Predicting multiple linear stapler firings in double stapling technique with an MRI-based deep-learning model.

Multiple linear stapler firings is a risk factor for anastomotic leakage (AL) in laparoscopic low anterior resection (LAR) using double stapling technique (DST) anastomosis. In this study, our objective was to establish the risk factors for ≥ 3 linear stapler firings, and to create and validate a predictive model for ≥ 3 linear stapler firings in laparoscopic LAR using DST anastomosis. We retrospectively enrolled 328 mid-low rectal cancer patients undergoing laparoscopic LAR using DST anastomosis. With a split ratio of 4:1, patients were randomly divided into 2 sets: the training set (n = 260) and the testing set (n = 68). A clinical predictive model of ≥ 3 linear stapler firings was constructed by binary logistic regression. Based on three-dimensional convolutional networks, we built an image model using only magnetic resonance (MR) images segmented by Mask region-based convolutional neural network, and an integrated model based on both MR images and clinical variables. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and Youden index were calculated for each model. And the three models were validated by an independent cohort of 128 patients. There were 17.7% (58/328) patients received ≥ 3 linear stapler firings. Tumor size ≥ 5 cm (odds ratio (OR) = 2.54, 95% confidence interval (CI) = 1.15-5.60, p = 0.021) and preoperative carcinoma embryonic antigen (CEA) level > 5 ng/mL [OR = 2.20, 95% CI = 1.20-4.04, p = 0.011] were independent risk factors associated with ≥ 3 linear stapler firings. The integrated model (AUC = 0.88, accuracy = 94.1%) performed better on predicting ≥ 3 linear stapler firings than the clinical model (AUC = 0.72, accuracy = 86.7%) and the image model (AUC = 0.81, accuracy = 91.2%). Similarly, in the validation set, the integrated model (AUC = 0.84, accuracy = 93.8%) performed better than the clinical model (AUC = 0.65, accuracy = 65.6%) and the image model (AUC = 0.75, accuracy = 92.1%). Our deep-learning model based on pelvic MR can help predict the high-risk population with ≥ 3 linear stapler firings in laparoscopic LAR using DST anastomosis. This model might assist in determining preoperatively the anastomotic technique for mid-low rectal cancer patients.

Magnetic resonance imaging-based deep learning model to predict multiple firings in double-stapled colorectal anastomosis.

BACKGROUND: Multiple linear stapler firings during double stapling technique (DST) after laparoscopic low anterior resection (LAR) are associated with an increased risk of anastomotic leakage (AL). However, it is difficult to predict preoperatively the need for multiple linear stapler cartridges during DST anastomosis. AIM: To develop a deep learning model to predict multiple firings during DST anastomosis based on pelvic magnetic resonance imaging (MRI). METHODS: We collected 9476 MR images from 328 mid-low rectal cancer patients undergoing LAR with DST anastomosis, which were randomly divided into a training set (n = 260) and testing set (n = 68). Binary logistic regression was adopted to create a clinical model using six factors. The sequence of fast spin-echo T2-weighted MRI of the entire pelvis was segmented and analyzed. Pure-image and clinical-image integrated deep learning models were constructed using the mask region-based convolutional neural network segmentation tool and three-dimensional convolutional networks. Sensitivity, specificity, accuracy, positive predictive value (PPV), and area under the receiver operating characteristic curve (AUC) was calculated for each model. RESULTS: The prevalence of ≥ 3 linear stapler cartridges was 17.7% (58/328). The prevalence of AL was statistically significantly higher in patients with ≥ 3 cartridges compared to those with ≤ 2 cartridges (25.0% vs 11.8%, P = 0.018). Preoperative carcinoembryonic antigen level > 5 ng/mL (OR = 2.11, 95%CI 1.08-4.12, P = 0.028) and tumor size ≥ 5 cm (OR = 3.57, 95%CI 1.61-7.89, P = 0.002) were recognized as independent risk factors for use of ≥ 3 linear stapler cartridges. Diagnostic performance was better with the integrated model (accuracy = 94.1%, PPV = 87.5%, and AUC = 0.88) compared with the clinical model (accuracy = 86.7%, PPV = 38.9%, and AUC = 0.72) and the image model (accuracy = 91.2%, PPV = 83.3%, and AUC = 0.81). CONCLUSION: MRI-based deep learning model can predict the use of ≥ 3 linear stapler cartridges during DST anastomosis in laparoscopic LAR surgery. This model might help determine the best anastomosis strategy by avoiding DST when there is a high probability of the need for ≥ 3 linear stapler cartridges.

Prognostic value of apical lymph node metastasis at the inferior mesenteric artery in sigmoid and rectal cancer patients who undergo laparoscopic surgery.

BACKGROUND AND OBJECTIVES: Evidence supporting the importance of apical lymph nodes (LNs) and the potential long-term impact of LN metastases at the inferior mesenteric artery (IMA) lymphectomy remains limited. This study aimed to evaluate the prognostic value of LNs at the IMA (IMA-LN) in sigmoid and rectal cancer patients undergoing laparoscopic surgery. METHODS: We retrospectively evaluated 265 consecutive patients who underwent laparoscopic sigmoid or rectal cancer surgery between August 2016 and May 2020. They were divided into two groups according to the pathological results of the IMA LNs: IMA-LN negative (n = 248) and IMA-LN positive (n = 17). RESULTS: The IMA-LN negative group had significantly better overall survival (OS) (p = .020) and disease-free survival (DFS) (p = .000) than did the IMA-LN positive group. IMA-LN metastasis was associated with worse OS and DFS regardless of the pN stage. Patients with IMA-LN metastasis had a higher risk of postoperative recurrence, especially liver (p = .000) and lung (p = .025) metastasis, than did those without metastasis. However, there was no significant difference in the local recurrence rate between the two groups. CONCLUSIONS: IMA-LN metastasis is an independent risk factor for poor prognosis in sigmoid and rectal cancer. Dissecting and evaluating IMA-LN separately is a more accurate and practical method for predicting prognosis.