Impact of Preoperative Magnetic Resonance Imaging on Surgical Outcomes in Women with Invasive Breast Cancer: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background. Currently, whether magnetic resonance imaging (MRI) should be routinely applied to patients with breast cancer before surgery remains controversial. A pooled analysis of the association between preoperative MRI and surgical outcomes in female patients with newly diagnosed invasive breast cancer was conducted to provide evidence‐based medicine for clinical practice. Methods. Three independent researchers searched the following databases: PubMed, Medline, Embase, Ovid, Cochrane Library, and Web of Science from inception to April 2022. Literature was included and excluded according to Cochrane’s principles. The basic information from eligible documents was extracted. Systematic evaluation and meta‐analysis were performed, and the odds ratio (OR) was analyzed by the random‐effect model. The quality of the literature was assessed using the modified Jadad scale and the Newcastle‐Ottawa (NOS) mean scale. Results. A total of 19 studies were included, including 4 randomized controlled trials and 15 observational comparative studies. Among them, most studies were not limited to a specific pathological type, with the exception of 3 that were limited to invasive lobular carcinoma. The results showed that preoperative MRI examination would significantly reduce the reoperation rate (OR = 0.77, P = 0.02) and increase the mastectomy rate (OR = 1.36, P = 0.001). In comparison, preoperative MRI did not significantly affect the rate of secondary mastectomy (OR = 0.77, P = 0.02), the rate of positive margin (OR = 1.08, P = 0.66), the rate of mastectomy (OR = 1.00, P < 0.05), and reoperations (OR = 0.65, P = 0.19) in the subgroup analysis of patients with invasive lobular carcinoma. Conclusion. Available evidence suggests that preoperative MRI examination increases the rate of mastectomy and reduces the rate of reoperations. The results indicate that preoperative MRI examination has the potential to benefit patients with breast cancer, but more high‐quality studies are needed for confirmation.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".