Publications
Browse peer-reviewed publications featuring Volpara Health science and technologies.
*full bibliography available upon request
Browse peer-reviewed publications featuring Volpara Health science and technologies.
*full bibliography available upon request
“The incremental cancer detection rate in the second round was 5.8 per 1000 screening examinations—compared with 16.5 per 1000 screening examinations in the first round. This was accompanied by a strong reduction in the number of false positive results.”
Veenhuizen, de Lange, Bakker, Pijnappel, Mann, Monninkhof, Emaus, de Koekkoek-Doll, Bisschops, Lobbes, de Jong, Duvivier, Veltman, Karssemeijer, de Koning, van Diest, Mali, van den Bosch, van Gils, Veldhuis
Waade, Danielsen, Holen, Larsen, Hanestad, Hopland, Kalcheva, Hofvind
“AI has great potential in evaluating breast position criteria in mammography by reducing subjectivity.”
Whelehan, Ali, Vinnicombe, Ball, Cox, Farry, Jenkin, Lowry, McIntosh, Nutt, Oeppen, Reilly, Stahnke, Steel, Sim, Warwick, Wilkinson, Zafeiris, Evans
“Where available, DBT merits first-line use in the under 60 age group in symptomatic breast clinics, particularly in women known to have very dense breasts.”
Ward, Heller, Hudson, Wilkinson
“A total of 40,760 women who underwent screening during the
study period had Volpara data available … There was a significant correlation between a diagnosis of cancer and nodular PP compared to not nodular PP (p = 0.043).”
Wang, Qiu, Ren, Liu, Wu, Li, Niu, Li
“A series of glandular tissue dose conversion coefficients for dose estimation in mammography were calculated. The conversion coefficients calculated in this study were compared with those estimated with the simple breast model. A discrepancy of 5.4–38.0% was observed.”
Wang, Azziz, Fan, Malkov, Klifa, Newitt, Yitta, Hylton, Kerlikowske, Shepherd
“Automated volumetric fibroglandular tissue measures from screening digital mammograms were in substantial agreement with MRI and if associated with breast cancer could be used in clinical practice to enhance risk assessment and prevention.”
Wanders, van Gils, Karssemeijer, Holland, Kallenberg, Peeters, Nielsen, Lillholm
“Deep-learning-based texture pattern scores, measured automatically on digital mammograms, are associated with breast cancer risk, independently of volumetric mammographic density, and augment the capacity to discriminate between future breast cancer and non-breast cancer cases.”
Wanders, Holland, Karssemeijer, Peeters, Veldhuis, Mann, van Gils
“Our results suggest that both absolute dense volume and percentage dense volume are strong markers of breast cancer risk, but that they are even stronger markers for predicting the occurrence of tumors that are not detected during mammography breast cancer screening.”
Waade, Sebuødegård, Hogg, Hofvind
“Breast compression force, compression pressure and compressed breast thickness increased across consecutive screening examinations,
which might be of influence for the females’ experiences of discomfort and pain during the examination and for image quality.”
Waade, Moshina, Sebuødegård, Hogg, Hofvind
“A wide variation in applied compression force was observed between the breast centres in the NBCSP. This variation indicates a need for evidence based recommendations for compression force aimed at optimizing the image quality and individualizing breast compression.”
Toriola, Appleton, Zong, Luo, Weilbaecher, Tamimi, Colditz
“These findings support the inhibition of RANKL signaling as a pathway to reduce mammographic density and possibly breast cancer incidence in high-risk women with dense breasts.”