Feasibility of Cancer Detection Through X-Ray Diffraction of Human Nails
11 affiliations
- 1Deutsches Elektronen-Synchrotron DESY, 22607 Hamburg, Germany
- 2Bragg Analytics, Inc., 1900 S. Norfolk Street, #350, San Mateo, CA 94403, USA
- 3Matur UK Ltd., 5 New Street Square, London EC4A 3TW, UK
- 4Scientific Center of Nanotechnology and Functional Nanomaterials, Buketov Karaganda National Research University, 28 University Street, Karaganda 100028, Kazakhstan
- 5Surgery Department, Regional Oncology Center of Karaganda, 189 Amanzholov Street, Karaganda 100019, Kazakhstan
- 6Department of Surgical Diseases, Karaganda Medical University, 40 Gogol Street, Karaganda 100012, Kazakhstan
- 7Department of Science and Commercialization, Kazakh National University of Sports, 15 Karkaraly Street, Astana 010000, Kazakhstan
- 8Biology-Geography Faculty, Buketov Karaganda National Research University, 28 University Street, Karaganda 100028, Kazakhstan
- 9Department of Ecology and Assessment, Karaganda University of Kazpotrebsoyuz, 9 Academic Street, Karaganda 100009, Kazakhstan
- 10Faculty of Engineering and Applied Sciences, Shrivenham Campus, Cranfield University, Swindon SN6 8LA, UK
- 11Physics Department, Queens College, City University of New York, 65-30 Kissena Blvd, Flushing, NY 11367, USA
Abstract
Background/Objectives: Establishing biomarkers is crucial for early cancer detection. Structural biomarkers can be revealed by noninvasive X-ray diffraction. We applied this approach to human nails and observed structural changes in the major nail tissue component, keratin, which coincided with a cancer diagnosis. Methods: X-ray diffraction patterns were obtained from 230 nail samples (control/cancer split: 92/138) belonging to 133 patients (48/85) in wide (WAXS) and small (SAXS) ranges of scattering angles. For both ranges, we obtained one-dimensional scattering profiles in meridional and equatorial directions. We examined these profiles using machine-learning techniques such as logistic regression and linear discriminant analysis. Results: We obtained good classification metrics that allow us to distinguish participants with cancer from controls. The results are similar for both machine-learning methods. Combining all four profiles (WAXS meridional, WAXS equatorial, SAXS meridional, and SAXS equatorial) produced a sensitivity of 0.753 and a specificity of 0.771 for the Linear Discriminant Analysis at the Youden point. For the limited set of patients of the same age, we showed that the patterns can still be distinguished, supporting our hypothesis that the observed aberrations are cancer-induced. Conclusions: Our exploratory research demonstrated the feasibility of cancer detection using the X-ray diffraction of human nails.
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BibTeX
@article{conceicao2026feasibility,
title = {{Feasibility of Cancer Detection Through X-Ray Diffraction of Human Nails}},
author = {Andre L. C. Conceiçao and Alexander Alekseev and Viacheslav Shcherbakov and Dmitriy Shestakov and Uldana Sagyngali and Kairat Shakeev and Assylbek Zeinidenov and Assel Zhumina and Aitbek Aimukhanov and Galym Turgambekov and Anna Kryzhanovskaya and Keith Rogers and Lev Mourokh and Pavel Lazarev},
journal = {Cancers},
year = {2026},
month = oct,
doi = {10.3390/cancers18203270},
url = {https://www.mdpi.com/2072-6694/18/20/3270},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Feasibility of Cancer Detection Through X-Ray Diffraction of Human Nails
AU - Andre L. C. Conceiçao
AU - Alexander Alekseev
AU - Viacheslav Shcherbakov
AU - Dmitriy Shestakov
AU - Uldana Sagyngali
AU - Kairat Shakeev
AU - Assylbek Zeinidenov
AU - Assel Zhumina
AU - Aitbek Aimukhanov
AU - Galym Turgambekov
AU - Anna Kryzhanovskaya
AU - Keith Rogers
AU - Lev Mourokh
AU - Pavel Lazarev
PY - 2026
DA - 2026/10/01
JO - Cancers
DO - 10.3390/cancers18203270
UR - https://www.mdpi.com/2072-6694/18/20/3270
AB - Background/Objectives: Establishing biomarkers is crucial for early cancer detection. Structural biomarkers can be revealed by noninvasive X-ray diffraction. We applied this approach to human nails and observed structural changes in the major nail tissue component, keratin, which coincided with a cancer diagnosis. Methods: X-ray diffraction patterns were obtained from 230 nail samples (control/cancer split: 92/138) belonging to 133 patients (48/85) in wide (WAXS) and small (SAXS) ranges of scattering angles. For both ranges, we obtained one-dimensional scattering profiles in meridional and equatorial directions. We examined these profiles using machine-learning techniques such as logistic regression and linear discriminant analysis. Results: We obtained good classification metrics that allow us to distinguish participants with cancer from controls. The results are similar for both machine-learning methods. Combining all four profiles (WAXS meridional, WAXS equatorial, SAXS meridional, and SAXS equatorial) produced a sensitivity of 0.753 and a specificity of 0.771 for the Linear Discriminant Analysis at the Youden point. For the limited set of patients of the same age, we showed that the patterns can still be distinguished, supporting our hypothesis that the observed aberrations are cancer-induced. Conclusions: Our exploratory research demonstrated the feasibility of cancer detection using the X-ray diffraction of human nails.
SN - 2072-6694
ER -