Dr. Nabihah Tayob Profile

Dr. Nabihah Tayob

at Univ of Texas MD Anderson Cancer Ctr

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Publications (2)

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Proceedings Article | 13 March 2019 Paper

Temporal mammographic registration for evaluation of architecture changes in cancer risk assessment

Kayla Mendel, Hui Li, Nabihah Tayob, Randa El-Zein, Isabelle Bedrosian, Maryellen Giger

Proceedings Volume 10950, 1095041 (2019) https://doi.org/10.1117/12.2512792

KEYWORDS: Image registration, Mammography, Breast, Cancer, Breast cancer, Tissues, Error analysis, Image segmentation, Image processing, Image resolution

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While breast cancer screening recommendations vary by agency, all agencies recommend mammographic screening with some frequency over some portion of a woman’s lifetime. Temporal evaluation of these images may inform personalized risk of breast cancer. However, due to the highly deformable nature of breast tissue, the positioning of breast tissue may vary widely between exams. Therefore, registration of physical regions in the breast over time points is a critical first step in computerized analysis of changes in breast parenchyma over time. While a postregistration image is altered and therefore not appropriate for radiomic texture analysis, the registration process produces a mapping of points which may aid in aligning similar image regions across multiple time points. In this study, a total of 633 mammograms from 87 patients were retrospectively collected. These images were sorted into 1144 temporal pairs, where each combination of images of a given women of a given laterality was used to form a temporal pair. B-splines registration and multi-resolution registration were performed on each mammogram pair. While the B-splines took an average of 552.8 CPU seconds per registration, multi-resolution registration took only an average of 346.2 CPU seconds per registration. Multi-resolution registration had a 15% lower mean square error, which was significantly different than that of B-splines (p<0.001). While previous work aimed to allow radiologists to visually evaluate the registered images, this study identifies corresponding points on images for use in assessing interval change for risk assessment and early detection of cancer through deep learning and radiomics.

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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