Microcomputed Tomography Is a Precise Method That Allows for Topographical Characterization of Lymph Nodes and Lymphatic Vessels
Bibliographic record
Abstract
Background: Surgical excision and/or radiation targeting of regional lymph nodes are an essential component in the clinical management of cancer. Importantly, a more accurate understanding of lymphatic anatomy could enable refinement of present treatment strategies. Given the spatial resolution limitations of contemporary imaging methods, our group sought to utilize noncontrast-enhanced microcomputed tomography (μCT) imaging to clarify regional lymphatic anatomy. Methods and Results: This study was conducted with embalmed en bloc lymphatic tissue packets from six donors (three females and three males: median age of death = 78 years). All specimens were investigated with noncontrast-enhanced μCT imaging using a conebeam-CT imaging system. Adipose and lymphatic tissues were segmented by radiodensity based on sampling regions of interest. To confirm the observations from μCT, lymph nodes from each packet were exposed to hematoxylin and eosin staining and anti-D240 immunostaining. Following μCT imaging, mean peak radiodensities of −203.14 ± 19.35 Hounsfield units (HU) and 37.25 ± 31.95 HU were revealed for adipose and lymphatic tissues, respectively ( p < 0.01). By analyzing histograms of the radiodensity distributions, we determined a threshold of −82.42 HU to differentiate adipose and lymphatic tissue, to generate three-dimensional renderings, and to calculate quantitative metrics. On average, adipose tissue comprised 9.62 ± 3.60 cm 3 (73.6%) of the total packet volume, whereas lymphatic tissue comprised 3.47 ± 2.71 cm 3 (26.4%). Moreover, each en bloc packet contained four small lymph nodes (1–5 mm) and three to four large lymph nodes (>5 mm). Histology corroborated the observations from μCT. Conclusions: Altogether, a precise understanding of regional lymphatic anatomy elucidated by the present imaging modality may help refine clinical cancer treatment strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".