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Vitacrystallography: Structural Biomarkers of Breast Cancer Obtained by X-ray Scattering
by
, ,
Sergey Denisov
1,2,
Benjamin Blinchevsky
1,3,
Jonathan Friedman
3,4,
Barbara Gerbelli
5,
Ash Ajeer
5,
Lois Adams
5,
Charlene Greenwood
3,5,
Keith Rogers
3,6,
Lev Mourokh
3,4,* and
Pavel Lazarev
1,3 1
Matur UK Ltd., 5 New Street Square, London EC4A 3TW, UK
2
Institut de Chimie Physique, UMR8000, CNRS, Université Paris-Saclay, Bât. 349, 91405 Orsay, France
3
EosDx, Inc., 1455 Adams Drive, Menlo Park, CA 94025, USA
4
Physics Department, Queens College, City University of New York, 65-30 Kissena Blvd, Flushing, NY 11367, USA
5
School of Chemical and Physical Sciences, Keele University, Keele ST5 5BG, UK
6
Shrivenham Campus, Cranfield University, Swindon SN6 8LA, UK
*
Author to whom correspondence should be addressed.
Cancers 2024, 16(14), 2499; https://doi.org/10.3390/cancers16142499 (registering DOI)
Submission received: 27 May 2024
/
Revised: 26 June 2024
/
Accepted: 8 July 2024
/
Published: 9 July 2024
(This article belongs to the Section Methods and Technologies Development)
Simple Summary
Breast cancer ranks as the most prevalent cancer among women. Current screening includes regular mammography and subsequent biopsy if the mammography results are abnormal. These procedures are costly and uncomfortable. We propose an alternative non-invasive method based on X-ray scattering. Using a machine learning approach, we have examined almost 3000 measurements of cancerous and non-cancerous samples belonging to 110 patients and shown excellent results on cancer/non-cancer separation. This can lead to patient-friendly, fast, and economical solutions for breast cancer screening to complement mammography and reduce biopsy. It should be emphasized that this approach can be readily extended to other types of cancer and even other diseases.
Abstract
With breast cancer being one of the most widespread causes of death for women, there is an unmet need for its early detection. For this purpose, we propose a non-invasive approach based on X-ray scattering. We measured samples from 107 unique patients provided by the Breast Cancer Now Tissue Biobank, with the total dataset containing 2958 entries. Two different sample-to-detector distances, 2 and 16 cm, were used to access various structural biomarkers at distinct ranges of momentum transfer values. The biomarkers related to lipid metabolism are consistent with those of previous studies. Machine learning analysis based on the Random Forest Classifier demonstrates excellent performance metrics for cancer/non-cancer binary decisions. The best sensitivity and specificity values are 80% and 92%, respectively, for the sample-to-detector distance of 2 cm and 86% and 83% for the sample-to-detector distance of 16 cm.
