Robotic-assisted segmentectomy is a pulmonary resection procedure that is emerging as an alternative to lobectomy for the treatment of early-stage lung cancer tumours less than 2 cm in maximal diameter. Segmentectomy offers better lung function after surgery by only removing a few segments of the lobe that contain the tumour, and sparing remaining healthy lung tissue. As tumours are being more frequently detected in their early-stages, segmentectomy has gained considerable attention for its potential as a primary treatment option for suspected nodules less than 3 cm in maximal diameter. However, there is a reluctance in adopting segmentectomy due to technical challenges while performing the operation, and the lack of high-quality prospective data compared to lobectomy, which is the current standard of care.
From a technical standpoint, segmentectomy is difficult to perform because the pulmonary lines that separate segments, or intersegmental planes, are invisible. This poses a challenge for the operating surgeon in determining where to resect the lung tissue to obtain adequate margin distance from the tumour. Near-infrared mapping (NIF) with indocyanine green dye (ICG) is a recent advancement in robotic-assisted segmentectomy that provides a complete delineation of the intersegmental plane. Previous work at our center has also shown that this technique was associated with an increase in the oncological margin distance compared to the surgeons’ initially estimated resection line. Given that segmentectomy is associated with a learning curve, we evaluated whether this was observed due to our early experience in robotic-assisted segmentectomy, and hypothesized that the added benefit of ICG would diminish as more cases were performed. In Chapter 2, we used a temporal analysis to monitor surgeon experience over time, and found that the clinical utility of NIF mapping diminished after approximately 42 cases with ICG, and the surgeon began to identify the location of the intersegmental plane more accurately and consistently without ICG injection since.
The second barrier in the adoption of segmentectomy is the lack of high quality-prospective data. Current evidence pertaining to the effectiveness of segmentectomy in terms of cancer-related outcomes is inconclusive and difficult to generalize to the current lung cancer population. In Chapter 3, we performed a secondary analysis of a prospectively collected database of participants who underwent robotic-assisted segmentectomy or lobectomy for tumours less than 3 cm. The oncological efficacy of segmentectomy can be evaluated by the measuring the number of lymph node stations sampled intraoperatively and rates of nodal upstaging, and comparing these outcomes to pulmonary lobectomy. These are important surrogate outcomes that can be readily evaluated, and have been shown to predict overall survival after lung resection. We observed that these outcomes, including overall survival, were similar between patients who underwent segmentectomy and lobectomy for tumours less than 3 cm. While these findings were consistent for patients that underwent segmentectomy for tumours between 2 and 3 cm, recurrence-free survival was found to be significantly lower after segmentectomy compared to lobectomy.
In conclusion, the clinical utility of near-infrared mapping diminishes over time, which is indicative of an improved ability to perform robotic-assisted segmentectomy as more cases were attempted. Second, adequate lymph node evaluation can be expected after segmentectomy, reducing the likelihood of missing positive lymph nodes. Although patients who underwent segmentectomy for tumours greater than 2 cm may be at a greater risk of experiencing recurrence compared to lobectomy, this population did not experience any reductions in overall survival. / Thesis / Master of Health Sciences (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/28033 |
| Date | January 2022 |
| Creators | Alaichi, Jacob |
| Contributors | Dr. Waël Hanna, Clinical Health Sciences (Health Research Methodology) |
| Source Sets | McMaster University |
| Detected Language | English |
| Type | Thesis |
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