Mammary Cancer Bone Metastasis Follow-up Using Multimodal Small-Animal MR and PET Imaging
Bibliographic record
Abstract
UNLABELLED: Despite tremendous progress in the management of breast cancer, the survival rate of this disease is still correlated with the development of metastases-most notably, those of the bone. Diagnosis of bone metastasis requires a combination of multiple imaging modalities. MR imaging remains the best modality for soft-tissue visualization, allowing for the distinction between benign and malignant lesions in many cases. On the other hand, PET imaging is frequently more specific at detecting bone metastasis by measuring the accumulation of radiotracers, such as (18)F-sodium fluoride ((18)F-NaF) and (18)F-FDG. Thus, the main purpose of this study was to longitudinally monitor bone tumor progression using PET/MR image coregistration to improve noninvasive imaging-assisted diagnoses. METHODS: After surgical implantation of mammary MRMT-1 cells in a rat femur, we performed minimally invasive imaging procedures at different time points throughout tumor development. The procedure consisted of sequential coregistered MR and PET image acquisition, using gadolinium-diethylenetriaminepentaacetic acid (DTPA) as a contrast agent for MR imaging and (18)F-FDG, (11)C-methionine, and (18)F-NaF as molecular tracers for PET imaging. The animals were then euthanized, and complementary radiologic (micro-CT scans) and histologic analyses were performed. RESULTS: In this preclinical study, we demonstrated that coregistered MR and PET images provide helpful information in a rat mammary-derived bone cancer model. First, MR imaging provided a high-definition anatomic resolution that made the localization of bone resorption and tumor extension detectable between days 9 and 18 after the injection of cancer cells in the medullary channel of the femur. Indeed, the calculation of mean standardized uptake value (SUVmean) and maximal SUV (SUVmax) in bone and soft-tissue regions, as defined from the gadolinium-DTPA contrast-enhanced MR images, showed (18)F-NaF uptake modifications and increased (18)F-FDG or (11)C-methionine uptake in the bone and surrounding soft tissues. (18)F-FDG and (11)C-methionine were compared in terms of the magnitude of change in their uptake and variability. We observed that (11)C-methionine SUVmean variations in the tumor were more important than those of (18)F-FDG. We also found fewer interindividual variations using SUVmean as a quantitative parameter than SUVmax. CONCLUSION: This preclinical evaluation demonstrated that a PET/MR image coregistration protocol provided a powerful tool to evaluate bone tumor progression in a rat model of bone metastasis and that this protocol could be translated to improve the clinical outcome for metastatic breast cancer management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".