Vitacrystallography: structural biomarkers of breast cancer obtained by X-ray scattering

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dc.contributor.authorDenisov, Sergey
dc.contributor.authorBlinchevsky, Benjamin
dc.contributor.authorFriedman, Jonathan
dc.contributor.authorGerbelli, Barbara
dc.contributor.authorAjeer, Ash
dc.contributor.authorAdams, Lois
dc.contributor.authorGreenwood, Charlene
dc.contributor.authorRogers, Keith
dc.contributor.authorMourokh, Lev
dc.contributor.authorLazarev, Pavel
dc.date.accessioned2024-08-05T14:49:35Z
dc.date.available2024-08-05T14:49:35Z
dc.date.freetoread2024-08-05
dc.date.issued2024-07-09
dc.description.abstractSimple 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.
dc.description.journalNameCancers
dc.format.extentArticle number 2499
dc.identifier.citationDenisov S, Blinchevsky B, Friedman J, et al., (2024) Vitacrystallography: structural biomarkers of breast cancer obtained by X-ray scattering. Cancers, Volume 16, Issue 14, July 2024, Article number 2499
dc.identifier.eissn2072-6694
dc.identifier.urihttps://doi.org/10.3390/cancers16142499
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22721
dc.language.isoen
dc.publisherMDPI
dc.publisher.urihttps://www.mdpi.com/2072-6694/16/14/2499
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectstructural biomarkers
dc.subjectX-ray scattering
dc.subjectextracellular matrix
dc.subjectcancer detection
dc.subjectmachine learning
dc.titleVitacrystallography: structural biomarkers of breast cancer obtained by X-ray scattering
dc.typeArticle
dcterms.dateAccepted2024-07-08

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