Objectives: The aim of this study was to evaluate the feasibility, complications, margin status, and functional outcome (on urinary and sexual functions) of nerve-sparing radical hysterectomy (NSRH) performed laparoscopically.
Methods: Patients with cervical carcinoma of stage Ia2 and Ib1 underwent laparoscopic NSRH along with pelvic lymphadenectomy. We performed the technique in simple comprehensible steps with anatomic delineation of the autonomic nerves and selective cutting of the uterine and cervical branches.
Results: Laparoscopic NSRH was feasible in 85.7% of patients. Mean operative time was 160 minutes and all 7 patients had clear surgical margins. There were no complications and no blood transfusions were required. The median hospital stay was 3 days. The median return time for normal bladder function was 2 days and none of them required catheterization beyond 2 weeks. The mean residual urine volume was <50 mL. Urodynamic studies performed at 3 weeks after the operation showed no impairment of maximum flow rate (maximal flow rate: 20 ± 2 mL). The postoperative results of sexual dysfunction were inconclusive.
Conclusion: Understanding this technique and the knowledge of laparoscopic anatomy of pelvic autonomic nerves is important in both benign and malignant pelvic surgeries. These preliminary results indicate that nerve sparing is easier done laparoscopically and its results are comparable to that of conventional laparoscopic radical hysterectomy in terms of lateral margin status and lymph node yield. Whether quality of life can be benefited by L-NRSH technique and its long-term oncological sequelae need further evaluation.
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http://dx.doi.org/10.1089/lap.2009.0342 | DOI Listing |
Am J Gastroenterol
November 2024
Division of Neurogastroenterology/Motility, Medical College of Georgia, Augusta University, Augusta, Georgia.
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Dept. of Cardiology, Leiden University Medical Center, Leiden, The Netherlands.
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Modular Implantable Neurotechnologies (MINE) Laboratory, Università Vita-Salute San Raffaele & Scuola Superiore Sant'Anna, Milan, Italy.
Electrical stimulation of peripheral nerves via implanted electrodes has been shown to be a promising approach to restore sensation, movement, and autonomic functions across a wide range of illnesses and injuries. While in principle computational models of neuromodulation can allow the exploration of large parameter spaces and the automatic optimization of stimulation devices and strategies, their high time complexity hinders their use on a large scale. We recently proposed the use of machine learning-based surrogate models to estimate the activation of nerve fibers under electrical stimulation, producing a considerable speed-up with respect to biophysically accurate models of fiber excitation while retaining good predictivity.
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