigates this association and examines the differential impacts among different age and race/ethnicity subgroups.

METHODS

The Surveillance, Epidemiology, and End Results database was queried to identify patients with breast cancer aged 40-74 years diagnosed between 2005 and 2016. Using a difference-in-differences multinomial logistic model, the odds of being diagnosed at different stages of cancer relative to the localized stage depending on legislation and individual characteristics were examined. Analyses were conducted in 2020-2021.

RESULTS

The study included 689,641 cases. Overall, the impact of notification legislation was not significant, whereas insurance coverage legislation was associated with 6% lower odds (OR=0.94, 95% CI=0.91, 0.96) of being diagnosed at the regional stage. The association between insurance coverage legislation and stage of diagnosis was even stronger among women aged 40-49 years, with 11% lower odds (OR=0.89, 95% CI=0.82, 0.96) of being diagnosed at the regional stage and 12% lower odds (OR=0.88, 95% CI=0.81, 0.96) of being diagnosed at the distant stage. Hispanic women benefited from notification laws, with 11% lower odds (OR=0.89, 95% CI=0.82, 0.97) of being diagnosed at distant stage. Neither notification nor supplemental screening insurance coverage legislation showed a substantial impact on Black women.

CONCLUSIONS

The findings imply that improving insurance coverage is more important than being notified overall. Raising awareness is important among Hispanic women; improving communication about dense breasts and access to screening might be more important than legislation among Black women.

Investigating the breast cancer screening-treatment-mortality pathway of women diagnosed with invasive breast cancer: Results from linked health data

OBJECTIVE

To examine the screening-treatment-mortality pathway among women with invasive breast cancer in 2006-2014 using linked data.

METHODS

BreastScreen histories of South Australian women diagnosed with breast cancer (n = 8453) were investigated. Treatments recorded within 12 months from diagnosis were obtained from linked registry and administrative data. Associations of screening history with treatment were investigated using logistic regression and with cancer mortality outcomes using competing risk analyses, adjusting for socio-demographic, cancer and comorbidity characteristics.

RESULTS AND CONCLUSION

For screening ages of 50-69 years, 70% had participated in BreastScreen SA ≤ 5 years and 53% ≤ 2 years of diagnosis. Five-year disease-specific survival post-diagnosis was 90%. Compared with those not screened ≤5 years, women screened ≤2 years had higher odds, adjusted for socio-demographic, cancer and comorbidity characteristics, and diagnostic period, of breast-conserving surgery (aOR 2.5, 95% CI 1.9-3.2) and radiotherapy (aOR 1.2, 95% CI 1.1-1.3). These women had a lower unadjusted risk of post-diagnostic cancer mortality (SHR 0.33, 95% CI 0.27-0.41), partly mediated by stage (aSHR 0.65, 95% CI 0.51-0.81), and less breast surgery (aSHR 0.78, 95% CI 0.62-0.99). Screening ≤2 years and conserving surgery appeared to have a greater than additive association with lower post-diagnostic mortality (interaction term SHR 0.42, 95% CI 0.23-0.78). The screening-treatment-mortality pathway was investigated using linked data.

Evaluation of Different Breast Cancer Screening Strategies for High-Risk Women in Beijing, China: A Real-World Population-Based Study

BACKGROUND

Mammography-based breast cancer screening has been widely implemented in many developed countries. Evidence was needed on participation and diagnostic performance of population-based breast cancer screening using ultrasound in China.

METHODS

We used data from the Cancer Screening Program in Urban China in Beijing from 2014 to 2019 and was followed up until July 2020 by matching with the Beijing Cancer Registry database. Eligible women between the ages of 45 and 69 years were recruited from six districts and assessed their risk of breast cancer through an established risk scoring system. Women evaluated to be at high risk of breast cancer were invited to undergo both ultrasound and mammography. Participation rates were calculated, and their associated factors were explored. In addition, the performance of five different breast cancer screening modalities was evaluated in this study.

RESULTS

A total of 49,161 eligible women were recruited in this study. Among them, 15,550 women were assessed as high risk for breast cancer, and 7,500 women underwent ultrasound and/or mammography as recommended, with a participation rate of 48.2%. The sensitivity of mammography alone, ultrasound alone, combined of ultrasound and mammography, ultrasound for primary screening followed by mammography for triage, and mammography for preliminary screening followed by ultrasound for triage were19.2%, 38.5%, 50.0%, 46.2%, and 19.2%, and the specificity were 96.1%, 98.6%, 94.7%, 97.6%, 95.7%, respectively. The sensitivity of combined ultrasound and mammography, ultrasound for primary screening followed by mammography for triage, was significantly higher than mammography alone (p=0.008 and p=0.039). Additionally, ultrasound alone (48,323 RMB ($7,550)) and ultrasound for primary screening followed by mammography for triage (55,927 RMB ($8,739)) were the most cost-effective methods for breast cancer screening than other modalities.

CONCLUSIONS

Ultrasound alone and ultrasound for primary screening and mammography are superior to mammography for breast cancer screening in high-risk Chinese women.

Systematic review and meta-analysis of the efficacy of general anesthesia combined with a thoracic nerve block in modified breast cancer surgery

BACKGROUND

Breast cancer is a malignant tumor disease that poses a significant threat to women's health. In recent years, the incidence of breast cancer in China has been increasing. This report aims to explore the effects of general anesthesia combined with a thoracic nerve block in modified breast cancer surgery.

METHODS

A computer-based search of PubMed, Web of Science, Embase, and the Cochrane Library was performed to identify randomized controlled studies on breast cancer, general anesthesia combined with a thoracic nerve block, modified breast cancer surgery, and other breast cancer treatments. Further search criteria included postoperative pain score, postoperative morphine equivalents given 24 hours after surgery, and operation duration. After an initial selection process, the studies were evaluated using the Jadad scale and the Cochrane Handbook for Systematic Reviews of Interventions to assess their suitability for inclusion in the subsequent meta-analysis of the experimental data, which was carried out using RevMan 5.3.

RESULTS

A total of 8 studies comprising a total of 624 patients were selected for inclusion in this report. According to the meta-analysis, the analytical structure of the thoracic nerve group and the control group had a mean difference (MD) of -1.27 [95% confidence interval (CI): -1.68 to -0.86], the structure of the statistical test was Z=6.08 (P<0.00001), the MD of the total analysis structure of morphine equivalents was -2.71 (95% CI: -4.98 to -0.44), and the statistical test structure was Z=2.34 (P=0.02).

DISCUSSION

General anesthesia combined with a thoracic nerve block in breast cancer surgery may effectively improve postoperative pain in patients and reduce the need for analgesic drugs. However, the outcome indicators included in this study are not sufficient. It is necessary to increase both the sample size and the number of outcome indicators to provide further theoretical evidence for the subsequent application of thoracic nerve block in modified breast cancer surgery.

Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms

OBJECTIVES

To evaluate the performance of interpretable machine learning models in predicting breast cancer molecular subtypes.

METHODS

We retrospectively enrolled 600 patients with invasive breast carcinoma between 2012 and 2019. The patients were randomly divided into a training (n = 450) and a testing (n = 150) set. The five constructed models were trained based on clinical characteristics and imaging features (mammography and ultrasonography). The model classification performances were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity. Shapley additive explanation (SHAP) technique was used to interpret the optimal model output. Then we choose the optimal model as the assisted model to evaluate the performance of another four radiologists in predicting the molecular subtype of breast cancer with or without model assistance, according to mammography and ultrasound images.

RESULTS

The decision tree (DT) model performed the best in distinguishing triple-negative breast cancer (TNBC) from other breast cancer subtypes, yielding an AUC of 0.971; accuracy, 0.947; sensitivity, 0.905; and specificity, 0.941. The accuracy, sensitivity, and specificity of all radiologists in distinguishing TNBC from other molecular subtypes and Luminal breast cancer from other molecular subtypes have significantly improved with the assistance of DT model. In the diagnosis of TNBC versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.090, 0.125, 0.114, and 0.060, 0.090, 0.083, respectively. In the diagnosis of Luminal versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.084, 0.152, 0.159, and 0.020, 0.100, 0.048.

CONCLUSIONS

This study established an interpretable machine learning model to differentiate between breast cancer molecular subtypes, providing additional values for radiologists.

KEY POINTS

• Interpretable machine learning model (MLM) could help clinicians and radiologists differentiate between breast cancer molecular subtypes. • The Shapley additive explanations (SHAP) technique can select important features for predicting the molecular subtypes of breast cancer from a large number of imaging signs. • Machine learning model can assist radiologists to evaluate the molecular subtype of breast cancer to some extent.

Radiomic Evaluations of the Diagnostic Performance of DM, DBT, DCE MRI, DWI, and Their Combination for the Diagnosisof Breast Cancer

Objectives

This study aims to evaluate digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced (DCE), and diffusion-weighted (DW) MRI, individually and combined, for the values in the diagnosis of breast cancer, and propose a visualized clinical-radiomics nomogram for potential clinical uses.

Methods

A total of 120 patients were enrolled between September 2017 and July 2018, all underwent preoperative DM, DBT, DCE, and DWI scans. Radiomics features were extracted and selected using the least absolute shrinkage and selection operator (LASSO) regression. A radiomics nomogram was constructed integrating the radiomics signature and important clinical predictors, and assessed with the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

The radiomics signature derived from DBT plus DM generated a lower area under the ROC curve (AUC) and sensitivity, but a higher specificity compared with that from DCE plus DWI. The nomogram integrating the combined radiomics signature, age, and menstruation status achieved the best diagnostic performance in the training (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.975 vs. 0.964 vs. 0.782) and validation (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.983 vs. 0.978 vs. 0.680) cohorts. DCA confirmed the potential clinical usefulness of the nomogram.

Conclusions

The DBT plus DM provided a lower AUC and sensitivity, but a higher specificity than DCE plus DWI for detecting breast cancer. The proposed clinical-radiomics nomogram has diagnostic advantages over each modality, and can be considered as an efficient tool for breast cancer screening.

Breast cancer screening literacy information on online platforms: A content analysis of YouTube videos

BACKGROUND

The YouTube platform has great potential of serving as a healthcare resource due to its easy accessibility, navigability and wide audience reach. Breast cancer screening is an important preventative measure that can reduce breast cancer mortality by 40%. Therefore, platforms being used as a healthcare resources, such as YouTube, can and should be used to advocate for essential preventative measures such as breast cancer screening.

METHODS

In this study, the usefulness of videos related to breast cancer and breast cancer screening were analyzed. Videos were first screened for inclusion and then were categorized into very useful, moderately useful, somewhat useful, and not useful categories according to a 10-point criteria scale developed by medical professionals based on existing breast cancer screening guidelines. Two reviewers independently assessed each video using the scale.

RESULTS

200 videos were identified in the preliminary analysis (100 for the search phrase 'breast cancer' and 100 for the search phrase 'breast cancer screening'). After exclusion of duplicates and non-relevant videos, 162 videos were included in the final analysis. We found the following distribution of videos: 4.3% very useful, 17.9% moderately useful, 39.5% somewhat useful, and 38.3% not useful videos. There was a significant association between each of the following and the video's level of usefulness: video length, the number of likes, and the uploading source. Longer videos were very useful, somewhat useful videos were the most liked, personally produced videos were the most not useful, and advertisements produced the highest ratio of very useful to not useful videos.

CONCLUSION

It is necessary to create more reliable and useful healthcare resources for the general population as well as to monitor health information on easily accessible social platforms such as YouTube.

Non-Participation in Breast Cancer Screening in Spain and Potential Application in the Present and Future: A Cross Sectional Study

Simple Summary

Breast cancer screening programmes have the aim of reducing breast cancer mortality. This article is an observational, descriptive, cross-sectional and retrospective study of 2507 women who were invited to participate in the Breast Cancer Prevention Programme in Extremadura (Spain) and did not attend their appointment. We analysed the different reasons why women do not participate in the Breast Cancer Early Detection Programme in Extremadura (Spain) and discuss the results offering possible tools to improve the screening programs. Women who did not participate in the breast cancer screening programme in Extremadura had low educational levels and were older women.

Abstract

Background: Currently, we are beginning to observe a stabilisation and even a decrease in breast cancer mortality in the world, which may be related, among other reasons, to breast cancer screening. Methods: The objective of this study was to analyse the different reasons why women do not participate in the Breast Cancer Early Detection Programme in Extremadura (Spain) and to discuss the results, offering possible tools to improve the screening programs. This is an observational, descriptive, cross-sectional and retrospective study. A questionnaire with 14 questions was carried out by telephone or mail. Results: A total of 3970 questionnaires were collected. However, only 2507 were valid. A total of 70.36% of young and educated women underwent mammographic controls. The type of women who did not attend the screening programme appointment corresponded to a woman of approximately 60 years of age, with no formal studies, married, with children, who does not work outside their home and who lived in the health area of Badajoz. Among the main reasons for not going to the appointment, 53.9% of the women surveyed indicated that they had check-ups with their gynaecologist, and this specialist referred them for a mammograph. These women were younger and have a higher level of education. Women with a lower educational level and older women did not have any mammography done and did not undergo screening. They indicated that they did not go to the appointment because they were afraid of having a mammography (44%) or because they did not receive the appointment in time (31.6%). A total of 26.9% of the women who did not attend the appointment for other reasons stated that they had problems in attending because they had a physical limitation (dependency). Conclusions: Women who did not participate in the breast cancer screening programme in Extremadura had low educational levels and were older women. Specifically, fear of having a mammogram was the main argument raised by these women. In addition, a small group stated that they did not consider mammography to be useful. At present and in the future, good quality screening programs must be carried out to contribute to the reduction in breast cancer mortality. Furthermore, enhancing the participation of women is essential to increase the attendance rate and, therefore, the success of the screening programmes.

The effect of a smartphone application on women's performance and health beliefs about breast self-examination: a quasi-experimental study

BACKGROUND

Breast Self-Examination (BSE) is a simple and inexpensive method for early diagnosis of breast cancer. This study aimed to determine the effect of a smartphone application on women's performance and health beliefs regarding BSE.

METHODS

In this quasi-experimental study, 150 women referring to therapeutic clinics in Jahrom, Iran from December 2019 to May 2020 were randomly assigned to an intervention or a control group. The intervention group participants had access to a smartphone application including BSE reminder, training, alarm, and feedback to the therapist. The application also contained educational movies and self-assessment. The study data were collected using Champion's Health Belief Model Scale and BSE information record form before and six months after the intervention. Then, the data were entered into the SPSS 21 software and were analyzed using descriptive statistics, paired t-test, independent t-test, Chi-square, ANCOVA, Mann-Whitney, and Wilcoxon tests.

RESULTS

After the intervention, the largest number of BSEs was four times among 60% of the participants in the intervention group and once among 24% of the participants in the control group during four months (p = 0.001). After the intervention, the mean differences of the scores of perceived susceptibility (1.03 ± 2.65 vs. 0.01 ± 0.42, p = 0.001), BSE barriers (2.80 ± 5.32 vs.  0.04 ± 1.43, p = 0.001), self-efficacy (10.75 ± 7.63 vs. - 2.75 ± 2.44, p = 0.001), and health motivation (2.77 ± 3.70 vs. - 0.29 ± 0.63, p = 0.001) were significantly higher in the intervention group compared to the control group. However, no significant difference was observed between the two groups with regard to perceived severity and BSE benefits after the intervention.

CONCLUSIONS

Access to the smartphone application enhanced the participants' performance and health beliefs regarding BSE in the areas of perceived susceptibility, self-efficacy, and health motivation. Therefore, we recommend using the same smartphone application to improve women's performance and health beliefs regarding BSE.

Relationship between ultrasound values and pathology and metastasis in patients with breast cancer

PURPOSE

This study was designed to investigate the relationship between ultrasound values of breast cancer and its pathology and metastasis.

METHODS

A retrospective study was conducted on 80 patients diagnosed with breast cancer by pathologic examination in our hospital. The tumor size, tumor type, tumor grade, and the presence of distant metastasis were recorded. Vascular invasion, molecular subtype, pathobiologic indicators, and other measures were analyzed to explore the correlation between ultrasound measurements and pathology and metastasis in breast cancer patients.

RESULT

The proportion of ultrasound scores did not differ significantly among the groups (P > 0.05). The enrolled subjects were grouped according to tumor types (intraductal carcinoma, invasive ductal carcinoma, invasive lobular carcinoma, and special types), tumor grade (grade 1-3), metastasis, vascular invasion, and pathobiologic indicators (positive or negative ER/PR and HER-2 expression). These factors affected the ultrasound scores of breast cancer patients, resulting in significant differences in the proportions of scores between the groups (P < 0.05).

CONCLUSION

The ultrasound scores of breast cancer are closely related to its pathologic changes, and this has implications for the types of pathological tissues, biologic indicators, and presence of metastasis. Therefore, ultrasound values may be useful as a primary pathologic screening method for breast cancer patients.

Racial disparities in routine health checkup and adherence to cancer screening guidelines among women in the United States of America

PURPOSE

Routine health checkup and cancer screening rates among women are suboptimal, partially due to the health care disparities by race/ethnicity in the USA. This study examined the previously understudied associations between routine health checkup, cervical cancer screening, and breast cancer screening by race/ethnicity using the national representative sample of women.

METHODS

Data were obtained from three cycles (2017, 2018, and 2019) of the Health Information National Trends Survey (HINTS) (n = 12,227). Survey-weighted logistic regressions were evaluated to assess associations between routine health checkup and cervical and breast cancer screening compliance with the established guidelines with the age criteria and frequency of screening by race/ethnicity (Black, White, Hispanic, and Other).

RESULTS

This study included 6,941 women in the cervical cancer screening and 8,005 women for breast cancer screening, considering the age criteria. Women who had received routine health checkups were more likely to meet the cervical cancer screening guideline (Odds ratio 3.24, p < 0.05) and breast cancer screening guideline (OR 5.86, p < 0.05) compared to women who did not receive routine health checkups. While routine health checkups were associated with both types of cancer screenings in most racial/ethnic groups, analyses stratified by race/ethnicity suggest that Hispanic women and Other women did not benefit from routine health checkup in relation to cervical and breast cancer screening, respectively.

CONCLUSION

Promotion of routine health checkups could promote cancer screening among women across racial/ethnic groups, although specific racial/ethnic groups may require additional support.

Deep adversarial domain adaptation for breast cancer screening from mammograms

The early detection of breast cancer greatly increases the chances that the right decision for a successful treatment plan will be made. Deep learning approaches are used in breast cancer screening and have achieved promising results when a large-scale labeled dataset is available for training. However, they may suffer from a dramatic decrease in performance when annotated data are limited. In this paper, we propose a method called deep adversarial domain adaptation (DADA) to improve the performance of breast cancer screening using mammography. Specifically, our aim is to extract the knowledge from a public dataset (source domain) and transfer the learned knowledge to improve the detection performance on the target dataset (target domain). Because of the different distributions of the source and target domains, the proposed method adopts an adversarial learning technique to perform domain adaptation using the two domains. Specifically, the adversarial procedure is trained by taking advantage of the disagreement of two classifiers. To evaluate the proposed method, the public well-labeled image-level dataset Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) is employed as the source domain. Mammography samples from the West China Hospital were collected to construct our target domain dataset, and the samples are annotated at case-level based on the corresponding pathological reports. The experimental results demonstrate the effectiveness of the proposed method compared with several other state-of-the-art automatic breast cancer screening approaches.

Effect of mammography screening and sociodemographic factors on stage of female breast cancer at diagnosis in New South Wales

Objective The aims of this study were to assess the effects of screening through BreastScreen NSW on the stage of cancer at diagnosis, and differences in cancer stage at diagnosis according to sociodemographic factors. Methods Using linked BreastScreen NSW screening attendance records and NSW Cancer Registry data, the effects of screening participation and sociodemographic characteristics on stage at diagnosis were investigated using Kruskal-Wallis analysis of variance or the Mann-Whitney U-test for the 2002-13 diagnostic period. Multivariate logistic regression was used to investigate predictors of stage at diagnosis. Results The association between BreastScreen NSW participation and earlier stage at diagnosis was strongest when the last screening episode occurred within 24 months of the cancer diagnosis, with an odds ratio of localised versus non-localised cancer of 1.61 (95% confidence interval 1.51-1.72). Women aged ≥70 years, Aboriginal women, residents of major cities and women living in areas of socioeconomic disadvantage were more likely to have distant than non-distant stage at diagnosis. A trend towards more distant stage in more recent diagnostic years was evident after adjusting for screening participation. Conclusions The strongest and most consistent predictor of earlier stage at diagnosis was BreastScreen NSW participation. Continued efforts to increase screening participation are important to achieve earlier stage at diagnosis, particularly for sociodemographic groups with more advanced disease. What is known about the topic? Earlier cancer stage at diagnosis is a prerequisite for mortality reduction from screening. Past research indicated that screening participation in New South Wales (NSW) was strongly associated with early stage at diagnosis and mortality reduction. More contemporary data are needed to monitor screening performance in NSW and assess differences in cancer stage across sociodemographic subgroups. What does this paper add? Using data linkage, this paper indicates associations between screening, sociodemographic factors and stage at diagnosis for the NSW population in 2002-13. Contrary to expectations, major city residents tended to have a lower proportion of early stage breast cancer at diagnosis, which may be indicative of lower screening coverage and barriers to screening. Compared with past research, similar effects of screening and other sociodemographic factors on the stage of breast cancer at diagnosis were observed. This paper compares screening histories across sociodemographic groups, indicating statistically significant differences. What are the implications for practitioners? Increasing screening participation is particularly important for sociodemographic groups who are diagnosed at more advanced stages, including women from lower socioeconomic areas, Aboriginal and Torres Strait Islander women and residents of major cities. In particular, the results reinforce the need to further develop targeted strategies to increase screening participation among NSW women from lower socioeconomic areas and Aboriginal and Torres Strait Islander women. Further investigation into screening coverage and barriers to screening for residents in major cities is needed.

Navigating the Cancer Screening Decision for Patients with Dementia

PURPOSE OF REVIEW

The risks of developing cancer and dementia both increase with age, giving rise to the complex question of whether continued cancer screening for older dementia patients is appropriate. This paper offers a practice-based clinical approach to determine an answer to this challenging question.

RECENT FINDINGS

There is no consensus on the prevalence of cancer and dementia as co-diagnoses. Persons with dementia are screened less often compared to those without dementia. There is significant literature focusing on screening in the geriatric population, but there is little evidence to support decision-making for screening for older patients with dementia. Given this lack of evidence, individualized decisions should be made in collaboration with patients and family caregivers. Four considerations to help guide this process include prognosis, behavioral constraints, cognitive capacity, and goals for care. Future research will be challenging due to variability of factors that inform screening decisions and the vulnerable nature of this patient population.

Multi-Omics Marker Analysis Enables Early Prediction of Breast Tumor Progression

Ductal carcinoma in situ (DCIS) is a preinvasive form of breast cancer with a highly variable potential of becoming invasive and affecting mortality of the patients. Due to the lack of accurate markers of disease progression, many women with detected DCIS are currently overtreated. To distinguish those DCIS cases who are likely to require therapy from those who should be left untreated, there is a need for robust and predictive biomarkers extracted from molecular or genetic profiles. We developed a supervised machine learning approach that implements multi-omics feature selection and model regularization for the identification of biomarker combinations that could be used to distinguish low-risk DCIS lesions from those with a higher likelihood of progression. To investigate the genetic heterogeneity of disease progression, we applied this approach to 40 pure DCIS and 259 invasive breast cancer (IBC) samples profiled with genome-wide transcriptomics, DNA methylation, and DNA copy number variation. Feature selection using the multi-omics Lasso-regularized algorithm identified both known genes involved in breast cancer development, as well as novel markers for early detection. Even though the gene expression-based model features led to the highest classification accuracy alone, methylation data provided a complementary source of features and improved especially the sensitivity of correctly classifying DCIS cases. We also identified a number of repeatedly misclassified DCIS cases when using either the expression or methylation markers. A small panel of 10 gene markers was able to distinguish DCIS and IBC cases with high accuracy in nested cross-validation (AU-ROC = 0.99). The marker panel was not specific to any of the established breast cancer subtypes, suggesting that the 10-gene signature may provide a subtype-agnostic and cost-effective approach for breast cancer detection and patient stratification. We further confirmed high accuracy of the 10-gene signature in an external validation cohort (AU-ROC = 0.95), profiled using distinct transcriptomic assay, hence demonstrating robustness of the risk signature.

Anxiety and Its Association With Screening Mammography

Anxiety is often cited as a risk of screening mammography, and organizations such as the U.S. Preventive Services Task Force list anxiety as a screening-associated "harm" that should be mitigated. However, the level of mammography-related anxiety risk is difficult to assign clearly for myriad reasons, including the variability of individuals' baseline susceptibility to anxiety, the self-reported nature of subjective anxiety states, and the multiple sources of breast cancer screening-related anxiety. In addition, anxiety measures differ between studies and psychological responses to screening mammography vary across racial and ethnic groups. Nonetheless, breast radiology practices should acknowledge the existence of mammography-associated anxiety and consider strategies to decrease it. These strategies include immediate screening interpretations, patient education efforts, and relaxation techniques.

Breast cancer recurrence prediction with ensemble methods and cost-sensitive learning

Breast cancer is one of the most common cancers in women all over the world. Due to the improvement of medical treatments, most of the breast cancer patients would be in remission. However, the patients have to face the next challenge, the recurrence of breast cancer which may cause more severe effects, and even death. The prediction of breast cancer recurrence is crucial for reducing mortality. This paper proposes a prediction model for the recurrence of breast cancer based on clinical nominal and numeric features. In this study, our data consist of 1,061 patients from Breast Cancer Registry from Shin Kong Wu Ho-Su Memorial Hospital between 2011 and 2016, in which 37 records are denoted as breast cancer recurrence. Each record has 85 features. Our approach consists of three stages. First, we perform data preprocessing and feature selection techniques to consolidate the dataset. Among all features, six features are identified for further processing in the following stages. Next, we apply resampling techniques to resolve the issue of class imbalance. Finally, we construct two classifiers, AdaBoost and cost-sensitive learning, to predict the risk of recurrence and carry out the performance evaluation in three-fold cross-validation. By applying the AdaBoost method, we achieve accuracy of 0.973 and sensitivity of 0.675. By combining the AdaBoost and cost-sensitive method of our model, we achieve a reasonable accuracy of 0.468 and substantially high sensitivity of 0.947 which guarantee almost no false dismissal. Our model can be used as a supporting tool in the setting and evaluation of the follow-up visit for early intervention and more advanced treatments to lower cancer mortality.

Bahcesehir long-term population-based screening compared to National Breast Cancer Registry Data: effectiveness of screening in an emerging country

PURPOSE

We aimed to show the effects of long-term screening on clinical, pathologic, and survival outcomes in patients with screen-detected breast cancer and compare these findings with breast cancer patients registered in the National Breast Cancer Registry Data (NBCRD).

METHODS

Women aged 40-69 years, living in Bahcesehir county, Istanbul, Turkey, were screened every 2 years using bilateral mammography. The Bahcesehir National Breast Cancer Registry Data (BMSP) data were collected during a 10-year screening period (five rounds of screening). BMSP data were compared with the NBCRD regarding age, cancer stage, types of surgery, tumor size, lymph node status, molecular subtypes, and survival rates.

RESULTS

During the 10-year screening period, 8758 women were screened with 22621 mammograms. Breast cancer was detected in 130 patients; 51 (39.2%) were aged 40-49 years. The comparison of breast cancer patients in the two programs revealed that BMSP patients had earlier stages, higher breast-conserving surgery rates, smaller tumor size, more frequent negative axillary nodal status, lower histologic grade, and higher ductal carcinoma in situ rates than NBCRD patients (p = 0.001, for all).

CONCLUSION

These results indicate the feasibility of successful population-based screening in middle-income countries.

Downregulated mRNA Expression of ZNF385B Is an Independent Predictor of Breast Cancer

Background

ZNF385B, a zinc finger protein, has been known as a potential biomarker in some neurological and hematological studies recently. Although numerous studies have demonstrated the potential function of zinc finger proteins in tumor progression, the effects of ZNF385B in breast cancer (BC) are less studied.

Methods

The Oncomine database and “ESurv” tool were used to explore the differential expression of ZNF385B in pan-cancer. Furthermore, data of patients with BC were downloaded from The Cancer Genome Atlas (TCGA). The receiver operating characteristic (ROC) curve of ZNF385B expression was established to explore the diagnostic value of ZNF385B and to obtain the cut-off value of high or low ZNF385B expression in BC. The chi-square test as well as Fisher exact test was used for identification of the relationships between clinical features and ZNF385B expression. Furthermore, the effects of ZNF385B on BC patients' survival were evaluated by the Kaplan-Meier and Cox regression. Data from the Gene Expression Omnibus (GEO) database were employed to validate the results of TCGA. Protein expression of ZNF385B in BC patient specimens was detected by immunohistochemistry (IHC) staining.

Results

ZNF385B expression was downregulated in most types of cancer including BC. Low ZNF385B expression was related with survival status, overall survival (OS), and recurrence of BC. ZNF385B had modest diagnostic value, which is indicated by the area under the ROC curve (AUC = 0.671). Patients with lower ZNF385B expression had shorter OS and RFS (relapse-free survival). It had been demonstrated that low ZNF385B expression represented independent prognostic value for OS and RFS by multivariate survival analysis. The similar results were verified by datasets from the GEO database as well. The protein expression of ZNF385B was decreased in patients' samples compared with adjacent tissues by IHC.

Conclusions

Low ZNF385B expression was an independent predictor for worse prognosis of BC patients.

Disruptions in preventive care: Mammograms during the COVID-19 pandemic

OBJECTIVE

To measure the extent to which the provision of mammograms was impacted by the COVID-19 pandemic and surrounding guidelines.

DATA SOURCES

De-identified summary data derived from medical claims and eligibility files were provided by Independence Blue Cross for women receiving mammograms.

STUDY DESIGN

We used a difference-in-differences approach to characterize the change in mammograms performed over time and a queueing formula to estimate the time to clear the queue of missed mammograms.

DATA COLLECTION

We used data from the first 30 weeks of each year from 2018 to 2020.

PRINCIPAL FINDINGS

Over the 20 weeks following March 11, 2020, the volume of screening mammograms and diagnostic mammograms fell by 58% and 38% of expected levels, on average. Lowest volumes were observed in week 15 (April 8 to 14), when screening and diagnostic mammograms fell by 99% and 74%, respectively. Volumes began to rebound in week 19 (May), with diagnostic mammograms reaching levels to similar to previous years' and screening mammograms remaining 14% below expectations. We estimate it will take a minimum of 22 weeks to clear the queue of missed mammograms in our study sample.

CONCLUSIONS

The provision of mammograms has been significantly disrupted due to the COVID-19 pandemic.

Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach

Breast cancer remains a global challenge, causing over 600,000 deaths in 2018 (ref. ¹). To achieve earlier cancer detection, health organizations worldwide recommend screening mammography, which is estimated to decrease breast cancer mortality by 20-40% (refs. ^2,3). Despite the clear value of screening mammography, significant false positive and false negative rates along with non-uniformities in expert reader availability leave opportunities for improving quality and access^4,5. To address these limitations, there has been much recent interest in applying deep learning to mammography^6-18, and these efforts have highlighted two key difficulties: obtaining large amounts of annotated training data and ensuring generalization across populations, acquisition equipment and modalities. Here we present an annotation-efficient deep learning approach that (1) achieves state-of-the-art performance in mammogram classification, (2) successfully extends to digital breast tomosynthesis (DBT; '3D mammography'), (3) detects cancers in clinically negative prior mammograms of patients with cancer, (4) generalizes well to a population with low screening rates and (5) outperforms five out of five full-time breast-imaging specialists with an average increase in sensitivity of 14%. By creating new 'maximum suspicion projection' (MSP) images from DBT data, our progressively trained, multiple-instance learning approach effectively trains on DBT exams using only breast-level labels while maintaining localization-based interpretability. Altogether, our results demonstrate promise towards software that can improve the accuracy of and access to screening mammography worldwide.

Lung Screening Benefits and Challenges: A Review of The Data and Outline for Implementation

Lung cancer is the leading cause of cancer-related deaths worldwide, accounting for almost a fifth of all cancer-related deaths. Annual computed tomographic lung cancer screening (CTLS) detects lung cancer at earlier stages and reduces lung cancer-related mortality among high-risk individuals. Many medical organizations, including the U.S. Preventive Services Task Force, recommend annual CTLS in high-risk populations. However, fewer than 5% of individuals worldwide at high risk for lung cancer have undergone screening. In large part, this is owing to delayed implementation of CTLS in many countries throughout the world. Factors contributing to low uptake in countries with longstanding CTLS endorsement, such as the United States, include lack of patient and clinician awareness of current recommendations in favor of CTLS and clinician concerns about CTLS-related radiation exposure, false-positive results, overdiagnosis, and cost. This review of the literature serves to address these concerns by evaluating the potential risks and benefits of CTLS. Review of key components of a lung screening program, along with an updated shared decision aid, provides guidance for program development and optimization. Review of studies evaluating the population considered "high-risk" is included as this may affect future guidelines within the United States and other countries considering lung screening implementation.

Chimeric antigen receptor T-cell immunotherapy in breast cancer: development and challenges

Chimeric antigen receptor (CAR) T-cell therapy is an innovative form of immunotherapy wherein autologous T-cells are genetically modified to express chimeric receptors encoding an antigen-specific single-chain variable fragment and costimulatory molecules. Moreover, CAR T-cell therapy can only work successfully in patients who have an intact immune system. Therefore, patients receiving cytotoxic chemotherapy will be immunosuppressed making CAR-T therapy less effective. In adoptive CD8+ T-cell therapy (ACT), numerous tumor-specific, engineered T-cells are sourced from patients, expanded in vitro, and infused back expressing tumor-specific antigen receptors. The most successful ACT, anti-CD19 chimeric antigen receptor T-cell therapy directed against B-cell lymphoma, has proved to be efficacious. However, current efforts to utilize this approach for solid tumors, like breast cancer, have shown only modest improvement. Nevertheless, the potential efficacy of CAR-T therapy is promising in an era of immunological advances. By appropriately manipulating CAR T-cells to combat the immunosuppressive forces of the tumor microenvironment, significant eradication of the solid tumor may occur. This review discusses CAR T-cell therapy and its specificity and safety in adoptive cell transfers in breast cancer. We will highlight novel discoveries in CAR T-cell immunotherapy and the formidable barriers including suppression of T-cell function and localization at tumor sites.

Comparative study between contrast-enhanced mammography, tomosynthesis, and breast ultrasound as complementary techniques to mammography in dense breast parenchyma

Background

Mammography is accused of having low sensitivity and specificity in dense breast parenchyma. Also, women with dense breasts show an increased risk of developing breast cancer. Breast ultrasound has been used for several years for a better characterization of breast lesions. Contrast-enhanced mammography and tomosynthesis are relative novel imaging techniques that have been implicated in breast cancer detection and diagnosis. We aimed to compare breast tomosynthesis, contrast-enhanced mammography, and breast ultrasound as complementary techniques to mammography in dense breast parenchyma.

Results

The study included 37 patients with 63 inconclusive mammography breast lesions. They all performed contrast-enhanced mammography, single-view tomosynthesis, and breast ultrasound. Mammography had a sensitivity of 83%, a specificity of 48%, a positive predictive value of 68%, a negative predictive value of 68%, and a diagnostic accuracy of 68%. Contrast-enhanced mammography had a sensitivity of 89%, a specificity of 89%, a positive predictive value of 91%, a negative predictive value of 86%, and a diagnostic accuracy of 89%. Tomosynthesis had a sensitivity of 86%, a specificity of 81%, a positive predictive value of 86%, a negative predictive value of 81%, and a diagnostic accuracy of 84%. Breast ultrasound had a sensitivity of 97%, a specificity of 85%, a positive predictive value of 90%, a negative predictive value of 96%, and a diagnostic accuracy of 92%.

Conclusion

Breast ultrasound, tomosynthesis, and contrast-enhanced mammography showed better performance compared to mammography in dense breasts. However, ultrasound being safe with no radiation hazards should be the second step modality of choice after mammography in the assessment of mammography dense breasts. Adding tomosynthesis to mammography in screening increases its sensitivity. Contrast-enhanced mammography should be reserved for cases with inconclusive sonomammographic results.

Visualization of Microvascular Angiogenesis Using Dual-Frequency Contrast-Enhanced Acoustic Angiography: A Review

Cancerous tumor growth is associated with the development of tortuous, chaotic microvasculature, and this aberrant microvascular morphology can act as a biomarker of malignant disease. Acoustic angiography is a contrast-enhanced ultrasound technique that relies on superharmonic imaging to form high-resolution 3-D maps of the microvasculature. To date, acoustic angiography has been performed with dual-element transducers that can achieve high contrast-to-tissue ratio and resolution in pre-clinical small animal models. In this review, we first describe the development of acoustic angiography, including the principle, transducer design, and optimization of superharmonic imaging techniques. We then detail several preclinical applications of this microvascular imaging method, as well as the current and future development of acoustic angiography as a pre-clinical and clinical diagnostic tool.

Proteomic analysis of circulating extracellular vesicles identifies potential markers of breast cancer progression, recurrence, and response

Proteomic profiling of circulating small extracellular vesicles (sEVs) represents a promising, noninvasive approach for early detection and therapeutic monitoring of breast cancer (BC). We describe a relatively low-cost, fast, and reliable method to isolate sEVs from plasma of BC patients and analyze their protein content by semiquantitative proteomics. sEV-enriched fractions were isolated from plasma of healthy controls and BC patients at different disease stages before and after surgery. Proteomic analysis of sEV-enriched fractions using reverse phase protein array revealed a signature of seven proteins that differentiated BC patients from healthy individuals, of which FAK and fibronectin displayed high diagnostic accuracy. The size of sEVs was significantly reduced in advanced disease stage, concomitant with a stage-specific protein signature. Furthermore, we observed protein-based distinct clusters of healthy controls, chemotherapy-treated and untreated postsurgery samples, as well as a predictor of high risk of cancer relapse, suggesting that the applied methods warrant development for advanced diagnostics.

External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms

Importance

A computer algorithm that performs at or above the level of radiologists in mammography screening assessment could improve the effectiveness of breast cancer screening.

Objective

To perform an external evaluation of 3 commercially available artificial intelligence (AI) computer-aided detection algorithms as independent mammography readers and to assess the screening performance when combined with radiologists.

Design, Setting, and Participants

This retrospective case-control study was based on a double-reader population-based mammography screening cohort of women screened at an academic hospital in Stockholm, Sweden, from 2008 to 2015. The study included 8805 women aged 40 to 74 years who underwent mammography screening and who did not have implants or prior breast cancer. The study sample included 739 women who were diagnosed as having breast cancer (positive) and a random sample of 8066 healthy controls (negative for breast cancer).

Main Outcomes and Measures

Positive follow-up findings were determined by pathology-verified diagnosis at screening or within 12 months thereafter. Negative follow-up findings were determined by a 2-year cancer-free follow-up. Three AI computer-aided detection algorithms (AI-1, AI-2, and AI-3), sourced from different vendors, yielded a continuous score for the suspicion of cancer in each mammography examination. For a decision of normal or abnormal, the cut point was defined by the mean specificity of the first-reader radiologists (96.6%).

Results

The median age of study participants was 60 years (interquartile range, 50-66 years) for 739 women who received a diagnosis of breast cancer and 54 years (interquartile range, 47-63 years) for 8066 healthy controls. The cases positive for cancer comprised 618 (84%) screen detected and 121 (16%) clinically detected within 12 months of the screening examination. The area under the receiver operating curve for cancer detection was 0.956 (95% CI, 0.948-0.965) for AI-1, 0.922 (95% CI, 0.910-0.934) for AI-2, and 0.920 (95% CI, 0.909-0.931) for AI-3. At the specificity of the radiologists, the sensitivities were 81.9% for AI-1, 67.0% for AI-2, 67.4% for AI-3, 77.4% for first-reader radiologist, and 80.1% for second-reader radiologist. Combining AI-1 with first-reader radiologists achieved 88.6% sensitivity at 93.0% specificity (abnormal defined by either of the 2 making an abnormal assessment). No other examined combination of AI algorithms and radiologists surpassed this sensitivity level.

Conclusions and Relevance

To our knowledge, this study is the first independent evaluation of several AI computer-aided detection algorithms for screening mammography. The results of this study indicated that a commercially available AI computer-aided detection algorithm can assess screening mammograms with a sufficient diagnostic performance to be further evaluated as an independent reader in prospective clinical trials. Combining the first readers with the best algorithm identified more cases positive for cancer than combining the first readers with second readers.

Autoantibodies in Early Detection of Breast Cancer

In spite of the progress made in treatment and early diagnosis, breast cancer remains a major public health issue worldwide. Although modern image-based screening modalities have significantly improved early diagnosis, around 15% to 20% of breast cancers still go undetected. In underdeveloped countries, lack of resources and cost concerns prevent implementing mammography for routine screening. Noninvasive, low-cost, blood-based markers for early breast cancer diagnosis would be an invaluable alternative that would complement mammography screening. Tumor-specific autoantibodies are excellent biosensors that could be exploited to monitor disease-specific changes years before disease onset. Although clinically informative autoantibody markers for early breast cancer screening have yet to emerge, progress has been made in the development of tools to discover and validate promising autoantibody signatures. This review focuses on the current progress toward the development of autoantibody-based early screening markers for breast cancer.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Study on automatic detection and classification of breast nodule using deep convolutional neural network system

Backgrounds

Conventional ultrasound manual scanning and artificial diagnosis approaches in breast are considered to be operator-dependence, slight slow and error-prone. In this study, we used Automated Breast Ultrasound (ABUS) machine for the scanning, and deep convolutional neural network (CNN) technology, a kind of Deep Learning (DL) algorithm, for the detection and classification of breast nodules, aiming to achieve the automatic and accurate diagnosis of breast nodules.

Methods

Two hundred and ninety-three lesions from 194 patients with definite pathological diagnosis results (117 benign and 176 malignancy) were recruited as case group. Another 70 patients without breast diseases were enrolled as control group. All the breast scans were carried out by an ABUS machine and then randomly divided into training set, verification set and test set, with a proportion of 7:1:2. In the training set, we constructed a detection model by a three-dimensionally U-shaped convolutional neural network (3D U-Net) architecture for the purpose of segment the nodules from background breast images. Processes such as residual block, attention connections, and hard mining were used to optimize the model while strategies of random cropping, flipping and rotation for data augmentation. In the test phase, the current model was compared with those in previously reported studies. In the verification set, the detection effectiveness of detection model was evaluated. In the classification phase, multiple convolutional layers and fully-connected layers were applied to set up a classification model, aiming to identify whether the nodule was malignancy.

Results

Our detection model yielded a sensitivity of 91% and 1.92 false positive subjects per automatically scanned imaging. The classification model achieved a sensitivity of 87.0%, a specificity of 88.0% and an accuracy of 87.5%.

Conclusions

Deep CNN combined with ABUS maybe a promising tool for easy detection and accurate diagnosis of breast nodule.

A Mathematical Model to Predict Diagnostic Periods for Secondary Distant Metastases in Patients with ER/PR/HER2/Ki-67 Subtypes of Breast Cancer

Previously, a consolidated mathematical model of primary tumor (PT) growth and secondary distant metastasis (sdMTS) growth in breast cancer (BC) (CoMPaS) was presented. The aim was to detect the diagnostic periods for visible sdMTS via CoMPaS in patients with different subtypes ER/PR/HER2/Ki-67 (Estrogen Receptor/Progesterone Receptor/Human Epidermal growth factor Receptor 2/Ki-67 marker) of breast cancer. CoMPaS is based on an exponential growth model and complementing formulas, and the model corresponds to the tumor-node-metastasis (TNM) staging system and BC subtypes (ER/PR/HER2/Ki-67). The CoMPaS model reflects (1) the subtypes of BC, such as ER/PR/HER2/Ki-67, and (2) the growth processes of the PT and sdMTSs in BC patients without or with lymph node metastases (MTSs) in accordance with the eighth edition American Joint Committee on Cancer prognostic staging system for breast cancer. CoMPaS correctly describes the growth of the PT in the ER/PR/HER2/Ki-67 subtypes of BC patients and helps to calculate the different diagnostic periods, depending on the tumor volume doubling time of sdMTS, when sdMTSs might appear. CoMPaS and the corresponding software tool can help (1) to start the early treatment of small sdMTSs in BC patients with different tumor subtypes (ER/PR/HER2/Ki-67), and (2) to consider the patient almost healthy if sdMTSs do not appear during the different diagnostic periods.

Range of Radiologist Performance in a Population-based Screening Cohort of 1 Million Digital Mammography Examinations

Background There is great interest in developing artificial intelligence (AI)-based computer-aided detection (CAD) systems for use in screening mammography. Comparative performance benchmarks from true screening cohorts are needed. Purpose To determine the range of human first-reader performance measures within a population-based screening cohort of 1 million screening mammograms to gauge the performance of emerging AI CAD systems. Materials and Methods This retrospective study consisted of all screening mammograms in women aged 40-74 years in Stockholm County, Sweden, who underwent screening with full-field digital mammography between 2008 and 2015. There were 110 interpreting radiologists, of whom 24 were defined as high-volume readers (ie, those who interpreted more than 5000 annual screening mammograms). A true-positive finding was defined as the presence of a pathology-confirmed cancer within 12 months. Performance benchmarks included sensitivity and specificity, examined per quartile of radiologists' performance. First-reader sensitivity was determined for each tumor subgroup, overall and by quartile of high-volume reader sensitivity. Screening outcomes were examined based on the first reader's sensitivity quartile with 10 000 screening mammograms per quartile. Linear regression models were fitted to test for a linear trend across quartiles of performance. Results A total of 418 041 women (mean age, 54 years ± 10 [standard deviation]) were included, and 1 186 045 digital mammograms were evaluated, with 972 899 assessed by high-volume readers. Overall sensitivity was 73% (95% confidence interval [CI]: 69%, 77%), and overall specificity was 96% (95% CI: 95%, 97%). The mean values per quartile of high-volume reader performance ranged from 63% to 84% for sensitivity and from 95% to 98% for specificity. The sensitivity difference was very large for basal cancers, with the least sensitive and most sensitive high-volume readers detecting 53% and 89% of cancers, respectively (P < .001). Conclusion Benchmarks showed a wide range of performance differences between high-volume readers. Sensitivity varied by tumor characteristics. © RSNA, 2020 Online supplemental material is available for this article.

Staying abreast of imaging - Current status of breast cancer detection in high density breast

OBJECTIVES

The aim of this paper is to illustrate the current status of imaging in high breast density as we enter a new decade of advancing medicine and technology to diagnose breast lesions.

KEY FINDINGS

Early detection of breast cancer has become the chief focus of research from governments to individuals. However, with varying breast densities across the globe, the explosion of breast density information related to imaging, phenotypes, diet, computer aided diagnosis and artificial intelligence has witnessed a dramatic shift in new screening recommendations in mammography, physical examination, screening younger women and women with comorbid conditions, screening women at high risk, and new screening technologies. Breast density is well known to be a risk factor in patients with suspected/known breast neoplasia. Extensive research in the field of qualitative and quantitative analysis on different tissue characteristics of the breast has rapidly become the chief focus of breast imaging. A summary of the available guidelines and modalities of breast imaging, as well as new emerging techniques under study that can potentially provide an augmentation or even a replacement of those currently available.

CONCLUSION

Despite all the advances in technology and all the research directed towards breast cancer, detection of breast cancer in dense breasts remains a dilemma.

IMPLICATIONS FOR PRACTICE

It is of utmost importance to develop highly sensitive screening modalities for early detection of breast cancer.

Contrasting DCIS and invasive breast cancer by subtype suggests basal-like DCIS as distinct lesions

Ductal carcinoma in situ (DCIS) is a non-invasive type of breast cancer with highly variable potential of becoming invasive and affecting mortality. Currently, many patients with DCIS are overtreated due to the lack of specific biomarkers that distinguish low risk lesions from those with a higher risk of progression. In this study, we analyzed 57 pure DCIS and 313 invasive breast cancers (IBC) from different patients. Three levels of genomic data were obtained; gene expression, DNA methylation, and DNA copy number. We performed subtype stratified analyses and identified key differences between DCIS and IBC that suggest subtype specific progression. Prominent differences were found in tumors of the basal-like subtype: Basal-like DCIS were less proliferative and showed a higher degree of differentiation than basal-like IBC. Also, core basal tumors (characterized by high correlation to the basal-like centroid) were not identified amongst DCIS as opposed to IBC. At the copy number level, basal-like DCIS exhibited fewer copy number aberrations compared with basal-like IBC. An intriguing finding through analysis of the methylome was hypermethylation of multiple protocadherin genes in basal-like IBC compared with basal-like DCIS and normal tissue, possibly caused by long range epigenetic silencing. This points to silencing of cell adhesion-related genes specifically in IBC of the basal-like subtype. Our work confirms that subtype stratification is essential when studying progression from DCIS to IBC, and we provide evidence that basal-like DCIS show less aggressive characteristics and question the assumption that basal-like DCIS is a direct precursor of basal-like invasive breast cancer.