Abstract
Minimally invasive approaches to cancer treatment are intended to decrease surgical morbidity and thus reduce the negative impact on the quality of life for patients. This thesis focuses on evaluating the minimally invasive approach to the treatment of early-stage cervical cancer.
In the first part of this thesis, we examined
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various aspects of sentinel lymph node (SLN) mapping, a minimally invasive technique to assess whether there is metastasis to the lymph nodes. The concept of SLN mapping is based on the existence of an orderly and predictable pattern of lymphatic drainage with a first lymph node, i.e. the sentinel lymph node, to which tumour cells spread from a primary tumour. Sentinel lymph nodes can be mapped by injecting tracers around the tumour. Recently, the fluorescent tracer indocyanine green (ICG) has emerged as an alternative to the conventional combined approach of a radiotracer and blue dye. The presumed advantages of ICG, that is, being cheaper, non-radioactive, and logistically more attractive than the combined approach, are only valuable if its detection rate proves to be at least non-inferior. We showed that the effectiveness of ICG needs to be properly investigated before we can safely omit the currently used tracers. This finding led to setting up a prospective study comparing the SLN detection of the combined approach of a radiotracer and blue dye with ICG.
Once excised, SLNs are processed according to the pathological ultrastaging protocol, consisting of serial sectioning and immunohistochemical staining. Ultrastaging enhances the detection of low volume disease, defined as isolated tumour cells or micrometastasis, which is rarely found with conventional sectioning and staining. We showed that the use of immunohistochemical staining is of added value but at high cost. Therefore, it is important to research more selective use of this technique and, in the future, the use of artificial intelligence.
In the second part of this thesis, we investigated the learning curve of robot-assisted surgery, an innovative and minimally invasive surgical technique. As surgical performance tends to improve with experience, learning curve effects seem unavoidable when adopting new and complex surgical technologies. We showed that the introduction of robot-assisted surgery can be accompanied by a learning phase in which the outcomes are inferior to the more experienced phase. By using a risk-adjusted cumulative sum (RA-CUSUM) analysis, we established the number of cases needed to ascertain surgical proficiency in terms of satisfying recurrence rates. Our findings also suggest that learning curve effects can be reduced by structured training. With this thesis, we aim to raise awareness of learning curve effects and the importance of structured learning curricula for robot-assisted surgery.
The work in this thesis resulted in an evaluation of existing minimally invasive techniques applied in early-stage cervical cancer. The gained knowledge helps us to further improve the SLN mapping technique and robot-assisted surgery while continuously investigating each step. Ultimately, when adopting new minimally invasive approaches in the field of early-stage cervical cancer, the key issue is to minimise harm for our patients while maintaining oncological safety.
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