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Record W4414358574 · doi:10.3390/curroncol32090523

Pulmonary Embolism Associated with Olaparib in BRCA2-Mutated Prostate Cancer: A Case Report

2025· article· en· W4414358574 on OpenAlexvenueno aff
Shuhei Ishii, Shigekatsu Maekawa, Fumiko Amano, Daichi Kikuchi, Daiki Ikarashi, Renpei Kato, Mitsugu Kanehira, Ryo Takata, Jun Sugimura, Wataru Obara

Bibliographic record

VenueCurrent Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsnot available
Fundersnot available
KeywordsOlaparibRivaroxabanPulmonary embolismProstate cancerChest painPARP inhibitorProstate

Abstract

fetched live from OpenAlex

Olaparib, a poly (ADP-ribose) polymerase (PARP) inhibitor approved for treating metastatic castration-resistant prostate cancer (mCRPC) with BRCA mutations, has significant clinical benefits. However, evidence suggests an increased risk of venous thromboembolism, including pulmonary embolism (PE), particularly in patients with PC. However, no case reports of olaparib-associated PE in mCRPC have been published. Here, we report the case of a 70-year-old man with mCRPC harboring a BRCA2 mutation, who developed PE during olaparib therapy. Diagnostic evaluations included contrast-enhanced computed tomography and serum D-dimer level measurement. Clinical decision tools, such as the Wells score and the Khorana score, were used to support the diagnosis and risk assessment. The patient developed acute dyspnea and chest pain 7 months after olaparib initiation. Imaging confirmed multiple pulmonary emboli; laboratory testing revealed markedly elevated D-dimer levels. Anticoagulation therapy with apixaban led to rapid clinical and radiological improvement. However, mCRPC eventually progressed after olaparib discontinuation, and the patient died 15 months after olaparib initiation. This is the first reported case of olaparib-associated PE in mCRPC. It underscores the importance of vigilance for thromboembolic complications during PARP inhibitor therapy. The integration of clinical scoring systems and biomarkers may facilitate timely PE diagnosis and management, potentially improving patient outcomes.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0020.001

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 head0.064
GPT teacher head0.433
Teacher spread0.369 · 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 designCase report
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
Published2025
Admission routes1
Has abstractyes

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