Introduction

Prostate cancer (PCa) is one of the most frequent cancers in men, with about 6600 new cases diagnosed per year.1 Despite its increasing incidence, a global mortality decline has been observed, in most world regions.2 The detection of low-stage cancers (≤T2), with a better prognosis, related to the increasing incidence of the tumour and proactive screening, may contribute towards a decline in the overall mortality trend, along with improvements in PCa treatment, which is unrelated to the number of cases diagnosed.2

Low-stage cancer includes PCa confined to the prostate gland. Local advanced disease includes extraprostatic extension, which is defined as cancer located outside the confines of the prostate, often mixed with adipose tissue. It may also include seminal vesicle involvement. It is very useful to differentiate T2 (organ confined PCa) from T3 (local advanced disease) in order to differentiate patients who may benefit from radical surgery.

Radical prostatectomy has been the gold standard for surgical treatment for organ confined PCa.3 Robotic-assisted radical prostatectomy introduced a minimally invasive surgical treatment for PCa and has been gaining acceptance for surgeons and patients.4

The detection of extraprostatic extension prior to treatment, in patients with PCa, affects the cancer prognosis. Positive surgical margin is defined as cancer cells touching an inked surgical margin on the pathological specimen. Extracapsular extension (ECE) is a cause of high frequency of PMS, biochemical recurrence, metastatic disease and lower cancer-specific survival after radical prostatectomy.4–8

So far, to predict the risk of advanced disease, clinicians often use staging nomograms. These nomograms, as D’AMICO or CAPRA, are based on prostate specific antigen results in blood test, biopsy Gleason score and clinical T stage on digital rectal examination; additionally, patient’s age and percentage of positive biopsy cores are also included in European patients for CAPRA classification.9 10

Several studies demonstrated an improved accuracy in predicting ECE (pECE), by incorporating information from MRI. The combined use of MRI and clinical risk further improves prediction of pathologic ECE.11

MR scanners able of acquiring high spatial resolution imaging, along with functional sequences (perfusion, DWI), paved the way to the so-called multiparametric MRI (mpMRI) that has radically changed the non-invasive perception for primary diagnosis and staging of prostate carcinoma.12 MpMRI is a well-established imaging modality in PCa assessment, particularly for depicting clinically significant PCa and improves the yields of transrectal US-guided biopsy.13–16 MpMRI is also an accurate method for local staging of PCa, as proven by a recent meta-analysis, but with a heterogeneous sensitivity across the several studies.17

Current mpMRI criteria, used to detect and pECE on pathology analysis, pathologic lymph nodes status and local or biochemical recurrence (BCR), are mostly qualitative with high reader variability, owing to strong interpreter dependency and experience.17 These include capsular contact length, capsular irregularity and/or bulging, obliteration of rectoprostatic angle, asymmetry of the neurovascular bundles, invasion of the periprostatic fat and seminal vesicle invasion.18 Some studies that applied confidence levels to diagnose ECE, such as a Likert scale from 1 to 5, showed only a moderate inter-reader agreement, but no imaging MRI scale for PCa local staging has been proposed or accepted, so far.19 Additionally, a few studies that have used specific Prostate Imaging Reporting and Data System findings as criteria for the assessment of pECE, revealed only a moderate overall accuracy (63%).20 21 There is, still, a great deal of heterogeneity in the protocols and methods used in the various published studies. This variability is one of the reasons why mpMRI is not yet accepted as the standard technique for local staging, both by the European Association of Urology and the National Comprehensive Cancer Network Guidelines.22 The only exception is, since 2016, the acceptance of mpMRI for assessing clinical tumour stage in high-risk patients.23

On the other hand, pECE is dependent on the mechanical effects introduced by the histological processing of the specimen, which may create additional bias and discrepancy regarding in vivo mpMRI observations. By the time, the classical MRI reported features, mentioned before, are not specific, but only suspicious for microscopic ECE. It is crucial to introduce predictive models for ‘early ECE’ before prostatectomy.

Artificial intelligence (AI) or machine learning (ML) is inspired by artificial neural networks (ANN), which in turn are biologically inspired algorithms. The term network is named after the topology, since neural networks represent the synergistic effect of composing and associate many different layers of information together to derive the final output. AI will lead to significant changes for radiology and how radiologists will practice.24 ANN have been applied to the detection of PCa by several groups and successfully applied to determine the probability of malignancy using data obtained by mpMRI.25

Radiomics is a recent methodology that has the goal of extracting a large amount of diagnostic features (kurtosis, skewness, wavelets, etc) from specific regions in medical images through data-characterisation algorithms, in order to classify a specific physiological target.26 These features can be used to provide valuable information to the clinician and may assist on the choice of personalised therapies. This novel method combines ideas from different scientific disciplines, transforming medical images into minable data in the form of features, exploiting that data to train ML models, so they can provide equivalent or even more accurate performance to tackle common diagnostic problems.26 27 Radiomics have been recruited to detect, characterise but also predict clinical outcomes in various diseases, including PCa.26 These algorithms need significantly high number of patients for training the models and hold out test sets to validate them. Due to the small patient sample for the AI standards, various dimensionality reduction methods will be used to minimise overfitting, including ensemble and transfer learning. The final Radiomic signatures will be compared in terms of performance with standard clinical nomograms and in addition novel nomograms including radiomic features will be developed.28 As far as we know, clinically accepted and validated algorithms to pECE, obtained from mpMRI features have not been described.