Are NCCN Resource-Stratified Guidelines for Breast Cancer Systemic Therapy Achievable? A Population-Based Study of Global Need and Economic Impact
Bibliographic record
Abstract
PURPOSE: Resource-stratified guidelines (RSG) for cancer provide a hierarchy of interventions, based on resource availability. We quantify treatment need and cost if National Comprehensive Cancer Network (NCCN) RSGs for breast cancer (BC) are adopted globally. METHODS: We developed decision trees for first-course systemic therapy, merged with SEER and Global Cancer Observatory 2018 incidence data to estimate treatment need and cost if NCCN RSG are implemented globally based on country-level income. Simulations were used to quantify need and cost of globally scaling up services to Maximal. RESULTS: Based on NCCN RSG, first-course chemotherapy is indicated in 0% (Basic), 87% (Core), and 86% (Enhanced) but declined to 50% (Maximal) because of incorporation of genomic profiling. First-course endocrine therapy (ET) is indicated in 80% in all settings. In 2018, treatment need was 1.4 million people for chemotherapy, 183,943 for human epidermal growth factor receptor 2 (HER2) therapies and 1.6 million for ET. The cost per person for chemotherapy or HER2 or immunotherapy increased by 17-fold from Core to Maximal ($1,278-$22,313 Australian dollars [AUD]). The cost of ET per person rose eight-fold from Basic to Maximal ($1,236-$9,809 AUD). If all patients with BC globally were treated with Maximal resources, the need for chemotherapy would decline by 28%, whereas cost of first-course treatment would rise by 1.8-fold ($21-$37 billion AUD) because of more costly therapies. CONCLUSION: NCCN RSGs for BC could result in chemotherapy overtreatment in Core and Enhanced settings. The absence of chemotherapy in Basic settings should be reconsidered, and future iterations of RSG should perform cross-tumor comparisons to ensure equitable resource distribution and maximize population-level outcomes. Our model is flexible and can be tailored to the costs, population attributes, and resource availability of any institution or country for health-services planning.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".