Abstract
Background Inclusion of mammographic breast density (BD) in breast cancer risk models improves accuracy, but accuracy remains modest. Interval cancer (IC) risk prediction may be improved by combining assessments of BD and an artificial intelligence (AI) cancer detection system. Purpose To evaluate the performance of a neural network (NN)-based model that combines the assessments of BD and an AI system in the prediction of risk of developing IC among women with negative screening mammography results. Materials and Methods This retrospective nested case-control study performed with screening examinations included women who developed IC and women with normal follow-up findings (from January 2011 to January 2015). An AI cancer detection system analyzed all studies yielding a score of 1-10, representing increasing likelihood of malignancy. BD was automatically computed using publicly available software. An NN model was trained by combining the AI score and BD using 10-fold cross-validation. Bootstrap analysis was used to calculate the area under the receiver operating characteristic curve (AUC), sensitivity at 90% specificity, and 95% CIs of the AI, BD, and NN models. Results A total of 2222 women with IC and 4661 women in the control group were included (mean age, 61 years; age range, 49-76 years). AUC of the NN model was 0.79 (95% CI: 0.77,0.81), which was higher than AUC of the AI cancer detection system or BD alone (AUC, 0.73 [95% CI: 0.71, 0.76] and 0.69 [95% CI: 0.67, 0.71], respectively; P < .001 for both). At 90% specificity, the NN model had a sensitivity of 50.9% (339 of 666 women; 95% CI: 45.2, 56.3) for prediction of IC, which was higher than that of the AI system (37.5%; 250 of 666 women; 95% CI: 33.0, 43.7; P < .001) or BD percentage alone (22.4%; 149 of 666 women; 95% CI: 17.9, 28.5; P < .001). Conclusion The combined assessment of an artificial intelligence detection system and breast density measurements enabled identification of a larger proportion of women who would develop interval cancer compared with either method alone. Published under a CC BY 4.0 license.
Highlights
On the basis of these findings, we hypothesized that short-term risk prediction of interval cancer (IC) could be further improved by combining assessment of an artificial intelligence (AI) breast cancer detection system and breast density (BD) within one neural network (NN)-based risk model. In this nested case-control study, we evaluated the performance of an NN-based model that combines the assessments of BD and an AI system in predicting risk of developing IC among women with negative screening mammograms
All mammograms were acquired with the same digital mammography systems (Selenia Dimensions; Hologic)
We found that a short-term risk model combining assessments of an artificial intelligence (AI) cancer radiology.rsna.org n Radiology: Volume 000: Number 0—Month 2022
Summary
This retrospective nested case-control study performed with screening examinations included women who developed IC and women with normal follow-up findings (from January 2011 to January 2015). Our study was performed with anonymized, retrospectively collected digital mammograms obtained from screening examinations. Study Sample Within a consecutive screening cohort of 1 163 147 women participating in two regions of the Dutch breast cancer screening program between January 2011 and January 2015, the last screening study of women with IC was included. A woman was classified as having IC if screening findings were negative but histologically proven breast cancer was diagnosed within the 20 months. For every selected woman with IC, at least two control studies were included, originating from women who had at least 2 years of normal follow-up findings after negative screening findings.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
