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Bone metastases and use of bone protective agents (BPA) for metastatic renal cell carcinoma (mRCC): A contemporary national real-world analysis.

2025· article· en· W4407699263 on OpenAlexaffabout
Braden Millan, Sunita Ghosh, Naveen S. Basappa, Lori Wood, Bimal Bhindi, Frédéric Pouliot, Rodney H. Breau, Antonio Finelli, Rahul Bansal, Jeffrey Graham, Georg A. Bjarnason, Dominick Bossé, Vincent Castonguay, Eric Winquist, Daniel Yick Chin Heng, Christian Kollmannsberger, Aly‐Khan A. Lalani

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsMcMaster UniversityUniversity of CalgaryLondon Health Sciences CentreBC Cancer AgencyHôtel-Dieu de QuébecUniversity of OttawaSunnybrook Health Science CentreOttawa HospitalCancerCare ManitobaSt. Joseph’s Healthcare HamiltonUniversity of ManitobaUniversity of AlbertaQueen Elizabeth II Health Sciences CentreUniversité LavalWestern UniversityUniversity of TorontoDalhousie University
Fundersnot available
KeywordsMedicineRenal cell carcinomaOncologyInternal medicineBone metastasisKidney cancerCancer researchPathologyMetastasisCancer

Abstract

fetched live from OpenAlex

490 Background: Bone Metastases (BM) occur in approximately 30% of mRCC patients (pts) with evidence suggesting that they portend a worse prognosis. Systemic therapies such as tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICI) are felt to be active in these pts, with radiation therapy (RT) often employed for BM-directed treatment. The evidence for and use of BPAs, such as bisphosphonates and denosumab, is limited in the current ICI era. We investigated the outcomes of mRCC pts with BM and use of BPA in a contemporary real-world cohort. Methods: We analyzed data collected from the prospectively maintained, multi-institutional Canadian Kidney Cancer Information System (CKCis). Patients with clear cell mRCC treated between January 2011-June 2023 were included. Outcomes were compared between BM+ and BM- pts using Cox proportional hazards models. Kaplan-Meier estimates were used to assess time to treatment failure (TTF) and overall survival (OS) adjusted by IMDC criteria. Results: Of 2,307 patients with clear cell mRCC, 893 (39%) had BM+, of whom 691 (77%) underwent RT, and 139 (16%) received a BPA. Median follow-up was longer for BM- patients (34.6 vs 29.1 mos). Significant differences were noted in Karnofsky performance status (> 80%: 84% vs 78%), IMDC risk group (intermediate/poor: 76% versus 82%) use of RT (35% vs 78%), and use of BPAs (2% vs 16%) between BM- vs BM+ pts (all p < 0.05). In our cohort, first-line treatment in BM+ pts was monotherapy TKI (67%), doublet ICI (20%), and combination TKI+ICI (13%). Median TTF was similar in BM- vs BM+ pts: 9.6 vs 8.6 mos (HR 0.92; 95% CI 0.91-1.02). However, median OS was higher in BM- vs BM+ pts: 57.6 vs 35.8 mos (HR 0.67; 0.58-0.76; p<0.0001); this was consistent irrespective of type of systemic therapy. In BM+ pts, those who did not receive BPAs had a lower TTF (7.9 vs 13.4 mos) and OS (34.0 vs 46.0 mos); however, these were not statistically different when adjusted by IMDC criteria (TTF HR 1.2; 0.95-1.51 and OS HR 1.12; 0.85-1.46). There was no significant difference in TTF (HR 0.89; 0.72-1.10) or OS (HR 0.94; 0.73-1.22) in BM+ pts selected to receive RT. Conclusions: BM remain a poor prognostic factor in mRCC in this contemporary cohort of patients. While BPA use was limited, we observed no improvement in TTF or OS in BM+ pts with their use. These data warrant further investigation of BPA in mRCC including assessment of SREs and potential complications.

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 score0.165
Threshold uncertainty score0.328

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.255
GPT teacher head0.502
Teacher spread0.246 · 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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Citations1
Published2025
Admission routes2
Has abstractyes

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