Lymph Node Metastases Detection Using Gd<sub>2</sub>O<sub>3</sub>@PCD as Novel Multifunctional Contrast Imaging Agent in Metabolic Magnetic Resonance Molecular Imaging
Bibliographic record
Abstract
Axillary lymph node detection is crucial to staging and prognosis of the lymph node metastatic spread in breast cancer. Currently, lymphoscintigraphy and blue dye, as the conventional methods to localize sentinel lymph nodes (SLNs), are invasive and can only be performed during surgery. This study has had a novel hybrid gadolinium oxide nanoparticle coating with Cyclodextrin‐based polyester as a high‐relaxivity T1 magnetic resonance molecular imaging (MRMI) contrast agent (CA). Twelve female BALB/c mice were randomly divided into three groups of four mice; each group was injected with 4T1 cells to obtain metastasis lymph nodes and diagnosed by using the 3D T1W (VIBE) MRI (Siemens 3T, Prisma). The synthesized Gd2O3@PCD nanoparticles with a suitable particle size range of 20–40 nm have had much higher longitudinal relaxivity (r1) for Gd2O3@PCD and Gd‐DOTA (Dotarem) with the values of 3.98 mM−1·s−1 ± 0.003 and 2.71 mM−1·s−1 ± 0.005, respectively. Identical MR images in coronal views were subsequently obtained to create time‐intensity curves of the right axillary lymph nodes and to measure the contrast ratio (CR). The peak CR and qualitative assessment of axillary lymph nodes at five‐time points were evaluated. After subcutaneous injection, the contrast ratio of axillary lymph node and tumor in mice exhibited CR peak of Gd2O3@PCD and Dotarem with the values of 2.21 ± 0.06 and 0.40 ± 0.004 for lymph node and 2.54 ± 0.04 and 1.21 ± 0.007 for the tumor, respectively. Furthermore, the lumbar‐aortic lymph node is weakly visible in the original coronal image. In conclusion, the use of Gd2O3@PCD nanoparticles as novel MRMI CAs enables high resolution for the detection of lymph node metastasis in mice with the potential capability for breast cancer diagnostic imaging.
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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 | 0.000 | 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 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".