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Prevalence of intra-patient inter-metastatic heterogeneity in mCRPC patients based on triple-tracer PET imaging: The 3TMPO study.

2023· article· en· W4379282451 on OpenAlexafffund
Frédéric Pouliot, Fred Saad, Patrick O. Richard, Stephan Probst, Étienne Rousseau, Éric Lévesque, Vincent Castonguay, Nicolas Marcoux, Michele Lodde, Jean‐Baptiste Lattouf, François-Alexandre Buteau, Daniel Juneau, Zineb Hamilou, Michel Pavic, Jean‐François Castilloux, Atefeh Zamanian, Guillaume F. Bouvet, Amélie Têtu, Brigitte Guérin, Jean‐Mathieu Beauregard

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Hospitalier Universitaire de SherbrookeCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanCentre Hospitalier de l’Université de MontréalHôtel-Dieu de QuébecJewish General HospitalUniversité de SherbrookeUniversité Laval
FundersCancer Research Society
KeywordsMedicineProstate cancerNuclear medicineImaging biomarkerPositron emission tomographyLesionPet imagingBiopsyRadiologyTarget lesionCancerInternal medicineMagnetic resonance imagingPathology

Abstract

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5033 Background: Intra-patient inter-metastatic heterogeneity (IIH) has been demonstrated in metastatic castration resistant prostate cancer (mCRPC) patients based on genomic and imaging studies, most often after several lines of therapies. IIH is of utmost importance for PSMA radioligand therapy (RLT) eligibility, biopsy-based precision medicine and/or treatment intensification decision-making. The Triple-Tracer strategy against Metastatic PrOstate cancer (3TMPO) study (NCT04000776) is a prospective multicenter PET imaging study that was designed to determine the prevalence of IIH in mCRPC patients and to determine candidacy for RLT. Methods: 3TMPO is a PET-imaging trial including mCRPC patients showing at least 3 metastases on conventional imaging with evidence of biochemical or radiographic progression, at least 3 months after initiation of the last systemic therapy. 68Ga-PSMA-617 and 18F-FDG PET/CT scans were performed within 10 days and analyzed quantitatively. A third scan with 68Ga-Octreotate was done when a PSMA-/FDG+ lesion was found. For all tracers, positivity of a lesion was defined as its SUVpeak being 1.5 times higher than the SUVmean of the liver. Lesions smaller than 1 cc and likely benign foci of uptake were excluded. IIH prevalence was the primary outcome, defined as the percentage of patients having at least two lesions with discordant features on PET imaging. Results: We included 98 patients in the final analysis. Patients had a mean age of 69 years and a median PSA of 51.1 ng/mL. Number of metastases were <5 in 46.9% and ≥10 in 33.7% of patients and 10.2, 20.4, 18.4 and 51.0% had received 0, 1, 2 or >2 lines of systemic therapies for mCRPC, respectively. Prevalence of IIH was 83.7% based on pre-specified PET criteria. Overall, seven different combinations of lesion phenotypes were found among patients based on FDG and PSMA PETs, and at least one PSMA-/FDG+ lesion was found in 46 patients (46.9%). Of the 44 patients who underwent Octreotate PET, six (13.6%) had at least one Octreotate-positive lesion. In this FDG/PSMA/Octreotate-imaged subgroup, 11 different combinations of phenotypes were observed between metastases of individual patients. Overall, 52.0% (IC95: 41.7-62.2) of patients were found to be candidate for PSMA RLT, but none for Octreotate RLT. Conclusions: The majority of mCRPC patients showed IIH. Based on a multi-tracer approach, up to 11 lesion phenotype combinations were found amongst patients. Correlation of these imaging phenotypes with genomics and treatment response will be highly relevant for optimized precision medicine, especially with respect to PSMA-RLT. Clinical trial information: NCT04000776 . [Table: see text]

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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.136
GPT teacher head0.502
Teacher spread0.367 · 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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Citations2
Published2023
Admission routes2
Has abstractyes

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Same venueJournal of Clinical Oncology→Same topicProstate Cancer Treatment and Research→French-language works237,207→