Atypical architectural distortion detection in digital breast tomosynthesis: a computer-aided detection model with adaptive receptive field

Combination of Deep Learning Grad-CAM and Radiomics for Automatic Localization and Diagnosis of Architectural Distortion on DBT

RATIONALE AND OBJECTIVES

Detection and diagnosis of architectural distortion (AD) on digital breast tomosynthesis (DBT) is challenging. This study applied artificial intelligence (AI) using deep learning (DL) algorithms to detect AD, followed by radiomics for classification.

MATERIALS AND METHODS

500 cases with AD on DBT reports were identified; the earlier 292 cases for training, and the later 208 cases for testing. The DL Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to automatically localize abnormalities and generate a region of interest (ROI), which was put into the radiomics model to estimate the malignancy probability for constructing ROC curves. Radiologists delineated ROI manually for comparison. Cases were categorized into pure AD and AD associated with other features, including mass, regional high-density, and calcifications. The ROC curves were compared using the DeLong test.

RESULTS

The overall malignancy rate was 57% (285/500). Of them, 267 cases were classified as pure AD, and the malignancy rate (106/267 = 39.7%) was significantly lower compared to AD cases associated with other features (179/233 = 76.8%, p < 0.01). In the testing set, the diagnostic AUC was 0.82 when using the manual ROI and 0.84 when using the DL-generated ROI. In the more challenging pure AD cases, DL-generated ROI yielded an AUC of 0.77, significantly lower than 0.86 for AD associated with other features.

CONCLUSION

DL could detect AD on DBT, and the diagnostic performance was comparable to manual ROI. The strategy worked for pure AD, but the performance was worse than that for AD with other features.

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.

Atypical architectural distortion detection in digital breast tomosynthesis: a multi-view computer-aided detection model with ipsilateral learning

Objective.Breast architectural distortion (AD), a common imaging symptom of breast cancer, is associated with a particularly high rate of missed clinical detection. In clinical practice, atypical ADs that lack an obvious radiating appearance constitute most cases, and detection models based on single-view images often exhibit poor performance in detecting such ADs. Existing multi-view deep learning methods have overlooked the correspondence between anatomical structures across different views.Approach.To develop a computer-aided detection (CADe) model for AD detection that effectively utilizes the craniocaudal (CC) and mediolateral oblique (MLO) views of digital breast tomosynthesis (DBT) images, we proposed an anatomic-structure-based multi-view information fusion approach by leveraging the related anatomical structure information between these ipsilateral views. To obtain a representation that can effectively capture the similarity between ADs in images from ipsilateral views, our approach utilizes a Siamese network architecture to extract and compare information from both views. Additionally, we employed a triplet module that utilizes the anatomical structural relationship between the ipsilateral views as supervision information.Main results.Our method achieved a mean true positive fraction (MTPF) of 0.05-2.0, false positives (FPs) per volume of 64.40%, and a number of FPs at 80% sensitivity (FPs@0.8) of 3.5754; this indicates a 6% improvement in MPTF and 16% reduction in FPs@0.8 compared to the state-of-the-art baseline model.Significance.From our experimental results, it can be observed that the anatomic-structure-based fusion of ipsilateral view information contributes significantly to the improvement of CADe model performance for atypical AD detection based on DBT. The proposed approach has the potential to lead to earlier diagnosis and better patient outcomes.