Dr. Olivier Alonzo-Proulx Profile

Dr. Olivier Alonzo-Proulx

at Sunnybrook Research Institute

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Proceedings Article | 13 March 2019 Presentation + Paper

Use of convolutional neural networks to predict risk of masking by mammographic density

Theo Cleland, James Mainprize, Olivier Alonzo-Proulx, Jennifer Harvey, Roberta Jong, Anne Martel, Martin Yaffe

Proceedings Volume 10950, 109501X (2019) https://doi.org/10.1117/12.2513063

KEYWORDS: Mammography, Cancer, Breast, Convolutional neural networks, Medical imaging, Computer aided diagnosis and therapy, Analytical research

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Sensitivity of screening mammography is reduced by increased mammographic density (MD). MD can obscure or “mask” developing lesions making them harder to detect. Predicting masking risk may be an effective tool for a stratified screening program where selected women can receive alternative screening modalities that are less susceptible to masking. Here, we investigate whether the use of artificial intelligence can accurately predict the masking risk and compare its performance to that of conventional BI-RADS density classification. The analysis was based on mammograms of 214 subjects comprised of 147 women with a screen-detected (SD) or “non-masked” cancer and 67 that developed a non-screen detected (NSD) or presumably masked cancer within 2 years following a negative screen. Prior to analysis, mammograms were pre-processed into quantitative MD maps using an in-house algorithm. A transfer learning approach was used to train a convolutional neural network (CNN) based on VGG-16 in a seven cross-fold approach to classify masking status. A two-step transfer learning method was also used where the pre-trained CNN was initially trained on 5,865 mammograms to classify by BI-RADS density category and then trained for masking status. Using BI-RADS density as a masking risk predictor has an AUC of 0.64 [0.57 - 0.71 95CI]. The CNN-mask yielded an AUC of 0.76 [0.68 - 0.81]. Combining the CNN-mask with our previous hand-crafted masking risk predictor, the AUC improved to 0.78 [0.70 - 0.83]. The combined AUC improved to 0.81 [0.72-0.90] when analysis was restricted to NSD cancers surfacing clinically within one year after a negative screen. The two-step transfer learning yielded similar performance. This work suggests that a CNN masking risk predictor can be used to guide a stratified screening program to overcome the limitations of screening mammography in dense breasts.

Proceedings Article | 6 July 2018 Paper

Masking risk predictors in screening mammography

James Mainprize, Olivier Alonzo-Proulx, Taghreed Alshafeiy, James Patrie, Jennifer Harvey, Martin Yaffe

Proceedings Volume 10718, 107180E (2018) https://doi.org/10.1117/12.2318074

KEYWORDS: Cancer, Mammography, Breast, Diagnostics, Breast cancer, Imaging systems, Breast imaging, Digital mammography, Image analysis, Performance modeling

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High mammographic density reduces the diagnostic accuracy of mammography by masking tumors, leading to interval cancers and late stage diagnosis. In this study, various models to predict masking risk are computed on a cohort of 90 interval or undiagnosed (“masked”) invasive cancers and 186 screen-detected invasive cancers, based on biometric (age and BMI) and image-based parameters (BI-RADS density, volumetric breast density (VBD) and detectability). Univariate logistic regressions were computed to predict masked cancers, and the accuracy of the regressions was evaluated using the area under receiver operator characteristic curve (AUC). The univariate AUC for BMI, age, BIRADS density, VBD and mean detectability were 0.61 [0.54-0.68], 0.65 [0.58-0.73], 0.67 [0.61–0.73], 0.72 [0.65-0.78] and 0.75 [0.68-0.81] respectively (95% confidence intervals are noted in the brackets). The models were applied to a set of 248 mammography exams from cancer-free women of the same population. A stratified screening model was tested by computing the fraction of disease-free women identified as masked (the recruitment rate) as a function of the fraction of masked cancers that were correctly identified. For BI-RADS densities 3 or 4 (4th edition), up to 60% of masked cancers could potentially be detected by supplemental tests, requiring 43% of women to be recruited for extra screening. Selecting by mean detectability would require a 29% recruitment rate for the same potential capture. Future work to develop multivariate masking risk predictors could yield more efficient stratified screening approaches for breast cancer detection.

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