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Record W3004436801 · doi:10.1016/j.euo.2020.01.001

Synchronous Versus Metachronous Metastatic Disease: Impact of Time to Metastasis on Patient Outcome—Results from the International Metastatic Renal Cell Carcinoma Database Consortium

2020· article· en· W3004436801 on OpenAlexafffund
Frede Donskov, Wanling Xie, A. J. Overby, J. Connor Wells, Anna Paola Fraccon, Cosimo Sacco, Camillo Porta, Igor Stukalin, Jae‐Lyun Lee, Konstantinos Koutsoukos, Takeshi Yuasa, Ian D. Davis, Carmel Pezaro, Ravindran Kanesvaran, Georg A. Bjarnason, Hao‐Wen Sim, Nityam Rathi, Christian Kollmannsberger, Christina M. Canil, Toni K. Choueiri, Daniel Y.C. Heng

Bibliographic record

VenueEuropean Urology Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsBaker Hughes (Canada)Ottawa HospitalBC Cancer AgencyPrincess Margaret Cancer Centre
FundersIpsen BiopharmaceuticalsSanofiHealth Research Fund of Central Denmark RegionAstraZenecaNational Health and Medical Research CouncilAmgenPfizerHealth Research FoundationRoche
KeywordsMedicineRenal cell carcinomaMetastasisOncologyDiseaseInternal medicineOutcome (game theory)Cancer

Abstract

fetched live from OpenAlex

BACKGROUND: Patients with metastatic renal cell carcinoma (mRCC) may present with primary metastases (synchronous disease) or develop metastases during follow-up (metachronous disease). The impact of time to metastasis on patient outcome is poorly characterised. OBJECTIVE: To characterise overall survival (OS) and time to treatment failure (TTF) based on time to metastasis in mRCC patients treated with targeted therapy (tyrosine kinase inhibitors [TKIs]). DESIGN, SETTING, AND PARTICIPANTS: We used the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) to compare synchronous (metastases within ≤3 mo of initial diagnosis of cancer) versus metachronous disease (evaluated by >3-12 mo, >1-2 yr, >2-7 yr, and >7 yr intervals). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: OS and TFF were assessed using Kaplan-Meier curves. Cox multivariable regressions analyses (MVAs) were adjusted for baseline factors. RESULTS AND LIMITATIONS: Of 7386 patients with mRCC treated with first-line TKIs, 3906 (53%) and 3480 (47%) had synchronous and metachronous metastasis, respectively. More patients with synchronous versus metachronous disease had higher T stage (T1-2: 19% vs 34%), N1 disease (21% vs 6%), presence of sarcomatoid differentiation (15.8% vs 7.9%), Karnofsky performance status <80 (25.9% vs 15.1%), anaemia (62.5% vs 42.3%), elevated neutrophils (18.9% vs 10.9%), elevated platelets (21.6% vs 11.4%), bone metastases (40.4% vs 29.8%), and IMDC poor risk (40.6% vs 11.3%). Synchronous versus metachronous disease by intervals >3-12 mo, >1-2 yr, >2-7 yr, and >7 yr correlated with poor TTF (5.6 mo vs 7.3, 8.0, 10.8, and 13.3 mo, p < 0.0001) and poor OS (median 16.7 mo vs 23.8, 30.2, 34.8, and 41.7 mo, p < 0.0001). In MVAs, the adjusted hazard ratios (95% confidence intervals) were 1.00 (reference), 0.98 (0.90-1.06), 0.81 (0.73-0.91), 0.74 (0.68-0.81), and 0.60 (0.54-0.67), respectively, for OS (p < 0.0001), and 1.00 (reference), 0.99 (0.92-1.06), 0.98 (0.90-1.07), 0.83 (0.77-0.89), and 0.66 (0.60-0.72), respectively, for TTF (p < 0.0001). Data were collected retrospectively. CONCLUSIONS: Timing of metastases after initial RCC diagnosis may impact the outcomes from targeted therapy in mRCC. PATIENT SUMMARY: We looked at the impact of the timing of metastatic outbreak on survival outcomes in kidney cancer patients treated with targeted therapy. We found that the longer time to metastatic development was associated with improved outcome.

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)0.004
metaresearch head score (Gemma)0.008
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.059
GPT teacher head0.312
Teacher spread0.253 · 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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Citations57
Published2020
Admission routes2
Has abstractyes

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