Real-World Prevalence, Treatment Patterns, and Economic Impact of EGFR- and ALK-Targeted Therapies in Non-Small Cell Lung Cancer: A Nationwide Analysis from Greece
Bibliographic record
Abstract
OBJECTIVES: To determine the prescribing prevalence of epidermal growth factor receptor (EGFR)- and anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC) patients in Greece and examine patterns of first-line tyrosine kinase inhibitor (TKI) utilization and associated treatment costs using nationwide real-world data. METHODS: A retrospective analysis of the national e-prescription database was performed, identifying patients initiating first-line treatment (FLT) for EGFR- or ALK-positive NSCLC between 1 January 2020 and 31 December 2022. Demographic characteristics, prescribing prevalence data, drug utilization patterns, total annual drug expenditures, and per patient treatment costs were assessed. All statistical analyses were performed using the statistical software SPSS-v.29. RESULTS: Overall, 1188 EGFR-positive (mean age of 70.93 ± 11.6) and 246 (mean age of 64.26 ± 12.6) ALK-positive NSCLC patients initiated FLT during the three-year study period. EGFR mutations were slightly more common in females (53%), peaking in the 70-79 age group (35%). ALK mutations were also more common among females (52%), particularly within the 60-79 age group. In EGFR-positive patients, osimertinib usage markedly increased from 41% in 2020 to 63% in 2022, primarily displacing afatinib (from 32% to 22%) and erlotinib (from 24% to 14%), with gefitinib prescriptions falling below 2%. Among ALK-positive patients, crizotinib utilization declined significantly from 60% to 16%, whereas alectinib increased to 59% by 2022. Annual EGFR-related total drug expenditures remained stable (€11.5 million in 2020 vs. €11.9 million in 2022), driven primarily by increasing osimertinib usage. Similarly, ALK-related annual drug expenditures showed stability, with costs predominantly attributed to rising alectinib utilization. CONCLUSIONS: This nationwide analysis highlights the rapid adoption of second- and third-generation TKIs for EGFR- and ALK-positive NSCLC in Greece, reflecting evolving clinical practice patterns. Although the target patient populations are relatively small, the associated economic burden is considerable. To ensure long-term sustainability of the Greek healthcare system, policymakers should critically assess the cost-effectiveness of these innovative therapies and align resource allocation with value-based care principles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".