Cystic Renal Cell Carcinomas: Do They Grow, Metastasize, or Recur?
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to evaluate the interval growth, tumor recurrence, and metastatic disease occurrence of cystic renal cell carcinoma (RCC). MATERIALS AND METHODS: Pre-and posttreatment imaging of 47 histologically proven cystic RCCs, with at least 6 months of pretreatment imaging monitoring or at least 2 years of posttreatment imaging follow-up, or both, was retrospectively reviewed. Tumor morphologic features, preoperative growth, histologic typing and grading, and the incidence of tumor recurrence or metastasis were evaluated. Growth rate of tumors were compared among various histologic subtypes and Fuhrman grades. RESULTS: Of 47 tumors, 27 (57.5%) were clear cell RCCs, 12 (25.5%) were multilocular RCCs, and eight (17%) were papillary cystic RCCs. Overall, 26 (55.3%) tumors were graded as Fuhrman grade 2, 17 (36.1%) were Fuhrman grade 1, and one tumor was Fuhrman grade 3. Of the 26 tumors with a minimum of 6 months of pretreatment imaging monitoring, 19 (73%) did not show a significant increase in tumor size. The differences in mean growth among the Fuhrman grades and different subtypes were not statistically significant. The average duration of posttreatment follow-up was 51 months. There were no local recurrences among the 43 patients who underwent posttreatment imaging, except for one patient who had metastasis at preoperative clinical presentation. CONCLUSION: Cystic RCCs exhibit slow indolent growth, if any, and show no significant metastatic or recurrence potential, with excellent clinical outcomes. We raise the need for revisiting current imaging protocols that may involve frequent pre-and posttreatment imaging in cystic RCCs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".