Predicting multiple linear stapler firings in double stapling technique with an mri-based deep-learning model
Predicting multiple linear stapler firings in double stapling technique with an mri-based deep-learning model"
- Select a language for the TTS:
- UK English Female
- UK English Male
- US English Female
- US English Male
- Australian Female
- Australian Male
- Language selected: (auto detect) - EN
Play all audios:
ABSTRACT 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. SIMILAR CONTENT BEING VIEWED BY OTHERS DEEP LEARNING HRNET FCN FOR BLOOD VESSEL IDENTIFICATION IN LAPAROSCOPIC PANCREATIC SURGERY Article Open access 01 May 2025
CT-BASED DEEP LEARNING ENABLES EARLY POSTOPERATIVE RECURRENCE PREDICTION FOR INTRAHEPATIC CHOLANGIOCARCINOMA Article Open access 19 May 2022 DEVELOPMENT OF A DEEP LEARNING-BASED SOFTWARE FOR
CALCULATING CLEANSING SCORE IN SMALL BOWEL CAPSULE ENDOSCOPY Article Open access 24 February 2021 INTRODUCTION Colorectal cancer (CRC) is the third most prevalent cancer worldwide with high
rate of cancer mortality, while approximately one third of all CRCs occur in rectum1. Total mesorectal excision (TME) is the primary treatment for localized rectal cancer patients, and has
high rate of cancer control2,3. It is a widely applied surgical technique to perform laparoscopic low anterior resection (LAR) using double-stapling technique (DST) anastomosis4,5. In
mid–low rectal cancer patients, anastomotic leakage (AL) is the most common postoperative complication after LAR6. In addition to increasing the incidence of reoperation and mortality, AL
may even negatively affect long-term survival7,8,9. Despite advances in surgical techniques and anastomotic equipment over the past few decades10,11,12, there has been no significant
decrease in the incidence of AL after LAR13. DST simplifies colorectal reconstruction in LAR, especially in laparoscopic surgery, but the incidence of postoperative AL was not reduced by the
application of this technique14,15. Several studies have proved that ≥ 3 linear stapler firings in laparoscopic LAR is an independent risk factor for AL16,17,18. Thus, the Chinese expert
consensus statement proposes ≤ 2 linear stapler firings in LAR surgery19. To avoid multiple linear stapler firings, a predictive model should be created to predict ≥ 3 linear stapler firings
in anastomosis, and alternative techniques could be considered. Several studies have shown that pelvic anatomical features (such as the anteroposterior diameter and the transverse diameter
of the pelvic outlet, the anteroposterior diameter of the pelvic inlet) are related to surgical difficulty and the number of the linear stapler firings20,21,22. Those studies mainly measured
pelvic parameters, but did not include the influence of rectal and mesenteric conditions. How to effectively integrate above parameters to predict ≥ 3 linear stapler firing needs further
research. With the advancement of imaging technology, pelvic magnetic resonance imaging (MRI) is a preferred tool for local staging of mid-low rectal cancer before surgery23,24. MRI can
obtain relevant parameters of tumor, meso-rectum and pelvis comprehensively and accurately. Mask region-based convolutional neural network (Mask R-CNN)25 and three-dimensional convolutional
network (C3D)26 image recognition are current techniques for advanced-recognition artificial intelligence (AI), and have been applied in various medical fields27,28,29. With advanced
deep-learning technology, pelvic MRIs’ complex data can be identified, extracted, analyzed, and integrated efficiently, and we can create a predictive model to screen out high-risk patients
with ≥ 3 linear stapler firings based on the database. In this study, we aimed to establish the risk factors for ≥ 3 linear stapler firings in laparoscopic LAR using DST anastomosis, and to
create and validate a predictive model for ≥ 3 linear stapler firings with deep-learning technology based on MRI. METHODS PATIENTS A total of 328 mid–low rectal cancer patients who underwent
laparoscopic LAR at Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, China, between January 2016 and June 2021 were retrospectively analyzed as the
deep-learning set. Clinical data were obtained from Ruijin hospital database and medical records. All methods were performed in accordance with the relevant guidelines and regulations, and
the study was approved by the Medical Ethics Committee of Ruijin Hospital (No. 2019-82). The need for informed consent was waived by Ethics Committee of Ruijin Hospital. With a split ratio
of 4:1, 260 patients were divided into the training set and 68 patients were divided into the testing set on the basis of an unbiased random sampling method. The prospective validation set
comprised 128 patients from an independent clinical trial in our institution (Transanal versus laparoscopic total mesorectal excision for rectal cancer, ClinicalTrials.gov Identifier:
NCT03359616). The inclusion criteria were: (1) histopathologically confirmed rectal cancer; (2) pelvic MRI examination within 14 days before surgery; (3) tumor distance from the anal verge ≤
10 cm; and (4) laparoscopic LAR with DST anastomosis. The exclusion criteria were: (1) LAR without anastomosis (e.g., Hartmann’s operation); (2) anastomotic techniques rather than DST
(e.g., manual rectal anastomosis through the anus); (3) number of linear stapler firings was not recorded on surgical reports; and (4) robotic surgery. SURGICAL PROCEDURE Laparoscopic LAR
was performed by the gastrointestinal surgery team with experience in completing more than 100 rectal cancer operations every year. The laparoscopic LAR surgical procedure was carried out in
strict accordance with the national guidelines for laparoscopic radical resection of CRC (2018 edition). During dissection of the distal rectum, the surgeon manually fired endoscopic linear
staplers (Endo-GIA™ Ultra Universal Stapler Reload with Tri-stapler™ Technology; Covidien LLC), which was loaded with 60- or 45-mm staple cartridges which have three types of heights: 3.0,
3.5 and 4.0 mm. DATA COLLECTION AND MODEL BUILDING We collected clinical data that potentially correlated with the number of linear staplers used in surgery, including baseline
characteristics, including age, gender and body mass index (BMI); biochemical data, including hemoglobin, albumin, and carcinoma embryonic antigen (CEA); and tumor characteristics, including
tumor distance from the anal verge, tumor stage, tumor size and circumferential resection margin (CRM). A predictive model of ≥ 3 linear stapler firings was constructed by binary logistic
regression. The variables of the clinical model included: gender, BMI, serum CEA level (> or ≤ 5 ng/mL), tumor distance from the anal verge, tumor size and CRM. MRI AND TARGET AREA
LABELING During MRI, patients were in the supine position and scanned using a Philips INGENIA™ scanner with 3.0 T field strength. The pelvic phased-array surface coil covered from the aortic
bifurcation to the anal verge. The scanning parameters were: layer thickness 5 mm; field of view 250 × 340 × 166 mm; echo time 80 ms; repetition time 3565 ms; and image matrix 312 × 357.
With the Picture Archiving and Communication System, fat-suppressed fast spin-echo (FSE) T2-weighted sequences in the axial plane of the pelvis were used for image segmentation. Then, pelvic
MRI specialists who have > 10 years of experience built an image database by an online annotation tool called Labelme (labelme.csail.mit.edu/)30, and labeled three kinds of target area
on each of the T2-weighted images (tumor body, mesorectum, and pelvis represented by green, yellow, and drab, respectively, Fig. 1A,B). Then, all data were transformed into a COCO dataset
for segmentation experiments31. SEGMENTATION MODEL BASED ON MASK R-CNN As an effective two-stage detection and segmentation algorithm, Mask R-CNN was adopted to identify and segment the
three kinds of target areas in the image (Fig. 1C,D). In the first stage, the ResNet-101-FPN network served as the backbone to extract multiscale and discriminative feature maps. The Region
Proposal Network scanned the feature map in a sliding-window and selected the rough detection rectangle that contained the object. After the regions of interest alignment process, the
candidate regions entered the next stage. This consisted of three functional branches: classification, detection and segmentation, based on fully connected layers and convolutional layers.
We trained the Mask R-CNN network on the training set for 200 epochs, and evaluated the performance of the testing set with standard COCO metrics. We evaluated the trained Mask R-CNN model,
obtained average precision (AP) by calculating the precision–recall curve under different intersection-over-union thresholds, and then calculated the three types of targets. The respective
AP values of the regions were weighted to obtain the class-wide mean average precision (mAP). mAP > 50 indicated that the model performed well32. DEEP-LEARNING MODEL BASED ON C3D We used
C3D networks to address 24 images of a case simultaneously and learn 3D spatial features. The C3D network consisted of eight 3D-convolution layers, a softmax layer, two fully connected
layers and five pooling layers. It took the entire image of the case as input and output the probability of ≥ 3 linear stapler firings, and the sample with probability > 50% (empirical
value) was judged as positive. The C3D network was trained until convergence (~ 1000 epochs) and evaluated the performance of the deep-learning model. In the training set, two C3D-based
models were trained, including an image model using only MR images and an integrated model based on both MR images and the six clinical variables in clinical model. In the testing set, the
clinical model, image model and integrated model were examined, and receiver operating characteristic (ROC) curves were plotted. Area under the curve (AUC), sensitivity, specificity,
accuracy, positive predictive value (PPV), and Youden index were calculated. PROSPECTIVE VALIDATION We used clinical data and T2-weighted images of patients from the validation set to
validate the predictive performance of the above three models. ROC curves were plotted, and sensitivity, specificity, accuracy, Youden index, PPV, and AUC were calculated. The flow chart of
the design is shown in Fig. 2. STATISTICAL ANALYSIS Statistical analysis was performed using SPSS (version 25.0). Categorical variables were analyzed by Fisher’s exact test or Pearson’s
chi-square. In our binary logistic regression models, only factors with a P value < 0.10 in the univariate analysis were entered into the multivariate analysis. All tests were two-sided,
and differences were considered statistically significant at p < 0.05. RESULTS CLINICAL CHARACTERISTICS OF PATIENTS IN DEEP-LEARNING SET The 328 patients had a median age of 63 (24–87)
years, including 227 men and 101 women. The proportion of patients who received ≥ 3 linear stapler firings was 17.7% (58/328). Clinical characteristics of patients with ≥ 3 firings of the
linear stapler and those with ≤ 2 firings in the deep-learning set were compared (Table 1). There were no significant differences in age, gender, BMI, diabetes mellitus, neoadjuvant
chemoradiotherapy ratio, operation time, hemoglobin, albumin, tumor distance from the anal verge, T stage and N stage between the two groups. Patients with ≥ 3 firings showed significantly
higher incidence of AL than those with ≤ 2 firings (p = 0.021), and patients with ≥ 3 firings of the linear stapler showed significantly higher CEA level (p = 0.007), larger tumor size (p =
0.004) and higher rate of positive CRM (p = 0.014). In univariate and multivariate analyses, tumor size ≥ 5 cm (odds ratio (OR) = 2.54, 95% confidence interval (CI) = 1.15–5.60, p = 0.021)
and serum CEA > 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 (Table 2). PREDICTING PERFORMANCE IN TESTING
SET The AUCs of the clinical, imaging and integrated models were obtained as 0.72, 0.81 and 0.88, respectively (Fig. 3A–C). The sensitivity, specificity, accuracy, PPV and Youden index of
the clinical model were: 70.0%, 81.0%, 79.4%, 38.9%, and 0.51, respectively. The relevant indicators of the image model were: 50.0%, 98.3%, 91.2%, 83.3%, and 0.48, respectively. The relevant
indicators of the integrated model were: 70.0%, 98.3%, 94.1%, 87.5%, and 0.68, respectively. PROSPECTIVE VALIDATION In the validation set, the proportion of patients who received ≥ 3 linear
stapler firings was 12.5% (16/128). All related clinical characteristics between the deep-learning and validation sets were comparable (Table 3). The AUCs of the clinical, imaging and
integrated models were obtained as 0.65, 0.75 and 0.84, respectively (Fig. 3D–F). The sensitivity, specificity, accuracy, PPV and Youden index of the clinical model were: 62.5%, 66.1%,
65.6%, 21.0%, and 0.29, respectively. The relevant indicators of the image model were: 68.8%, 95.5%, 92.1%, 68.8%, and 0.64, respectively. The relevant indicators of the integrated model
were: 68.8%, 97.3%, 93.8%, 78.5%, and 0.66, respectively. DISCUSSION In this study, we built an MRI-based deep-learning model to predict ≥ 3 linear stapler firings in LAR using DST
anastomosis. This model aimed to help determine the surgical strategy for mid–low rectal cancer patients by predicting the probability of ≥ 3 firings of the linear stapler before surgery.
Thus, we can reduce the occurrence of AL by using other more suitable anastomosis techniques. Our findings suggest that clinical information alone may not be sufficient to predict cases with
≥ 3 firings of the linear stapler. Compared with the clinical or image model, the integrated model that combined clinical information with pelvic MR images achieved better AUC and higher
PPV. LAR using DST anastomosis is currently a widely applied surgical technique for mid–low rectal cancer, a series of high-quality randomized controlled trials has confirmed its feasibility
and safety5,33. The technique greatly reduces the difficulty of reconstruction of the digestive tract. However, some studies have reported that multiple linear stapler firings is closely
related to AL17,34, and AL is more likely to occur at the intersection of two staples35. In some cases, due to the limitation of the pelvic space or thickness of the rectum, the surgeons
have to trigger more linear stapler firings during rectal dissection36. In the high-risk populations with ≥ 3 linear stapler firings, other anastomosis techniques rather than DST anastomosis
could be considered, such as transanal anastomosis after transanal transection of the rectum37 and manual purse-string suture after endoluminal transection of the rectum (e.g., Transanal
total mesorectal excision)38. Although some studies have shown that the above techniques do not reduce the incidence of AL39, these techniques can minimize the anastomotic difficulty in
patients with a narrow pelvis and avoid excessive use of linear stapler firings. Akiyoshi et al. used clinical data and pelvic parameters to predict surgical difficulty and the incidence of
AL in patients undergoing LAR using DST anastomosis. They found that tumor distance from the anal verge, BMI, pelvic outlet, and tumor invasive depth were independent predictors of operation
time and occurrence of AL10. Foo et al. reported a predictive model for predicting ≥ 3 linear stapler firings. The model included the following parameters: tumor distance from the anal
verge, gender, pelvic entrance, internodal distance and interspinous distance40. Compared with the above two studies, our predictive model has several advantages. (1) Using AI-based image
segmentation, pelvic measurements can be identified comprehensively, rather than obtaining certain pelvic parameters separately. Thus, all anatomical features of the pelvis can be entirely
integrated into the image model. (2) Clinically, the space between the pelvis, mesorectum and tumor mass affects the number of linear stapler firings. Our model takes into account not only
pelvic parameters, but also the influence of meso-rectal factors and tumor conditions. (3) The predicting time of this AI-based warning model is only 100 ms. It greatly reduces the time and
labor of manual measurement. It should be noted that our study has some limitations. First, the cohort of 328 patients was too small for training the deep-learning model, further study with
larger sample size is needed. Second, other technical factors that were difficult to quantify can also affect the number of linear stapler firings, such as the correct angle between the
stapler and rectum and precompression before stapler firings35,36. Therefore, no 100% accuracy were achieved in our three models. Third, the number of linear stapler firings was just one of
anastomotic factors related to AL, the circular end-to-end anastomosis, intersections of staple lines41, and the distance between the linear staple line36 were also risk factors for AL.
Finally, our deep-learning traning is only performed on T2-weighted MR sequences. Other MR sequences or contrast-enhanced MRI could be investigated in future studies. In conclusion, the
pelvic MR-based deep-learning model can help identify the high-risk population with ≥ 3 linear stapler firings in laparoscopic LAR surgery. It might help determine the anastomotic technique
for mid–low rectal cancer patients preoperatively. However, it is still necessary to verify its value through clinical application. DATA AVAILABILITY The datasets used during the current
study available from the corresponding author on reasonable request. REFERENCES * Bray, F. _et al._ Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for
36 cancers in 185 countries. _CA Cancer J. Clin._ 68(6), 394–424. https://doi.org/10.3322/caac.21492 (2018). Article PubMed Google Scholar * MacFarlane, J., Ryall, R. & Heald, R.
Mesorectal excision for rectal cancer. _Lancet_ 341(8843), 457–460. https://doi.org/10.1016/0140-6736(93)90207-w (1993). Article CAS PubMed Google Scholar * Rullier, E. _et al._ Organ
preservation for rectal cancer (GRECCAR 2): A prospective, randomised, open-label, multicentre, phase 3 trial. _Lancet_ 390(10093), 469–479. https://doi.org/10.1016/s0140-6736(17)31056-5
(2017). Article PubMed Google Scholar * Griffen, F., Knight, C., Whitaker, J. & Knight, C. The double stapling technique for low anterior resection. Results, modifications, and
observations. _Ann. Surg._ 211(6), 745–751. https://doi.org/10.1097/00000658-199006000-00014 (1990) (DISCUSSION 51-2). Article CAS PubMed PubMed Central Google Scholar * Shrikhande, S.
V. _et al._ Outcomes of resection for rectal cancer in India: The impact of the double stapling technique. _World J. Surg. Oncol._ 5(1), 35. https://doi.org/10.1186/1477-7819-5-35 (2007).
Article PubMed PubMed Central Google Scholar * Climent-Agustín, M. & Martin, S. T. Complications of laparoscopic rectal cancer surgery. _Mini-invasive Surg._ 2, 45.
https://doi.org/10.20517/2574-1225.2018.62 (2018). Article Google Scholar * Koedam, T. _et al._ Oncological outcomes after anastomotic leakage after surgery for colon or rectal cancer:
Increased risk of local recurrence. _Ann. Surg._ 275(2), e420–e427. https://doi.org/10.1097/sla.0000000000003889 (2020). Article Google Scholar * Kverneng Hultberg, D. _et al._ The impact
of anastomotic leakage on long-term function after anterior resection for rectal cancer. _Dis. Colon Rectum_ 63(5), 619–628. https://doi.org/10.1097/dcr.0000000000001613 (2020). Article
PubMed Google Scholar * Taflampas, P., Christodoulakis, M. & Tsiftsis, D. Anastomotic leakage after low anterior resection for rectal cancer: Facts, obscurity, and fiction. _Surg.
Today_ 39(3), 183–188. https://doi.org/10.1007/s00595-008-3835-2 (2009). Article PubMed Google Scholar * Akiyoshi, T. _et al._ Factors affecting the difficulty of laparoscopic total
mesorectal excision with double stapling technique anastomosis for low rectal cancer. _Surgery_ 146(3), 483–489. https://doi.org/10.1016/j.surg.2009.03.030 (2009). Article PubMed Google
Scholar * Huang, B. Y., Yang, L. C. & Ding-Li, M. Modified rectocolon end-to-side anastomosis for middle and low rectal cancer after anterior resection. _J. Mod. Oncol._ 11(2), 118–119
(2003). Google Scholar * Ishii, Y., Hasegawa, H., Nishibori, H., Endo, T. & Kitajima, M. The application of a new stapling device for open surgery (Contour Curved Cutter Stapler) in the
laparoscopic resection of rectal cancer. _Surg. Endosc._ 20(8), 1329–1331. https://doi.org/10.1007/s00464-005-0633-4 (2006). Article CAS PubMed Google Scholar * Shearer, R., Gale, M.,
Aly, O. E. & Aly, E. H. Have early postoperative complications from laparoscopic rectal cancer surgery improved over the past 20 years?. _Colorectal Dis._ 15(10), 1211–1226.
https://doi.org/10.1111/codi.12302 (2013). Article CAS PubMed Google Scholar * Bernhard, D. _et al._ Anastomotic leakage after low anterior resection for rectal cancer: Comparison of
stapled versus compression anastomosis. _Langenbeck’s Arch. Surg._ 398(7), 957–964. https://doi.org/10.1007/s00423-013-1103-4 (2013). Article Google Scholar * Kawada, K. & Sakai, Y.
Preoperative, intraoperative and postoperative risk factors for anastomotic leakage after laparoscopic low anterior resection with double stapling technique anastomosis. _World J.
Gastroenterol._ 22(25), 5718–5727. https://doi.org/10.3748/wjg.v22.i25.5718 (2016). Article PubMed PubMed Central Google Scholar * Balciscueta, Z. _et al._ Impact of the number of
stapler firings on anastomotic leakage in laparoscopic rectal surgery: A systematic review and meta-analysis. _Tech. Coloproctol._ 24(9), 919–925. https://doi.org/10.1007/s10151-020-02240-7
(2020). Article CAS PubMed Google Scholar * Park, J. S. _et al._ Multicenter analysis of risk factors for anastomotic leakage after laparoscopic rectal cancer excision: The Korean
laparoscopic colorectal surgery study group. _Ann. Surg._ 257(4), 665–671. https://doi.org/10.1097/SLA.0b013e31827b8ed9 (2013). Article PubMed Google Scholar * Ito, M. _et al._
Relationship between multiple numbers of stapler firings during rectal division and anastomotic leakage after laparoscopic rectal resection. _Int. J. Colorectal Dis._ 23(7), 703–707.
https://doi.org/10.1007/s00384-008-0470-8 (2008). Article PubMed Google Scholar * Zhang, Z. T. Chinese expert consensus statement on the diagnostic, prevention and treation of the
anastomotic leakage for rectal cancer (article in Chinese). _Zhonghua Wei Chang Wai Ke Za Zhi_ 22(3), 201–206. https://doi.org/10.3760/cma.j.issn.1671-0274.2019.03.001 (2019). Article
Google Scholar * Bertani, E. _et al._ The impact of pelvimetry on anastomotic leakage in a consecutive series of open, laparoscopic and robotic low anterior resections with total mesorectal
excision for rectal cancer. _Hepatogastroenterology_ 61(134), 1574–1581. https://doi.org/10.5754/hge13724 (2014). Article PubMed Google Scholar * Killeen, T. _et al._ Magnetic resonance
(MR) pelvimetry as a predictor of difficulty in laparoscopic operations for rectal cancer. _Surg. Endosc._ 24(12), 2974–2979. https://doi.org/10.1007/s00464-010-1075-1 (2010). Article
PubMed Google Scholar * Zhou, X. _et al._ CT pelvimetry and clinicopathological parameters in evaluation of the technical difficulties in performing open rectal surgery for mid-low rectal
cancer. _Oncol. Lett._ 11(1), 31–38. https://doi.org/10.3892/ol.2015.3827 (2016). Article ADS PubMed Google Scholar * Balyasnikova, S. & Brown, G. Optimal imaging strategies for
rectal cancer staging and ongoing management. _Curr. Treat. Options Oncol._ 17(6), 32. https://doi.org/10.1007/s11864-016-0403-7 (2016). Article PubMed PubMed Central Google Scholar *
Jhaveri, K. S. & Sadaf, A. Role of MRI for staging of rectal cancer. _Expert Rev. Anticancer Ther._ 9(4), 469–481. https://doi.org/10.1586/era.09.13 (2009). Article PubMed Google
Scholar * Rajjak, S. & Kureshi, A. K. Multiple-object detection and segmentation based on deep learning in high-resolution video using Mask-RCNN. _Int. J. Pattern Recogn. Artif.
Intell._ https://doi.org/10.1142/S0218001421500385 (2021). Article Google Scholar * Tran, D., Bourdev, L., Fergus, R., Torresani, L. & Paluri, M. Learning spatiotemporal features with
3D convolutional networks. In _IEEE International Conference on Computer Vision_, 4489–4497 https://doi.org/10.1109/ICCV.2015.510 (2015). * Li, W., Wang, G., Fidon, L., Ourselin, S. &
Cardoso, M. J. _Vercauteren T (2017) On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task_ Vol. 10265, 348–360 (Springer,
2017). https://doi.org/10.1007/978-3-319-59050-9_28. Book Google Scholar * Meng, J., Xue, L., Chang, Y., Zhang, J. & Yang, K. Automatic detection and segmentation of adenomatous
colorectal polyps during colonoscopy using Mask R-CNN. _Open Life Sci._ 15(1), 588–596. https://doi.org/10.1515/biol-2020-0055 (2020). Article PubMed PubMed Central Google Scholar *
Qadir, H. A., Shin, Y., Solhusvik, J., Bergsland, J. & Balasingham, I. Polyp detection and segmentation using Mask R-CNN: Does a deeper feature extractor CNN always perform better? In
_2019 13th International Symposium on Medical Information and Communication Technology (ISMICT)_, 1–6 https://doi.org/10.1109/ISMICT.2019.8743694 (2019) . * Russell, B. C., Torralba, A.,
Murphy, K. P. & Freeman, W. T. LabelMe: A database and web-based tool for image annotation. _Int. J. Comput. Vis._ 77(1–3), 157–173. https://doi.org/10.1007/s11263-007-0090-8 (2008).
Article Google Scholar * Lin, T. Y., Maire, M., Belongie, S., Hays, J. & Zitnick, C. L. _Microsoft COCO: Common Objects in Context_ (Springer International Publishing, 2014).
https://doi.org/10.1007/978-3-319-10602-1_48. Book Google Scholar * Hu, J (2013). Proceedings of the 2013 IEEE International Conference on Computer Vision. IEEE International Conference on
Computer Vision. * Huang, M. J. _et al._ Laparoscopic-assisted versus open surgery for rectal cancer: A meta-analysis of randomized controlled trials on oncologic adequacy of resection and
long-term oncologic outcomes. _Int. J. Colorectal Dis._ 26(4), 415. https://doi.org/10.1007/s00384-010-1091-6 (2011). Article PubMed Google Scholar * Kim, C. _et al._ Anastomotic leakage
after low anterior resection for rectal cancer is different between minimally invasive surgery and open surgery. _Ann. Surg._ 263(1), 130–137. https://doi.org/10.1097/sla.0000000000001157
(2016). Article PubMed Google Scholar * Kawada, K. _et al._ Risk factors for anastomotic leakage after laparoscopic low anterior resection with DST anastomosis. _Surg. Endosc._ 28(10),
2988–2995. https://doi.org/10.1007/s00464-014-3564-0 (2014). Article PubMed PubMed Central Google Scholar * Kuroyanagi, H. _et al._ Standardized technique of laparoscopic intracorporeal
rectal transection and anastomosis for low anterior resection. _Surg. Endosc._ 22(2), 557–561. https://doi.org/10.1007/s00464-007-9626-9 (2008). Article PubMed Google Scholar * Nakagoe,
T. _et al._ Oncological outcome of ultra-low anterior resection with total mesorectal excision for carcinoma of the lower third of the rectum: Comparison of intrapelvic double-stapled
anastomosis and transanal coloanal anastomosis. _Hepato-gastroenterology_ 52(66), 1692–1697. https://doi.org/10.1246/bcsj.40.2063 (2005). Article PubMed Google Scholar * Spinelli, A. _et
al._ Transanal Transection and Single-Stapled Anastomosis (TTSS): A comparison of anastomotic leak rates with the double-stapled technique and with transanal total mesorectal excision
(TaTME) for rectal cancer. _Eur. J. Surg. Oncol._ 47(12), 3123–3129. https://doi.org/10.1016/j.ejso.2021.08.002 (2021). Article PubMed Google Scholar * Penna, M. _et al._ Incidence and
risk factors for anastomotic failure in 1594 patients treated by transanal total mesorectal excision: Results From the International TaTME Registry. _Ann. Surg._ 269(4), 700–711.
https://doi.org/10.1097/sla.0000000000002653 (2019). Article PubMed Google Scholar * Foo, C., Hung, H., Ho, Y., Lam, W. & Law, W. Predicting the level of difficulty of the
double-stapling technique in laparoscopic total mesorectal excision. _Surg. Endosc._ 34(8), 3382–3387. https://doi.org/10.1007/s00464-019-07112-2 (2020). Article PubMed Google Scholar *
Lee, S., Ahn, B. & Lee, S. The relationship between the number of intersections of staple lines and anastomotic leakage after the use of a double stapling technique in laparoscopic
colorectal surgery. _Surg. Laparosc. Endosc. Percutaneous Tech._ 27(4), 273–281. https://doi.org/10.1097/sle.0000000000000422 (2017). Article CAS Google Scholar Download references
FUNDING This study was funded by Shanghai Jiaotong University [grant number YG2019QNB24 to J.M.]. AUTHOR INFORMATION Author notes * These authors contributed equally: Zhanwei Fu and Shuchun
Li. AUTHORS AND AFFILIATIONS * Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, People’s Republic
of China Zhanwei Fu, Shuchun Li, Lu Zang, Feng Dong, Zhenghao Cai & Junjun Ma Authors * Zhanwei Fu View author publications You can also search for this author inPubMed Google Scholar *
Shuchun Li View author publications You can also search for this author inPubMed Google Scholar * Lu Zang View author publications You can also search for this author inPubMed Google Scholar
* Feng Dong View author publications You can also search for this author inPubMed Google Scholar * Zhenghao Cai View author publications You can also search for this author inPubMed Google
Scholar * Junjun Ma View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS Z.F., S.L. and Z.C. wrote the main manuscript text and prepared
figures. J.M., L.Z. and F.D. enrolled the participating patients . All authors reviewed the manuscript. CORRESPONDING AUTHORS Correspondence to Zhenghao Cai or Junjun Ma. ETHICS DECLARATIONS
COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER'S NOTE Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations. RIGHTS AND PERMISSIONS OPEN ACCESS This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,
sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or
exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints
and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Fu, Z., Li, S., Zang, L. _et al._ Predicting multiple linear stapler firings in double stapling technique with an MRI-based deep-learning
model. _Sci Rep_ 13, 18906 (2023). https://doi.org/10.1038/s41598-023-46225-6 Download citation * Received: 11 March 2023 * Accepted: 30 October 2023 * Published: 02 November 2023 * DOI:
https://doi.org/10.1038/s41598-023-46225-6 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not
currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative
Trending News
Page not foundLoading......
George e. Harron ii dies at 90George E. Harron II, a North Merrick resident died on Dec. 14 at 90. He was the husband to his late wife Helen and fathe...
Linda DoyleEnvironment Why Extinction Rebellion is demanding a citizens’ assembly to combat the climate crisis The UK public deserv...
Shamed former mp natalie mcgarry jailed for embezzlementMcGarry had previously admitted taking £21,000 from the Women For Independence group and the SNP’s Glasgow Regional Asso...
Mazgaon metropolitan magistrate court raps family for accusing daughter-in-law of black magicThe court was hearing a case of domestic violence filed by Nazma Shaikh (42) Mumbai Central resident against her in-laws...
Latests News
Predicting multiple linear stapler firings in double stapling technique with an mri-based deep-learning modelABSTRACT Multiple linear stapler firings is a risk factor for anastomotic leakage (AL) in laparoscopic low anterior rese...
Anti-islamization rally in germany draws counter-demonstratorsRENEE MONTAGNE, HOST: The growth of radical Islam in Europe has led to a backlash against immigrants from the Muslim wor...
Stormy daniels teases for the love of dilfs season 2Thankfully, the streaming platform Froot.TV has confirmed For the Love of DILFs will indeed return, and Daniels will be ...
Andhra Pradesh to step up activities to inculcate Covid Appropriate BehaviourNewsletters ePaper Sign in HomeIndiaKarnatakaOpinionWorldBusinessSportsVideoEntertainmentDH SpecialsOperation SindoorNew...
What is the difference between css grid and css flex?> CSS grid and CSS flex are the most recent CSS layout options, and > these come with incredible benefits. However...