High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4

Grading of cancer histopathology slides requires more pathologists and expert clinicians as well as it is time consuming to look manually into whole-slide images. Hence, an automated classification of histopathological breast cancer sub-type is useful for clinical diagnosis and therapeutic responses. Recent deep learning methods for medical image analysis suggest the utility of automated radiologic imaging classification for relating disease characteristics or diagnosis and patient stratification. To develop a hybrid model using the convolutional neural network (CNN) and the long short-term memory recurrent neural network (LSTM RNN) to classify four benign and four malignant breast cancer subtypes. The proposed CNN-LSTM leveraging on ImageNet uses a transfer learning approach in classifying and predicting four subtypes of each. The proposed model was evaluated on the BreakHis dataset comprises 2480 benign and 5429 malignant cancer images acquired at magnifications of 40×, 100×, 200× and 400×. The proposed hybrid CNN-LSTM model was compared with the existing CNN models used for breast histopathological image classification such as VGG-16, ResNet50, and Inception models. All the models were built using three different optimizers such as adaptive moment estimator (Adam), root mean square propagation (RMSProp), and stochastic gradient descent (SGD) optimizers by varying numbers of epochs. From the results, we noticed that the Adam optimizer was the best optimizer with maximum accuracy and minimum model loss for both the training and validation sets. The proposed hybrid CNN-LSTM model showed the highest overall accuracy of 99% for binary classification of benign and malignant cancer, and, whereas, 92.5% for multi-class classifier of benign and malignant cancer subtypes, respectively. To conclude, the proposed transfer learning approach outperformed the state-of-the-art machine and deep learning models in classifying benign and malignant cancer subtypes. The proposed method is feasible in classification of other cancers as well as diseases.     

Classification of breast masses in mammograms using genetic programming and feature selection

Mammography is a widely used screening tool and is the gold standard for the early detection of breast cancer. The classification of breast masses into the benign and malignant categories is an important problem in the area of computer-aided diagnosis of breast cancer. A small dataset of 57 breast mass images, each with 22 features computed, was used in this investigation; the same dataset has been previously used in other studies. The extracted features relate to edge-sharpness, shape, and texture. The novelty of this paper is the adaptation and application of the classification technique called genetic programming (GP), which possesses feature selection implicitly. To refine the pool of features available to the GP classifier, we used feature-selection methods, including the introduction of three statistical measures—Student’s t test, Kolmogorov–Smirnov test, and Kullback–Leibler divergence. Both the training and test accuracies obtained were high: above 99.5% for training and typically above 98% for test experiments. A leave-one-out experiment showed 97.3% success in the classification of benign masses and 95.0% success in the classification of malignant tumors. A shape feature known as fractional concavity was found to be the most important among those tested, since it was automatically selected by the GP classifier in almost every experiment.     

Computerized Determination Scheme for Histological Classification of Breast Mass Using Objective Features Corresponding to Clinicians’ Subjective Impressions on Ultrasonographic Images

It is often difficult for clinicians to decide correctly on either biopsy or follow-up for breast lesions with masses on ultrasonographic images. The purpose of this study was to develop a computerized determination scheme for histological classification of breast mass by using objective features corresponding to clinicians’ subjective impressions for image features on ultrasonographic images. Our database consisted of 363 breast ultrasonographic images obtained from 363 patients. It included 150 malignant (103 invasive and 47 noninvasive carcinomas) and 213 benign masses (87 cysts and 126 fibroadenomas). We divided our database into 65 images (28 malignant and 37 benign masses) for training set and 298 images (122 malignant and 176 benign masses) for test set. An observer study was first conducted to obtain clinicians’ subjective impression for nine image features on mass. In the proposed method, location and area of the mass were determined by an experienced clinician. We defined some feature extraction methods for each of nine image features. For each image feature, we selected the feature extraction method with the highest correlation coefficient between the objective features and the average clinicians’ subjective impressions. We employed multiple discriminant analysis with the nine objective features for determining histological classification of mass. The classification accuracies of the proposed method were 88.4 % (76/86) for invasive carcinomas, 80.6 % (29/36) for noninvasive carcinomas, 86.0 % (92/107) for fibroadenomas, and 84.1 % (58/69) for cysts, respectively. The proposed method would be useful in the differential diagnosis of breast masses on ultrasonographic images as diagnosis aid.     

Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses on Breast DCE-MRI Images Using Deep Convolutional Neural Network with Bayesian Optimization

Although magnetic resonance imaging (MRI) has a higher sensitivity of early breast cancer than mammography, the specificity is lower. The purpose of this study was to develop a computer-aided diagnosis (CAD) scheme for distinguishing between benign and malignant breast masses on dynamic contrast material-enhanced MRI (DCE-MRI) by using a deep convolutional neural network (DCNN) with Bayesian optimization. Our database consisted of 56 DCE-MRI examinations for 56 patients, each of which contained five sequential phase images. It included 26 benign and 30 malignant masses. In this study, we first determined a baseline DCNN model from well-known DCNN models in terms of classification performance. The optimum architecture of the DCNN model was determined by changing the hyperparameters of the baseline DCNN model such as the number of layers, the filter size, and the number of filters using Bayesian optimization. As the input of the proposed DCNN model, rectangular regions of interest which include an entire mass were selected from each of DCE-MRI images by an experienced radiologist. Three-fold cross validation method was used for training and testing of the proposed DCNN model. The classification accuracy, the sensitivity, the specificity, the positive predictive value, and the negative predictive value were 92.9% (52/56), 93.3% (28/30), 92.3% (24/26), 93.3% (28/30), and 92.3% (24/26), respectively. These results were substantially greater than those with the conventional method based on handcrafted features and a classifier. The proposed DCNN model achieved high classification performance and would be useful in differential diagnoses of masses in breast DCE-MRI images as a diagnostic aid.     

Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images

Prompt and widely available diagnostics of breast cancer is crucial for the prognosis of patients. One of the diagnostic methods is the analysis of cytological material from the breast. This examination requires extensive knowledge and experience of the cytologist. Computer-aided diagnosis can speed up the diagnostic process and allow for large-scale screening. One of the largest challenges in the automatic analysis of cytological images is the segmentation of nuclei. In this study, four different clustering algorithms are tested and compared in the task of fast nuclei segmentation. K-means, fuzzy C-means, competitive learning neural networks and Gaussian mixture models were incorporated for clustering in the color space along with adaptive thresholding in grayscale. These methods were applied in a medical decision support system for breast cancer diagnosis, where the cases were classified as either benign or malignant. In the segmented nuclei, 42 morphological, topological and texture features were extracted. Then, these features were used in a classification procedure with three different classifiers. The system was tested for classification accuracy by means of microscopic images of fine needle breast biopsies. In cooperation with the Regional Hospital in Zielona Góra, 500 real case medical images from 50 patients were collected. The acquired classification accuracy was approximately 96–100%, which is very promising and shows that the presented method ensures accurate and objective data acquisition that could be used to facilitate breast cancer diagnosis.     

Cystic breast lesions by conventional ultrasonography: sonographic subtype-pathologic correlation and BI-RADS Assessment

Introduction

Appropriate categorization is very important because the clinical management of each subtype of cystic breast lesions (CBLs) differs. The purpose was to evaluate the sonographic subtype-pathologic correlation, and to identify the effectiveness of the Breast Imaging Reporting and Data System (BI-RADS)-ultrasound (US) for differentiation of benign and malignant CBLs.

Material and methods

A database from December 1, 2007 and November 30, 2009 was identified in the Department of Diagnostic Ultrasound, Rui Jin Hospital, School of Medicine, Shanghai Jiao Tong University, China. Those patients with palpable or clinical symptomatic breast masses were associated with a cystic component in lesions on breast US. All patients underwent a subsequent fine-needle/core-needle biopsy or surgical excision. The sonographic findings were analyzed according to the BI-RADS-US, and were categorized by two different methods of subtype categorization compared with the pathologic results.

Results

Ninety-nine breast cystic lesions in 83 women were included, among whom 16 patients were identified with bilateral cystic lesions. The total malignancy rate of CBLs was 14.1% (95% confidence interval 7.3–21.0%). Among 99 CBLs, 14 malignant lesions were associated with sonographic appearances of complex cystic lesions, while the remaining subtypes were benign. Shape, margin, echo pattern, orientation, calcification, and vascularity were statistically significantly different between the benign and malignant lesions (p = 0.010, p = 0.004, p < 0.001, p < 0.001, p = 0.036, and p < 0.001, respectively) (degrees of freedom = 1).

Conclusions

By comparison of the two different methods of subtype categorization of CBLs, the appropriate 5-variety classification should be suggested. The BI-RADS-US was useful for differentiating benign from malignant cystic lesions.     

DeepCAT: Deep Computer-Aided Triage of Screening Mammography

Although much deep learning research has focused on mammographic detection of breast cancer, relatively little attention has been paid to mammography triage for radiologist review. The purpose of this study was to develop and test DeepCAT, a deep learning system for mammography triage based on suspicion of cancer. Specifically, we evaluate DeepCAT’s ability to provide two augmentations to radiologists: (1) discarding images unlikely to have cancer from radiologist review and (2) prioritization of images likely to contain cancer. We used 1878 2D-mammographic images (CC & MLO) from the Digital Database for Screening Mammography to develop DeepCAT, a deep learning triage system composed of 2 components: (1) mammogram classifier cascade and (2) mass detector, which are combined to generate an overall priority score. This priority score is used to order images for radiologist review. Of 595 testing images, DeepCAT recommended low priority for 315 images (53%), of which none contained a malignant mass. In evaluation of prioritizing images according to likelihood of containing cancer, DeepCAT’s study ordering required an average of 26 adjacent swaps to obtain perfect review order. Our results suggest that DeepCAT could substantially increase efficiency for breast imagers and effectively triage review of mammograms with malignant masses.     

Negative prognostic value of intra-ductal fat infiltrate in breast cancer

BackgroundRecent studies showed a correlation between Body Mass Index and both breast cancer occurrence and progression. Nevertheless, no study reported an accurate evaluation of intra-ductal fat infiltrate. Therefore, the main aim of this study was to evaluate the putative association between intra-ductal fat infiltrate (IDFi) and breast cancer subtypes by using digital pathology.MethodsWe retrospectively collected 220 breast biopsies. Paraffin serial sections were used for haematoxylin and eosin staining and immunohistochemical evaluation of the following markers: estrogen receptor (ER), progesterone receptor (PR), Ki67 and c-erb2. Three haematoxylin and eosin sections for each paraffin block were digitalized. Digital slides were used to evaluate the areas of IDFi. Five randomized areas were evaluated for each slide. By using GraphPad software IDFi areas was correlated with a) breast cancer histotype, b) presence of microcalcifications and c) biomarkers expression.ResultsBreast biopsies were classified as follow: 20 normal breast, 50 benign lesions, and 150 malignant lesions (85 ductal in situ carcinomas; 65 ductal infiltrating carcinomas). Statistical analysis showed a significant increase of IDFi in malignant lesions as compared to both normal breast and benign lesions. We noted higher IDFi in breast ductal carcinomas as compared to lobular lesions. Significant differences were observed between breast lesions with microcalcifications respect to lesions without calcifications. Noteworthy, we also found a positive association between IDFi and the expression of both ER and Ki67.ConclusionResults of our study highlighted the possible role of fat in breast cancer progression suggesting a negative prognostic value of IDFi.     

Boundary modelling and shape analysis methods for classification of mammographic masses

The problem of computer-aided classification of benign and malignant breast masses using shape features is addressed. The aim of the study is to look at the exceptions in shapes of masses such as circumscribed malignant tumours and spiculated benign masses which are difficult to classify correctly using common shape analysis methods. The proposed methods of shape analysis treat the object's boundary in terms of local details. The boundaries of masses analysed using the proposed methods were manually drawn on mammographic images by an expert radiologist (JELD). A boundary segmentation method is used to separate major portions of the boundary and to label them as concave or convex segments. To analyse the shape information localised in each segment, features are computed through an iterative procedure for polygonal modelling of the mass boundaries. Features are based on the concavity fraction of a mass boundary and the degree of narrowness of spicules as characterised by a spiculation index. Two features comprising spiculation index (SI) and fractional concavity (f_cc) developed in the present study when used in combination with the global shape feature of compactness resulted in a benign/malignant classification accuracy of 82%, with an area (A_z) of 0.79 under the receiver operating characteristics (ROC) curve with a database of the boundaries of 28 benign masses and 26 malignant tumours. SI alone resulted in a classification accuracy of 80% with A_z of 0.82. The combination of all the three features achieved 91% accuracy of circumscribed versus spiculated classification of masses based on shape.     

基于双分支多视角深度自注意力编码器的乳腺肿瘤分类方法

自动乳腺全容积超声成像(ABVS)系统因其高效、无辐射等特性成为筛查乳腺癌的重要方式。针对ABVS图像进行计算机辅助乳腺肿瘤良恶性分类的研究,有利于帮助临床医生准确、快速地进行乳腺癌的诊断,甚至可辅助提高低年资医生的诊断水平。ABVS系统产生的三维乳腺图像数据量较大,造成常规的深度学习方式训练时间长、占用资源巨大。本研究设计了一种基于ABVS数据的多视角图像提取方式,替代常规的三维数据输入,在降低参数量的同时弥补二维深度学习中的空间关联性;其次,基于交叉视角图像的空间位置关系,提出一种深度自注意力编码器(Transformer)网络,用于获得图像的有效特征表达。实验是基于自有ABVS数据库的153例容积图像,良恶性分类的准确率为86.88%,F1-评分为81.70%,AUC达到0.831 6。所提出的方法有望应用于ABVS图像的乳腺肿瘤良恶性筛查。     

“Hippocrates-mst”: a prototype for computer-aided microcalcification analysis and risk assessment for breast cancer

One of the most common cancer types among women is breast cancer. Regular mammographic examinations increase the possibility for early diagnosis and treatment and significantly improve the chance of survival for patients with breast cancer. Clustered microcalcifications have been considered as important indicators of the presence of breast cancer. We present “Hippocrates-mst”, a prototype system for computer-aided risk assessment of breast cancer. Our research has been focused in developing software to locate microcalcifications on X-ray mammography images, quantify their critical features and classify them according to their probability of being cancerous. A total of 260 cases (187 benign and 73 malignant) have been examined and the performance of the prototype is presented through receiver operating characteristic (ROC) analysis. The system is showing high levels of sensitivity identifying correctly 98.63% of malignant cases.     

Dynamic infrared imaging in identification of breast cancer tissue with combined image processing and frequency analysis

Five combinations of image-processing algorithms were applied to dynamic infrared (IR) images of six breast cancer patients preoperatively to establish optimal enhancement of cancer tissue before frequency analysis. mid-wave photovoltaic (PV) IR cameras with 320×254 and 640×512 pixels were used. The signal-to-noise ratio and the specificity for breast cancer were evaluated with the image-processing combinations from the image series of each patient. Before image processing and frequency analysis the effect of patient movement was minimized with a stabilization program developed and tested in the study by stabilizing image slices using surface markers set as measurement points on the skin of the imaged breast. A mathematical equation for superiority value was developed for comparison of the key ratios of the image-processing combinations. The ability of each combination to locate the mammography finding of breast cancer in each patient was compared. Our results show that data collected with a 640×512-pixel mid-wave PV camera applying image-processing methods optimizing signal-to-noise ratio, morphological image processing and linear image restoration before frequency analysis possess the greatest superiority value, showing the cancer area most clearly also in the match centre of the mammography estimation.     

CD44+/CD24− Cells Are Transit Progenitors and Do Not Determine the Molecular Subtypes and Clinical Parameters in Breast Carcinomas

CD44⁺/CD24^? cells have been associated with breast cancer stem/progenitor cell features. However, the status of this phenotype cells in normal, benign and malignant breast tissues has not been studied, and the clinical correlation of this subpopulation in breast cancer is not fully understood. The present study sought to identify these cells in a series of normal, benign, and malignant breast tissues and explore their correlation to the molecular subtypes of breast carcinoma and conventional pathological features. Double-staining immunohistochemistry (DIHC) of CD44 and CD24 was performed on 30 normal breast tissues, 30 breast fibroadenomas (FA), 60 breast invasive ductal carcinomas (IDC), and 3 breast cancer cell lines (MCF-7, MDA-MB-468, and MDA-MB-231). In the normal breast tissues and FAs, three phenotypes were observed including CD44⁺/CD24⁺, CD44⁺/CD24^?, and CD44^?/CD24^? cells. In the IDCs, CD44^?/CD24⁺ cells were detected, in addition to the three aforementioned phenotypes. The strong positive rate (+++, incidence >60%) of CD44⁺/CD24^? was significantly increased from normal breast tissue, FAs to IDCs (0.0%→6.7%→21.7%). However, the CD44⁺/CD24^? cells didn’t correlate with ages of patients, lymph node metastasis, tumor size, molecular subtypes, and the expression of ER, PR, HER-2, PS2, Bcl-2, nm23. The proportion of CD44⁺/CD24^? cells in MCF-7, MDA-MB-468, and MDA-MB-231 was about 1, 5, and 80%, respectively. The results indicate that the CD44⁺/CD24^? cells are transit progenitors and have no association with the molecular subtypes and clinicopathological parameters in the IDCs.     

Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses in Breast DCE-MRI

Our purpose in this study was to develop a computer-aided diagnosis (CAD) scheme for distinguishing between benign and malignant breast masses in dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI). Our database consisted 90 DCE-MRI examinations, each of which contained four sequential phase images; this database included 28 benign masses and 62 malignant masses. In our CAD scheme, we first determined 11 objective features of masses by taking into account the image features and the dynamic changes in signal intensity that experienced radiologists commonly use for describing masses in DCE-MRI. Quadratic discriminant analysis (QDA) was employed to distinguish between benign and malignant masses. As the input of the QDA, a combination of four objective features was determined among the 11 objective features according to a stepwise method. These objective features were as follows: (i) the change in signal intensity from 2 to 5 min; (ii) the change in signal intensity from 0 to 2 min; (iii) the irregularity of the shape; and (iv) the smoothness of the margin. Using this approach, the classification accuracy, sensitivity, and specificity were shown to be 85.6 % (77 of 90), 87.1 % (54 of 62), and 82.1 % (23 of 28), respectively. Furthermore, the positive and negative predictive values were 91.5 % (54 of 59) and 74.2 % (23 of 31), respectively. Our CAD scheme therefore exhibits high classification accuracy and is useful in the differential diagnosis of masses in DCE-MRI images.     

Typing and grading breast carcinoma on fine-needle aspiration: Is this clinically useful information?

The ability of fine-needle aspiration (FNA) to diagnose breast cancer is beyond question. The established role of cytopathology is to maintain a low benign to malignant biopsy ratio by reducing the number of benign lesions excised. Both typing and grading of breast cancers on FNA have received attention in the cytology literature but how this knowledge can influence management has not been fully explored. Recently we described a method for the cytological grading of breast cancer that compares well with the established Bloom and Richardson grades. In this paper we present our experience of 1,387 breast cancer FNAs reported by us with histological verification. We show that cytologically typing and grading breast cancers are valid exercises that can predict the true nature of the neoplasm. This information may assist in the clinical approach to the malignant breast. © 1995 Wiley-Liss, Inc.     

A quantifier-based fuzzy classification system for breast cancer patients

ObjectivesRecent studies of breast cancer data have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a range of different clustering techniques. Consensus between unsupervised classification algorithms has been successfully used to categorise patients into these specific groups, but often at the expenses of not classifying the whole set. It is known that fuzzy methodologies can provide linguistic based classification rules. The objective of this study was to investigate the use of fuzzy methodologies to create an easy to interpret set of classification rules, capable of placing the large majority of patients into one of the specified groups.Materials and methodsIn this paper, we extend a data-driven fuzzy rule-based system for classification purposes (called ‘fuzzy quantification subsethood-based algorithm’) and combine it with a novel class assignment procedure. The whole approach is then applied to a well characterised breast cancer dataset consisting of ten protein markers for over 1000 patients to refine previously identified groups and to present clinicians with a linguistic ruleset. A range of statistical approaches was used to compare the obtained classes to previously obtained groupings and to assess the proportion of unclassified patients.ResultsA rule set was obtained from the algorithm which features one classification rule per class, using labels of High, Low or Omit for each biomarker, to determine the most appropriate class for each patient. When applied to the whole set of patients, the distribution of the obtained classes had an agreement of 0.9 when assessed using Kendall's Tau with the original reference class distribution. In doing so, only 38 patients out of 1073 remain unclassified, representing a more clinically usable class assignment algorithm.ConclusionThe fuzzy algorithm provides a simple to interpret, linguistic rule set which classifies over 95% of breast cancer patients into one of seven clinical groups.     

HPD overexpression predicts poor prognosis in breast cancer

BackgroundThe enzyme, 4-hydroxyphenylpyruvate dioxygenase (HPD), is critical to tyrosine metabolism; its deficiency can cause tyrosinemia. However, its precise contribution to tumorigenesis is unclear. Here, we investigated the correlation between HPD expression and prognosis in patients with breast cancer.Methods145 breast cancer specimens were selected to analyze HPD protein expression by immunohistochemistry and evaluate its relationship to patients’ clinicopathological features. HPD localization was confirmed in MCF-7 and MDA-MB-231 breast cancer cells, using immunofluorescence staining. The expression of HPD protein was detected in breast cancer and cancer-adjacent normal tissues using Western blot analysis. Survival rates were calculated by the Kaplan–Meier method.ResultsWe found that HPD protein was mainly located in the cytoplasm/nucleoli/perinucleus in breast cancer cells, as shown by immunofluorescence staining in MCF-7 and MDA-MB-231 cells, and immunohistochemistry in breast cancer and adjacent normal tissues (HPD protein expression—breast cancer: 46.9% [68/145], ductal carcinoma in situ [DCIS]: 22.6% [12/53], and normal tissues: only 4.8% [2/42]). Similarly, the Western blot results further confirmed the increased expression of HPD in breast cancer compared with cancer-adjacent normal tissues (P < 0.05). HPD expression level was positively correlated with histological grade and clinical stage, and inversely correlated with 10-year overall survival (OS) rates, in patients with breast cancer. Among patients with breast cancer, those with high HPD expression had worse OS rates than those with low HPD expression. Additionally, when patients were subgrouped by disease stage or grade, those with high HPD expression had worse OS rates than those with low HPD expression for each respective stage or grade.ConclusionsOur findings indicate that HPD may be a useful prognostic predictor, and a potential therapeutic target for patients with breast cancer.     

Development of a stiffness measurement accessory for ultrasound in breast cancer diagnosis

When ultrasound imaging is used for breast cancer diagnosis, the lesion's morphology is usually used to determine if the lesion is benign or malignant. Sometimes, the information provided by the procedure may not be adequate to make an accurate judgment. Additional information is needed, such as the stiffness of the lesion relative to its surrounding tissue. This paper presents an ultrasound accessory device designed to achieve this purpose. The device is easy to operate and is similar in use to a normal clinical breast ultrasound examination. A sonologist must only attach an ultrasound probe to the device and then slide it across the lesion maintaining a constant compression depth. The built-in inverse biomechanical model of the device will then calculate the predicted stiffness ratio of the lesion relative to its surrounding tissue based on the measured palpation data. Modelling and experiments have been performed on phantoms with embedded inclusions. The experimental results show that the stiffness ratio of the inclusion to its surrounding material can be accurately predicted by the handheld device. A preliminary clinical test was also performed to demonstrate the use of this device in vivo, to offer additional information to aid classification of the tumor as benign or malignant when complemented with ultrasound images.     

A Comprehensive Methodology for Determining the Most Informative Mammographic Features

This study aims to determine the most informative mammographic features for breast cancer diagnosis using mutual information (MI) analysis. Our Health Insurance Portability and Accountability Act-approved database consists of 44,397 consecutive structured mammography reports for 20,375 patients collected from 2005 to 2008. The reports include demographic risk factors (age, family and personal history of breast cancer, and use of hormone therapy) and mammographic features from the Breast Imaging Reporting and Data System lexicon. We calculated MI using Shannon’s entropy measure for each feature with respect to the outcome (benign/malignant using a cancer registry match as reference standard). In order to evaluate the validity of the MI rankings of features, we trained and tested naïve Bayes classifiers on the feature with tenfold cross-validation, and measured the predictive ability using area under the ROC curve (AUC). We used a bootstrapping approach to assess the distributional properties of our estimates, and the DeLong method to compare AUC. Based on MI, we found that mass margins and mass shape were the most informative features for breast cancer diagnosis. Calcification morphology, mass density, and calcification distribution provided predictive information for distinguishing benign and malignant breast findings. Breast composition, associated findings, and special cases provided little information in this task. We also found that the rankings of mammographic features with MI and AUC were generally consistent. MI analysis provides a framework to determine the value of different mammographic features in the pursuit of optimal (i.e., accurate and efficient) breast cancer diagnosis.     

Understanding clinical communication about mood disturbance symptoms among breast cancer patients: A mixed methods analysis

ObjectivesWe aimed to characterize the relationships between breast cancer patient mood symptom severity and demographic/medical factors with clinical communication about mood, and to explore mood discussion content.Methods134 breast cancer patients (mean age=58.3; 14% minority; 13% metastatic) had oncology clinic visits audio-recorded, transcribed, and coded for mood communication. Patient Care Monitor assessed mood symptoms (anxiety/depression presence/severity). Logistic regressions measured associations between mood, demographic/medical factors, and communication. Thematic analysis characterized discussion topics.ResultsOver half of patients (55%; n = 73) reported mood symptoms. Worse mood symptoms were associated with younger age and current treatment (p’s < 0.05). 19% of clinic visits (n = 26/134) contained mood discussions. Discussions were more common for younger women and those with non-metastatic disease (p’s < 0.05). Odds of discussing mood increased with symptom severity (OR=4.52, p = 0.018). Cancer-related anxiety and medication management were among the most common topics discussed.ConclusionsCommunication about mood occurred infrequently, with women currently undergoing treatment, with metastatic disease, or with mild mood symptoms at potentially increased risk for inadequate discussion. Both patient-focused and provider-focused interventions to improve clinical communication about mood symptoms could be beneficial.Practice implicationsClinicians hold a key role in supporting cancer patients’ well-being by using and encouraging effective communication about patients’ mood.