Emerging Role of Targeted Therapies Combined With Radiotherapy in Inoperable Stages I to III NSCLC: A Review From the IASLC ART Subcommittee
Bibliographic record
Abstract
Precision oncology has transformed the management of NSCLC by tailoring treatment to the specific genetic alterations driving oncogenesis. Targeted therapies, such as tyrosine kinase inhibitors, have been found to dramatically improve survival in patients with advanced-stage NSCLC. However, treatment options remain limited for patients with early or locally advanced stage (I-III) NSCLC harboring driver mutations, when the disease is not resectable, or the patient is unsuitable for surgery due to poor fitness or comorbidities. There is growing interest in combining targeted therapies with radiotherapy to optimize treatment outcomes for this patient group. Notably, a progression-free survival benefit has recently been reported with the third-generation tyrosine kinase inhibitor osimertinib in patients with inoperable, EGFR-mutated, stage III NSCLC after chemoradiotherapy. A narrative review of the literature was performed using PubMed, OVID (EMBASE), and ClinicalTrials.gov to identify studies evaluating the combination of targeted therapies and radiotherapy in inoperable stages I to III NSCLC. This review provides a comprehensive overview of the incidence of actionable driver alterations and emerging clinical evidence on combining targeted therapies with thoracic radiotherapy in patients with inoperable stages I to III NSCLC. The toxicity profile of combination treatments, optimal sequencing strategies, ongoing clinical trials, and future perspectives in this field are highlighted. In summary, a clear biological rationale supports the synergistic effects of combining targeted therapies with radiotherapy in the neoadjuvant, concurrent, and adjuvant settings. Advanced clinical trial methodologies may facilitate further research in this area, particularly for rare genetic alterations, to improve outcomes for these patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 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) | 0.003 | 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".