Role of [68Ga]Ga-PSMA-11 PET radiomics to predict post-surgical ISUP grade in primary prostate cancer

Purpose The aim of this study is to investigate the role of [ 68 Ga]Ga-PSMA-11 PET radiomics for the prediction of post-surgical International Society of Urological Pathology ( PS ISUP) grade in primary prostate cancer (PCa). Methods This retrospective study included 47 PCa patients who underwent [...

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Bibliographic Details
Published in:European journal of nuclear medicine and molecular imaging 2023-07, Vol.50 (8), p.2548-2560
Main Authors: Ghezzo, Samuele, Mapelli, Paola, Bezzi, Carolina, Samanes Gajate, Ana Maria, Brembilla, Giorgio, Gotuzzo, Irene, Russo, Tommaso, Preza, Erik, Cucchiara, Vito, Ahmed, Naghia, Neri, Ilaria, Mongardi, Sofia, Freschi, Massimo, Briganti, Alberto, De Cobelli, Francesco, Gianolli, Luigi, Scifo, Paola, Picchio, Maria
Format: Article
Language:English
Subjects:
Advanced Image Analyses (Radiomics and Artificial Intelligence)
Algorithms
Biomarkers
Biopsy
Cardiology
Entropy
Gallium Radioisotopes
Humans
Imaging
Learning algorithms
Machine learning
Male
Medical imaging
Medicine
Medicine & Public Health
Nuclear Medicine
Oncology
Original Article
Orthopedics
Positron emission
Positron emission tomography
Positron Emission Tomography Computed Tomography - methods
Predictions
Prostate cancer
Prostatectomy
Prostatic Neoplasms - diagnostic imaging
Prostatic Neoplasms - pathology
Prostatic Neoplasms - surgery
Radiology
Radiomics
Redundancy
Retrospective Studies
Standardization
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Summary:Purpose The aim of this study is to investigate the role of [ 68 Ga]Ga-PSMA-11 PET radiomics for the prediction of post-surgical International Society of Urological Pathology ( PS ISUP) grade in primary prostate cancer (PCa). Methods This retrospective study included 47 PCa patients who underwent [ 68 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. Features were then selected using the minimum redundancy maximum relevance algorithm and a combination of the 4 most relevant RFs was used to train 12 radiomics machine learning models for the prediction of PS ISUP grade: ISUP ≥ 4 vs ISUP  0.05). Conclusion These findings support the role of [ 68 Ga]Ga-PSMA-11 PET radiomics for the accurate and non-invasive prediction of PS ISUP grade.
ISSN:1619-7070
1619-7089
DOI:10.1007/s00259-023-06187-3