Publications by authors named "Erik Preza"

Purpose: The aim of this study is to investigate the role of [Ga]Ga-PSMA-11 PET radiomics for the prediction of post-surgical International Society of Urological Pathology (ISUP) grade in primary prostate cancer (PCa).

Methods: This retrospective study included 47 PCa patients who underwent [Ga]Ga-PSMA-11 PET at IRCCS San Raffaele Scientific Institute before radical prostatectomy. The whole prostate was manually contoured on PET images and 103 image biomarker standardization initiative (IBSI)-compliant radiomic features (RFs) were extracted.

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Introduction: State of the art artificial intelligence (AI) models have the potential to become a "one-stop shop" to improve diagnosis and prognosis in several oncological settings. The external validation of AI models on independent cohorts is essential to evaluate their generalization ability, hence their potential utility in clinical practice. In this study we tested on a large, separate cohort a recently proposed state-of-the-art convolutional neural network for the automatic segmentation of intraprostatic cancer lesions on PSMA PET images.

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The aim of the present study is to investigate and compare the performances of Ga-PSMA and Ga-DOTA-RM2 PET/MRI in identifying recurrent prostate cancer (PCa) after primary treatment and to explore the association of dual-tracer PET findings with clinical and histopathological characteristics. Thirty-five patients with biochemical relapse (BCR) of PCa underwent Ga PSMA PET/MRI for restaging purpose, with 31/35 also undergoing Ga-DOTA-RM2 PET/MRI scan within 16 days (mean: 3 days, range: 2-16 days). Qualitative and quantitative image analysis has been performed by comparing Ga-PSMA and Ga-DOTA-RM2 PET/MRI findings both on a patient and lesion basis.

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The aim of the present study is to investigate the synergic role of Ga-PSMA PET/MRI and Ga-DOTA-RM2 PET/MRI in prostate cancer (PCa) staging. We present pilot data on twenty-two patients with biopsy-proven PCa that underwent Ga-PSMA PET/MRI for staging purposes, with 19/22 also undergoing Gaa-DOTA-RM2 PET/MRI. TNM classification based on image findings was performed and quantitative imaging parameters were collected for each scan.

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We present the current clinical applications of radiomics in the context of prostate cancer (PCa) management. Several online databases for original articles using a combination of the following keywords: "(radiomic or radiomics) AND (prostate cancer or prostate tumour or prostate tumor or prostate neoplasia)" have been searched. The selected papers have been pooled as focus on (i) PCa detection, (ii) assessing the clinical significance of PCa, (iii) biochemical recurrence prediction, (iv) radiation-therapy outcome prediction and treatment efficacy monitoring, (v) metastases detection, (vi) metastases prediction, (vii) prediction of extra-prostatic extension.

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