Can We Predict Skeletal Lesion on Bone Scan Based on Quantitative PSMA PET/CT Features?

OBJECTIVE: The increasing use of PSMA-PET/CT for restaging prostate cancer (PCa) leads to a patient shift from a non-metastatic situation based on conventional imaging (CI) to a metastatic situation. Since established therapeutic pathways have been designed according to CI, it is unclear how this should be translated to the PSMA-PET/CT results. This study aimed to investigate whether PSMA-PET/CT and clinical parameters could predict the visibility of PSMA-positive lesions on a bone scan (BS).

METHODS: In four different centers, all PCa patients with BS and PSMA-PET/CT within 6 months without any change in therapy or significant disease progression were retrospectively selected. Up to 10 non-confluent clear bone metastases were selected per PSMA-PET/CT and SUVmax, SUVmean, PSMAtot, PSMAvol, density, diameter on CT, and presence of cortical erosion were collected. Clinical variables (age, PSA, Gleason Score) were also considered. Two experienced double-board physicians decided whether a bone metastasis was visible on the BS, with a consensus readout for discordant findings. For predictive performance, a random forest was fit on all available predictors, and its accuracy was assessed using 10-fold cross-validation performed 10 times.

RESULTS: A total of 43 patients were identified with 222 bone lesions on PSMA-PET/CT. A total of 129 (58.1%) lesions were visible on the BS. In the univariate analysis, all PSMA-PET/CT parameters were significantly associated with the visibility on the BS (p < 0.001). The random forest reached a mean accuracy of 77.6% in a 10-fold cross-validation.

CONCLUSIONS: These preliminary results indicate that there might be a way to predict the BS results based on PSMA-PET/CT, potentially improving the comparability between both examinations and supporting decisions for therapy selection.

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:15

Enthalten in:

Cancers - 15(2023), 22 vom: 19. Nov.

Sprache:

Englisch

Beteiligte Personen:

Laudicella, Riccardo [VerfasserIn]
Bauckneht, Matteo [VerfasserIn]
Maurer, Alexander [VerfasserIn]
Heimer, Jakob [VerfasserIn]
Gennari, Antonio G [VerfasserIn]
Di Raimondo, Tania [VerfasserIn]
Paone, Gaetano [VerfasserIn]
Cuzzocrea, Marco [VerfasserIn]
Messerli, Michael [VerfasserIn]
Eberli, Daniel [VerfasserIn]
Burger, Irene A [VerfasserIn]

Links:

Volltext

Themen:

Generalized linear mixed model
Journal Article
PSMA PET
Prediction
Prostate cancer
Staging

Anmerkungen:

Date Revised 27.11.2023

published: Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.3390/cancers15225471

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM364929499