Computed Tomography–Based Distribution of Involved Lymph Nodes in Patients with Upper Esophageal Cancer
Bibliographic record
Abstract
BACKGROUND: Delineating the nodal clinical target volume (ctvn) remains a challenging task in patients with cervical or upper thoracic esophageal carcinoma (ec). In particular, the extent of the lymph area that should be included in the irradiation field remains controversial. In the present study, the extent of the ctvn was determined based on the incidence of lymph node involvement mapped by computed tomography (ct) imaging. METHODS: Our study included 468 patients who were diagnosed with cervical and upper thoracic ec and who received staging information between June 2005 and April 2011. The anatomic distribution of metastatic regional lymph nodes was mapped using ct images and grouped using the levels established by the Radiation Therapy Oncology Group. The probability of the various groups being involved was examined. If a lymph node group had a probability of 10% or more of being involved, it was considered at high risk for metastasis, and elective treatment as part of the ctvn was recommended. RESULTS: Lymph node involvement was mapped by ct in 256 patients (54.7%). Not all lymph node groups should be included in the ctvn. For cervical lesions, the involved lymph nodes were located mainly between the hyoid bone and the arcus aortae; the recommended ctvn should consist of the neck lymph nodes at levels iii and iv (supraclavicular group) and thoracic groups 2 and 3P. In upper thoracic ec patients, most of the involved lymph nodes were distributed between the cricoid cartilage and the subcarinal area; the ctvn should cover the supraclavicular group and thoracic nodal groups 2, 3P, 4, 5, and 7. CONCLUSIONS: Our ct-based study indicates a specific distribution and incidence of metastatic lymph node groups in patients with cervical and upper thoracic ec. The results suggest that regional lymph node groups should be electively included in the ctvn for precise radiation administration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".