Utility of 3D-printed vascular modeling in microsurgical breast reconstruction: a systematic review
Bibliographic record
Abstract
BACKGROUND: Microsurgical breast reconstruction presents a technical challenge in preoperative planning and flap harvest. Given the limitations of computed tomographic angiography as a preoperative aid, 3D printing has emerged as an avenue for creating patient-specific anatomical models for pre- and intraoperative use. This systematic review assesses the current use and utility of 3D-printed vascular models (3DVMs) in microsurgical breast reconstruction. METHODS: MEDLINE, Embase, and CENTRAL were searched for English articles published from 1946 to 2024. Studies utilizing 3D-printed vascular modeling in the context of microsurgical breast reconstruction were included if they reported surgical, model-, or user-related outcomes. The Newcastle-Ottawa Scale and Joanna Briggs Institute checklists were used for quality assessment. Results were reported according to PRISMA guidelines. RESULTS: Six hundred and nineteen records were retrieved. Following specific inclusion and exclusion criteria, 29 studies underwent full-text review. Eight studies totaling 181 patients and 261 flaps were included in the final analysis. 3DVMs were used to model deep inferior epigastric perforator (DIEP) and muscle-sparing transverse rectus abdominis myocutaneous (MS-TRAM) flap perforator origin, course, distribution, and surrounding anatomy. They were used for perforator selection, flap harvest, and training. Use of 3DVMs reduced harvest time by up to 23 min per case. No complications or preoperative plan deviations were reported in 3DVM-guided cases. Surgeons endorsed significant model utility in anatomical visualization, preoperative planning, and flap harvest. Model cost, production time, and adoption were identified as barriers to use. CONCLUSIONS: 3DVMs can enhance preoperative planning, intraoperative decision-making, and operative efficiency in unilateral DIEP and bilateral MS-TRAM flap breast reconstructions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".