DOI 10.1016/j.ultras.2026.107995
Rib quality assessment using quantitative ultrasound imaging of bone.
Аннотация
This feasibility study evaluated quantitative ultrasound imaging of bone (QUSIB) for non-ionizing assessment of rib quality in the context of breast cancer treatment. In silico, microcomputed tomography-based rib models simulated five-year of the effects of radiation and bisphosphonate therapy. Broadband ultrasound propagation (6-MHz center frequency, 128-element array) yielded backscatter and attenuation coefficients, which were related to structural and material parameters via univariate and partial least squares (PLS) regression analyses with 5-fold cross-validation. The strongest univariate correlations were observed for trabecular total bone volume fraction (BV/TVtb) and cortical porosity with attenuation at 7-8 MHz. PLS models significantly predicted trabecular BV/TVtb (R2 = 0.50; p < 0.001) and cortical porosity (R2 = 0.58; p < 0.001). Treatment-dependent spectral shifts in backscatter and attenuation coefficients confirmed sensitivity to pathological changes. In-vivo QUSIB measurements at the antero-lateral 4th-6th ribs and at the tibia midshaft in n = 10 healthy volunteers produced apparent integrated backscatter and attenuation values that closely matched in-silico distributions (p > 0.01) and did not differ significantly from tibia measurements. These results demonstrate that QUSIB backscatter biomarkers robustly reflect rib microstructure and treatment-induced alterations, supporting their potential for fracture-risk assessment in breast cancer patients.
Предметные рубрики MeSH
Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Тип публикации указан по данным источника. Уровень доказательности автоматически не присваивается.