Learning Laparoscopic Radical Hysterectomy: Are We Facing an Emerging Situation?

Abstract

Despite wide screening campaigns and early detection, cervical cancer remains the fourth most common cancer among women. Radical hysterectomy, whether by open, laparoscopic or by robotic-assisted techniques, is the mainstay treatment. However, for adequate surgical results and good oncological prognosis, a gynecological surgeon should be trained to perform those procedures. The learning curve of radical hysterectomy, especially by laparoscopy, is influenced by several factors. The LACC trial, the decrease in cervical cancer incidence and radical hysterectomy procedures have widely reduced the learning curve for surgeons. This article mainly discusses the learning curve of laparoscopic radical hysterectomy for cervical cancers, and how several factors are influencing it negatively, with the need to have medical authorities reset specific surgical training programs and allocate them to special oncological centers.

Beyond Sentinel Lymph Node: Outcomes of Indocyanine Green-Guided Pelvic Lymphadenectomy in Endometrial and Cervical Cancer

“…The results of the LACC trial also have important implications for the education and training of young gynecological surgeons and residents [ 45 ]. Lewiki et al [ 46 ] showed that since the publication of the LACC trial, the minimally invasive approach for cervical neoplasms has been adopted less and less, even in academic centers.…”

“…Teaching future surgeons the correct minimally invasive approach for gynecological neoplasms, including for CC, could help to identify the correct time on their learning curve to leave them alone to operate on this kind of tumor. Therefore, it would be advisable to design trials analyzing only oncological outcomes from teams with proven experience and undergoing proper training for minimally invasive surgery [ 45 ]. The more experienced gynecological surgeons should therefore teach minimally invasive techniques even for the management of CC as well, so that if new evidence leads to a new minimally invasive surgical indication in the future (for instance from the ongoing “Robot-assisted approach to cervical cancer” international multi-center, open-label randomized controlled trial [ 48 ]), there will be still adequate surgical proficiency to treat these patients.…”

Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images

“…create a multimodal ensemble model using a large-scale model, including the model we developed and other essential data besides images, for various purposes, including predicting the occurrence and recurrence risk, in addition to cervical cancer diagnosis. Furthermore, the development of multimodal models using diverse data has great potential for various applications, such as difficult treatment decision-making, determining follow-up frequency, and making decisions about the use of low-invasive surgery, which requires a wide range of operations [38][39][40][41][42]. However, the datasets now available are limited, and the approaches are diverse.…”