Jing Wang1, Pam Gottschal2, Lilu Ding3, DaniëlleW A van Veldhuizen4, Wenli Lu5, Nehmat Houssami6, Marcel J W Greuter7, Geertruida H de Bock8. 1. University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands. Electronic address: j.wang@umcg.nl. 2. University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands. Electronic address: p.gottschal.1@student.rug.nl. 3. University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands. Electronic address: l.ding@umcg.nl. 4. University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands. Electronic address: w.a.van.veldhuizen@student.rug.nl. 5. Department of Epidemiology and Health Statistics, School of Public Health, Tianjin Medical University, Tianjin, China. Electronic address: luwenli@tmu.edu.cn. 6. Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Australia. Electronic address: nehmat.houssami@sydney.edu.au. 7. University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, the Netherlands; Robotics and Mechatronics (RaM) Group, Faculty of Electrical Engineering Mathematics and Computer Science, Technical Medical Centre, University of Twente, Enschede, the Netherlands. Electronic address: m.j.w.greuter@umcg.nl. 8. University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands. Electronic address: g.h.de.bock@umcg.nl.
Abstract
BACKGROUND: Instead of a single value for mammographic sensitivity, a sensitivity function based on tumor size more realistically reflects mammography's detection capability. Because previous models may have overestimated size-specific sensitivity, we aimed to provide a novel approach to improve sensitivity estimation as a function of tumor size. METHODS: Using aggregated data on interval and screen-detected cancers, observed tumor sizes were back-calculated to the time of screening using an exponential tumor growth model and a follow-up time of 4 years. From the observed number of detected cancers and an estimation of the number of false-negative cancers, a model for the sensitivity as a function of tumor size was determined. A univariate sensitivity analysis was conducted by varying follow-up time and tumor volume doubling time (TVDT). A systematic review was conducted for external validation of the sensitivity model. RESULTS: Aggregated data of 22,915 screen-detected and 10,670 interval breast cancers from the Dutch screening program were used. The model showed that sensitivity increased from 0 to 85% for tumor sizes from 2 to 20 mm. When TVDT was set at the upper and lower limits of the confidence interval, sensitivity for a 20-mm tumor was 74% and 93%, respectively. The estimated sensitivity gave comparable estimates to those from two of three studies identified by our systematic review. CONCLUSION: Derived from aggregated breast screening outcomes data, our model's estimation of sensitivity as a function of tumor size may provide a better representation of data observed in screening programs than other models.
BACKGROUND: Instead of a single value for mammographic sensitivity, a sensitivity function based on tumor size more realistically reflects mammography's detection capability. Because previous models may have overestimated size-specific sensitivity, we aimed to provide a novel approach to improve sensitivity estimation as a function of tumor size. METHODS: Using aggregated data on interval and screen-detected cancers, observed tumor sizes were back-calculated to the time of screening using an exponential tumor growth model and a follow-up time of 4 years. From the observed number of detected cancers and an estimation of the number of false-negative cancers, a model for the sensitivity as a function of tumor size was determined. A univariate sensitivity analysis was conducted by varying follow-up time and tumor volume doubling time (TVDT). A systematic review was conducted for external validation of the sensitivity model. RESULTS: Aggregated data of 22,915 screen-detected and 10,670 interval breast cancers from the Dutch screening program were used. The model showed that sensitivity increased from 0 to 85% for tumor sizes from 2 to 20 mm. When TVDT was set at the upper and lower limits of the confidence interval, sensitivity for a 20-mm tumor was 74% and 93%, respectively. The estimated sensitivity gave comparable estimates to those from two of three studies identified by our systematic review. CONCLUSION: Derived from aggregated breast screening outcomes data, our model's estimation of sensitivity as a function of tumor size may provide a better representation of data observed in screening programs than other models.