Strategies for obtaining bone biopsy specimens from breast cancer patients – Past experience and future directions
Bibliographic record
Abstract
BACKGROUND: Cancer and its treatment can have multiple effects on the bone. Despite the widespread use of in vivo and in vitro models, it is still necessary to understand these effects in humans. Obtaining human bone biopsies is technically challenging and in this article we review the experiences from the Ottawa Bone Oncology Program. METHODS: A series of bone biopsy studies in breast cancer patients with and without bone metastasis have been performed. We reviewed the results of these studies and present them in a descriptive manner. We discuss lessons learned from each project and how they have affected future directions for research. RESULTS: Since 2009, 5 studies have been performed accruing 97 breast cancer patients. Study endpoints have ranged from comparing the yield of malignant cells from CT-guided versus standard iliac crest biopsies, to studies assessing the feasibility of micro-CT analysis on Jedhadi trephines to evaluate bisphosphonate effects on bone micro-architecture. More recently, we have assessed the feasibility of performing repeat bone biopsies in the same patient as well as evaluating the practicality of obtaining bone tissue at the time of orthopaedic surgery. CONCLUSION: Human bone tissue is an important biological resource. Our experience suggests that obtaining bone biopsies is feasible and can yield adequate amount of tumour cells for many studies. However, these remain technically challenging specimens to obtain and given the rapid advances in cancer therapeutics and the use of potent adjuvant bone-targeted agents, more centres need to be involved in these types of studies.
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".