Dissecting patterns of care in patients with variant histology of bladder cancer and lymph node invasion
Bibliographic record
Abstract
Objectives Lymph node invasion (LNI) is related to long-term survival in patients with muscle-invasive bladder cancer. However, in the case of variant histology (VH), data on pelvic lymph node dissection (PLND) and LNI are sparse. We described the pattern of care of PLND in patients with VHs of bladder cancer, exploring predictors of LNI. Methods Using the 2001–2016 SEER registry, 20 767 bladder cancer patients who underwent PLND were identified. Included histological variants were pure urothelial carcinoma (UC), micropapillary UC, sarcomatoid UC, lymphoepithelioma-like UC, adenocarcinoma, sarcoma, giant and spindle cell carcinoma, squamous cell carcinoma (SCC), and neuroendocrine tumor. Uni- and multivariable logistic regression analyses tested for LNI predictors. Cox regression was used to test for predictors of overall mortality (OM) among both LNI positive and LNI negative patients. Results Overall, 2464 (11.9%) harbored a VH. On multivariate analysis, only micropapillary UC was associated with higher risk (OR = 3.39) of LNI. This association was maintained when only the subset of patients treated without perioperative chemotherapy were analyzed (OR = 3.30). Similarly, higher T stage (T2 stage OR = 2.24; T3–4 stage OR = 9.44) and the use of chemotherapy (OR = 2.29) were associated with a higher risk of LNI. Among patients with LNI (5299, 25.5%), SCC (HR = 1.87), T3–4 stage (HR = 1.94), age at diagnosis (HR = 1.01) and geographic region (south) (HR = 1.22) were predictors of higher risk of OM. Conversely, chemotherapy (HR = 0.69) and number of removed LN (HR = −0.99) were associated with lower risk of OM. Finally, in a subgroup of patients without LNI, sarcomatoid UC (HR = 1.58) and giant and spindle cell carcinoma (HR = 1.83) were the only VH predictors of OM. Conclusions We described different patterns of care in patients with VHs of bladder cancer. Micropapillary UC was an independent risk factor for LNI. Among patients harboring LNI, those with SCC VH had higher OM compared to pure UC. Conversely, sarcomatoid UC and giant and spindle cell carcinoma were predictors of OM in patients without nodal involvement.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".