Measures of divergence of oriented patterns for the detection of architectural distortion in prior mammograms

A Novel Structure Fusion Attention Model to Detect Architectural Distortion on Mammography

Architectural distortion (AD) is one of the most common findings on mammograms, and it may represent not only cancer but also a lesion such as a radial scar that may have an associated cancer. AD accounts for 18-45% missed cancer, and the positive predictive value of AD is approximately 74.5%. Early detection of AD leads to early diagnosis and treatment of the cancer and improves the overall prognosis. However, detection of AD is a challenging task. In this work, we propose a new approach for detecting architectural distortion in mammography images by combining preprocessing methods and a novel structure fusion attention model. The proposed structure-focused weighted orientation preprocessing method is composed of the original image, the architecture enhancement map, and the weighted orientation map, highlighting suspicious AD locations. The proposed structure fusion attention model captures the information from different channels and outperforms other models in terms of false positives and top sensitivity, which refers to the maximum sensitivity that a model can achieve under the acceptance of the highest number of false positives, reaching 0.92 top sensitivity with only 0.6590 false positive per image. The findings suggest that the combination of preprocessing methods and a novel network architecture can lead to more accurate and reliable AD detection. Overall, the proposed approach offers a novel perspective on detecting ADs, and we believe that our method can be applied to clinical settings in the future, assisting radiologists in the early detection of ADs from mammography, ultimately leading to early treatment of breast cancer patients.

A Novel Fusion-Based Texture Descriptor to Improve the Detection of Architectural Distortion in Digital Mammography

Architectural distortion (AD) is the earliest sign of breast cancer that can be detected on a mammogram, and it is usually associated with malignant tumors. Breast cancer is one of the major causes of death among women, and the chance of cure can increase significantly when detected early. Computer-aided detection (CAD) systems have been used in clinical practice to assist radiologists with the task of detecting breast lesions. However, due to the complexity and subtlety of AD, its detection is still a challenge, even with the assistance of CAD. Recently, the fusion of descriptors has become a trend for improving the performance of computer vision algorithms. In this work, we evaluated some local texture descriptors and their possible combinations, considering different fusion approaches, for application in CAD systems to improve AD detection. In addition, we present a novel fusion-based texture descriptor, the Completed Mean Local Mapped Pattern (CMLMP), which is based on complementary information between three LMP operators (signal, magnitude and center) and the local differences between pixel values and the mean value of a neighborhood. We compared the performance of the proposed descriptor with two other well-known descriptors: the Completed Local Binary Pattern (CLBP) and the Completed Local Mapped Pattern (CLMP), for the task of detecting AD in 350 digital mammography clinical images. The results showed that the descriptor proposed in this work outperforms the others, for both individual and fused approaches. Moreover, the choice of the fusion operator is crucial because it results in different detection performances.

Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI

Background

The purpose of this study was to investigate the value of wavelet-transformed radiomic MRI in predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) for patients with locally advanced breast cancer (LABC).

Methods

Fifty-five female patients with LABC who underwent contrast-enhanced MRI (CE-MRI) examination prior to NAC were collected for the retrospective study. According to the pathological assessment after NAC, patient responses to NAC were categorized into pCR and non-pCR. Three groups of radiomic textures were calculated in the segmented lesions, including (1) volumetric textures, (2) peripheral textures, and (3) wavelet-transformed textures. Six models for the prediction of pCR were Model I: group (1), Model II: group (1) + (2), Model III: group (3), Model IV: group (1) + (3), Model V: group (2) + (3), and Model VI: group (1) + (2) + (3). The performance of predicting models was compared using the area under the receiver operating characteristic (ROC) curves (AUC).

Results

The AUCs of the six models for the prediction of pCR were 0.816 ± 0.033 (Model I), 0.823 ± 0.020 (Model II), 0.888 ± 0.025 (Model III), 0.876 ± 0.015 (Model IV), 0.885 ± 0.030 (Model V), and 0.874 ± 0.019 (Model VI). The performance of four models with wavelet-transformed textures (Models III, IV, V, and VI) was significantly better than those without wavelet-transformed textures (Model I and II). In addition, the inclusion of volumetric textures or peripheral textures or both did not result in any improvements in performance.

Conclusions

Wavelet-transformed textures outperformed volumetric and/or peripheral textures in the radiomic MRI prediction of pCR to NAC for patients with LABC, which can potentially serve as a surrogate biomarker for the prediction of the response of LABC to NAC.

A New Method for Detecting Architectural Distortion in Mammograms by NonSubsampled Contourlet Transform and Improved PCNN

Breast cancer is the leading cause of cancer death in women, and early detection can reduce mortality. Architectural distortion (AD) is a feature of clinical manifestations for breast cancer, however, due to its complex structure and low detection accuracy, which cause a high mortality of breast cancer. In order to improve the accuracy of AD detection and reduce the mortality of breast cancer, this paper proposes a new method by combining the non-subsampled contourlet transform (NSCT) with the improved pulse coupled neural network (PCNN). Firstly, the top–bottom hat transformation and the exponential transformation are employed to enhance the image. Secondly, the NSCT is employed to expand the overall contrast of the mammograms and filter out the noise. Finally, the improved PCNN by the maximum inter-class variance threshold selection method is employed to complete the AD detection. This proposed approach is tested on the public and authoritative database—Digital Database for Screening Mammography (DDSM). The specificity of the method is 98.73%, the accuracy is 93.16%, and the F1-score is 79.80%, and the area under curve (AUC) of the receiver operating characteristic (ROC) curve is 0.93, these results clearly demonstrate that the proposed method is comparable with those methods in recent literatures. This proposed method is simple, furthermore it can achieve high accuracy and help doctors to perform computer-aided detection of AD effectively.

A complementary scheme for automated detection of high-uptake regions on dedicated breast PET and whole-body PET/CT

In this study, we aimed to develop a hybrid method for automated detection of high-uptake regions in the breast and axilla using dedicated breast positron-emission tomography (db PET) and whole-body PET/computed tomography (CT) images. In our proposed method, high-uptake regions in the breast and axilla were detected using db PET images and whole-body PET/CT images. In db PET images, high-uptake regions in the breast were detected using adaptive thresholding technique based on the noise characteristics. In whole-body PET/CT images, the region of the breast that includes the axilla was first extracted using CT images. Next, high-uptake regions in the extracted breast region were detected on the PET images. By integration of the results of the two types of PET images, a final candidate region was obtained. In the experiments, the accuracy of extracting the region of the breast and detection ability was evaluated using clinical data. As a result, all breast regions were extracted correctly. The sensitivity of detection was 0.765, and the number of false positive cases were 1.8, which was 30% better than those on whole-body PET/CT alone. These results suggested that the proposed method, combining the two types of PET images is effective for improving detection performance.

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

Background

In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).

Methods

A total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR⁺, HER2⁻) and triple-negative or HER2⁺ (TN/HER2⁺) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance.

Results

Among all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR⁺, HER2⁻ group using DLDA and 0.93 ± 0.018 within the TN/HER2⁺ group using a naive Bayes classifier. In HR⁺, HER2⁻ breast cancers, non-pCR was characterized by elevated peritumoral heterogeneity during initial contrast enhancement. However, TN/HER2⁺ tumors were best characterized by a speckled enhancement pattern within the peritumoral region of nonresponders. Radiomic features were found to strongly predict pCR independent of choice of classifier, suggesting their robustness as response predictors.

Conclusions

Through a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-017-0846-1) contains supplementary material, which is available to authorized users.

An automated method for detecting architectural distortions on mammograms using direction analysis of linear structures

Architectural distortion is one of the most important findings when evaluating mammograms for breast cancer. Abnormal breast architecture is characterized by the presence of spicules, which are distorted mammary structures that are not accompanied by an increased density or mass. We have been developing an automated method for detecting spiculated architectural distortions by analyzing linear structures extracted by normal curvature. However, some structures that are possibly related to distorted areas are not extracted using this method. The purpose of this study was to develop a new automated method for direction analysis of linear structures to improve detection performance in mammography. The direction of linear structures in each region of interest (ROI) was first determined using a direction filter and a background filter that can define one of eight directions (0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, and 157.5°). The concentration and isotropic indexes were calculated using the determined direction of the linear structures in order to extract the candidate areas. Discriminant analysis was performed to eliminate false positives results. Our database consisted of 168 abnormal images containing 174 distorted areas and 580 normal images. The sensitivity of the new method was 81%. There were 2.6 and 4.2 false positives per image using the new and previous methods, respectively. These findings show that our new method is effective for detecting spiculated architectural distortions.

Detection of architectural distortion in prior mammograms via analysis of oriented patterns

We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion. Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.