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Introduction: Three-dimensional image-guided surgical (3D-IGS) systems for minimally invasive partial nephrectomy (MIPN) can potentially improve the efficiency and accuracy of intraoperative anatomical localization and tumor resection. This review seeks to analyze the current state of research regarding 3D-IGS, including the evaluation of clinical outcomes, system functionality, and qualitative insights regarding 3D-IGS's impact on surgical procedures. Methods: We have systematically reviewed the clinical literature pertaining to 3D-IGS deployed for MIPN. For inclusion, studies must produce a patient-specific 3D anatomical model from two-dimensional imaging. Data extracted from the studies include clinical results, registration (alignment of the 3D model to the surgical scene) method used, limitations, and data types reported. A subset of studies was qualitatively analyzed through an inductive coding approach to identify major themes and subthemes across the studies. Results: Twenty-five studies were included in the review. Eight (32%) studies reported clinical results that point to 3D-IGS improving multiple surgical outcomes. Manual registration was the most utilized (48%). Soft tissue deformation was the most cited limitation among the included studies. Many studies reported qualitative statements regarding surgeon accuracy improvement, but quantitative surgeon accuracy data were not reported. During the qualitative analysis, six major themes emerged across the nine applicable studies. They are as follows: 3D-IGS is necessary, 3D-IGS improved surgical outcomes, researcher/surgeon confidence in 3D-IGS system, enhanced surgeon ability/accuracy, anatomical explanation for qualitative assessment, and claims without data or reference to support. Conclusions: Currently, clinical outcomes are the main source of quantitative data available to point to 3D-IGS's efficacy. However, the literature qualitatively suggests the benefit of accurate 3D-IGS for robotic partial nephrectomy.

Image-guidance during partial nephrectomy enables navigation within the operative field alongside a 3-dimensional roadmap of renal anatomy generated from patient-specific imaging. Once a process is performed by the human mind, the technology will allow standardization of the task for the benefit of all patients undergoing robot-assisted partial nephrectomy. Any surgeon will be able to visualize the kidney and key subsurface landmarks in real-time within a 3-dimensional simulation, with the goals of improving operative efficiency, decreasing surgical complications, and improving oncologic outcomes. For similar purposes, image-guidance has already been adopted as a standard of care in other surgical fields; we are now at the brink of this in urology. This review summarizes touch-based approaches to image-guidance during partial nephrectomy, as the technology begins to enter in vivo human evaluation. The processes of segmentation, localization, registration, and re-registration are all described with seamless integration into the da Vinci surgical system; this will facilitate clinical adoption sooner., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Aim: Image-guided surgery (IGS) allows for accurate, real-time localization of subsurface critical structures during surgery. No prior IGS systems have described a feasible method of intraoperative reregistration after manipulation of the kidney during robotic partial nephrectomy (PN). We present a method for seamless reregistration during IGS and evaluate accuracy before and after tumor resection in two validated kidney phantoms. Materials and Methods: We performed robotic PN on two validated kidney phantoms-one with an endophytic tumor and one with an exophytic tumor-with our IGS system utilizing the da Vinci Xi robot. Intraoperatively, the kidney phantoms' surfaces were digitized with the da Vinci robotic manipulator via a touch-based method and registered to a three-dimensional segmented model created from cross-sectional CT imaging of the phantoms. Fiducial points were marked with a surgical marking pen and identified after the initial registration using the robotic manipulator. Segmented images were displayed via picture-in-picture in the surgeon console as tumor resection was performed. After resection, reregistration was performed by reidentifying the fiducial points. The accuracy of the initial registration and reregistration was compared. Results: The root mean square (RMS) averages of target registration error (TRE) were 2.53 and 4.88 mm for the endophytic and exophytic phantoms, respectively. IGS enabled resection along preplanned contours. Specifically, the RMS averages of the normal TRE over the entire resection surface were 0.75 and 2.15 mm for the endophytic and exophytic phantoms, respectively. Both tumors were resected with grossly negative margins. Point-based reregistration enabled instantaneous reregistration with minimal impact on RMS TRE compared with the initial registration (from 1.34 to 1.70 mm preresection and from 1.60 to 2.10 mm postresection). Conclusions: We present a novel and accurate registration and reregistration framework for use during IGS for PN with the da Vinci Xi surgical system. The technology is easily integrated into the surgical workflow and does not require additional hardware.