Defining the role of radiofrequency ablation and stereotactic ablative radiotherapy in patients with high‐risk, early‐stage non‐small cell lung cancer
Bibliographic record
Abstract
We read with interest the report by Dupuy et al describing 2-year outcomes in patients with medically inoperable, early-stage non-small cell lung cancer (ES-NSCLC) who were treated with computed tomography-guided radiofrequency ablation (RFA).1 Their data add to the growing body of evidence demonstrating a low incidence of regional disease recurrence in patients with ES-NSCLC after local ablative therapy without any invasive lymph node staging procedures. It is questionable, however, whether their data support the overarching suggestion that RFA is a reasonable first-line alternative to stereotactic ablative radiotherapy (SABR) for patients with medically inoperable ES-NSCLC. The authors argue that the 2-year overall survival (OS) between RFA and SABR is similar, “even in an older and sicker [RFA] cohort.” Although competing risks have a clear impact on survival in these high-risk patients,2 details regarding comorbidities were lacking. The importance of comorbidity in any survival comparison with historical SABR data is highlighted by the example of a 2-year actuarial OS rate of 100% noted among in potentially operable patients from 2 randomized trials who were undergoing lung SABR.3 A purported benefit of RFA over SABR is the preservation of lung function, and the authors cite the Radiation Therapy Oncology Group 0236 trial as reporting a 12% reduction in the diffusing capacity for carbon monoxide after SABR.4 We would like to point out that the article cited by Dupuy et al1 did not report any analysis of pulmonary function. In fact, a subsequent report on the Radiation Therapy Oncology Group 0236 trial noted a nonsignificant decline in the diffusing capacity for carbon monoxide of 6% at 2 years.5 The authors argue that local recurrence rates of 40% were acceptable in their study because of the ability to salvage failures with either SABR or repeat RFA, and because local failures did not appear to impact on OS. This finding needs to be considered within the context of the study's small sample size and risk of making a type II error (ie, failure to reject a false null hypothesis). It would be particularly enlightening if the authors had provided the number of patients at risk in the Kaplan-Meier analyses. Nonetheless, any perceived acceptability of local recurrence rates should be tempered by the psychological impact of recurrent disease, as well as the morbidity and resource implications of salvage treatments. We concur with the editorial accompanying the article that the high local recurrence rates after RFA decrease enthusiasm for its use as a first-line option for high-risk patients who are also eligible for SABR or surgery.6 RFA may have a role in previously irradiated, frail patients, although determining the efficacy of RFA versus best supportive care in a clinical trial would be challenging. Modeling studies can be helpful in such situations; for example, we previously constructed a Markov model to simulate quality-adjusted life years in extremely comorbid patients with ES-NSCLC who were receiving either SABR or best supportive care.7 Combining such data with robust cost information, which can differ greatly from reimbursement rates,8 is crucial to inform cost-effectiveness in the era of increasing awareness of the financial burdens associated with cancer treatment.9 The VU University Medical Center has a research agreement with Varian Medical Systems. Dr. Senan has received honoraria and travel support from Varian Medical Systems for work performed as part of the current study. He has also acted as a paid member of the Advisory Board for Lilly Oncology for work performed outside of the current study. Alexander V. Louie, MD, MSc, FRCPC Department of Radiation Oncology London Regional Cancer Program London, Ontario, Canada; Department of Radiation Oncology VU University Medical Center Amsterdam, the Netherlands Shankar Siva, MBBS, FRANZCR Department of Radiation Oncology Peter MacCallum Cancer Centre East Melbourne, Victoria, Australia Suresh Senan, MRCP, FRCR, PhD Department of Radiation Oncology VU University Medical Center Amsterdam, the Netherlands
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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".