Distribution of subtypes of metastatic renal‐cell carcinoma: Correlating findings of fine‐needle aspiration biopsy and surgical pathology
Bibliographic record
Abstract
Clear-cell (CRCC), papillary (PRCC), and chromophobe (CHRCC) renal-cell carcinoma (RCC) are the three most frequent subtypes of RCC. The rate and distribution of their metastatic lesions have not been well studied in cytopathological materials. Sixty-two fine-needle aspiration biopsy cases of metastatic RCC were studied and correlated with surgical pathology of RCCs with and without metastasis. Special stains for glycogen and immunostaining for cytokeratins, vimentin epithelial membrane antigen (EMA), and carcinoembryonic antigens, and electron microscopic studies were performed. Fifty-nine cases of CRCC and three of PRCC subtypes were retrieved from the cytopathology files at the Ottawa Hospital in a period of 10 years. Of these cases, 10 metastatic CRCC and one metastatic PRCC were diagnosed prior to the diagnosis of the primary tumor. CHRCC and sarcomatoid RCC were not represented in cytopathological specimens. CRCC displayed characteristic filmy cytoplasm and nuclei with prominent nucleoli. PRCC was characterized by dense cytoplasm, large nuclei with prominent nucleoli, and papillary architectures. In addition, all RCCs were characterized by the presence of glycogen and the absence of mucin by using histochemical techniques and electron microscopic studies and positive reactivity for cytokeratins (CK) and vimentin (VIM). In the same period, there were a total of 380 patients with RCC divided into 310 CRCCs, 55 PRCCs, and 15 CHRCCs associated with metastases in 142, 9, and 1 case, respectively. CRCC is by far the most common subtype found in metastases sampled in cytopathology. PRCC, CHRCC, and sarcomatoid RCC were underrepresented. Awareness of this propensity of RCC and the characteristic cytopathological, histochemical, immunohistochemical, and ultrastructural features are helpful in the diagnosis of metastatic RCC.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".