Computer methods and programs in biomedicine · 2026
Frequency-Domain Dynamic Light Scattering (FEDSA) for in vivo screening of breast tissue abnormalities: A proof-of-concept study.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
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.
Fernández-Pinto Janeth, Gómez-Torrado Álvaro, Miranda David A
Аннотация
BACKGROUND AND OBJECTIVE: Dynamic light scattering (DLS) provides valuable information on nanoscale and microscale dynamics, but its systematic application to in vivo tissue evaluation remains limited. This study introduces field effect detection by spectral analysis (FEDSA), a frequency-domain approach designed to analyze backscattered light signals and identify tissue abnormalities associated with the field cancerization effect in breast tissue. The objective was to establish a proof of concept showing that FEDSA can differentiate normal from abnormal tissue.
METHODS: A two-stage proof-of-concept study was conducted. First, FEDSA was validated using suspensions of alumina particles (60-300 nm and 100-400 nm) and polystyrene particles (315 nm) to test its performance as a dynamic light scattering technique. Second, in vivo measurements were obtained from 26 women (19 with normal tissue and 7 with abnormal tissue confirmed by imaging or clinical diagnosis). Power spectra were decomposed into frequency bands, transformed through principal component analysis, and analyzed by logistic regression.
RESULTS: FEDSA reproduced the expected behavior of a dynamic light scattering-type technique when applied to suspensions of particles. In breast tissue experiments, statistically significant differences were observed between normal and abnormal groups, particularly in the 150-160 kHz frequency band. A PCA-logistic regression model showed discriminatory potential. The ROC analysis yielded an AUC of 0.83; however, cross-validation grouped with patients provided a more conservative performance estimate (AUC ≈ 0.68-0.74), supporting the feasibility of the approach while suggesting uncertainty due to the limited cohort size.
CONCLUSIONS: This proof-of-concept study demonstrates the feasibility of FEDSA as a non-invasive, low-cost, and non-ionizing frequency-domain technique inspired by DLS principles to differentiate normal from abnormal breast tissue. Although further validation with larger and more diverse cohorts is required, these findings suggest the potential of FEDSA as a complementary tool for early breast cancer risk assessment.
METHODS: A two-stage proof-of-concept study was conducted. First, FEDSA was validated using suspensions of alumina particles (60-300 nm and 100-400 nm) and polystyrene particles (315 nm) to test its performance as a dynamic light scattering technique. Second, in vivo measurements were obtained from 26 women (19 with normal tissue and 7 with abnormal tissue confirmed by imaging or clinical diagnosis). Power spectra were decomposed into frequency bands, transformed through principal component analysis, and analyzed by logistic regression.
RESULTS: FEDSA reproduced the expected behavior of a dynamic light scattering-type technique when applied to suspensions of particles. In breast tissue experiments, statistically significant differences were observed between normal and abnormal groups, particularly in the 150-160 kHz frequency band. A PCA-logistic regression model showed discriminatory potential. The ROC analysis yielded an AUC of 0.83; however, cross-validation grouped with patients provided a more conservative performance estimate (AUC ≈ 0.68-0.74), supporting the feasibility of the approach while suggesting uncertainty due to the limited cohort size.
CONCLUSIONS: This proof-of-concept study demonstrates the feasibility of FEDSA as a non-invasive, low-cost, and non-ionizing frequency-domain technique inspired by DLS principles to differentiate normal from abnormal breast tissue. Although further validation with larger and more diverse cohorts is required, these findings suggest the potential of FEDSA as a complementary tool for early breast cancer risk assessment.