Mr. Chun-Wai Chan Profile

Chun-Wai Chan

at Univ of Chicago

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Publications (2)

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Proceedings Article | 15 February 2021 Presentation + Paper

Parenchymal field effect analysis for breast cancer risk assessment: evaluation of FFDM radiomic similarity

Natalie Baughan, Hui Li, Li Lan, Chun-Wai Chan, Matthew Embury, Gary Whitman, Randa El-Zein, Isabelle Bedrosian, Maryellen Giger

Proceedings Volume 11597, 115970Z (2021) https://doi.org/10.1117/12.2581791

KEYWORDS: Tumors, Breast cancer, Breast, Statistical analysis, Quantization, Nipple, Cancer

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Histologically normal areas of the breast parenchyma have been shown to share molecular similarity with breast tumors, suggesting the presence of a field effect in breast cancer. To further understand a potential cancer field effect, we compared mammographic parenchymal texture features across four regions of the breast. The study included 103 FFDMs with at least one identified malignant tumor. All FFDM images (12-bit quantization and 70 micron pixels) were acquired with a Hologic Lorad Selenia system and retrospectively collected under an IRB-approved protocol. Regions of interest (ROI) of 128x128 and 256x256 pixels were selected from four regions across the craniocaudal projection: within the identified tumor, adjacent to the tumor, distant from the tumor, and behind the nipple in the contralateral breast. Radiographic texture analysis was used to extract 45 features in each region. Kolmogorov-Smirnov (KS) and Pearson correlation tests assessed similarity between features in each region. KS test results, with a 95% confidence interval on the KS test statistic bootstrapped with 2000 iterations indicated that 81.8% (128x128) and 88.4% (256x256) of feature distributions across all ROI regions showed equivalence with a threshold equal to the critical value at the p = 0.05 level. Pearson correlation results demonstrated a majority of structure-based feature comparisons which reached statistical significance, and less intensity-based feature comparisons which reached statistical significance. These results support our hypothesis of a potential cancer field effect across tumor and non-tumor regions and support the development of computerized analysis of mammographic parenchymal patterns to assess breast cancer risk.

Proceedings Article | 27 February 2018 Paper

Temporal assessment of radiomic features on clinical mammography in a high-risk population

Kayla Mendel, Hui Li, Li Lan, Chun-Wai Chan, Lauren King, Nabihah Tayob, Gary Whitman, Randa El-Zein, Isabelle Bedrosian, Maryellen Giger

Proceedings Volume 10575, 105753Q (2018) https://doi.org/10.1117/12.2293368

KEYWORDS: Cancer, Mammography, Breast, Feature extraction, Breast cancer, Control systems, Medical imaging, Biopsy, Image classification, Tissues

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Extraction of high-dimensional quantitative data from medical images has become necessary in disease risk assessment, diagnostics and prognostics. Radiomic workflows for mammography typically involve a single medical image for each patient although medical images may exist for multiple imaging exams, especially in screening protocols. Our study takes advantage of the availability of mammograms acquired over multiple years for the prediction of cancer onset. This study included 841 images from 328 patients who developed subsequent mammographic abnormalities, which were confirmed as either cancer (n=173) or non-cancer (n=155) through diagnostic core needle biopsy. Quantitative radiomic analysis was conducted on antecedent FFDMs acquired a year or more prior to diagnostic biopsy. Analysis was limited to the breast contralateral to that in which the abnormality arose. Novel metrics were used to identify robust radiomic features. The most robust features were evaluated in the task of predicting future malignancies on a subset of 72 subjects (23 cancer cases and 49 non-cancer controls) with mammograms over multiple years. Using linear discriminant analysis, the robust radiomic features were merged into predictive signatures by: (i) using features from only the most recent contralateral mammogram, (ii) change in feature values between mammograms, and (iii) ratio of feature values over time, yielding AUCs of 0.57 (SE=0.07), 0.63 (SE=0.06), and 0.66 (SE=0.06), respectively. The AUCs for temporal radiomics (ratio) statistically differed from chance, suggesting that changes in radiomics over time may be critical for risk assessment. Overall, we found that our two-stage process of robustness assessment followed by performance evaluation served well in our investigation on the role of temporal radiomics in risk assessment.

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