Current Radiotherapy Management of Extensive-Stage Small-Cell Lung Cancer in the Immunotherapy Era: An Italian National Survey on Behalf of the Italian Association of Radiotherapy and Clinical Oncology (AIRO)
Bibliographic record
Abstract
BACKGROUND: Extensive-stage small-cell lung cancer (ES-SCLC) treatment has recently been revolutionized by the advent of immune checkpoint inhibitors. This survey was conducted to evaluate the current pattern of care among Italian clinicians, in particular about the integration with radiation therapy (RT). METHODS: In June 2023, 225 Italian cancer care professionals were invited to complete a 21-question web-based survey about ES-SCLC management through personal contacts and the Italian Association for Radiotherapy and Clinical Oncology (AIRO) network. RESULTS: We received 90 responses; the majority were radiation oncologists (89%) with more than 10 years of experience (51%). The preferred management of ES-SCLC in patients with a good performance status was concomitant chemo-immunotherapy (84%). Almost all respondents recommended prophylactic cranial irradiation (PCI) (85%), taking into account age and thoracic response; PCI was performed mainly between the end of chemotherapy and before starting immunotherapy (37%), with a three-dimensional conformal technique (46%). Furthermore, 83% of respondents choose to deliver thoracic RT in the case of both an intrathoracic and extrathoracic response, with an RT schedule of 30 Gy/10 fractions. Stereotactic RT is increasingly being used in oligoprogressions. CONCLUSIONS: Our analysis showed the variability of real-world management of ES-SCLC. Future clinical trials and developments are needed to improve the multidisciplinary treatment of these patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".