Abstract P4-07-04: Bc cancer ipsilateral breast tumor recurrence (BCC IBTR) nomogram
Bibliographic record
Abstract
Abstract INTRODUCTION:. Local and systemic treatments for breast cancer have evolved in the past decades. This study reports the development of a modern population-based nomogram to individualize local recurrence (LR) risk estimates for patients treated with breast conservation surgery (BCS). The magnitude of benefit of adjuvant breast radiotherapy (BRT) depends on individual patient, tumor, and treatment characteristics (1). Nomograms can provide accurate predictions of LR and the absolute LR benefit of BRT that can assist patients in making informed decisions regarding BRT. This nomogram is based on a large cohort of women with prospectively captured biomarker data and modern systemic therapies including anti-human epidermal growth factor receptor 2 (HER2) therapy. METHOD:. Study Population:. The study cohort included women treated curatively for newly diagnosed breast cancer between 1st January 2005 and 31st December 2014. Inclusion criteria were: age >16 years, invasive ductal or lobular carcinoma, stage I-III, and BCS. Patients with metastatic disease, prior or synchronous contralateral breast cancer, unknown tumor or treatment characteristics, or treated with neoadjuvant therapy or mastectomy were excluded. Nomogram Development and Validation:. Age, tumor size, number of positive lymph nodes, grade, margin status, lymphovascular invasion (LVI), extensive intraductal component (EIC), estrogen receptor (ER), progesterone receptor (PR), HER2 status, use of chemotherapy, hormonal therapy, and radiotherapy with or without boost were recorded for each patient. The endpoint was LR as the first event. Fine and Gray’s competing risk model, with distant recurrence and death as competing risks, was used for the multivariable analysis, adjusting for demographics, tumor, and treatment factors. Hazard ratio (HR) and 95% confidence interval (CI) for each variable were calculated. The multivariable model forms the basis for the nomogram, which is being internally validated using the bootstrap and cross-validation. RESULTS:Of 11,310 patients, there were 429 LR (crude risk = 3.8%). The HR and 95% CIs from the Fine and Gray model for each of the variables in the nomogram are presented in the table. Age, number of positive nodes, grade, ER, LVI, margins, hormone therapy, chemotherapy, and radiotherapy were independent prognostic factors for LR. For patients treated with RT, the predicted 10-year cumulative incidence of LR ranged from 2.4% in patients with low-risk disease to 12.5% in patients with high-risk disease. CONCLUSION:A new nomogram for local recurrence, based on patients who had ER/PR/HER2 testing and who received modern systemic therapies is being developed. It will assist clinicians and patients individualize estimates of local recurrence risk and improve shared decision-making regarding the use of BRT in contemporary practice. REFERENCES:. (1)Sanghani, M., et al J Clin Oncol.,2010; 28(5), 718-722. Cox regression hazard ratios and confidence intervals for variablesCharacteristicHR95%CIp-valueAge0.980.970.99<0.01T-size1.011.001.010.14No. nodes1.041.011.070.005GradeGrade1---Grade21.751.322.33<0.001Grade32.541.813.56<0.001ERNeg---Pos1.441.012.060.046PRNeg---Pos0.790.611.030.084Her2Neg---Pos0.960.731.250.8LVINeg---Pos1.961.552.47<0.001Unk1.360.722.580.3Margin StatusNeg---Close1.411.081.840.011Pos1.681.082.590.200Extensive DCISNo---Yes1.140.841.550.4HTNo---Yes0.520.410.65<0.001ChemoNo---Yes0.540.420.71<0.001RTNo---Yes0.330.260.42<0.001BoostNo---Yes0.800.621.040.094 Citation Format: Dylan Narinesingh, Alan Nichol, Pauline Truong, Lovedeep Gondara, Caroline Speers, Laveniya Kugathasan, Caroline Lohrisch, Dave Voduc, Nafisha Lalani. Bc cancer ipsilateral breast tumor recurrence (BCC IBTR) nomogram [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-07-04.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.008 | 0.003 |
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".