68Ga-Prostate-Specific Membrane Antigen PET Radiomics For the Prediction of PostSurgical International Society of Urological Pathology Grade in Patients with Primary Prostate Cancer
Authors: Ghezzo Samuele , Brembilla Giorgio , Russo Tommaso , Gotuzzo Irene , Preza Erik , Samanes Gajate Ana Maria , Mapelli Paola , Bezzi Carolina , Cucchiara Vito , Mongardi Sofia , Neri Ilaria , Gandaglia Giorgio , Montorsi Francesco , Briganti Alberto , De Cobelli Francesco , Scifo Paola , Picchio Maria 
Published: April 2023

INTRODUCTION
Radiomics has been proven effective for the
characterisation of primary prostate cancer
(PCa).1,2 However, the limited interpretability of
the proposed models represents one of the major
limitations in this field.3,4 This study investigated 68Ga-prostate-specific membrane antigen
(PSMA) PET radiomics for the prediction of
post-surgical International Society of Urological
Pathology (ISUP) grade in patients with primary
PCa, ensuring model interpretability.
MATERIALS AND METHODS
Forty-seven patients with PCa were examined
with 68Ga-PSMA PET at the authors’ institution.
Those patients were enrolled in this study
prior to radical prostatectomy. Images were
acquired using either PET/MRI or PET/CT. ISUP
grade was available at both biopsy and radical
prostatectomy for all patients. A radiologist
manually segmented the whole prostate on PET
images using the co-registered CT or MRI for
anatomical localisation on 3D Slicer software
(Brigham and Women’s Hospital, Boston,
Massachusetts, USA).5 The whole prostate was
used as volume of interest (VOI) to avoid the
limitations of radiomics for small volumes.6 VOIs
were normalised, resampled, and discretised.
A total of 103 image biomarker standardisation initiative-compliant, radiomic features (RF) were
extracted using PyRadiomics (Python Software
Foundation, Beaverton, Oregon, USA).7
RFs
were harmonised with the ComBat method8 to
control for the scanner effect, and selected using
the minimum redundancy maximum relevance
algorithm. Combinations of the four most
relevant RFs were used to train 12 radiomics
machine learning models for the prediction
of post-surgical ISUP ≥4 versus ISUP <4 that
were validated by five-fold repeated stratified
cross-validation. To ensure that results were
not driven by spurious associations, two ad hoc
control models were generated. The first one
Creative Commons Attribution-Non Commercial 4.0 ● April 2023 ● Urology 37
EAU 2023 • Abstract
had SUVmax and VOI volume as input (radiomics
baseline), while the other was made by setting
to zero all voxel values prior features extraction
(PET zeros). Balanced accuracy, sensitivity,
specificity, and positive and negative predictive
values were collected. The performance of the
best developed model was compared with that
of ISUP grade biopsy.
RESULTS
ISUP grade at biopsy was upgraded in 9 out of
47 patients after prostatectomy, resulting in a
balanced accuracy of 85.9%; sensitivity of 71.9%;
specificity of 100.0%; positive predicted value of
100.0%; and negative predictive value of 62.5%.
The best performing radiomic model yielded a
balanced accuracy of 87.6%; sensitivity of 88.6%;
specificity of 86.7%; positive predicted value of
94.0%; and negative predicted value of 82.5%.
All radiomic models trained with at least two
RFs (grey level size zone matrix; zone entropy
and shape; least axis length) outperformed
the control models. Conversely, no significant
differences were found for radiomic models
trained with two or more RFs (Mann–Whitney U
test; p>0.05). See Table 1 for a detailed report of
all the generated models’ performance.
CONCLUSION
These findings support the role of 68Ga-PSMA
PET radiomics for the accurate and non-invasive
prediction of post-surgical ISUP grade. Future
multicentre studies will be needed to establish
with certainty the accuracy and reproducibility of
the radiomic signature proposed here.