Computer-Integrated Methods for Breast Cancer Surgery and Brachytherapy Guidance
Bibliographic record
Abstract
Breast cancer is a leading cause of cancer-related death in women in Canada. Lumpectomy and brachytherapy are treatments of choice for early stage breast cancer, but the soft nature of the breast makes it difficult to deliver accurate treatment to the intended sites. Navigation systems offer improved accuracy, reduced cost, and reduced time requirement. Unfortunately navigation systems are still limited because anatomy can move relative to the body. This thesis makes three contributions to this area. \n \nIn navigated lumpectomy, the tumour is tracked rigidly relative to a tracked needle. However, no rigid relationship exists and the needle can spin in place around the tumour, resulting in erroneous tracking. I propose a method to isolate different types of change in position and orientation, then apply it to computationally compensate for lack of a physical rotation constraint. A phantom study shows that this method improved tumour tracking by 15\\% of its volume over the existing method. Fewer degrees of freedom are required, enabling smaller and more affordable hardware to be used. \n \nNavigated lumpectomy can rely on a technician's presence. This contributes to cost and crowding in the operating room. I propose to reduce reliance on the technician by delegating display-related tasks to a computer. Two methods are discussed - one to allow the surgeon to point an instrument to create their desired view, and one to automatically center the view on the target. Experiments show these methods eliminate over 100 manual interactions required by the technician in navigated lumpectomy. \n \nBeast brachytherapy is a technically-demanding soft tissue intervention that requires precise catheter placement. I propose an electromagnetic navigation system that consists of a tracked needle guide and a small sensor for catheter reconstruction. Phantom experiments show that needles can be placed more accurately when using the tracked needle guide compared to ultrasound guidance. Catheter reconstruction is shown to be accurate within 0.6 mm relative to computed tomography. \n \nIn conclusion, I have made synergistic contributions toward navigation in soft tissue surgery and brachytherapy. These contributions have been demonstrated in various experiments for two different procedures.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".