Browsing by Author "Adams, Lois"

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    Prostate microcalcification crystallography as a marker of pathology
    (Springer, 2025-04-29) Gosling, Sarah B.; Arnold, Emily L.; Adams, Lois; Cool, Paul; Geraki, Kalotina; Kitchen, Mark O.; Lyburn, Iain D.; Rogers, Keith D.; Snow, Tim; Stone, Nick; Greenwood, Charlene E.
    Prostate cancer remains the most common male cancer; however, treatment regimens remain unclear in some cases due to a lack of agreement in current testing methods. Therefore, there is an increasing need to identify novel biomarkers to better counsel patients about their treatment options. Microcalcifications offer one such avenue of exploration. Microfocus spectroscopy at the i18 beamline at Diamond Light Source was utilised to measure X-ray diffraction and fluorescence maps of calcifications in 10 µm thick formalin fixed paraffin embedded prostate sections. Calcifications predominantly consisted of hydroxyapatite (HAP) and whitlockite (WH). Kendall’s Tau statistics showed weak correlations of ‘a’ and ‘c’ lattice parameters in HAP with GG (rτ = − 0.323, p = 3.43 × 10–4 and rτ = 0.227, p = 0.011 respectively), and a negative correlation of relative zinc levels in soft tissue (rτ = − 0.240, p = 0.022) with GG. Negative correlations of the HAP ‘a’ axis (rτ = − 0.284, p = 2.17 × 10–3) and WH ‘c’ axis (rτ = − 0.543, p = 2.83 × 10–4) with pathological stage were also demonstrated. Prostate calcification chemistry has been revealed for the first time to correlate with clinical markers, highlighting the potential of calcifications as biomarkers of prostate cancer.
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    Vitacrystallography: structural biomarkers of breast cancer obtained by X-ray scattering
    (MDPI, 2024-07-09) Denisov, Sergey; Blinchevsky, Benjamin; Friedman, Jonathan; Gerbelli, Barbara; Ajeer, Ash; Adams, Lois; Greenwood, Charlene; Rogers, Keith; Mourokh, Lev; Lazarev, Pavel
    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.