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Advancing minimally invasive rectal cancer surgery

A decade of comparative outcomes and predictive analytics
PhD ceremony:Mr R.T.J. (Ritch) GeitenbeekWhen:November 03, 2025 Start:12:45Supervisor:prof. dr. E.C.J. (Esther) ConstenCo-supervisors:dr. T.A. Burghgraef, dr. R. HompesWhere:Academy building RUG / Student Information & AdministrationFaculty:Medical Sciences / UMCG
Advancing minimally invasive rectal cancer surgery

Advancing Minimally Invasive Surgery for Rectal Cancer: Insights from Large International Studies

Over the past three decades, surgical techniques for the treatment of rectal cancer have evolved significantly, with minimally invasive approaches—laparoscopic (L-TME), robotic (R-TME), and transanal (TaTME) total mesorectal excision—gaining prominence. This thesis of Ritch Geitenbeek compares these techniques to determine their safety, effectiveness, and impact on patients.

By analyzing data from international collaborations, including high-volume centers across Europe, this research highlights the advantages of robot-assisted over laparoscopic surgery, such as lower conversion rates and improved mesorectal excision quality, though at the cost of longer operative times. Additionally, specific patient subgroups, like those with obesity, may particularly benefit from robotic surgery. Long-term analysis confirms that both techniques offer similar outcomes regarding cancer recurrence and overall survival.

Beyond surgical effectiveness, this thesis evaluates patient-reported quality of life, functional recovery, and economic factors. While all minimally invasive techniques are safe, robotic and transanal approaches may lead to better functional outcomes and fewer permanent stomas, despite higher initial costs.

Lastly, the research explores the role of artificial intelligence (AI) and predictive models in improving surgical planning and patient selection. AI-driven pelvic measurements on magnetic resonance imaging (pelvimetry) enhance risk assessment for postoperative complications, and machine learning (ML) models offer new insights into predicting cancer recurrence.

These findings contribute to the growing evidence supporting minimally invasive rectal cancer surgery and provide valuable guidance for surgeons, healthcare providers, and policymakers aiming to optimize patient outcomes.

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