Modern Imaging Guidelines for 3-D Modeling of Pediatric Solid Tumors: A New Era of Surgical Oncology Operative Planning
Bibliographic record
Abstract
Background: Three-dimensional (3-D) models are gaining interest for surgical planning in the field of pediatric surgical oncology. The diverse tumor types in pediatric patients and the non-uniform presentation and anatomy make 3D modeling in pediatric oncology particularly challenging. In addition, centers have variability in technique and experience with these approaches. Guidelines for model creation and quality assurance are notably absent. Objective: We developed national recommendations pertaining to image acquisition and model creation for 3-D renderings based on the existing literature and multidisciplinary expertise. Materials and Methods: The literature was critically appraised, and the authors developed minimum standard guidelines for imaging and modeling pediatric solid tumors. Strength of recommendation scores for each guideline were independently determined by the authors. Results: Twelve specific guidelines were developed and scored with overall strength of recommendation ranging from moderate to very strong. Guidelines focused on image acquisition were uniformly scored very strongly while scores for those pertaining to segmentation and model display were slightly less consistent. Conclusions: We propose minimum standards for image acquisition and modern 3-D modeling of pediatric solid tumors based on literature and expertise. The overall level of agreement among our multidisciplinary team was high.
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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 | 0.034 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".