Leiden University Scholarly Publications

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Azargoshasb, S.; Barros, H.A. de; Rietbergen, D.D.D.; Dell'Oglio, P.; Leeuwen, P.J. van; Wagner, C.; ... ; Leeuwen, F.W.B. van (2023)

Artificial intelligence-supported video analysis as a means to assess the Impact of DROP-IN image guidance on robotic surgeons: radioguided sentinel lymph node versus PSMA-targeted prostate cancer surgery

Article / Letter to editor
The introduction of the tethered DROP-IN gamma probe has enabled targeted robot-assisted radioguided prostate cancer (PCa) resection of pelvic sentinel lymph nodes (SLNs) and prostate-specific membrane antigen (PSMA)-positive lesions. While both procedures use Tc-99m-isotopes, the two vary in signal and background intensity. To understand how the different levels of image guidance impact surgical decision-making, computer-vision algorithms are used to extract the DROP-IN probe kinematic form clinical videos. 44 PCa patients undergo SLN (25) and PSMA-targeted (19) resections. PSMA-PET/CT and SPECT/CT create preoperative roadmaps, and intraoperative probe signal intensities are recorded. Using neural network-based software, probe trajectories are extracted from videos to extract multiparametric kinematics and generate decision-making and dexterity scores. PSMA-targeted resections yield significantly lower nodal signal intensities in preoperative SPECT-CT scans (three-fold; p = 0.01),...Show moreThe introduction of the tethered DROP-IN gamma probe has enabled targeted robot-assisted radioguided prostate cancer (PCa) resection of pelvic sentinel lymph nodes (SLNs) and prostate-specific membrane antigen (PSMA)-positive lesions. While both procedures use Tc-99m-isotopes, the two vary in signal and background intensity. To understand how the different levels of image guidance impact surgical decision-making, computer-vision algorithms are used to extract the DROP-IN probe kinematic form clinical videos. 44 PCa patients undergo SLN (25) and PSMA-targeted (19) resections. PSMA-PET/CT and SPECT/CT create preoperative roadmaps, and intraoperative probe signal intensities are recorded. Using neural network-based software, probe trajectories are extracted from videos to extract multiparametric kinematics and generate decision-making and dexterity scores. PSMA-targeted resections yield significantly lower nodal signal intensities in preoperative SPECT-CT scans (three-fold; p = 0.01), intraoperative probe readouts (eight-fold; p < 0.001), and signal-to-background ratios (SBR; two-fold; p < 0.001). Kinematics assessment reveal that the challenges encounter during PSMA-targeted procedures converted to longer target identification times and increase in probe pick-ups (both five-fold; p < 0.001). This results in a fourfold reduction in the decision-making score (p < 0.001). Reduced signal intensities and intraoperative SBR values negatively affect the impact that image-guided surgery strategies have on the surgical decision-making process.Show less
artificial intelligence
image-guided surgery
prostate cancer
robot-assisted surgery
sentinel nodes
All authors
Azargoshasb, S.; Barros, H.A. de; Rietbergen, D.D.D.; Dell'Oglio, P.; Leeuwen, P.J. van; Wagner, C.; Stricker, P.; Vidal-Sicart, S.; Briganti, A.; Maurer, T.; Poel, H.G. van der; Oosterom, M.N. van; Leeuwen, F.W.B. van
Date
2023-07-09
Journal
Advanced Intelligent Systems
DOI
doi:10.1002/aisy.202300192