Breast cancer risk prediction and mammography biopsy decisions: a model-based study

Action Augmentation of Tactile Perception for Soft-Body Palpation

Medical palpation is a diagnostic technique in which physicians use the sense of touch to manipulate the soft human tissue. This can be done to enable the diagnosis of possibly life-threatening conditions, such as cancer. Palpation is still poorly understood because of the complex interaction dynamics between the practitioners' hands and the soft human body. To understand this complex of soft body interactions, we explore robotic palpation for the purpose of diagnosing the presence of abnormal inclusions, or tumors. Using a Bayesian framework for training and classification, we show that the exploration of soft bodies requires complex, multi-axis, palpation trajectories. We also find that this probabilistic approach is capable of rapidly searching the large action space of the robot. This work progresses “robotic” palpation, and it provides frameworks for understanding and exploiting soft body interactions.

[Diagnosis of breast diseases in a certified breast center]

The beginning of the 21st century has seen immense improvements in the quality of diagnosis and treatment of breast cancer due to several, simultaneous developments. In particular, the introduction of a certification program from the German Cancer Society based on level III guidelines has enhanced the transparency and quality of treatment of breast diseases for all actors. As a result, patients have benefited from intensified cooperation especially between core disciplines in breast disease, gynecology, pathology, and radiology. The standardized and synoptic reading of multiple diagnostic modalities has enabled precise sampling of histologic specimen, which has improved prognosis and the successful individualization of therapy. In this article the benefits of breast cancer diagnosis and therapy in a certified breast center are illustrated using four case examples.

Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants

The predictive capability of combining demographic risk factors, germline genetic variants, and mammogram abnormality features for breast cancer risk prediction is poorly understood. We evaluated the predictive performance of combinations of demographic risk factors, high risk single nucleotide polymorphisms (SNPs), and mammography features for women recommended for breast biopsy in a retrospective case-control study (n = 768) with four logistic regression models. The AUC of the baseline demographic features model was 0.580. Both genetic variants and mammography abnormality features augmented the performance of the baseline model: demographics + SNP (AUC =0.668), demographics + mammography (AUC =0.702). Finally, we found that the demographics + SNP + mammography model (AUC = 0.753) had the greatest predictive power, with a significant performance improvement over the other models. The combination of demographic risk factors, genetic variants and imaging features improves breast cancer risk prediction over prior methods utilizing only a subset of these features.

Association Between Imaging Characteristics and Different Molecular Subtypes of Breast Cancer

RATIONALE AND OBJECTIVE

Breast cancer can be divided into four major molecular subtypes based on the expression of hormone receptor (estrogen receptor and progesterone receptor), human epidermal growth factor receptor 2, HER2 status, and molecular proliferation rate (Ki67). In this study, we sought to investigate the association between breast cancer subtype and radiological findings in the Chinese population.

MATERIALS AND METHODS

Medical records of 300 consecutive invasive breast cancer patients were reviewed from the database: the Breast Imaging Reporting and Data System. The imaging characteristics of the lesions were evaluated. The molecular subtypes of breast cancer were classified into four types: luminal A, luminal B, HER2 overexpressed (HER2), and basal-like breast cancer (BLBC). Univariate and multivariate logistic regression analyses were performed to assess the association between the subtype (dependent variable) and mammography or 15 magnetic resonance imaging (MRI) indicators (independent variables).

RESULTS

Luminal A and B subtypes were commonly associated with "clustered calcification distribution," "nipple invasion," or "skin invasion" (P <0.05). The BLBC subtype was more commonly associated with "rim enhancement" and persistent inflow type enhancement in delayed phase (P <0.05). HER2 overexpressed cancers showed association with persistent enhancement in the delayed phase on MRI and "clustered calcification distribution" on mammography (P <0.05).

CONCLUSION

Certain radiological features are strongly associated with the molecular subtype and hormone receptor status of breast tumor, which are potentially useful tools in the diagnosis and subtyping of breast cancer.

Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy

RATIONALE AND OBJECTIVES

The discovery of germline genetic variants associated with breast cancer has engendered interest in risk stratification for improved, targeted detection and diagnosis. However, there has yet to be a comparison of the predictive ability of these genetic variants with mammography abnormality descriptors.

MATERIALS AND METHODS

Our institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study utilized a personalized medicine registry in which participants consented to provide a DNA sample and to participate in longitudinal follow-up. In our retrospective, age-matched, case-controlled study of 373 cases and 395 controls who underwent breast biopsy, we collected risk factors selected a priori based on the literature, including demographic variables based on the Gail model, common germline genetic variants, and diagnostic mammography findings according to Breast Imaging Reporting and Data System (BI-RADS). We developed predictive models using logistic regression to determine the predictive ability of (1) demographic variables, (2) 10 selected genetic variants, or (3) mammography BI-RADS features. We evaluated each model in turn by calculating a risk score for each patient using 10-fold cross-validation, used this risk estimate to construct Receiver Operator Characteristic Curve (ROC) curves, and compared the area under the ROC curve (AUC) of each using the DeLong method.

RESULTS

The performance of the regression model using demographic risk factors was not statistically different from the model using genetic variants (P = 0.9). The model using mammography features (AUC = 0.689) was superior to both the demographic model (AUC = .598; P < 0.001) and the genetic model (AUC = .601; P < 0.001).

CONCLUSIONS

BI-RADS features exceeded the ability of demographic and 10 selected germline genetic variants to predict breast cancer in women recommended for biopsy.

TCM tongue diagnosis index of early-stage breast cancer

OBJECTIVES

This paper investigates discriminating tongue features to distinguish between early stage breast cancer (BC) patients and non-breast cancer individuals through non-invasive traditional Chinese medicine (TCM) tongue diagnosis.

DESIGN

The tongue features for 67 patients with 0 and 1 stages of BC, and 70 non-breast cancer individuals are extracted by the automatic tongue diagnosis system (ATDS). A total of nine tongue features, namely, tongue color, tongue quality, tongue fissure, tongue fur, red dot, ecchymosis, tooth mark, saliva, and tongue shape are identified for each tongue. Features extracted are further sub-divided according to the areas located, i.e., spleen-stomach, liver-gall-left, liver-gall-right, kidney, and heart-lung areas. This study focuses on deriving significant tongue features (p<0.05) to discriminate early-stage BC patients from non-breast cancer individuals.

RESULTS

The Mann-Whitney test shows that the amount of tongue fur (p=0.024), maximum covering area of tongue fur (p=0.009), thin tongue fur (p=0.009), the average area of red dot (p=0.049), the maximum area of red dot (p=0.009), red dot in the spleen-stomach area (p=0.000), and red dot in the heart-lung area (p=0.000) demonstrate significant differences. The data collected are further classified into two groups. The training group consists of 57 early-stage BC patients and 60 non-breast cancer individuals, while the testing group is composed of 10 early-stage BC patients and 10 non-breast cancer individuals. The logistic regression by utilizing these 7 tongue features with significant differences in Mann-Whitney test as factors is performed. In order to reduce the number of tongue features employed in prediction, tongue features with the least amount of significant difference, namely, maximum area of red dot and average area of red dot, are removed progressively. The tongue features of the testing group are employed in the aforementioned three models to test the power of significant tongue features identified in predicting early-stage BC. An accuracy of 80%, 80% and 90% is reached on non-breast cancer individuals by applying the 7, 6 and 5 significant tongue features obtained through Mann-Whitney test, respectively, while 60%, 60% and 50% is reached on the corresponding early-stage BC patients.

CONCLUSION

The TCM tongue diagnosis can serve as a preliminary screening procedure in the early detection of BC in light of its simple and non-invasive nature, followed by other more accurate testing process. To the best of our knowledge, this is the first attempt in applying non-invasive TCM tongue diagnosis to the discrimination of early-stage BC patients and non-breast cancer individuals.

The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms

Introduction

Mammography screening results in a significant number of false-positives. The use of pretest breast cancer risk factors to guide follow-up of abnormal mammograms could improve the positive predictive value of screening. We evaluated the use of the Gail model, body mass index (BMI), and genetic markers to predict cancer diagnosis among women with abnormal mammograms. We also examined the extent to which pretest risk factors could reclassify women without cancer below the biopsy threshold.

Methods

We recruited a prospective cohort of women referred for biopsy with abnormal (BI-RADS 4) mammograms according to the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS). Breast cancer risk factors were assessed prior to biopsy. A validated panel of 12 single-nucleotide polymorphisms (SNPs) associated with breast cancer were measured. Logistic regression was used to assess the association of Gail risk factors, BMI and SNPs with cancer diagnosis (invasive or ductal carcinoma in situ). Model discrimination was assessed using the area under the receiver operating characteristic curve, and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. The distribution of predicted probabilities of a cancer diagnosis were compared for women with or without breast cancer.

Results

In the multivariate model, age (odds ratio (OR) = 1.05; 95% confidence interval (CI), 1.03 to 1.08; P < 0.001), SNP panel relative risk (OR = 2.30; 95% CI, 1.06 to 4.99, P = 0.035) and BMI (≥30 kg/m² versus <25 kg/m²; OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) were significantly associated with breast cancer diagnosis. Older women were more likely than younger women to be diagnosed with breast cancer. The SNP panel relative risk remained strongly associated with breast cancer diagnosis after multivariable adjustment. Higher BMI was also strongly associated with increased odds of a breast cancer diagnosis. Obese women (OR = 2.20; 95% CI, 1.05 to 4.58; P = 0.036) had more than twice the odds of cancer diagnosis compared to women with a BMI <25 kg/m². The SNP panel appeared to have predictive ability among both white and black women.

Conclusions

Breast cancer risk factors, including BMI and genetic markers, are predictive of cancer diagnosis among women with BI-RADS 4 mammograms. Using pretest risk factors to guide follow-up of abnormal mammograms could reduce the burden of false-positive mammograms.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-014-0509-4) contains supplementary material, which is available to authorized users.

Factors associated with mammography adherence among married Chinese women in Yanbian, China

BACKGROUND

Despite the efficacy of regular mammograms, the incidence and mortality rate of breast cancer have been increasing in China. Insufficient studies on the factors affecting mammography adherence in Chinese married women have been conducted. The purpose of the present study was to explore the factors associated with adherence to guidelines for regular mammography among Chinese married women.

MATERIALS AND METHODS

The participants were recruited conveniently and included Chinese and Korean Chinese women who were married, living at Yanbian City in China. Demographic information, status regarding eight risk factors of breast cancer, health responsibility, and perceived benefits/barriers of mammography were obtained. Descriptive analyses, t-test, and multivariate analysis were performed. Hierarchical logistic regression was conducted to explore the factors associated with regular mammography adherence in Chinese and Korean Chinese subgroups.

RESULTS

About 24% of the sample population was adherent in going for regular mammography. The adherent group was significantly more educated, had more children, and had a lower proportion experiencing early menarche and a greater menopausal proportion than the non-adherent group. The final model using logistic regression analysis showed that being Chinese [OR=2.199 (1.224-3.951)], having no or one child [OR=4.879 (1.835-12.976)], early menarche [OR=3.515 (1.057-11.694)], being menopausal [OR=3.120 (0.965-10.088)], aged 40-49 [OR=2.374 (1.099-5.124)], having low education [OR=0.400 (0.211-0.765)], and perceiving greater benefits in doing mammography [OR=1.080 (1.014-1.151)] were significantly associated with mammography adherence, after controlling for covariates.

CONCLUSIONS

Sociocultural sensitive intervention for minorities should be emphasized when improving the adherence of regular mammography. Intervention tailored for women with lower education should be delivered and the benefits of mammography should be propagated to women in rural areas of China.

Breast cancer index: a perspective on tongue diagnosis in traditional chinese medicine

Breast cancer (BC) ranks second in the cancer fatality rate among females worldwide. Mammogram, ultrasound, magnetic resonance imaging (MRI), blood testing, and fine needle aspiration biopsy are usually applied to discriminate BC patients from normal persons. False-negative results, undetectable calcifications, movement-incurred blurry image, infection, and sampling error are commonly associated with these traditional means of diagnosis. Traditional Chinese medicine (TCM) covers a broad range of medical practices sharing common theoretical concepts. Tongue diagnosis plays an important role in TCM. Organ conditions, properties, and variation of pathogens can be revealed through observation of tongue. In light of this observation, this paper investigates discriminating tongue features to distinguish between BC patients and normal people, and establishes differentiating index to facilitate the non-invasive detection of BC. The tongue features for 60 BC patients and 70 normal persons were extracted by the Automatic Tongue Diagnosis System (ATDS). The Mann-Whitney test showed that the amount of tongue fur (P = 0.007), tongue fur in the spleen-stomach area, maximum covering area of tongue fur, thin tongue fur, the number of tooth marks, the number of red dots, red dot in the spleen-stomach area, red dot in the liver-gall-left area, red dot in the liver-gall-right area, and red dot in the heart-lung area demonstrated significant differences (P < 0.05). The tongue features of the testing group were employed to test the power of significant tongue features identified in predicting BC. An accuracy of 80% was reached by applying the seven significant tongue features obtained through Mann–Whitney test. To the best of our knowledge, this is the first attempt in applying TCM tongue diagnosis to the discrimination of BC patients and normal persons.