Cryotherapy in the Treatment of Early-Stage Breast Cancer
Bibliographic record
Abstract
Breast cancer is one of the most common malignancies, affecting millions of people worldwide annually. The treatment paradigm for early-stage breast cancer is in flux. The focus is now on opportunities to de-escalation treatment to minimize morbidity and maximize patients' quality of life. Recently, percutaneous minimally invasive ablative techniques have been explored. Early trials in small population of patients demonstrated cryoablation to be effective, safe, and well-tolerated in an outpatient setting. Subsequent surgical resection was performed and the ablation success rate was the highest if the tumor was less than 1.5 cm and with < 25% ductal carcinoma in situ component. ACOSOG Alliance Z1072, a phase II trial with curative intent, demonstrated 100% ablation in all tumors smaller than 1 cm and 92% success in lesions without multifocal disease and less than 2 cm in size. There are ongoing prospective clinical trials to investigate the efficacy of cryoablation without surgical excision for treatment of early-stage breast cancer. FROST (Freezing Instead of Removal Of Small Tumors) started in 2016 is ongoing, ICE3 (Cryoablation of Low Risk Small Breast Cancer) started in 2014 just released 5 years results, and COOL-IT: Cryoablation vs Lumpectomy in T1 Breast Cancers is also ongoing. These prospective trials will expand our knowledge on the safety and value of cryoablation. It is crucial to understand the indications, technical nuances, and distinctive imaging findings for cryoablation as it has potential to revolutionize standard surgical practice. World J Oncol. 2024;15(5):737-743 doi: https://doi.org/10.14740/wjon1909
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".