MRI as a viable alternative to CT for 3D surgical planning of cavitary bone tumors
Bibliographic record
Abstract
Cavitary bone defects, defined as a volumetric loss of native bone tissue, require accurate preoperative imaging for treatment planning. While CT (computed tomography) has traditionally been the gold standard for segmentation due to its superior resolution of cortical bone, MRI (magnetic resonance imaging) offers unique advantages, particularly in visualizing the soft tissue-bone interface. Furthermore, MRI eliminates the ionizing radiation associated with CT, making it an advantageous alternative, especially in the management of benign and low-grade malignant bone tumors. Despite these advantages, MRI's inherently lower spatial resolution may introduce artifacts, which can complicate segmentation accuracy. This study evaluates the feasibility of MRI as a viable alternative to CT in the preoperative planning of cavitary bone defect treatment. We analyzed CT and MRI scans from 80 patients with benign and locally aggressive primary bone tumors, generating three-dimensional (3D) models through manual segmentation in Mimics, validated using Geomagic Control X. Volumetric differences between the CT- and MRI-derived models were assessed using the Wilcoxon signed-rank test and paired t-test. The mean volumetric difference between MRI and CT scans was 2.68 ± 1.44 %, which was not statistically significant (p = 0.15). Additionally, multiple regression analysis examining sex, age, and diagnosis revealed no significant differences in the 3D model volumes derived from the two imaging modalities (sex: p = 0.51, age: p = 0.98, and diagnosis: p = 0.50). These results support MRI-based segmentation as a reliable, radiation-free alternative to CT, particularly when precise delineation of soft tissue boundaries is critical for surgical planning.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".