Abstract P3-02-09: Do radiographic features influence the decision to order a breast MRI? A prospective cohort study
Bibliographic record
Abstract
Abstract Background: The clinical impact of breast MRI in the neoadjuvant setting is unclear. It is assumed that MRI may help with surgical planning. Factors that may affect whether an MRI is ordered for breast cancer in the neoadjuvant setting may include both imaging and tumour characteristics. Literature suggests MRI can be used in the neoadjuvant therapy (NAT) if there is evidence of high density of breast tissue, multifocal disease, multi-centric disease, lymph node involvement or presence of calcifications. In a non-trial setting, it is unclear when MRI is ordered and if it is indeed ordered based on the above imaging criteria. We sought to determine how MRI is currently implemented in a provincial practice to determine which patients are selected for MRI prior to NAT. Specifically, we aimed to determine if the imaging characteristics determined likelihood of use of MRI in the neoadjuvant setting. Methods: Patients who received neoadjuvant therapy between May 2012 and May 2016 were captured in a prospective database at the BC Cancer Agency in Vancouver. Patients were reviewed and identified as those who either received a breast MRI or not. A random sample of 80 cases, 40 who received MRI and 40 who did not, was taken from this database. Charts were reviewed in detail, and detailed review of the radiographic features from mammogram and ultrasound imaging reports was recorded. Results: 80 patients were reviewed in detail. There were no differences in patient demographics or tumour characteristics. Imaging review demonstrated no statistical significant difference in use of MRI based on reported density, multi-centric disease, calcifications, and nodal involvement. The only radiographic feature that was different was presence of multifocal disease on conventional imaging, where 40.0% of patients who had an MRI had multifocal disease reported whereas only 17.5% of those who did not have an MRI had multifocality reported (p = 0.03). Discussion/Conclusions: Despite radiographic guidelines for use of MRI, the decision by the ordering physician regarding who should receive an MRI prior to NAT still appears to be unsystematic. This could be due to incorrect interpretation of radiographic reports by the ordering physicians and lack of availability or access to the interpreting radiologist. Results suggest that the ordering physician is already aware of multifocal disease and is utilizing MRI to verify this presence, rather than using MRI to investigate the possibility of multifocality in dense breast tissue. Based on this strategy of use it is unlikely that MRI will reduce the rate of mastectomy in this patient population. Table 1: Summary of radiographic features in MRI vs. non-MRI cohorts MRI (N=40)No MRI (N=40)Chi SquareDensity (C or D)25 (62.5%)16 (40.0%)p=0.11Multifocal Disease16 (40.0%)7 (17.5%)p=0.03Multi-centric Disease7 (17.5%)3 (7.5%)p=0.19Lymph Node Involvement31 (77.5%)25 (62.5%)p=0.25Calcifications Present23 (57.5%)21 (52.5%)p=0.56 Citation Format: Illmann C, Simmons CE, McDermott M, Xu J, Wilson C. Do radiographic features influence the decision to order a breast MRI? A prospective cohort study [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 P3-02-09.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".