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.

Efficacy of neoadjuvant CapeOX/mFOLFOX6 without radiation for patients with baseline resectable mid-low locally advanced rectal cancer.

OBJECTIVES: Chemotherapy without radiation therapy for locally advanced rectal cancer (LARC) has attracted increasing attention, but the optimal schema remains controversial. In this study, we aimed to assess the efficacy and toxicity of neoadjuvant chemotherapy (nCT) of two regimens for patients with mid-low baseline resectable LARC. METHODS: A retrospective study was performed in 131 patients with baseline resectable LARC in a single center between April 2016 and August 2020. All patients received four cycles of neoadjuvant CapeOX or mFOLFOX6 before surgery. Clinical characteristics, pathological response, and survival status were then assessed. RESULTS: After a 1:1 propensity score matching, 96 patients were enrolled, including 48 receiving CapeOX and 48 receiving mFOLFOX6. The objective regression rates were 50.00% and 58.33%, and the pathological complete response rates were 6.25% and 8.33%, respectively, in the CapeOX and mFOLFOX6 groups. Patients who received mFOLFOX6 had a better tumor regression grade (TRG) than those who received CapeOX (P = 0.005). Patients in both groups had similar survival outcomes. CONCLUSIONS: The nCT has shown promising tumor response and survival outcomes, which can be a treatment option for baseline resectable LARC. For the two regimens, mFOLFOX6 provided better TRG than CapeOX, although no differences were observed in disease-free survival and OS.