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Trends in local therapy utilization and survival of patients with de-novo metastatic prostate cancer treated by hormone therapy with or without systemic therapy intensification with chemotherapy.

2024· article· en· W4391303641 on OpenAlexaff
Siqi Hu, Zachary Melchiode, Jiaqiong Xu, Carlos Riveros, Emily Huang, Dharam Kaushik, Andrew Farach, Brian J. Miles, Guru Sonpavde, Eleni Efstathiou, Christopher J.D. Wallis, Raj Satkunasivam

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineSystemic therapyProstate cancerOncologyChemotherapyHormone therapyInternal medicineHormonal therapyCancerOverall survivalBreast cancer

Abstract

fetched live from OpenAlex

90 Background: Guideline-recommended treatment for de novo metastatic prostate cancer (mPCa) includes hormone therapy (HT) and androgen receptor axis-targeted (ARAT) therapy with or without chemotherapy. While retrospective data have implicated the potential survival benefit of treating the primary tumor with radical prostatectomy, prospective clinical trials have demonstrated a benefit of definitive local radiotherapy in the context of low-volume mPCa. Given this emerging data, we sought to assess population-based treatment trends in the utilization of local therapy (LT) for mPCa and the association between the receipt of contemporary LT and overall survival in patients with mPCa. Methods: Using the National Cancer Database from 2004 to 2020, we identified men aged 18-90+ who were diagnosed with de-novo mPCa. To mitigate potential confounding, propensity score matching (PSM) was employed to balance patient characteristics between the two groups, including metastatic volume. High-volume mPCa was defined as the presence of any visceral metastases or bone metastases with at least 1 distant invasion. Cox proportional hazard models with clustering were utilized to estimate hazard ratios (HRs) for the risk of all-cause mortality to account for the inherent correlation created by PSM. Results: Among 30,713 patients, 2,569 (8.36%) received both LT and systemic therapy, while 26,038 (84.78%) received systemic therapy alone. Of these, 5,453 (19.06%) had high-volume PCa, and 23,154 (80.94%) had low-volume PCa. No upward trend in LT utilization was observed from 2004 to 2020, with fluctuations in rates observed over time. After PSM, LT was associated with lower all-cause mortality risk (HR=0.87, 95% CI: 0.81-0.93, p<0.001). In patients without chemotherapy intensification, LT was correlated with an 18% lower all-cause mortality risk (HR=0.82, 95% CI: 0.26-0.70, p<0.001), specifically, radical prostatectomy with a 72% lower risk (HR=0.28, 95% CI: 0.20-0.38, p<0.001). For patients receiving chemotherapy intensification, definitive radiotherapy was related to an 18% increased all-cause mortality risk (HR=1.18, 95% CI: 1.01-1.39, p=0.04), while radical prostatectomy showed a 54% decreased risk (HR=0.46, 95% CI: 0.29-0.74, p=0.001). Conclusions: We did not observe an increasing population-based utilization of LT. This contemporary analysis showed that LT was associated with a 13% reduction in the risk of all-cause mortality. Importantly, this observation was also seen in patients receiving systemic therapy intensification with chemotherapy. The retrospective nature of this data, as well as residual confounding despite PSM (including metastatic volume), remain important limitations in this study. Ongoing Phase 3 trials (S1802) will be critical for informing future widespread uptake of LT in mPCa.

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 score0.014
Threshold uncertainty score0.027

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 integrity0.0000.001
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 head0.136
GPT teacher head0.467
Teacher spread0.331 · 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
Published2024
Admission routes1
Has abstractyes

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