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Record W4402516500 · doi:10.3390/curroncol31090402

Evaluating the Effects of Prostate Radiotherapy Intensified with Pelvic Nodal Radiotherapy and Androgen Deprivation Therapy on Myelosuppression: Single-Institution Experience

2024· article· en· W4402516500 on OpenAlexafffundvenue
Yousef Katib, Steven Tisseverasinghe, Ian J. Gerard, Benjamin Royal-Preyra, Ahmad Chaddad, Tania Sasson, Boris Bahoric, Federico Roncarolo, Tamim Niazi

Bibliographic record

VenueCurrent Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsMcGill UniversityJewish General HospitalUniversité de MontréalMcGill University Health Centre
FundersJewish General HospitalNational Natural Science Foundation of China
KeywordsMedicineRadiation therapyAndrogen deprivation therapyNODALProstate cancerProstateOncologyInternal medicineRadiologyCancer

Abstract

fetched live from OpenAlex

Background: Prostate cancer (PCa) management commonly involves the utilization of prostate radiotherapy (PRT), pelvic nodal radiotherapy (PNRT), and androgen deprivation therapy (ADT). However, the potential association of these treatment modalities with bone marrow (BM) suppression remains inadequately reported in the existing literature. This study is designed to comprehensively evaluate the risk of myelosuppression associated with PRT, shedding light on an aspect that has been underrepresented in prior research. Materials and Methods: We conducted a retrospective analysis of 600 patients with prostate cancer (PCa) treated with prostate radiotherapy (PRT) at a single oncology center between 2007 and 2017. Patients were categorized into four cohorts: PRT alone (n = 149), PRT + ADT, (n = 91), PRT + PNRT (n = 39), and PRT + PNRT + ADT (n = 321). To assess the risk of myelosuppression, we scrutinized specific blood parameters, such as hemoglobin (HGB), white blood cells (WBCs), neutrophils (NEUT), lymphocytes (LYM), and platelets (PLT) at baseline, mid-treatment (mRT), immediately post-RT (pRT), 1 month post-RT (1M-pRT), and 1 year post-RT (1Y-pRT). The inter-cohort statistical significance was evaluated with further stratification based on the utilized RT technique {3D conformal radiotherapy (3D-CRT), and intensity-modulated radiation therapy (IMRT)}. Results: Significant statistical differences at baseline were observed in HGB and LYM values among all cohorts (p < 0.05). Patients in the PRT + PNRT + ADT cohort had significantly lower HGB at baseline and 1M-pRT. In patients undergoing ADT, BMS had a significant impact at 1M-pRT {odds ratio (OR) 9.1; 95% Confidence Interval (CI) 4.8–17.1} and at 1Y-pRT (OR 2.84; CI 1.14–7.08). The use of 3D-CRT was linked to reduced HGB levels in the PRT + PNRT + ADT group at 1 month pRT (p = 0.015). Similarly, PNRT significantly impacted BMS at 1M-pRT (OR 6.7; CI 2.6–17.2). PNRT increased the odds of decreased WBC counts at 1Y-pRT (OR 6.83; CI: 1.02–45.82). Treatment with any RT techniques (3D-CRT or IMRT), particularly in the PRT + PNRT and PRT + PNRT + ADT groups, significantly increased the odds of low LYM counts at all time points except immediately pRT (p < 0.05). Furthermore, NEUT counts were considerably lower at 1M-pRT (p < 0.05) in the PRT + PNRT + ADT group. PLT counts were significantly decreased by PRT + PNRT + ADT at mRT (OR 2.57; 95% CI: 1.42–4.66) but were not significantly impacted by the RT technique. Conclusions: Treatment with PRT, ADT, PNRT, and 3D-CRT is associated with BMS. Despite this statistically significant risk, no patient required additional interventions to manage the outcome. While its clinical impact appears limited, its importance cannot be underestimated in the context of increased integration of novel systemic agents with myelosuppressive properties. Longer follow-up should be considered in future studies.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.087
GPT teacher head0.412
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".

Quick stats

Citations1
Published2024
Admission routes3
Has abstractyes

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