Complete mesocolic excision in comparison with conventional surgery for right colon cancer: a nationwide multicenter study of the Italian Society of Surgical Oncology colorectal cancer network (CoME-in trial). Study protocol for a randomized controlled trial.
Maurizio DegiuliMario SolejHogla Aridai Resendiz AguilarGiulia MarchioriRossella ReddavidPublished in: Japanese journal of clinical oncology (2022)
Complete mesocolic excision with central vascular ligation, or simply CME, includes the sharp dissection along the mesocolic visceral and parietal layers, with the ligation of the main vessels at their origins. To date, there is low evidence on its safety and efficacy. This is a study-protocol of a multicenter, randomized, superiority trial in patients with right-sided colon cancer. It aims to investigate whether the complete mesocolic excision improves the oncological outcomes as compared with conventional right hemicolectomy, without worsening early outcomes. Data on efficacy and safety of complete mesocolic excision are available only from a large trial recruiting eastern patients and from a low-volume single-center western study. No results on survival are still available. For this reason, complete mesocolic excision continues to be a controversial topic in daily practice, particularly in western world. This new nationwide multicenter large-volume trial aims to provide further data on western patients, concerning both postoperative and survival outcomes.
Keyphrases
- study protocol
- phase iii
- phase ii
- clinical trial
- double blind
- ejection fraction
- end stage renal disease
- south africa
- newly diagnosed
- open label
- randomized controlled trial
- cross sectional
- healthcare
- prognostic factors
- type diabetes
- patients undergoing
- electronic health record
- placebo controlled
- palliative care
- machine learning
- prostate cancer
- patient reported outcomes
- skeletal muscle
- coronary artery disease
- physical activity
- weight loss
- acute coronary syndrome
- insulin resistance
- working memory
- deep learning
- surgical site infection
- robot assisted
- network analysis