Editorial Comment to Feasibility and accuracy of computational robot‐assisted partial nephrectomy planning by virtual partial nephrectomy analysis

摘要

The use of preoperative imaging in surgery has, historically, been largely confined to the staging and grading of disease, with surgeons tending to leave these information-rich patient specific datasets at the door of the operating room. More recently, however, there has been a growth in image guidance research, with surgeons and engineers exploring the use of these datasets to assist in surgical planning and intraoperative decision-making.1, 2 In the present article, Isotani et al. presented a novel system that allows the surgeon to better inform decision-making both pre- and intraoperatively for patients undergoing partial nephrectomy.3 The system was conceived with three principal aims: (i) to inform the previously subjective decision-making process of whether or not to attempt selective clamping; (ii) to allow an automated estimation of wedge resection or tumor enucleation and the respective risk of collecting system opening; and (iii) to offer the surgeon an approximation of a patient's renal function post-resection, based on the ratio of post- to preoperative renal cortical volume. The potential benefits of a system with these facets are not insignificant. The ability to accurately predict the feasibility of a selective clamping technique has the ability to reduce unnecessary tissue dissection and as a result operative time, in addition to intraoperative blood loss. The prediction of a patient's post-procedural estimated glomerular filtration rate also has significant implications, allowing the surgeon to make considerably more informed decisions when deciding the management approach to take, particularly in those patients who have suboptimal renal function preoperatively. In addition, the preprocedural simulation can be displayed to the surgeon intraoperatively to help better orientate them to the renal vascular and tumor anatomy. Although the work presented falls short of showing that the proposed system can in fact offer a better prediction of these factors than a surgeon or radiologist, it demonstrates that this set of algorithms can offer a sufficiently accurate measure of all three to warrant further investigation. All advances in surgical technology should have the central aim of improving patient safety and outcome. Image guidance systems endeavor to achieve this by taking patient-specific datasets, and improving the way they are processed and presented to the surgeon. The authors of this article should be commended for keeping this dogma of patient safety and outcome central to their platform's design, offering the surgeon and patient the potential benefits of more informed decision-making both pre- and intraoperatively. None declared.