The role of conventional TRUS imaging in PCa diagnosis is currently limited to the guidance of the biopsy needle inside of the prostate to retrieve systematic cores or to target a suspect area previously identified by mpMRI . This limited use of gray-scale TRUS is due to its poor diagnostic performance in the detection of PCa that has been extensively studied in the past with an estimated sensitivity and specificity of approximately 40% and 50% . Since the introduction of ultrasound (US) scanners in clinical practice, around 30 years ago [8, 9], different tools and technologies have been proposed to overcome this problem. Among these advancements, some involve the use of advanced US modalities (such as Color/Power-Doppler US, Contrast-Enhanced US, Elastography and Micro-ultrasound), whilst others introduce a different way of processing the subvisual information that is already available inside the US image either analyzing the raw data of the tissue characteristics (Histoscanning) or using algorithms and Artificial Intelligence (AI) such as the Artificial Neural Network Analysis (ANNA) which is the object of our study.
The use of AI is constantly gaining more interest in healthcare, motivating numerous applications being proposed for several diseases and pathologies [10–12]. The ANNA approach is one of the AI approaches available, defined as a form of machine learning, composed of adaptative computational statistical models (designed to mimic a biological nervous system) which is able to recognize complex patterns in data and can predict outcomes after being trained with datasets including input and known output (or outcome) data [10, 11]. These methods have shown promising results in improving and automating the ability to diagnose, characterize, and assess the severity of PCa by interpolating clinical or image-based information. For these reasons, one of the earliest applications of ANNA was used to improve the diagnostic accuracy of the TRUS of the prostate. Loch et al. in a study with 61 patients used an ANNA system trained with 289 pathological confirmed digitalized images of TRUS showing a sensitivity of 79% and a specificity of 99%. . Ronco and Fernandez used an ANNA system combining fourteen TRUS variables (including length, diameter, prostate and central zone volume, echoic level, volume, the major and minor diameter of the pathological area and presence or absence of calcifications) with clinical parameters (such as age, PSA level and PSA density) of 442 cases showing a PPV, NPV and accuracy of 82%, 97% and 83% respectively . Tokas et al. reported results from a study with 71 patients with 12-year follow-up performed with serial ANNA/C-TRUS imaging and repeated prostate biopsy with only six targeted cores. They showed several findings: first, 97% of patients were correctly diagnosed either harbouring PCa or being without the disease. Second, during the follow-up, patients underwent several biopsy sessions but performed with targeted cores only, which allowed for less invasive procedures with no need for local anaesthesia and no decrease in diagnostic performance. Last but not least, thanks to the help of internal landmarks and the Ultrasound Computed Tomography (US-CT), which allows correlating sections on a mm level and record image changes over time (ANNA 2.0). This imaging method showed to be able to monitor patients safely as no csPCa was diagnosed after 12 years of follow-up in those patients with a non-suspicious ANNA/C-TRUS analysis .
With the introduction of mpMRI into the armamentarium of PCa imaging, a comparison between mpMRI and the ANNA/C-TRUS system in terms of diagnostic performance for PCa detection is warranted.
Recently, Harland et al. published results from 202 patients using the ANNA/C-TRUS system combined with robotic US-CT and mpMRI. In 44 patients the results were then compared with whole-mount sections of radical prostatectomy specimens. In this study they showed that the use of a robotic registration of TRUS images can help to overcome the problem of the operator dependency of the US techniques, allowing for a reassessment of the findings by different readers similar to what is done in mpMRI. Moreover, ANNA/C-TRUS detected PCa in 30 out of the 44 patients (68%) for whom prostatectomy specimen was available for comparison. The detection rate increased to 80% in combination with conventional gray-scale ultrasound .
Looking at the evidence coming from high-quality trials such as from the PROMIS trial, the values of sensitivity, specificity, NPV and PPV for MRI were reported to be 93%, 41%, 89% and 55% respectively (for csPCa defined as Gleason score ≥ 4+3 or cancer core length ≥ 6 mm). It is important to highlight that in the PROMIS trial, MRI images were reported by specifically trained radiologists. This detailed reduced substantially the inter-reader variability, which is well documented limitation of mpMRI in the literature and may limit the reproducibility of the PROMIS results beyond expert and academic MRI centers [5, 15, 16]. Conversely, ANNA/C-TRUS being based on an AI analysis of subvisual information of the gray-scale TURS image, can overcome limitations such as learning curve, inter-observer variability and need for expertise. It is interesting to notice how AI has also been applied to MRI in several studies, mainly to better differentiate and score the aggressiveness of PCa lesions, confirming the enormous attention and interest that nowadays AI has gained in the medical field [17–19].
Looking at the costs of imaging modalities for PCa diagnosis, TRUS and its variants appears to compare favorable. In fact, the average cost of an ultrasound scanner is around €100.000; whilst the cost for additional software and technologies which allow performing elastography, Doppler or contrast-enhanced ultrasound, is in the order of a few thousand dollars more. This total amount is still considered to be ten times less than the economic burden of an MRI scanner. The economic aspect is also at the base of the limited availability and access to MRI scans which, as mentioned above, require also additional expertise (radiologists, technicians, etc.) . On the other hand, TRUS is much more readily available, can be performed in-office by urologists and, thanks to technological advancements such as ANNA/C-TRUS, are associated with a more favorable learning curve and need less specific expertise. In fact, especially in the multicenter setup (C-TRUS-MS), which was the modality used in our study, ANNA/C-TRUS appears to be an accessible system, relatively less expensive, and user-friendly. This AI system, in its multicenter setup, allows urologists to perform TRUS utilizing any US scanner available, send images via any internet platform for analysis and receive, also via internet, results with valid and enriched information about a prostate harboring PCa or not. .
Strunk et al. , in a targeted biopsy-based study with 20 patients, proposed a combination between ANNA/C-TRUS and mpMRI. In this study, it was suggested that combining the two methods could improve PCa detection. Moreover, all patients with PCa were detected by the ANNA/C-TRUS, whilst MRI-target biopsy missed cancers in 3 patients (27%). These results, even though coming from an era in which standardization with PIRADS-score v.2 was not yet available and from a small sample size, highlights the possible difficulties encountered when transferring mpMRI information to US imaging for the purpose of targeting MRI lesions. This problem is, indeed, not yet solved even with the last modern software of fusion imaging due to the intrinsic differences in these two imaging modalities.