Techniques in coloproctology · 2026
Using contrast-enhancing software to improve sentinel lymph node detection with indocyanine green in colon cancer surgery.
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van de Laar Bart C T, Sikkenk Daan J, van Dam Bloem N, Verheijen Paul M, Nagengast Wouter B, Consten Esther C J
Аннотация
BACKGROUND: Colon cancer (CC) is primarily treated with segmental resection, a surgical procedure associated with high morbidity and potential mortality. Sentinel lymph node (SLN) biopsy using the near-infrared fluorescence (NIRF) tracer indocyanine green (ICG) may help mitigate these risks by enabling organ-preserving surgery without the need for an anastomosis. It is hypothesized that contrast-enhancing software for NIRF could further improve SLN detection.
METHODS: This retrospective study evaluated whether a contrast-enhanced NIRF view improved SLN detection compared to the standard NIRF view of the robotic surgical system (da Vinci Xi). The contrast-enhanced view was generated using surgical image analysis software (PerfusionWorks), which enhances NIRF visual contrast. A retrospective analysis was conducted using intraoperative videos from SLN mapping with ICG in 10 patients with CC. Fifteen clinicians with varying levels of experience annotated video clips using both the standard and the contrast-enhanced views (44 clips per clinician in total). The sensitivity, specificity, accuracy, time-to-detection, and confidence scores were compared between the two views.
RESULTS: The contrast-enhanced view significantly improved sensitivity (49.3% to 62.4%, p < 0.001) and accuracy (46.1% to 51.8%, p < 0.001) compared to the standard view, with a decrease in specificity that was not statistically significant (39.4% to 30.4%, p = 0.08). On a 6-point Likert scale, clinicians reported higher confidence in their annotations with the contrast-enhanced view (median 5 vs. 4, p < 0.001).
CONCLUSION: The contrast-enhancing software improved SLN detection in this test setting, improving sensitivity and accuracy. These findings support further investigation of its application in an intraoperative setting.
METHODS: This retrospective study evaluated whether a contrast-enhanced NIRF view improved SLN detection compared to the standard NIRF view of the robotic surgical system (da Vinci Xi). The contrast-enhanced view was generated using surgical image analysis software (PerfusionWorks), which enhances NIRF visual contrast. A retrospective analysis was conducted using intraoperative videos from SLN mapping with ICG in 10 patients with CC. Fifteen clinicians with varying levels of experience annotated video clips using both the standard and the contrast-enhanced views (44 clips per clinician in total). The sensitivity, specificity, accuracy, time-to-detection, and confidence scores were compared between the two views.
RESULTS: The contrast-enhanced view significantly improved sensitivity (49.3% to 62.4%, p < 0.001) and accuracy (46.1% to 51.8%, p < 0.001) compared to the standard view, with a decrease in specificity that was not statistically significant (39.4% to 30.4%, p = 0.08). On a 6-point Likert scale, clinicians reported higher confidence in their annotations with the contrast-enhanced view (median 5 vs. 4, p < 0.001).
CONCLUSION: The contrast-enhancing software improved SLN detection in this test setting, improving sensitivity and accuracy. These findings support further investigation of its application in an intraoperative setting.