scholarly journals A Novel Predictive Model for Anastomotic Leakage in Colorectal Cancer Using Auto-artificial Intelligence

2021 ◽  
Vol 41 (11) ◽  
pp. 5821-5825
Author(s):  
JUNICHI MAZAKI ◽  
KENJI KATSUMATA ◽  
YUKI OHNO ◽  
RYUTARO UDO ◽  
TOMOYA TAGO ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Predictive Model ◽  
Anastomotic Leakage

scholarly journals Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Tenghui Han ◽  
Jun Zhu ◽  
Xiaoping Chen ◽  
Rujie Chen ◽  
Yu Jiang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Predictive Model ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Subsequent Treatment ◽  
Training Dataset ◽  
Discriminative Ability ◽  
Fundamental Parameters

Abstract Background Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically. Methods We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model. Results A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956). Conclusion We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.

scholarly journals Nomogram for Benign Anastomotic Stricture After Surgery for Colorectal Cancer

2021 ◽  
Author(s):  
Xuhua Hu ◽  
Peiyuan Guo ◽  
Ning Zhang ◽  
Ganlin Guo ◽  
Baokun Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Predictive Model ◽  
Anastomotic Leakage ◽  
Anastomotic Stricture ◽  
Small Sample ◽  
Anal Margin ◽  
Anastomotic Site ◽  
Tertiary Center ◽  
Protective Stoma

Abstract Background Benign anastomotic stricture remains among the most prevalent complications following surgery for colorectal cancer, albeit its incidence is very low. Objective This study is aimed at identifying risk factors of anastomotic stricture as well as generating an effective nomogram for the stricture. Design: This is a retrospective study. Setting: This study was conducted from January, 2015 to December, 2019 in a single tertiary center with colorectal cancer. Patients: A total of 117 colorectal patients after surgery without recurrence including 39 with anastomotic stricture (the distance between anastomotic site and anal margin < = 20 cm) and 78 without the stricture were enrolled in this study. Main outcome measures: Their clinical and pathological data were collected. Multiple logistic regression analysis was conducted for identifying risk factors for anastomotic stricture, and the nomogram prediction model was generated. Results Multivariate analysis of the primary cohort led to identification of LCA (left colon artery) preservation (OR, 0.074; P = 0.0015), protective stoma (OR, 5.353; P = 0.012), anastomotic leakage (OR, 12.027; P = 0.005), and anastomotic distance (OR, 7.578; P = 0.012) as independent risk factors for anastomotic stricture. The following predictive model was derived: Logit (anastomotic stricture) = 0.074* LCA + 5.353* Protective stoma + 12.027* Anastomotic leakage + 7.578* Anastomotic distance. Assessment of the predictive model revealed that the area under curve (AUC) was 0.871, while the cutoff value was 15.444, with a sensitivity of 64.1% and a specificity of 94.8%. Limitations: A retrospective and case-controlled design with a small sample size from one single center is the main Limitation. Conclusions LCA preservation, protective stoma, anastomotic leakage, and anastomotic distance may affect the occurrence of anastomotic stricture following surgery for colorectal cancer. The nomogram model generated in the present study can be valuable in prediction of anastomotic stricture. Registered at Chinese Clinical Trial Registry (http://www.chictr.org.cn, ChiCTR 2100043775).

Application of Artificial Intelligence in a Real-World Research for Predicting the Risk of Liver Metastasis in T1 Colorectal Cancer

2021 ◽  
Author(s):  
Tenghui Han ◽  
Jun Zhu ◽  
Dong Xu ◽  
Rujie Chen ◽  
Shuai Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Predictive Model ◽  
Tumor Size ◽  
Area Under The Curve ◽  
Subsequent Treatment ◽  
Tumor Type ◽  
Discriminative Ability ◽  
N Stage

Abstract Background: The liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. However, there is still no effective model to predict the risk of LM in T1 CRC patients and we aim to develop a novel and accurate predictive model.Methods: We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER) and Xijing hospital. Artificial intelligence (AI) and machine learning methods were adopted to establish the predictive model.Results: A total of 16785 and 326 T1 CRC patients from SEER database and our hospital were incorporated respectively in the study. We found that age, gender, married status, primary site, tumor size, carcinoembryonic antigen (CEA), tumor type, grade, N stage and perineural invasion were significant independent factors for predicting the presence of LM, among which tumor size is the most important. The stacking bagging model showed the best predictive capability, achieving a sensitivity of 0.8452, a specificity of 0.9566, and an area under the curve of 0.9631. In addition, the stacking model had an excellent discriminative ability and accurately screened out eight LM cases from 326 T1 patients in the outer validation cohort. Ultimately, we authenticated the prognostic value of the stacking model, which is consistent with the predictive result of LM.Conclusion: We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in our dataset.

scholarly journals Early postoperative pain as a marker of anastomotic leakage in colorectal cancer surgery

2021 ◽  
Author(s):  
Petrus Boström ◽  
Johan Svensson ◽  
Camilla Brorsson ◽  
Martin Rutegård
Keyword(s):  
Colorectal Cancer ◽  
Postoperative Pain ◽  
Colorectal Surgery ◽  
Anastomotic Leakage ◽  
Rating Scale ◽  
Numerical Rating Scale ◽  
Population Based ◽  
Colorectal Cancer Surgery ◽  
Increased Risk ◽  
Independent Marker

Abstract Purpose Even though anastomotic leakage after colorectal surgery is a major clinical problem in need of a timely diagnosis, early indicators of leakage have been insufficiently studied. We therefore conducted a population-based observational study to determine whether the patient’s early postoperative pain is an independent marker of anastomotic leakage. Methods By combining the Swedish Colorectal Cancer Registry and the Swedish Perioperative Registry, we retrieved prospectively collected data on 3084 patients who underwent anastomotic colorectal surgery for cancer in 2014–2017. Postoperative pain, measured with the numerical rating scale (NRS), was considered exposure, while anastomotic leakage and reoperation due to leakage were outcomes. We performed logistic regression to evaluate associations, estimating odds ratios (ORs) and 95% confidence intervals (CIs), while multiple imputation was used to handle missing data. Results In total, 189 patients suffered from anastomotic leakage, of whom 121 patients also needed a reoperation due to leakage. Moderate or severe postoperative pain (NRS 4–10) was associated with an increased risk of anastomotic leakage (OR 1.69, 95% CI 1.21–2.38), as well as reoperation (OR 2.17, 95% CI 1.41–3.32). Severe pain (NRS 8–10) was more strongly related to leakage (OR 2.38, 95% CI 1.44–3.93). These associations were confirmed in multivariable analyses and when reoperation due to leakage was used as an outcome. Conclusion In this population-based retrospective study on prospectively collected data, increased pain in the post-anaesthesia care unit is an independent marker of anastomotic leakage, possibly indicating a need for further diagnostic measures.

scholarly journals Exploratory Analysis of a Prognosis Predictive Model for Metastatic Colorectal Cancer Treated with Chemotherapy

2014 ◽  
Vol 25 ◽  
pp. v73
Author(s):  
Mamoru Tanaka ◽  
Hozumi Kumagai ◽  
Junji Kishimoto ◽  
Satomi Mukaide ◽  
Hisanobu Oda ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Metastatic Colorectal Cancer ◽  
Predictive Model ◽  
Exploratory Analysis

scholarly journals Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer

2020 ◽  
Vol 9 (10) ◽  
pp. 3313 ◽  
Author(s):  
Hemant Goyal ◽  
Rupinder Mann ◽  
Zainab Gandhi ◽  
Abhilash Perisetti ◽  
Aman Ali ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Narrow Band Imaging ◽  
Screening Methods ◽  
Polyp Detection ◽  
Mortality And Morbidity ◽  
Magnifying Chromoendoscopy

Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a “second look” for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.

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