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Association between abnormal IMDC laboratory criteria after surgery and cancer-specific and overall survival in non-metastatic renal cell carcinoma: Potential biomarkers for adjuvant therapy patient selection.

2025· article· en· W4407700784 on OpenAlexaffabout
Caio Vinícius Suartz, Rodney H. Breau, Kaleem Atchia, Camilla Tajzler, Ranjeeta Mallick, Daniel Yick Chin Heng, Georg A. Bjarnason, Aly‐Khan A. Lalani, Bimal Bhindi, Lori Wood, Naveen S. Basappa, Simon Tanguay, Frédéric Pouliot

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsDalhousie UniversityMcMaster UniversityMcGill University Health CentreJuravinski Cancer CentreSunnybrook Health Science CentreUniversity of CalgaryOttawa HospitalUniversity of AlbertaQueen Elizabeth II Health Sciences CentreUniversité Laval
Fundersnot available
KeywordsMedicineRenal cell carcinomaOncologyInternal medicineCancerAdjuvant therapyKidney cancerOverall survivalAdjuvantSelection (genetic algorithm)Surgery

Abstract

fetched live from OpenAlex

567 Background: The International Metastatic RCC Database Consortium (IMDC) criteria are widely used for risk stratification in metastatic renal cell carcinoma (RCC), but their role in non-metastatic RCC is less defined, especially as a biomarker of recurrence. This study seeks to evaluate (1) the prevalence of IMDC abnormalities in non-metastatic RCC patients (nmRCCC), (2) the impact of nephrectomy on the normalization of these criteria, and (3) the association between post-operative IMDC abnormalities and oncological outcomes. Methods: Data from the Canadian Kidney Cancer Information System (CKCis) were analyzed to identify non-metastatic RCC patients diagnosed between January 2011 and April 2024 who underwent nephrectomy after diagnosis. Laboratory tests were evaluated preoperatively (within 6 months before surgery) and postoperatively (between 2 and 15 months). Univariable and multivariable analyses were performed to assess the association with overall survival, recurrence-free survival, and cancer-specific survival. Results: A total of 1,804 patients were analyzed, with 65.7% male and mean age was 62.5y. Tumors pathological characteristics were: pT1 (69.2%), pT2 (5.6%), pT3 (1.8%), pT4 (0.28%); tumors necrosis (21.8%); mean tumor size (4.7cm) and clear cell carcinoma (73.1%). Preoperative hemoglobin (Hb), neutrophils, platelets (Plt), and corrected calcium (Ca 2+ ) IMDC criteria abnormalities were identified in 19.4, 9.6, 4.3, and 3.2% of patients, respectively. After surgery, 45.4, 79.8, 80.4, and 76.9% of these abnormal cases normalized, respectively. Among patients with normal preoperative Hb, neutrophils, Plt, and Ca 2+ , 9.65, 4.4, 1.0, and 1.6% developed postoperative IMDC criteria abnormalities, respectively. In multivariate analysis, patients with normal Hb, neutrophils, and Ca 2+ , both pre- and postoperatively, had an increase in overall survival (OS) compared to those with abnormal postoperative values. Hazard ratios (HR[95%CI]) for increased OS were 3.6[2.8-4.7], 2.1[1.7-2.7] and 3.5[1.5-43.1], respectively, compared to those with abnormal postoperative values. Furthermore, those with normal Hb, neutrophils, Plt, and Ca 2+ , both pre- and postoperatively, had an increase in cancer-specific survival (CSS) with HR of 3.3, 4.3, 8.0 and 5.7 (p<0.05),indicating a significantly higher CSS probability than patients with abnormal postoperative values. Conclusions: Abnormal IMDC laboratory criteria are frequently found in nmRCC, especially anemia, and most normalize after surgery. Finding abnormal IMDC laboratory criteria post-surgery is associated with decreased CSS and OS. This knowledge may be useful in stratifying patients for intensified monitoring or for future adjuvant therapy clinical trials.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.049
GPT teacher head0.374
Teacher spread0.325 · 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 designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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