Cryoablation Protocols for Primary and Metastatic Lung Tumors: A Systematic Review and Meta-Analysis Evaluating Effectiveness and Safety of Percutaneous Cryoablation of Pulmonary Tumors
Bibliographic record
Abstract
PURPOSE: To evaluate 1-year local tumor control (LTC) after percutaneous cryoablation for lung tumors and to identify procedural protocol and patient/tumor characteristics associated with improved outcomes. MATERIALS AND METHODS: A systematic search of PubMed, Embase, and Web of Science was conducted. The primary outcome was LTC at 1 year. Secondary outcomes included the identification of factors associated with LTC and pooled adverse event rates, such as freezing lengths, number of cycles, tumor characteristics, and more. Data were pooled using a random-effects model, and meta-regression was used to analyze factors affecting LTC. RESULTS: Nineteen studies (786 patients, 1,048 tumors) yielded a pooled 1-year LTC of 90.5% (95% CI, 85.1%-94.1%). Multivariate meta-regression showed that smaller tumor size was significantly associated with improved LTC. Univariate analysis also identified that superior LTC was associated with a triple freeze-thaw protocols (vs double), a shorter first freeze duration, a longer final freeze duration, and ending the procedure with a thaw cycle. The incidence of adverse events (Common Terminology Criteria for Adverse Events [CTCAE] Grade ≥3) was 4.9% (95% CI, 2.9%-6.9%), with pneumothorax most common (28% of cases). CONCLUSIONS: Percutaneous cryoablation demonstrates high effectiveness for lung tumor control. Superior 1-year LTC is associated with smaller tumor size and a triple freeze-thaw protocol characterized by a short initial freeze followed by longer subsequent freezes. These findings provide a data-driven basis for standardizing cryoablation techniques.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.030 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".