Pelvimetric and Nutritional Factors Predicting Surgical Difficulty in Laparoscopic Resection for Rectal Cancer Following Preoperative Chemoradiotherapy

Abstract

Aim Laparoscopic total mesorectal excision (LaTME) following preoperative chemoradiotherapy (PCRT) in locally advanced rectal cancer (LARC) is technically demanding. The present study is intended to evaluate predictive factors of surgical difficulty of LaTME following PCRT by using pelvimetric and nutritional factors. Method Consecutive LARC patients receiving LaTME after PCRT were included. Surgical difficulty was classified based upon intraoperative (operation time, blood loss, and conversion) and postoperative outcomes (postoperative hospital stay and morbidities). Pelvimetry was performed using preoperative T2-weighted MRI. Nutritional factors such as albumin-toglobulin ratio (AGR) and prognostic nutritional index (PNI) were calculated. Multivariable logistic analysis was used to identify predictors of high surgical difficulty. A predictive nomogram was developed and validated internally. Results Among 294 patients included, 36 (12.4%) patients were graded as high surgical difficulty. Logistic regression analysis demonstrated that previous abdominal surgery (OR = 6.080, P = 0.001), tumor diameter (OR = 1.732, P = 0.003), intersphincteric resection (vs. low anterior resection, OR = 13.241, P \ 0.001), interspinous distance (OR = 0.505, P = 0.009), and preoperative AGR (OR = 0.041, P = 0.024) were independently predictive of high surgical difficulty of LaTME after PCRT. Then, a predictive nomogram was built (C-index = 0.867). Conclusion Besides previous abdominal surgery, type of surgery (intersphincteric resection), tumor diameter, and interspinous distance, we found that preoperative AGR could be useful for the prediction of surgical difficulty of LaTME after PCRT. A predictive nomogram for surgical difficulty may aid in planning an appropriate approach for rectal cancer surgery after PCRT.

Development of artificial blood loss and duration of excision score to evaluate surgical difficulty of total laparoscopic anterior resection in rectal cancer

“…In light of recent developments in machine learning and the accessibility of computing power, the application of the technique in the data mining and model development field has yielded promising results ( 42 ). Currently, most of the predictive tools are presented with limited clinical applicability, poor predictive ability, and lack of external validation ( 28 , 43 , 44 ) since they are developed according to the variables’ interaction in a linear and additive manner ( 45 ), but the surgical difficulty is multi-factorial, and the interaction between surgical difficulty and influencing factors cannot be completely linear. Machine learning algorithms could effectively overcome the shortcomings of traditional methods, which can be used as a more accurate and non-linear tool to predict the outcomes of patients ( 46 , 47 ).…”

Establishment of Surgical Difficulty Grading System and Application of MRI-Based Artificial Intelligence to Stratify Difficulty in Laparoscopic Rectal Surgery

“…A narrow pelvis, thick mesorectum, large tumor size, tissue edema, and indistinct anatomical layer were selected to account for the surgical difficulty in this grading system. In the narrow anatomical space of the deep pelvis, a larger tumor size and thicker mesorectum have adverse effects on operation [ 3 , 4 ], limiting the vision of laparoscopy and restricting the ability of surgeons to operate. Neoadjuvant radiotherapy reduces local recurrence and benefits survival [ 36 ], but it also results in severe tissue edema followed by fibrosis [ 37 ], which increases the difficulty of dissecting the mesorectum.…”

“…Recently, there has been an increasing interest in factors affecting the difficulty of performing surgery in the pelvic cavity. Several studies have shown that the tumor location, tumor size, gender, body mass index (BMI), pelvic dimensions and angles, previous abdominal surgery, and neoadjuvant radiotherapy affect this difficulty [ 3 , 4 , 5 , 6 ]. However, these objective indicators are unreliable and, may not able to reflect the intraoperative situation.…”

BMI and pelvimetry help to predict the duration of laparoscopic resection for low and middle rectal cancer

“…Recent studies have shown that several parameters are associated with the surgical difficulty of laparoscopic anterior resection for low and middle rectal cancer, but the results are inconsistent [ 15 – 18 ]. The present study demonstrated that a higher BMI and smaller pelvic inlet could help predict the duration of surgery, which might be helpful for preoperative assessment.…”

“…Moreover, the median operative time and postoperative hospital stay were 310 min and 18 days, respectively, so those criteria were also adjusted accordingly. However, in the studies by Sun [ 18 ] and Chen [ 16 ], the average postoperative hospital stay was 8.0 days and 7.7 days, respectively. As a result, these authors adjusted the standard critical value of postoperative hospital stay to 7 days for analysis.…”