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Record W3127487470 · doi:10.1038/s41379-021-00737-6

Novel, emerging and provisional renal entities: The Genitourinary Pathology Society (GUPS) update on renal neoplasia

2021· article· en· W3127487470 on OpenAlexaff
Kiril Trpkov, Sean R. Williamson, Anthony J. Gill, Adebowale Adeniran, Abbas Agaimy, Reza Alaghehbandan, Mahul B. Amin, Pedram Argani, Ying‐Bei Chen, Liang Cheng, Jonathan I. Epstein, John C. Cheville, Éva Compérat, Isabela Werneck da Cunha, Jennifer Gordetsky, Sounak Gupta, Huiying He, Michelle S. Hirsch, Peter A. Humphrey, Payal Kapur, Fumiyoshi Kojima, José I. López, Fiona Maclean, Cristina Magi‐Galluzzi, Jesse K. McKenney, Rohit Mehra, Santosh Menon, George J. Netto, Christopher G. Przybycin, Priya Rao, Qiu Rao, Victor E. Reuter, Rola Saleeb, Rajal B. Shah, Steven C. Smith, Satish K. Tickoo, Maria Tretiakova, Lawrence D. True, Virginie Verkarre, Sara E. Wobker, Ming Zhou, Ondřej Hes

Bibliographic record

VenueModern Pathology · 2021
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaRoyal Columbian HospitalUniversity of Calgary
Fundersnot available
KeywordsRenal cell carcinomaMedicinePathologyGenitourinary systemRenal pathologyHematopathologyKidneyInternal medicineBiologyCytogenetics

Abstract

fetched live from OpenAlex

The Genitourinary Pathology Society (GUPS) undertook a critical review of the recent advances in renal neoplasia, particularly focusing on the newly accumulated evidence post-2016 World Health Organization (WHO) classification. In the era of evolving histo-molecular classification of renal neoplasia, morphology is still key. However, entities (or groups of entities) are increasingly characterized by specific molecular features, often associated either with recognizable, specific morphologies or constellations of morphologies and corresponding immunohistochemical profiles. The correct diagnosis has clinical implications leading to better prognosis, potential clinical management with targeted therapies, may identify hereditary or syndromic associations, which may necessitate appropriate genetic testing. We hope that this undertaking will further facilitate the identification of these entities in practice. We also hope that this update will bring more clarity regarding the evolving classification of renal neoplasia and will further reduce the category of "unclassifiable renal carcinomas/tumors". We propose three categories of novel entities: (1) "Novel entity", validated by multiple independent studies; (2) "Emerging entity", good compelling data available from at least two or more independent studies, but additional validation is needed; and (3) "Provisional entity", limited data available from one or two studies, with more work required to validate them. For some entities initially described using different names, we propose new terminologies, to facilitate their recognition and to avoid further diagnostic dilemmas. Following these criteria, we propose as novel entities: eosinophilic solid and cystic renal cell carcinoma (ESC RCC), renal cell carcinoma with fibromyomatous stroma (RCC FMS) (formerly RCC with leiomyomatous or smooth muscle stroma), and anaplastic lymphoma kinase rearrangement-associated renal cell carcinoma (ALK-RCC). Emerging entities include: eosinophilic vacuolated tumor (EVT) and thyroid-like follicular renal cell carcinoma (TLFRCC). Finally, as provisional entities, we propose low-grade oncocytic tumor (LOT), atrophic kidney-like lesion (AKLL), and biphasic hyalinizing psammomatous renal cell carcinoma (BHP 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.004
Science and technology studies0.0010.002
Scholarly communication0.0030.006
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.020
GPT teacher head0.259
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations220
Published2021
Admission routes1
Has abstractyes

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