Abstract P3-07-16: Comparing surrogates of oncotype Dx recurrence scores in invasive ductal carcinoma: How complicated does it have to be?
Bibliographic record
Abstract
Abstract Background: The Oncotype DX Recurrence Score (ODX) is commonly used to estimate recurrence risk and chemotherapy benefit in ER positive, node negative breast cancer but is associated with significant cost. The Magee score (MS), a free online calculation using numerical values of commonly assessed pathological features (ER, PR, Her2/neu, and Ki-67), has been validated as an ODX surrogate. This includes the MS using classic H scores (HMagee) or Surrogate H scores derived from total % positive cells and average intensity or our use of Surrogate H scores derived from Allred scores (SMagee). Also Gage et al (2015) used a simple algorithm based on tumor grade, PR >1% and ER>20%. Here we compare three methods of predicting ODX scores. Design: 61 patients from The Ottawa Hospital with ER positive HER2/neu negative invasive ductal carcinoma (IDC), known ODX were assessed. Classic H scores (CH) were assessed using Image analysis. All cases had Ki67 (1000 cell hot spot score), tumor size, grade, and Allred scores available. Surrogate H scores (SH) were derived (average reported intensity x midpoint of reported Allred positive score range or absolute percentage if between 1-10%). The three MS were calculated with CH giving classic MS (HMagee) and with SH giving surrogate scores (SMagee). Each case was also categorized using rules published by Gage (2015): LOW predicted if Low Grade and PR positive (>1%), HIGH predicted if High Grade or Low ER (<20%). Agreement with classic ODX categories of low (<18), intermediate (18-30) and high risk (≥ 31) was assessed. Results: ODX included 35 low, 20 intermediate and 6 high risk (Mean 17.4, sd=10). There was a good correlation between corresponding HMagee and SMagee 1-3(0.820, 0.73, 0.84). Concentrating on the theoretically best MS1 that includes Ki67 and all clinical variables (Table 1); HMagee1 predicted 32 cases as low grade with 78% accuracy and SMagee1 predicted 26 low grade with 92.3% accuracy. Of note, all intermediate ODX predicted low by MS were ODX 18-20. None of the MS falsely predicted high risk in low risk ODX. The one case of high ODX which MS predicted low (false negative) is controversial as an Allred ER&PR 8 tumor gave low ODX PR score. Using SMagee1 in this population would safely leave 32 (52.5%) in the intermediate category. Of note, all intermediate ODX predicted low by MS were borderline ODX 18-20. The Gage algorithm in our population is less useful with higher discordance rate and left 69% in the intermediate category. Table 1TestMagee RiskNODX LowODX IntermediateODX High%DiscordantHMagee1Low (<18)3225/78%7/22%07/22% Moderate(≥18-30)2810/36%15/54%3/11%13/46% High (≥31)1001/1.6%0SMagee1Low (<18)2624/92%1/4%1/4%2/8% Moderate(≥18-30)3211/33%19/58%3/9%14/44% High (≥31)2002/100%0GageLow108/80%1/10%1/10%2/20% Moderate4225/60%16/38%1/2.4%26/62% High92/22%3/33%4/44%5/56% Conclusions: In IDC, while simpler algorithms without proliferation markers do not perform as well; SMagee1 based on Allred, performs at least as well in prediction of ODX as an MS based on classic H score and can potentially save considerable time and money. In our hands the simpler Gage algorithm does not perform as well. Citation Format: Robertson SJ, Petkiewicz SL, Arnaout A, Clemons M, Gravel DH, Pond GR. Comparing surrogates of oncotype Dx recurrence scores in invasive ductal carcinoma: How complicated does it have to be?. [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 P3-07-16.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".