Browsing by Author "Lazarev, Pavel"

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    Toward in-vivo cancer detection: X-ray scattering on thick phantom samples
    (MDPI, 2025-04-08) Kubytskyi, Viacheslav; Khonkhodzhaev, Masroor; Tanaka, Aika; Nguyen, Audrey; Lazarev, Alexander; Aram, Byron; Rogers, Keith; Mourokh, Lev; Lazarev, Pavel
    As the number of new breast cancer cases grows around the world, there is an unmet need for fast, accurate, and low-cost methods of early cancer detection. It was previously shown that X-ray scattering on lipid molecules can provide the necessary structural biomarker. However, these measurements were performed on small ex vivo samples, and to ensure the progress to in vivo diagnostics, the approach should be extended to larger tissues. We use the phantom fat samples to establish such a procedure. In the obtained X-ray scattering patterns, we observe the characteristic features for the inter-fatty-acid molecular distance. The large size of the samples led to the peak broadening; however, the features remain visible up to 10 cm in thickness. The experimental data are in excellent agreement with the Monte Carlo simulations based on the form factors obtained from the small samples. Our results usher the way for the in vivo monitoring of the structural biomarkers of breast 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.