Prediction of Near-Term Breast Cancer Occurrence using Subtraction of Temporally Sequential Digital Mammograms.

Breast cancer remains one of the leading cancers for women worldwide. Fortunately, with the introduction of mammography, the mortality rate has significantly decreased. However, earlier breast cancer prediction could effectively increase the survival rates, improve patient outcomes, and avoid unnecessary biopsies. For that purpose, prediction of breast cancer, using subtraction of temporally sequential digital mammograms and machine learning, is proposed. A new dataset was collected with 192 images from 32 patients (three screening rounds, with two views of each breast). This dataset included precise annotation of each individual malignant mass, present in the most recent mammogram, with the two priors being radiologically evaluated as normal. The most recent mammogram was considered as the "future" screening round and provided the location of the mass as the ground truth for the training. The two previous mammograms, the "current" and the "prior", were processed and a new, difference image was formed for the prediction. Ninety-six features were extracted and five feature selection algorithms were combined to identify the most important features. Ten classifiers were tested in leave-one-patient-out and k-fold-patient cross-validation (k = 4 and 8). Ensemble Voting achieved the highest performance in the prediction of the development of breast mass in the next screening round, with 85.7% sensitivity, 83.7% specificity, 83.7% accuracy and 0.85 AUC. The proposed methodology could lead to a new mammography-based model that could predict the short-term risk for developing a malignancy, thus providing an earlier diagnosis.

Computer-aided breast cancer detection and classification in mammography: A comprehensive review.

Cancer is the second cause of mortality worldwide and it has been identified as a perilous disease. Breast cancer accounts for ∼20% of all new cancer cases worldwide, making it a major cause of morbidity and mortality. Mammography is an effective screening tool for the early detection and management of breast cancer. However, the identification and interpretation of breast lesions is challenging even for expert radiologists. For that reason, several Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists to accurately detect and/or classify breast cancer. This review examines the recent literature on the automatic detection and/or classification of breast cancer in mammograms, using both conventional feature-based machine learning and deep learning algorithms. The review begins with a comparison of algorithms developed specifically for the detection and/or classification of two types of breast abnormalities, micro-calcifications and masses, followed by the use of sequential mammograms for improving the performance of the algorithms. The available Food and Drug Administration (FDA) approved CAD systems related to triage and diagnosis of breast cancer in mammograms are subsequently presented. Finally, a description of the open access mammography datasets is provided and the potential opportunities for future work in this field are highlighted. The comprehensive review provided here can serve both as a thorough introduction to the field but also provide indicative directions to guide future applications.

A Review of Computer-Aided Breast Cancer Diagnosis Using Sequential Mammograms.

Radiologists assess the results of mammography, the key screening tool for the detection of breast cancer, to determine the presence of malignancy. They, routinely, compare recent and prior mammographic views to identify changes between the screenings. In case a new lesion appears in a mammogram, or a region is changing rapidly, it is more likely to be suspicious, compared to a lesion that remains unchanged and it is usually benign. However, visual evaluation of mammograms is challenging even for expert radiologists. For this reason, various Computer-Aided Diagnosis (CAD) algorithms are being developed to assist in the diagnosis of abnormal breast findings using mammograms. Most of the current CAD systems do so using only the most recent mammogram. This paper provides a review of the development of methods to emulate the radiological approach and perform automatic segmentation and/or classification of breast abnormalities using sequential mammogram pairs. It begins with demonstrating the importance of utilizing prior views in mammography, through the review of studies where the performance of expert and less-trained radiologists was compared. Following, image registration techniques and their application to mammography are presented. Subsequently, studies that implemented temporal analysis or subtraction of temporally sequential mammograms are summarized. Finally, a description of the open access mammography datasets is provided. This comprehensive review can serve as a thorough introduction to the use of prior information in breast cancer CAD systems but also provides indicative directions to guide future applications.

Automatic Breast Mass Segmentation and Classification Using Subtraction of Temporally Sequential Digital Mammograms.

OBJECTIVE: Cancer remains a major cause of morbidity and mortality globally, with 1 in 5 of all new cancers arising in the breast. The introduction of mammography for the radiological diagnosis of breast abnormalities, significantly decreased their mortality rates. Accurate detection and classification of breast masses in mammograms is especially challenging for various reasons, including low contrast and the normal variations of breast tissue density. Various Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists with the accurate classification of breast abnormalities. METHODS: In this study, subtraction of temporally sequential digital mammograms and machine learning are proposed for the automatic segmentation and classification of masses. The performance of the algorithm was evaluated on a dataset created especially for the purposes of this study, with 320 images from 80 patients (two time points and two views of each breast) with precisely annotated mass locations by two radiologists. RESULTS: Ninety-six features were extracted and ten classifiers were tested in a leave-one-patient-out and k-fold cross-validation process. Using Neural Networks, the detection of masses was 99.9% accurate. The classification accuracy of the masses as benign or suspicious increased from 92.6%, using the state-of-the-art temporal analysis, to 98%, using the proposed methodology. The improvement was statistically significant (p-value < 0.05). CONCLUSION: These results demonstrate the effectiveness of the subtraction of temporally consecutive mammograms for the diagnosis of breast masses. Clinical and Translational Impact Statement: The proposed algorithm has the potential to substantially contribute to the development of automated breast cancer Computer-Aided Diagnosis systems with significant impact on patient prognosis.

Identification and Classification of Benign and Malignant Masses based on Subtraction of Temporally Sequential Digital Mammograms.

Breast cancer remains the leading cause of cancer deaths and the second highest cause of death, in general, among women worldwide. Fortunately, over the last few decades, with the introduction of mammography, the mortality rate of breast cancer has significantly decreased. However, accurate classification of breast masses in mammograms is especially challenging. Various Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists with the accurate classification of breast abnormalities. In this study, classification of benign and malignant masses, based on the subtraction of temporally sequential digital mammograms and machine learning, is proposed. The performance of the algorithm was evaluated on a dataset created for the purposes of this study. In total, 196 images from 49 patients, with precisely annotated mass locations and biopsy confirmed malignant cases, were included. Ninety-six features were extracted and five feature selection algorithms were employed to identify the most important features. Ten classifiers were tested using leave-one-patient-out and 7-fold cross-validation. Neural Networks, achieved the highest classification performance with 90.85% accuracy and 0.91 AUC, an improvement compared to the state-of-the-art. These results demonstrate the effectiveness of the subtraction of temporally consecutive mammograms for the classification of breast masses as benign or malignant.

Digital subtraction of temporally sequential mammograms for improved detection and classification of microcalcifications.

BACKGROUND: Our aim was to demonstrate that automated detection and classification of breast microcalcifications, according to Breast Imaging Reporting and Data System (BI-RADS) categorisation, can be improved with the subtraction of sequential mammograms as opposed to using the most recent image only. METHODS: One hundred pairs of mammograms were retrospectively collected from two temporally sequential rounds. Fifty percent of the images included no (BI-RADS 1) or benign (BI-RADS 2) microcalcifications. The remaining exhibited suspicious findings (BI-RADS 4-5) in the recent image. Mammograms cannot be directly subtracted, due to tissue changes over time and breast deformation during mammography. To overcome this challenge, optimised preprocessing, image registration, and postprocessing procedures were developed. Machine learning techniques were employed to eliminate false positives (normal tissue misclassified as microcalcifications) and to classify the true microcalcifications as BI-RADS benign or suspicious. Ninety-six features were extracted and nine classifiers were evaluated with and without temporal subtraction. The performance was assessed by measuring sensitivity, specificity, accuracy, and area under the curve (AUC) at receiver operator characteristics analysis. RESULTS: Using temporal subtraction, the contrast ratio improved ~ 57 times compared to the most recent mammograms, enhancing the detection of the radiologic changes. Classifying as BI-RADS benign versus suspicious microcalcifications, resulted in 90.3% accuracy and 0.87 AUC, compared to 82.7% and 0.81 using just the most recent mammogram (p = 0.003). CONCLUSION: Compared to using the most recent mammogram alone, temporal subtraction is more effective in the microcalcifications detection and classification and may play a role in automated diagnosis systems.