Dataset Information

Prediction of surgical difficulty in minimally invasive surgery for rectal cancer by use of MRI pelvimetry.

ABSTRACT:

Background

Technical difficulties in rectal surgery are often related to dissection in a limited surgical field. This study investigated the clinical value of MRI pelvimetry in the prediction of surgical difficulty associated with minimally invasive rectal surgery.

Methods

Patients with rectal cancer who underwent laparoscopic or robotic total mesorectal excision between 2005 and 2017 were reviewed retrospectively and categorized according to surgical difficulty on the basis of duration of surgery, conversion to an open procedure, use of the transanal approach, postoperative hospital stay, blood loss and postoperative complications. Preoperative clinical and MRI-related parameters were examined to develop a prediction model to estimate the extent of surgical difficulty, and to compare anastomotic leakage rates in the low- and high-grade surgical difficulty groups. Prognosis was investigated by calculating overall and relapse-free survival, and cumulative local and distant recurrence rates.

Results

Of 121 patients analysed, 104 (86·0 per cent) were categorized into the low-grade group and 17 (14·0 per cent) into the high-grade group. Multivariable analysis indicated that high-grade surgical difficulty was associated with a BMI above 25 kg/m² (odds ratio (OR) 4·45, P = 0·033), tumour size 45 mm or more (OR 5·42, P = 0·042), anorectal angle 123° or more (OR 5·98, P = 0·028) and pelvic outlet less than 82·7 mm (OR 6·62, P = 0·048). All of these features were used to devise a four-variable scoring model to predict surgical difficulty. In patients categorized as high grade for surgical difficulty, the anastomotic leakage rate was 53 per cent (9 of 17 patients), compared with 9·6 per cent (10 of 104) in the low-grade group (P < 0·001). The high-grade group had a significantly higher local recurrence rate than the low-grade group (P = 0·002).

Conclusion

This study highlights the impact of clinical variables and MRI pelvimetry in the prediction of surgical difficulty in minimally invasive rectal surgery.

SUBMITTER: Yamamoto T

PROVIDER: S-EPMC7397373 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction of surgical difficulty in minimally invasive surgery for rectal cancer by use of MRI pelvimetry.

Yamamoto T T Kawada K K Kiyasu Y Y Itatani Y Y Mizuno R R Hida K K Sakai Y Y

BJS open 20200428 4

<h4>Background</h4>Technical difficulties in rectal surgery are often related to dissection in a limited surgical field. This study investigated the clinical value of MRI pelvimetry in the prediction of surgical difficulty associated with minimally invasive rectal surgery.<h4>Methods</h4>Patients with rectal cancer who underwent laparoscopic or robotic total mesorectal excision between 2005 and 2017 were reviewed retrospectively and categorized according to surgical difficulty on the basis of du ...[more]

PMID: 32342670

Similar Datasets

Project description:(1) Background: The difficulty of pelvic operation is greatly affected by anatomical constraints. Defining this difficulty and assessing it based on conventional methods has some limitations. Artificial intelligence (AI) has enabled rapid advances in surgery, but its role in assessing the difficulty of laparoscopic rectal surgery is unclear. This study aimed to establish a difficulty grading system to assess the difficulty of laparoscopic rectal surgery, as well as utilize this system to evaluate the reliability of pelvis-induced difficulties described by MRI-based AI. (2) Methods: Patients who underwent laparoscopic rectal surgery from March 2019 to October 2022 were included, and were divided into a non-difficult group and difficult group. This study was divided into two stages. In the first stage, a difficulty grading system was developed and proposed to assess the surgical difficulty caused by the pelvis. In the second stage, AI was used to build a model, and the ability of the model to stratify the difficulty of surgery was evaluated at this stage, based on the results of the first stage; (3) Results: Among the 108 enrolled patients, 53 patients (49.1%) were in the difficult group. Compared to the non-difficult group, there were longer operation times, more blood loss, higher rates of anastomotic leaks, and poorer specimen quality in the difficult group. In the second stage, after training and testing, the average accuracy of the four-fold cross validation models on the test set was 0.830, and the accuracy of the merged AI model was 0.800, the precision was 0.786, the specificity was 0.750, the recall was 0.846, the F1-score was 0.815, the area under the receiver operating curve was 0.78 and the average precision was 0.69; (4) Conclusions: This study successfully proposed a feasible grading system for surgery difficulty and developed a predictive model with reasonable accuracy using AI, which can assist surgeons in determining surgical difficulty and in choosing the optimal surgical approach for rectal cancer patients with a structurally difficult pelvis.

Dataset Information

Prediction of surgical difficulty in minimally invasive surgery for rectal cancer by use of MRI pelvimetry.

Background

Methods

Results

Conclusion

Publications

Prediction of surgical difficulty in minimally invasive surgery for rectal cancer by use of MRI pelvimetry.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets