S
Serghei Malkov
Researcher at University of California, San Francisco
Publications - 48
Citations - 1112
Serghei Malkov is an academic researcher from University of California, San Francisco. The author has contributed to research in topics: Breast cancer & Mammography. The author has an hindex of 17, co-authored 48 publications receiving 952 citations. Previous affiliations of Serghei Malkov include University of California, Berkeley & Applied Materials.
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Journal ArticleDOI
Volume of Mammographic Density and Risk of Breast Cancer
John A. Shepherd,Karla Kerlikowske,Lin Ma,Frederick Duewer,Bo Fan,Jeff Wang,Serghei Malkov,Eric Vittinghoff,Steven R. Cummings +8 more
TL;DR: Volumetric measures of breast density are more accurate predictors of breast cancer risk than risk factors alone and than percent dense area, and fibroglandular volume improved the categorical risk classification of 1 in 5 women for both women with and without breast cancer.
Journal ArticleDOI
Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening
Kathleen R. Brandt,Christopher G. Scott,Lin Ma,Amir Pasha Mahmoudzadeh,Matthew R. Jensen,Dana H. Whaley,Fang Fang Wu,Serghei Malkov,Carrie B. Hruska,Aaron D. Norman,John N. Heine,John A. Shepherd,V. Shane Pankratz,Karla Kerlikowske,Celine M. Vachon +14 more
TL;DR: Automated and clinical assessments of breast density are similarly associated with breast cancer risk but differ up to 14% in the classification of women with dense breasts, which could have substantial effects on clinical practice patterns.
Journal ArticleDOI
Agreement of Mammographic Measures of Volumetric Breast Density to MRI
Jeff Wang,Ania Azziz,Bo Fan,Serghei Malkov,Catherine Klifa,David C. Newitt,Silaja Yitta,Nola M. Hylton,Karla Kerlikowske,John A. Shepherd +9 more
TL;DR: Automated volumetric fibroglandular tissue measures from screening digital mammograms were in substantial agreement with MRI and if associated with breast cancer could be used in clinical practice to enhance risk assessment and prevention.
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Single x-ray absorptiometry method for the quantitative mammographic measure of fibroglandular tissue volume.
TL;DR: An automated method for quantifying fibroglandular tissue volume has been developed that exhibited good accuracy and precision for a broad range of breast thicknesses, paddle tilt angles, and %FGV values.
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Compositional breast imaging using a dual-energy mammography protocol
TL;DR: A novel approach to dual-energy mammography, full-field digital compositional mammography (FFDCM), which can independently image the three compositional components of breast tissue: water, lipid, and protein, which has been derived and exhibited good compositional thickness accuracy on phantoms.