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Comment
. 2023 Jul;50(8):2548-2560.
doi: 10.1007/s00259-023-06187-3. Epub 2023 Mar 18.

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

Samuele Ghezzo  1   2 , Paola Mapelli  1   2 , Carolina Bezzi  1   2 , Ana Maria Samanes Gajate  2 , Giorgio Brembilla  1   3 , Irene Gotuzzo  4 , Tommaso Russo  1   3 , Erik Preza  2 , Vito Cucchiara  1   5   6 , Naghia Ahmed  7 , Ilaria Neri  1   2 , Sofia Mongardi  1 , Massimo Freschi  7 , Alberto Briganti  1   5   6 , Francesco De Cobelli  1   3 , Luigi Gianolli  2 , Paola Scifo  2 , Maria Picchio  8   9
Affiliations
Comment

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

Samuele Ghezzo et al. Eur J Nucl Med Mol Imaging. 2023 Jul.

Abstract

Purpose: The aim of this study is to investigate the role of [68Ga]Ga-PSMA-11 PET radiomics for the prediction of post-surgical International Society of Urological Pathology (PSISUP) grade in primary prostate cancer (PCa).

Methods: This retrospective study included 47 PCa patients who underwent [68Ga]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 PSISUP grade: ISUP ≥ 4 vs ISUP < 4. Machine learning models were validated by means of fivefold repeated cross-validation, and two control models were generated to assess that our findings were not surrogates of spurious associations. Balanced accuracy (bACC) was collected for all generated models and compared with Kruskal-Wallis and Mann-Whitney tests. Sensitivity, specificity, and positive and negative predictive values were also reported to provide a complete overview of models' performance. The predictions of the best performing model were compared against ISUP grade at biopsy.

Results: ISUP grade at biopsy was upgraded in 9/47 patients after prostatectomy, resulting in a bACC = 85.9%, SN = 71.9%, SP = 100%, PPV = 100%, and NPV = 62.5%, while the best-performing radiomic model yielded a bACC = 87.6%, SN = 88.6%, SP = 86.7%, PPV = 94%, and NPV = 82.5%. All radiomic models trained with at least 2 RFs (GLSZM-Zone Entropy and Shape-Least Axis Length) outperformed the control models. Conversely, no significant differences were found for radiomic models trained with 2 or more RFs (Mann-Whitney p > 0.05).

Conclusion: These findings support the role of [68Ga]Ga-PSMA-11 PET radiomics for the accurate and non-invasive prediction of PSISUP grade.

Keywords: ISUP grade; PET; PSMA; Prostate cancer; Radiomics.

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  • The added value of PSMA PET/MR radiomics for prostate cancer staging.
    Solari EL, Gafita A, Schachoff S, Bogdanović B, Villagrán Asiares A, Amiel T, Hui W, Rauscher I, Visvikis D, Maurer T, Schwamborn K, Mustafa M, Weber W, Navab N, Eiber M, Hatt M, Nekolla SG. Solari EL, et al. Eur J Nucl Med Mol Imaging. 2022 Jan;49(2):527-538. doi: 10.1007/s00259-021-05430-z. Epub 2021 Jul 13. Eur J Nucl Med Mol Imaging. 2022. PMID: 34255130 Free PMC article.

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