A prospective analysis of lymph node retrieval in colorectal cancer: discrepancies, neoadjuvant impact, and practical implications
Bibliographic record
Abstract
Introduction Accurate lymph node (LN) retrieval is vital for colorectal cancer (CRC) staging and determining adjuvant therapy. Methods In this prospective study of 122 CRC specimens, we evaluated LN retrieval times, discrepancies between gross and microscopic LN counts, and the impact of neoadjuvant therapy. Results On average, dissecting each specimen took 50 min (range 15–295 min), with rectal and descending/sigmoid colon specimens often requiring multiple passes. Macroscopic dissection yielded an average of 45.1 LNs per specimen, whereas microscopic examination confirmed only 35.7 LNs on average. Neoadjuvant therapy did not alter macroscopic yields (p = 0.105), yet significantly reduced microscopic LN counts (p = 2.676 × 105). T-stage correlated with total microscopic LN counts (p = 0.018) but not the number of cancer-positive nodes (p = 0.140). Rectal specimens showed the largest discrepancy between macroscopic and microscopic LN counts; in contrast, 8 specimens had higher microscopic than macroscopic counts, suggesting that some LNs were missed during manual palpation but detected microscopically in the extra submitted sections of mesenteric tissue. Overall, the mean absolute percentage error (MAPE) was 50.18%, rising to 97.87% for neoadjuvant-treated cases. The average pathology report turnaround time (TAT) was 3.3 business days, meeting the recommended 4-days threshold, with no significant delay due to cancer location or additional LN searches. A preliminary cost analysis indicates that missed or misidentified LNs can increase histology processing and pathologist review expenses, emphasizing the need for more efficient LN search protocols. Discussion Taken together, these findings emphasize the multifactorial nature of LN retrieval challenges, particularly in neoadjuvant-treated and anatomically complex cases. Refining dissection protocols, leveraging new technologies, and allocating adequate resources may help reduce retrieval errors, potentially improving staging accuracy and clinical decision-making.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".