Abstract P3-01-02: Trends in positive predictive values following transition from screen film to digital mammography
Bibliographic record
Abstract
Abstract Purpose Our health region converted from screen-film mammography (SFM) to digital mammography (DM) in 2005. DM has several advantages over SFM, including superior contrast resolution, less noise, and opportunities for image optimization through post-acquisition processing; although, the spatial resolution of DM is inferior to SFM. We sought to determine what effect this transition may have had on positive predictive values (PPVs) for malignant and premalignant lesions. Methods From our institution's breast biopsy database, we retrospectively reviewed core biopsy results for mammographic calcifications performed in the years 2001-2004 (SFM years) and 2009-2012 (DM years). We subsequently determined the PPV3 (detection of malignancy after biopsy) for each group of years (SFM and DM). We then performed subgroup analysis to calculate PPVs for each of ductal carcinoma in-situ (DCIS) without invasion, DCIS with invasion, and premalignant lesions. Premalignant lesions included atypical ductal hyperplasia, atypical lobular hyperplasia, and lobular carcinoma in-situ. Results A total of 4787 biopsies in 4633 patients were reviewed. The comparative detection of cancer after biopsy performed for mammographic calcification between SFM and DM was not statistically significant (PPV3 = 23.5% and 24.0%, respectively; P=.71). Upon further analysis, however, PPV for premalignant lesions increased (SFM=6.6% and DM=8.9%; P<.01) and PPV for DCIS without invasion increased (SFM=15.5% and DM=18.2%; P=.015), while PPV for DCIS with invasion decreased (SFM=8.0% and DM=5.8%; P<.01). Conclusion We observed no significant impact on PPV3 for calcifications following the transition from SFM to DM; however, our subgroup analysis suggests that with digital mammography we are now detecting a statistically significantly lower proportion of DCIS with invasion but greater proportions of DCIS without invasion and premalignant lesions. As the natural history of these lower-grade lesions, particularly in the premalignant category, is still not entirely understood, the significance of potentially detecting more of these earlier cancers/precancers is uncertain. Citation Format: Motiuk DA, Dahl P, Roy E, Docktor B-J, Burrowes P. Trends in positive predictive values following transition from screen film to digital mammography [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P3-01-02.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".