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Fasanella. Mini-invasive Surg 2024;8:5 https://dx.doi.org/10.20517/2574-1225.2023.79 Page 5 of 10

OCT was most successful in distinguishing angiomyolipoma and transitional cell carcinoma from normal
parenchyma, while clear cell tumors had a heterogeneous appearance on OCT, which precluded reliable
[38]
differentiation from normal parenchyma and between renal carcinoma subtypes . Hekman et al. have
[39]
shown its ability to differentiate between benign and malignant renal tissue . The next step will certainly
be to evaluate these technologies in vivo and evaluate their effective potential in renal surgery.

3D reconstruction and augmented reality
In the field of urology, 3D virtual reality (VR) is gaining attention, especially in developing models for
[40]
planning and simulating complex surgical procedures, such as conservative renal surgery . To generate 3D
kidney models, computed tomography (CT) and magnetic resonance imaging (MRI) can be used [41,42] . CT
represents the imaging technique of choice for renal tumors. MRI is a valid alternative but is currently
employed in cases of CT-indeterminate renal tumors or impaired renal function . A clear understanding
[43]
of the anatomy, location, and size of the tumor, along with the relationship of the renal tumor to the normal
parenchyma, collecting system, and vascular structures, is critical to achieving a successful surgical outcome
in RAPN. Additionally, 3D imaging has made it possible to obtain a better evaluation of these aspects by the
surgeon, compared to the original 2D imaging, thanks also to the possibility of manipulating the anatomical
structures at 360° along all axes, for better evaluating intraparenchymal anatomy and tumor extent .
[44]
Bertolo et al., in their study, demonstrated the reliability of 3D VR in the preoperative planning of complex
renal tumors amenable to conservative surgery, especially for the more accurate management of the renal
peduncle compared to standard 2D imaging-based planning . As regards the prediction of the surgical
[45]
complexity of a renal tumor, several nephrometric systems have been developed in recent years, such as the
PADUA and RENAL score [46,47] . Several recent studies have focused on a quantitative analysis of
nephrometry scores based on 3D imaging, finding a higher level of accuracy than 2D-based scoring systems,
especially in predicting major intra- and perioperative complications [48,49] . Huang et al. have developed a
scoring system termed ROADS that provides a standardized and quantitative 3D anatomical classification
to stratify renal sinus tumors and guide surgical strategy . A high ROADS score has been correlated with
[50]
greater surgical complexity, such as longer operative and ischemia times and higher estimated blood loss
and complication rates. Another important aspect is the relationship between postoperative renal function
and the volume of the resected cortical margin, calculated using the 3D reconstruction technique. The
margin volume of the healthy cortex calculated with the reconstruction technique was found to be an
[51]
independent risk factor for impaired postoperative renal function . Using precise 3D volumetric analysis,
Meyer et al. demonstrated that ischemia time, tumor size, and endophytic/exophytic characteristics of a
renal mass are the most important determinants of renal parenchymal volume loss . Furthermore, 3D
[52]
reconstruction techniques could provide an important aid for the super-selective clamping of renal artery
branches during RAPN for complex renal masses, allowing for reducing renal damage caused by
[53]
ischemia .
Augmented reality (AR) consists of the superimposition of acquired images acquired from the
intraoperative or preoperative field on real images or videos of a patient in real time . First, to perform an
[54]
AR procedure, it is necessary to acquire a 3D surgical model reconstruction, starting from standard images.
Subsequently, the image must be registered on the real anatomy, also obtaining the tracking of the surgical
[55]
instruments, the movements of the target organs, and the adjacent anatomical structures . Registration is
the process by which different sets of data are aligned into a single coordinate system such that the positions
of the corresponding points match in the best way possible . However, the superimposition of the images
[56]
requires very high accuracy and precision. The most important aspects to be evaluated in the image
[56]
superimposition in AR are the movements of the organs and the deformation of the tissues . A possible
solution to this problem may be using organ detectors to correct their breathing-related movements over
time. However, another important problem remains, represented by tissue deformation. Surgical dissection,