Natural history of renal cell carcinoma: An immunohistochemical analysis of growth rate in patients with delayed treatment
Bibliographic record
Abstract
BACKGROUND/PURPOSE: To investigate the natural history of renal cell carcinoma (RCC) with delayed treatment and to immunohistochemically analyze the correlation between some biomarkers and the growth rate of RCC. METHODS: We reviewed our institutional databases to identify renal tumors which were confirmed to be RCC by delayed surgical treatment after at least 12 months of active surveillance (AS). Growth rate was defined as the average growth rate of the maximal diameter on computed tomography or magnetic resonance imaging. The clinicopathological characteristics and immunohistochemical biomarkers (Ki-67, p53, bcl-2, and vascular endothelial growth factor) were analyzed the correlation with the growth rate of RCC. RESULTS: We identified 45 RCCs from 45 patients. The mean patient age was 54 years (range, 26-78 years). The mean tumor size increased from 2.39 cm (range, 0.10-6.70 cm) at presentation to 4.54 cm (range, 1.40-11.80 cm) after a mean time of 45.4 months (range, 12-155 months) of AS. The mean growth rate was 0.79 cm/y (range, 0.10-4.74 cm), and 36 (80.0%) tumors presented a growth rate ≤ 1.00 cm/y. Clear cell RCC had a trend of growing faster than other histological subtypes. Pathological grade was significantly correlated with the growth rate of RCC (p = 0.043). High positive ratio of Ki-67 (r = 0.351, p = 0.018) and being p53 positive (p = 0.019) were significantly correlated to the fast growth rate of RCC. CONCLUSION: In general, RCCs under AS are slow growing with a wide variation of growth rate, with a portion of RCCs presenting rapid growth kinetics. RCC with rapid growth during AS is characterized by a high histological grade, high positive ratio of Ki-67, and being p53 positive.
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 | 0.000 | 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".