Imaging and quantification of prostate cancer-associated bone by polarization-sensitive optical coherence tomography
Bibliographic record
Abstract
Abstract Prostate cancer frequently metastasizes to bone, leading to a spectrum of osteosclerotic and osteolytic lesions that cause debilitating symptoms. Accurate differentiation of bone tissue or lesion types can provide opportunity for local pathologic investigation, which is critical for understanding the bone metastasis and remains challenging. Current imaging methods lack the ability to directly differentiate tissues based on collagen organization and may induce invasive effect on bone tissues. We introduce Polarization-Sensitive Optical Coherence Tomography (PS-OCT) to investigate normal, osteosclerotic, and osteolytic bone tissues. High-resolution PS-OCT imaging reveals collagen fiber arrangement, enabling nuanced distinction of degree of collagen alignment among different bone tissue types or regions. We present a novel feature named degree of ordered organization (DOO), derived from the multiple contrasts of PS-OCT that can quantitatively evaluate bone samples from different pathologic groups, including control,osteoblastic and osteolytic tissues. The capacity of PS-OCT to differentiate trabecular/lamellar and irregular (woven bone) regions within the same specimen is tested and validated on ex-vivo samples extracted from 13 subjects. Our study is the first time that PS-OCT is applied to metastatic bone disease with the aim of enhancing the understanding of bone-related pathologies, and potentially impacting clinical practice. This work demonstrates that PS-OCT can provide useful insight into bone microstructures, and thus it has potential applications across diverse bone disorders.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".