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

Ghezzo, Samuele; Mapelli, Paola; Bezzi, Carolina; Gajate, Ana Maria Samanes; Brembilla, Giorgio; Gotuzzo, Irene; Russo, Tommaso; Preza, Erik; Cucchiara, Vito; Ahmed, Naghia; Neri, Ilaria; Mongardi, Sofia; Freschi, Massimo; Briganti, Alberto; Cobelli, Francesco De; Scifo, Paola; Picchio, Maria

doi:10.1007/s00259-023-06187-3

Cited by 9 publications

(5 citation statements)

References 51 publications

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“…A clear focus of PSMA PET/CT-derived radiomics has been in prediction of surgical pathology [6,[26][27][28][29][30][31], treatment outcomes [2,3,7,12,30,[32][33][34][35], progression, or survival [32,34,35]. This is in stark contrast to several recent reviews of mpMRI-derived radiomic models [1,2,36], which have concentrated on the initial diagnosis and staging of PC.…”

Section: Discussionmentioning

confidence: 99%

“…The literature included in this review is of no exception and, as such, conclusions are limited by small sample sizes [21,22,24,25], the inclusion of single institutions without external validation, and high variability in image preprocessing and during the identification of regions of interest. Cross-validation, resampling, and multiple segmentations were employed by many of these studies [2,3,6,7,[18][19][20]22,[26][27][28]30,33], but the fragility of radiomic features and the variability between models ought best to be addressed via external validation and reproducibility. Despite these concerns, however, of the 23 studies included in the present review, only 2 (8.7%) performed external validation of the radiomic model in an external cohort of patients.…”

Section: Discussionmentioning

confidence: 99%

“…Within these studies, four also included secondary classification of GS risk groups [26][27][28][29][30]. Solari and colleagues compared radiomic features extracted from different imaging sequences (i.e., T1w, T2w, and ADC) and found that the PET + ADC radiomics model outperformed other double and single modalities [27].…”

Section: Prediction Of Adverse Pathology Following Radical Prostatectomymentioning

confidence: 99%

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Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography-Derived Radiomic Models in Prostate Cancer Prognostication

Huynh,

Swanson,

Cima

et al. 2024

Cancers

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The clinical integration of prostate membrane specific antigen (PSMA) positron emission tomography and computed tomography (PET/CT) scans represents potential for advanced data analysis techniques in prostate cancer (PC) prognostication. Among these tools is the use of radiomics, a computer-based method of extracting and quantitatively analyzing subvisual features in medical imaging. Within this context, the present review seeks to summarize the current literature on the use of PSMA PET/CT-derived radiomics in PC risk stratification. A stepwise literature search of publications from 2017 to 2023 was performed. Of 23 articles on PSMA PET/CT-derived prostate radiomics, PC diagnosis, prediction of biopsy Gleason score (GS), prediction of adverse pathology, and treatment outcomes were the primary endpoints of 4 (17.4%), 5 (21.7%), 7 (30.4%), and 7 (30.4%) studies, respectively. In predicting PC diagnosis, PSMA PET/CT-derived models performed well, with receiver operator characteristic curve area under the curve (ROC-AUC) values of 0.85–0.925. Similarly, in the prediction of biopsy and surgical pathology results, ROC-AUC values had ranges of 0.719–0.84 and 0.84–0.95, respectively. Finally, prediction of recurrence, progression, or survival following treatment was explored in nine studies, with ROC-AUC ranging 0.698–0.90. Of the 23 studies included in this review, 2 (8.7%) included external validation. While explorations of PSMA PET/CT-derived radiomic models are immature in follow-up and experience, these results represent great potential for future investigation and exploration. Prior to consideration for clinical use, however, rigorous validation in feature reproducibility and biologic validation of radiomic signatures must be prioritized.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Prediction Of Adverse Pathology Following Radical Prostatectomymentioning

confidence: 99%

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Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography-Derived Radiomic Models in Prostate Cancer Prognostication

Huynh,

Swanson,

Cima

et al. 2024

Cancers

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“…Any focal uptake above the background in the prostate that couldn’t be explained by physiological uptake was considered a positive lesion. The SUVmax, the mean SUV (SUVmean), and PSMA tumor volume (PSMA-TV) were produced automatically from the volume of interest (VOI) with isocontours set at 41% of the maximum uptake within the respective focus [ 25 , 26 ]. PSMA total lesion (PSMA-TL) was calculated by multiplying the PSMA-TV and SUVmean.…”

Section: Methodsmentioning

confidence: 99%

[68Ga]Ga‑PSMA‑617 PET-based radiomics model to identify candidates for active surveillance amongst patients with GGG 1–2 prostate cancer at biopsy

Yang,

Xiao,

Zhou

et al. 2024

Cancer Imaging

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Purpose To develop a radiomics-based model using [68Ga]Ga-PSMA PET/CT to predict postoperative adverse pathology (AP) in patients with biopsy Gleason Grade Group (GGG) 1–2 prostate cancer (PCa), assisting in the selection of patients for active surveillance (AS). Methods A total of 75 men with biopsy GGG 1–2 PCa who underwent radical prostatectomy (RP) were enrolled. The patients were randomly divided into a training group (70%) and a testing group (30%). Radiomics features of entire prostate were extracted from the [68Ga]Ga-PSMA PET scans and selected using the minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression model. Logistic regression analyses were conducted to construct the prediction models. Receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve were employed to evaluate the diagnostic value, clinical utility, and predictive accuracy of the models, respectively. Results Among the 75 patients, 30 had AP confirmed by RP. The clinical model showed an area under the curve (AUC) of 0.821 (0.695–0.947) in the training set and 0.795 (0.603–0.987) in the testing set. The radiomics model achieved AUC values of 0.830 (0.720–0.941) in the training set and 0.829 (0.624–1.000) in the testing set. The combined model, which incorporated the Radiomics score (Radscore) and free prostate-specific antigen (FPSA)/total prostate-specific antigen (TPSA), demonstrated higher diagnostic efficacy than both the clinical and radiomics models, with AUC values of 0.875 (0.780–0.970) in the training set and 0.872 (0.678–1.000) in the testing set. DCA showed that the net benefits of the combined model and radiomics model exceeded those of the clinical model. Conclusion The combined model shows potential in stratifying men with biopsy GGG 1–2 PCa based on the presence of AP at final pathology and outperforms models based solely on clinical or radiomics features. It may be expected to aid urologists in better selecting suitable patients for AS.

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“…In addition, the performance of positron emission tomography (PET) molecular imaging with gallium-68 ( 68 Ga) or fluorine-18 ( 18 F) on prostate-specific membrane antigen (PSMA), which specifically targets PSMA peptides highly expressed on the surface of the prostate tumor cells and its accompanying metastases, allows noninvasive precise evaluation of prostate lesions. The intensity of PSMA uptake and the degree of PSMA enrichment in the tumor are reported to be positively related to Gleason grading and ISUP classification of the tumor [ 4 , 5 ]. Therefore, studies could prove the superiority of integrated PET/computed tomography (CT) or MRI systems for primary staging of prostate tumors in terms of diagnostic accuracy of results with fewer ambiguous findings compared with mpMRI [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Direct Patlak Reconstruction of [68Ga]Ga-PSMA PET for the Evaluation of Primary Prostate Cancer Prior Total Prostatectomy: Results of a Pilot Study

Rasul,

Geist,

Einspieler

et al. 2023

IJMS

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To investigate the use of kinetic parameters derived from direct Patlak reconstructions of [68Ga]Ga-PSMA-11 positron emission tomography/computed tomography (PET/CT) to predict the histological grade of malignancy of the primary tumor of patients with prostate cancer (PCa). Thirteen patients (mean age 66 ± 10 years) with a primary, therapy-naïve PCa (median PSA 9.3 [range: 6.3–130 µg/L]) prior radical prostatectomy, were recruited in this exploratory prospective study. A dynamic whole-body [68Ga]Ga-PSMA-11 PET/CT scan was performed for all patients. Measured quantification parameters included Patlak slope (Ki: absolute rate of tracer consumption) and Patlak intercept (Vb: degree of tracer perfusion in the tumor). Additionally, the mean and maximum standardized uptake values (SUVmean and SUVmax) of the tumor were determined from a static PET 60 min post tracer injection. In every patient, initial PSA (iPSA) values that were also the PSA level at the time of the examination and final histology results with Gleason score (GS) grading were correlated with the quantitative readouts. Collectively, 20 individual malignant prostate lesions were ascertained and histologically graded for GS with ISUP classification. Six lesions were classified as ISUP 5, two as ISUP 4, eight as ISUP 3, and four as ISUP 2. In both static and dynamic PET/CT imaging, the prostate lesions could be visually distinguished from the background. The average values of the SUVmean, slope, and intercept of the background were 2.4 (±0.4), 0.015 1/min (±0.006), and 52% (±12), respectively. These were significantly lower than the corresponding parameters extracted from the prostate lesions (all p < 0.01). No significant differences were found between these values and the various GS and ISUP (all p > 0.05). Spearman correlation coefficient analysis demonstrated a strong correlation between static and dynamic PET/CT parameters (all r ≥ 0.70, p < 0.01). Both GS and ISUP grading revealed only weak correlations with the mean and maximum SUV and tumor-to-background ratio derived from static images and dynamic Patlak slope. The iPSA demonstrated no significant correlation with GS and ISUP grading or with dynamic and static PET parameter values. In this cohort of mainly high-risk PCa, no significant correlation between [68Ga]Ga-PSMA-11 perfusion and consumption and the aggressiveness of the primary tumor was observed. This suggests that the association between SUV values and GS may be more distinctive when distinguishing clinically relevant from clinically non-relevant PCa.

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

Cited by 9 publications

References 51 publications

Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography-Derived Radiomic Models in Prostate Cancer Prognostication

Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography-Derived Radiomic Models in Prostate Cancer Prognostication

[68Ga]Ga‑PSMA‑617 PET-based radiomics model to identify candidates for active surveillance amongst patients with GGG 1–2 prostate cancer at biopsy

Direct Patlak Reconstruction of [68Ga]Ga-PSMA PET for the Evaluation of Primary Prostate Cancer Prior Total Prostatectomy: Results of a Pilot Study

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