Abstract P6-01-01: Impact of imaging surveillance on the risk of radiation induced malignancies in breast cancer survivors
Bibliographic record
Abstract
Abstract Introduction: After curative treatment of breast cancer (BC), relevant clinical guidelines recommend against the use of imaging procedures other than yearly mammography for surveillance, based on the lack of survival benefit for intensive surveillance strategies. Nevertheless, use of non-recommended imaging tests occurs frequently in this context. Most BC surveillance studies have focused on the potential benefit of detection of early relapse, on financial burden, and risk of false positives with different follow-up regimens. No study has analyzed the risk of imaging radiation induced malignancies (IRIM) in BC survivors exposed to repeated body imaging during surveillance. We previously reported on the IRIM risk in the BC clinical trials setting (Fresco R. The Oncologist 2015). In this current study we report on this risk during surveillance in clinical practice in BC survivors. Objective: To estimate IRIM risk in patients curatively treated for BC undergoing imaging tests during surveillance. Methodology: We defined 6 surveillance strategies with differing imaging requirements, from a non imaging-intensive one (yearly mammography only) to intensive ones (mammography + CT, Bone scan, PET-CT and/or MUGA) (Table 1). For each strategy we calculated the imaging dose and excess lifetime attributable cancer risk (LAR) for a 60 year-old BC survivor, using NCI's Radiation Risk Assessment Tool (RadRat). Results: Total effective imaging radiation dose received by a 60 year-old BC survivor during surveillance was 8.4 miliSieverts (mSv) when only yearly mammography is performed to 199.9 mSv when CT, MUGA and bone scan are added. Mean IRIM LAR ranges from 37.2/100,000 with the first strategy to 1,330/100,000 with the latter. Performing MUGA scans increased IRIM risk 31% compared to not performing it. The addition of any additional radiating imaging procedure to yearly mammography significantly increases LAR. Imaging effective dose and LAR in different surveillance strategiesFollow-up strategyImaging effective dose (mSv)Excess lifetime attributable cancer risk: mean (90% uncertainty range) (/100,000)Yearly mammography only8.437.2 (21.4-60.3)Yearly mammography + Chest/abdomen CT q6mo for 3y, then annually for 2y128.4857.0 (503.0-1,350.0)Yearly mammography + Chest/abdomen CT q6mo for 3y, then annually for 2y + Bone scan q12mo for 5y159.91,060.0 (603.0-1,640.0)Yearly mammography + Chest/abdomen CT q6mo for 3y, then annually for 2y + MUGA q6mo for 2y168.41,130.0 (642.0-1,800.0)Yearly mammography + Chest/abdomen CT q6mo for 3y, then annually for 2y + MUGA q6mo for 2y + Bone scan q12mo for 5y199.91,330.0 (792.0-2,080.0)Yearly mammography + PET-CT q6mo for 3y, then annually for 2y184.41,310.0 (802.0-1,990.0) Conclusions: A number of incremental second cancers could be derived from imaging performed during BC surveillance after curative treatment. Addition of non-recommended imaging for relapse detection increases IRIM risk compared to performing only mammography. This, in addition to the lack of proven benefit in BC endpoints, emphasizes the need to follow recommendations for surveillance clinical guidelines, and forgo imaging studies other than annual mammography to detect relapses. Substituting MUGA with echocardiogram for cardiac assessment could also reduce IRIM risk. Citation Format: Spera G, Gonzalez V, Meyer C, Fung H, Mackey JR, Fresco R. Impact of imaging surveillance on the risk of radiation induced malignancies in breast cancer survivors. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P6-01-01.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".