ArticleCancers2024
Vitacrystallography: Structural Biomarkers of Breast Cancer Obtained by X-ray Scattering.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- X-Ray Diffraction of Collagen-Structured Water Molecules for Cancer Detection.Molecules (Basel, Switzerland) · 2026Article
- Vitacrystallography: Appearance and Development of Cancer-Induced Structural Biomarkers in a Mouse Model.Life (Basel, Switzerland) · 2025Article
- Benign/Cancer Diagnostics Based on X-Ray Diffraction: Comparison of Data Analytics Approaches.Cancers · 2025Article
- Toward In Vivo Cancer Detection: X-Ray Scattering on Thick Phantom Samples.Molecules (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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