Development and validation of [18 F]-PSMA-1007 PET-based radiomics model to predict biochemical recurrence-free survival following radical prostatectomy.
Eur J Nucl Med Mol Imaging
; 51(9): 2806-2818, 2024 Jul.
Article
en En
| MEDLINE
| ID: mdl-38691111
ABSTRACT
PURPOSE:
Biochemical recurrence (BCR) following radical prostatectomy (RP) is a significant concern for patients with prostate cancer. Reliable prediction models are needed to identify patients at risk for BCR and facilitate appropriate management. This study aimed to develop and validate a clinical-radiomics model based on preoperative [18 F]PSMA-1007 PET for predicting BCR-free survival (BRFS) in patients who underwent RP for prostate cancer. MATERIALS ANDMETHODS:
A total of 236 patients with histologically confirmed prostate cancer who underwent RP were retrospectively analyzed. All patients had a preoperative [18 F]PSMA-1007 PET/CT scan. Radiomics features were extracted from the primary tumor region on PET images. A radiomics signature was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The performance of the radiomics signature in predicting BRFS was assessed using Harrell's concordance index (C-index). The clinical-radiomics nomogram was constructed using the radiomics signature and clinical features. The model was externally validated in an independent cohort of 98 patients.RESULTS:
The radiomics signature comprised three features and demonstrated a C-index of 0.76 (95% CI 0.60-0.91) in the training cohort and 0.71 (95% CI 0.63-0.79) in the validation cohort. The radiomics signature remained an independent predictor of BRFS in multivariable analysis (HR 2.48, 95% CI 1.47-4.17, p < 0.001). The clinical-radiomics nomogram significantly improved the prediction performance (C-index 0.81, 95% CI 0.66-0.95, p = 0.007) in the training cohort and (C-index 0.78 95% CI 0.63-0.89, p < 0.001) in the validation cohort.CONCLUSION:
We developed and validated a novel [18 F]PSMA-1007 PET-based clinical-radiomics model that can predict BRFS following RP in prostate cancer patients. This model may be useful in identifying patients with a higher risk of BCR, thus enabling personalized risk stratification and tailored management strategies.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Prostatectomía
/
Neoplasias de la Próstata
/
Tomografía Computarizada por Tomografía de Emisión de Positrones
Límite:
Aged
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
Eur J Nucl Med Mol Imaging
Asunto de la revista:
MEDICINA NUCLEAR
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Alemania