Abstract P3-02-02: Impact of breast density notification laws on radiology practices: A survey of 110 radiology facilities
Bibliographic record
Abstract
Abstract Purpose: Breast Density Notification laws, passed in 15 states as of April 2014, mandate that breast density information be given to patients often without guidance on modalities, patient selection or funding for supplemental screening. The purpose of this study is to assess the impact of breast density notification laws on radiology practices, specifically regarding breast cancer risk assessment and supplemental screening studies. Methods: We performed an anonymous 20-question web-based survey to Society of Breast Imaging radiologists using a Qualtrics Survey Tool between 8/2013-3/2014, with questions on radiology practices, breast cancer risk assessment, breast density measurement, supplemental screening tests, and support for referring physicians and patients. We compared survey results between groups using Fisher’s exact test. Results: 121 radiologists from 110 facilities (48 academic, 43 large private hospital, 15 small private hospital and 4 other) representing 34 USA states and 1 Canadian site responded. 49% of facilities (54/110) were in states with an enacted breast density notification law. 37% of facilities (40/109) performed risk assessment, 26% (28/109) did not perform risk assessment, and 38% (41/109) did not but reported family history/other risk factors, with no significant difference in performing risk assessment between facilities with or without an enacted law (p-value 0.71). Of the 37 facilities performing risk assessment, 60% used the Gail model, 22% used the Tyrer-Cuzick model and 11% used the modified Gail model (multiple answers allowed [m.a.a.]). Of the 15 facilities performing risk assessment, 40% answered "yes" when asked whether performing risk assessment is a new task because of the density law. Breast density was estimated by only visual assessment in 98% of facilities (103/105), and by computer-based determination with or without visual assessment in 2% (2/105). Supplemental screening studies offered included magnetic resonance imaging (MRI) (88%, 92/105), handheld whole breast ultrasound (HHWBUS) (48%, 50/105), tomosynthesis (39%, 41/105), and automated WBUS (8%, 8/105) (m.a.a.). There was no significant difference in supplemental screening studies offered between facilities with or without an enacted law (p-value 0.26). In anticipation of the law, facilities implemented HHWBUS (33%, 16/48), tomosynthesis (6%, 3/48), automated WBUS (6%, 3/48) or none (60%, 29/48) (m.a.a.). Facilities with the enacted law prepared for the law with referring physician discussions (69%, 34/49), website (49%, 24/49), educational talks for referring physicians (43%, 21/49) or patients (31%, 15/49) (m.a.a.). Conclusion: Our survey showed variations in available supplemental screening modalities and policy implementation at each facility. There was no significant difference in performing risk assessment and supplemental screening studies between facilities with or without an enacted breast density notification law. Citation Format: Lina Nayak, Kanae K Miyake, Yueyi Irene Liu, William R Thomas, Edward A Sickles, Bonnie N Joe, Karen Lindfors, R J Brenner, Stephen Feig, Lawrence W Bassett, Jessica W Leung, Haydee Ojeda-Fournier, Jonathan Hargreaves, Elissa Price, Jafi A Lipson, Allison W Kurian, Elyse Love, Donna D Walgenbach, Lauren Ryan, Meg Durbin, Bruce L Daniel, Linda Garcia, Debra M Ikeda. Impact of breast density notification laws on radiology practices: A survey of 110 radiology facilities [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P3-02-02.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".