Anum S. Kazerouni

Prognostic Biomarkers in Ductal Carcinoma in Situ: An Interobserver Agreement Study

Rachel L. Stewart, Daniel S. Hippe, Rebeca Alvarez, Sara H. Javid et autres

Ductal carcinoma in situ (DCIS) is a noninvasive form of breast cancer that accounts for 15% to 25% of all new breast cancer diagnoses. Clinical management approaches for DCIS include surgical excision, radiotherapy, and endocrine therapy (for patients with estrogen receptor (ER)-positive …

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PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns

Abishek Sankaranarayanan, Chenkai Zhao, Hernández, Elizabeth A. Clemens et autres

Abstract Multiplex imaging is a valuable tool for spatially examining tissue microenvironments at the single-cell level to uncover biological and clinical insights. However, most multiplex image analysis workflows currently require manual intervention for cell phenotyping, which slows progress, demands human effort, and …

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Deep Learning-Based Synthetic Contrast-Enhanced Breast MRI for Monitoring Response to Neoadjuvant Therapy

Suleeporn Sujichantararat, Debosmita Biswas, Anum S. Kazerouni, Edric D. Tsang et autres

Background/Objectives: Contrast-enhanced (CE) breast MRI is highly sensitive for evaluating breast cancer extent and response to neoadjuvant therapy (NAT) but requires intravenous administration of gadolinium-based contrast agents (GBCA), increasing cost, time, patient discomfort, and health concerns. This study explored the feasibility of …

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The Future of Quantitative Breast Imaging: Integrated to Integral Imaging Biomarkers for Precision Cancer Care, From the AJR Special Series on Quantitative Imaging

E. Robert McDonald, Anum S. Kazerouni, Pouya Metanet, Lev Barinov et autres

This review explores the potential of quantitative imaging biomarkers (QIBs) for guiding personalized breast cancer treatment through prognostic stratification and measurement of treatment response. Accumulating data from prospective clinical trials show that QIBs are more predictive of treatment outcome than conventional sizebased …

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220 Associations Between Immunohistochemical Markers and Molecular Recurrence Risk in Ductal Carcinoma in-Situ

Lakshmisree Vemulakonda, Daniel S. Hippe, Anum S. Kazerouni, Rebeca Alvarez et autres

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Abstract PS4-10-13: Clinical Feasibility and Reproducibility of Tumor Infiltrating Lymphocyte (TIL) Reporting for Early-Stage Triple Negative Breast Cancer Patients at the University of Washington

Lynn Symonds, Daniel J. Reiter, D. S. Hippe, Anum S. Kazerouni et autres

Abstract Background: Tumor Infiltrating Lymphocytes (TILs) have prognostic and predictive value in triple negative breast cancer (TNBC). Higher TILs correlate with improved rates of pathologic complete response (pCR) following neoadjuvant chemotherapy and lower risk of recurrence and death. While TILs have emerged …

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Validation of fully‑automated deep learning‑based fibroglandular tissue segmentation for efficient and reliable quantitation of background parenchymal enhancement in breast MRI

Anum S. Kazerouni, Vivian Youngjean Park, Wesley Surento, Suleeporn Sujichantararat et autres

Background parenchymal enhancement (BPE) on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) shows potential as a breast cancer risk marker. Clinically, BPE is qualitatively assessed by radiologists, but quantitative BPE measures offer potential for more precise risk evaluation. This study evaluated an …

DuCTSNet: MA‑SAM based fully automated DCIS segmentation and radiomic phenotyping for patient risk stratification in the ECOG‑ACRIN E4112 trial

Aaron Sossin, Kalina P. Slavkova, Somit Jain, Arunima Sharma et autres

Validation of fully‑automated deep learning‑based fibroglandular tissue segmentation for efficient and reliable quantitation of background parenchymal enhancement in breast MRI

Yu-Tzu Kuo, Anum S. Kazerouni, Vivian Park, Wesley Surento et autres

TILseg: Automated Whole Slide-Level Spatial Scoring of Tumor-Infiltrating Lymphocytes Reveals Prognostic Patterns in Triple Negative Breast Cancer

Lisa L. Carr, Abishek Sankaranarayanan, Khanh Ha, Meenal Rawlani et autres

Stromal tumor-infiltrating lymphocytes (sTILs) are promising biomarkers for predicting therapeutic outcomes in triple-negative breast cancer (TNBC), with higher sTIL levels correlating with improved chemotherapy response and survival outcomes. Currently, sTILs are manually evaluated by pathologists, which is prone to inter-reader variability. In …

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Establishing an Evidence-based Modern Breast MRI Program

Marco G Aru, Habib Rahbar, Debosmita Biswas, Suleeporn Sujichantararat et autres

Breast MRI has evolved over the past several decades into a cornerstone of breast imaging. Historically, dynamic contrast-enhanced (DCE) MRI has served as the foundation of breast MRI protocols for differentiation of benign and malignant lesions and was supplemented by additional sequences …

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Multiparametric MRI Markers Associated with Breast Cancer Risk in Women with Dense Breasts

Wesley Surento, Debosmita Biswas, Daniel S. Hippe, Anum S. Kazerouni et autres

Background/Objectives: This study explored the associations of normal breast tissue characteristics on multiparametric MRI with clinical assessments of breast cancer risk in women with dense breasts. Methods: Women with dense breasts who underwent multiparametric MRI were included. Breast cancer risk was determined …

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