Incidence and relevance of clinically indeterminate nonregional lymph nodes in the treatment of oesophageal cancer
Bibliographic record
Abstract
OBJECTIVES: Metastatic involvement of nonregional supraclavicular or superior mediastinal lymph nodes in distal oesophageal cancer is rare but has important implications for prognosis and management. The management of nonregional lymph nodes which appear indeterminate on CT and FDG PET-CT (subcentimeter nodes or those with preserved normal morphology, but increased FDG avidity) can present a diagnostic dilemma. This study investigates the incidence, work-up and clinical significance of nonregional clinically indeterminate FDG avid lymph nodes. METHODS: A single-centre retrospective review of all FDG PET-CT scans conducted over 5 years was conducted. Patients with mid- or distal oesophageal cancer with nonregional FDG avid nodes were identified. Subsequent work-up, management and outcomes were retrieved from electronic health records. RESULTS: Reports for 1189 PET-CT scans were reviewed. A total of 79 patients met the inclusion criteria. Of these, 18 (23%) were deemed to have disease and performance status potentially amenable to radical surgery and underwent further assessment. The indeterminate lymph nodes were successfully sampled via endobronchial ultrasound (EBUS) or ultrasound-guided fine-needle aspiration (US-FNA) in 100% of cases. 15/18 (83.3%) of samples were benign and proceeded to surgery. Outcomes for patients who proceeded to surgery were similar to other cohorts. None had pathology suggesting false-negative lymph node sampling. CONCLUSIONS: EBUS and US-FNA are effective means of sampling clinically indeterminate nonregional lymph nodes, and can significantly impact prognosis, and management. Further investigations in this context are of value in this cohort and should be pursued. Nonregional clinically indeterminate lymph nodes represent a diagnostic dilemma in oesophageal cancer staging. Additional investigations in the form of endobronchial ultrasound are effective at providing additional staging information, and can substantially influence patient care.
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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 | 0.002 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".