This work was supported by grants from the Collaborative Research and Training Experience Programme (CREATE) and a Discovery Grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada.

The authors have nothing to disclose.

We have presented a series of sophisticated techniques of digital image processing and pattern recognition, also known as machine learning and CAD, for the detection of architectural distortion in prior mammograms of interval-cancer cases. The methods are based on analysis of the oriented textural patterns present in mammographic images. Our methods, including several more features proposed in our related works, are capable of detecting early signs of breast cancer 15 months ahead of the time of clinical diagnosis, on th...

Breast cancer is a major disease affecting women and is the second leading cause of cancer related death among women 1,2. In order to improve the chance of survival and the prognosis of the affected patients through effective treatment at early stages of breast cancer, the disease needs to be detected as early as possible. In retrospective analysis of cases of breast cancer, subtle signs of abnormalities have been observed on previously acquired screening mammograms 3,4. Architectural distortion is one such localized mammographic sign of possibly early stages of breast cancer that is difficult to detect 5,6. The associated patterns are vaguely described as distortion of the normal architecture of the breast with no definite mass visible. Architectural distortion could appear at the initial stages of the formation of a breast mass or tumor. We hypothesize that screening mammograms obtained prior to the detection of breast cancer could contain subtle signs of early stages of breast cancer, in particular, architectural distortion.
Figure 1a shows a prior mammographic image of a case of screen-detected cancer. The region of abnormality identified by a radiologist (J.E.L.D.) is outlined with a red rectangle. The prior mammogram was taken 24 months before the detection mammogram shown in Figure 1b. The prior mammogram had been declared to be free of signs of cancer at the original instance of screening. In retrospective analysis and in comparison with the detection mammogram, a suspicious region related to the site of cancer detected was labeled by the radiologist, and is outlined in red on the prior mammogram. The suspicious region contains signs of architectural distortion, including spicules.
Computer-aided diagnosis (CAD) techniques and systems offer the potential for achieving increased sensitivity in the detection of breast cancer 2,7-9. However, in comparison with the number of publications that exist in the literature on the detection of other signs of breast cancer, such as masses and calcifications, only a small number of studies have been reported on the detection of architectural distortion in the absence of a central mass 10-17. Commercially available CAD systems have been found to perform poorly in the detection of architectural distortion 18. Studies on the detection of architectural distortion in prior mammograms of screen-detected or interval-cancer cases 3,4,19-22 could help in developing strategies for the detection and treatment of breast diseases at their early stages, and lead to improvement in the prognosis for the patient 23.
Preparation of Images for the Experiment
Experiments were conducted with 158 mammographic images including 106 prior mammograms of 56 individuals diagnosed with breast cancer and 52 images of 13 normal individuals. Ethics approval for the study was obtained from the Conjoint Health Research Ethics Board, Office of Medical Bioethics, University of Calgary, and the Calgary Regional Health Authority. The images were obtained from Screen Test: Alberta Program for the Early Detection of Breast Cancer 21,24,25.
Mammograms acquired in the last scheduled visit to the screening program prior to the diagnosis of cancer outside the screening program were labeled as prior mammograms of interval-cancer cases. The corresponding diagnostic mammograms were not available. All but two of the 106 prior mammograms had been declared to be free of any sign of breast cancer at the time of their acquisition and analysis in the screening program; the individuals corresponding to the other two mammograms had been referred for biopsy. The time interval between the diagnosis of cancer and prior mammograms ranged from 1.5 months to 24.5 months, with an average of 15 months and standard deviation of 7 months. All of the prior mammograms of interval-cancer cases available in the database have been included in the present study, except six images in which no suspicious parts could be identified.
The screen-film mammograms were digitized at the spatial resolution of 50 &mu;m and gray-scale resolution of 12 bits per pixel using the Lumiscan 85 laser scanner (Lumisys, Sunnyvale, CA). An expert radiologist specialized in mammography (J.E.L.D.) reviewed all of the 106 prior mammograms of interval-cancer cases and marked the suspected regions of architectural distortion with rectangular boxes based on the reports available on subsequent imaging or biopsy, or by detailed inspection of the mammograms. Of the 106 prior mammographic images in the dataset used in the present study, 38 images have visible architectural distortion, and the remaining 68 images contain questionable or no clearly evident architectural distortion. Each prior mammogram contains a single site of architectural distortion as identified by the rectangular box drawn by the radiologist. The average width, height, and area of the 106 suspicious parts of images marked by the radiologist are 56 mm, 39 mm, and 2,274 mm2, with standard deviation of 11.8 mm, 11.6 mm, and 1073.9 mm2, respectively.

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.

The three features, namely, node value, FD, and HF , provided AUC values of 0.61, 0.59, and 0.64, respectively, when each feature was used on its own. Combined use of the three features provided improved performance with AUC = 0.70. The FROC curve obtained with the combination of the three features is shown in Figure 11, which indicates a sensitivity of 80% at 5.6 FPs/patient and 89% at 7.5 FPs/patient. Use of only the node value provided a sensitivity of 80% at 8.1 FPs/patient and 89...

Watch this Scientific Journal Video about Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns at JoVE.com

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

We demonstrate methods for the detection of architectural distortion in prior mammograms. Oriented structures are analyzed using Gabor filters and phase portraits to detect sites of radiating tissue patterns. Each site is characterized and classified using measures to represent spiculating patterns. The methods should assist in the detection of breast cancer.

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 ...

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42.5K Views.  University of Calgary. We demonstrate methods for the detection of architectural distortion in prior mammograms. Oriented structures are analyzed using Gabor filters and phase portraits to detect sites of radiating tissue patterns. Each site is characterized and classified using measures to represent spiculating patterns. The methods should assist in the detection of breast cancer.