Abstract P3-12-10: Utilization patterns and temporal trends of internal mammary nodal irradiation (IMNI) at a tertiary cancer centre
Bibliographic record
Abstract
Abstract Purpose: Despite data from multiple randomized trials, the role and uptake of internal mammary nodal irradiation (IMNI) is variable. This study was designed to quantify the rates and determinants of IMNI at a tertiary cancer centre. Methods: Treatment records of consecutively treated breast cancer (BC) patients receiving adjuvant locoregional (LR) radiotherapy (RT) from January 1, 2012 to October 31, 2017 was studied. LR-RT and use of IMNI as a function of clinicopathological factors, use of deep inspiratory breath-hold (DIBH) and dosimetric parameters were retrieved. Patients were divided into two groups:Group1 received LR-RT that included the IMN's, supraclavicular (SCLV) ± axillary regions,Group2 received LR-RT directed only to the SCLV±axillary areas. For the purpose of utilization analysis and temporal trends, early(2012-2015) and late(2016-2017) cohorts were examined based on the year of RT delivery. To determine if the use of IMNI was dependent on BC risk, we defined 3 risk categories:1) pT1/2, N0); 2)pT1/2, N1; and 3) pT3/4, N2/3 disease. Differences between the risk categories and groups were evaluated using chi-square/ Fisher's and Mann Whitney test for categorical and continuous variables, respectively. Univariable and multivariable logistic regression analysis was done to determine factors associated with the receipt of IMNI. Results:A total of 1566 patients met eligibility (Group 1=376; Group 2=1190). Of these patients, the percentage receiving LR-RT remained constant (17%) over the study period but the proportion of patients receiving IMNI increased significantly each year (p<0.0001), and was higher in the late vs. early treatment cohort (55% vs 8%, p<0.0001).On univariable analysis, younger age, LVI positivity, medial/central location, increasing stage, PR negativity, mastectomy, axillary dissection ,receipt of chemotherapy and increasing number of positive nodes had higher odds of receiving IMNI. Radiation oncologists with < or ≥5 years of practice was predictive of IMNI (31.3% vs 20.5%, p<0.0001),staff having <5years in practice being more likely to recommend IMNI. The distribution of patients in the different risk categories was similar between Groups 1 and 2 (p=0.097), and identified that the majority of patients receiving IMNI were in risk category 2(83%). Further comparison of risk categories suggested that the odds of receiving IMNI was lower in risk category 3 vs. category 1 (p=0.033). On multivariable analysis, decreasing age age (p=<0.001) , medial quadrant(p=0.0026),PR negative(p=0.0011), mastectomy(p=0.0055) , increasing nodal positivity (p<0.0001)and late RT cohort (p=0.001) had increased odds of IMNI. Overall use of DIBH was significantly higher in those receiving IMNI (45% vs 26%, p<0.0001). Mean heart (2.2vs1.7Gy, p<0.0001) and total lung doses (7.8vs6.6Gy,p<0.0001) were also significantly higher with IMNI. Conclusion: There was a significant increase in utilization of IMNI from 2012 to 2017. Younger age, medial location, PR negativity and increasing number of positive nodes predicted for receipt of IMNI. Staff with<5years in practice were more likely to recommend LR-RT that included the IMNs. The use of DIBH significantly increased with IMNI and allowed for acceptable dosimetric constraints. Citation Format: Misra S, Lee G, Maganti M, Koch CA. Utilization patterns and temporal trends of internal mammary nodal irradiation (IMNI) at a tertiary cancer centre [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-12-10.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".