The impact of the new WHO classification of renal cell carcinoma on the diagnosis of hereditary leiomyomatosis and renal cell carcinoma
Bibliographic record
Abstract
Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) syndrome is caused by heterozygous germline variants in the fumarate hydratase (FH) gene. Inheritance follows an autosomal dominant pattern. Loss of FH confers a predisposition for various benign and malignant neoplasms, including cutaneous leiomyomas, uterine fibroids and FH-deficient renal cell carcinoma. While benign, cutaneous and uterine manifestations have a relevant impact on quality of life and risk for complications, the vast majority of FH-deficient RCCs exhibit an aggressive behaviour with invasive growth and the potential for early metastatic spread. Additionally, pathogenic germline FH variants have been associated with other neoplasms, such as adrenal gland and Leydig cell tumours. The aggressive behaviour of FH-deficient RCC challenges nephron-sparing resection strategies, as a wide margin is recommended. Even after early nephrectomy for surgical removal of FH-deficient renal cell carcinomas, there is a relevant risk for distant metastasis as well as the remaining predisposition for de novo primary renal tumours in the other kidney. Active screening is central to HLRCC care since no preventative HLRCC-specific treatment exists. Vascular endothelial growth factor/epidermal growth factor receptor-directed treatment regimes, such as erlotinib/bevacizumab, demonstrate efficacy against HLRCC-associated RCC. This emphasizes the importance of establishing the correct diagnosis in HLRCC early on to guide therapeutic decisions. Morphologic criteria as well as specific immunohistochemical staining and molecular genetics allow the identification of FH-deficient RCC. Changes made in the recent 2022 World Health Organization classification impact the diagnosis of HLRCC in multiple ways. This commentary aims to discuss this impact and raise awareness among pathologists as well as clinicians involved in the care of patients with HLRCC.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".