The feasibility of three-dimensional displays of the thorax for preoperative planning in the surgical treatment of lung cancer
Bibliographic record
Abstract
OBJECTIVE: Three-dimensional (3D) displays of anatomic structures have become feasible for preoperative planning in some surgical procedures. There have been no reports, however, on the use of 3D displays for surgical treatment of lung cancer. We hypothesized that 3D displays of the thorax are useful for preoperative planning for lung cancer. METHODS: Based on virtual reality technologies, we rendered 3D displays of the thorax from two-dimensional (2D) computed tomographic (CT) images of six anonymous patients, some of whom underwent surgical removal of lung cancer. For determining the resectability of lung cancer, we tested 17 participants with varying degrees of surgical skills to view 3D displays and read 2D CT images of these thoracic cavities in a randomized order. We measured their performance in terms of the accuracy of predicted resectability, the confidence of their prediction, planning time used, and workload experienced. RESULTS: The results demonstrated that viewing 3D displays of thoracic cavities has significant advantages over reading 2D CT images in determining the resectability of lung cancer: increasing the accuracy of predicted resectability by about 20%, enhancing the confidence of the prediction by about 20%, decreasing planning time by about 30%, and reducing workload by about 50%. All participants preferred viewing 3D displays to reading 2D CT images for preoperative planning. Junior residents found 3D displays of thoraces more useful than senior residents. CONCLUSIONS: It is feasible to use 3D displays of the thorax for preoperative planning in treating lung cancer. Using 3D displays in surgical treatment of lung cancer has potential benefits, once the technique is perfected.
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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 | 0.007 | 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".