Development and validation of artificial intelligence models for preoperative prediction of inferior mesenteric artery lymph nodes metastasis in left colon and rectal cancer

Wang, Xiaojie; Zheng, Zhifang; Xie, Zhongdong; Yu, Qian; Lu, Xingrong; Zhi, Zhao; Huang, Shenghui; Huang, Ying; Chi, Pan

doi:10.1016/j.ejso.2022.06.009

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…XGB [ 37 , 38 ] belongs to a kind of gradient enhanced tree, which is a combination of multiple weak learners from simple regression to complex decision tree. A number of studies accepted this model to discover disease biomarkers [ 39 ], develop artificial intelligence [ 40 ], monitor diabetes [ 41 ]. However, accuracy of the model is often at the expense of more computing time and complexity.…”

Section: Discussionmentioning

confidence: 99%

Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis

Wang

Wang³

et al. 2023

Inflamm. Res.

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Objective Diagnosis of lupus nephritis (LN) is a complex process, which usually requires renal biopsy. We aim to establish a machine learning pipeline to help diagnosis of LN. Methods A cohort of 681 systemic lupus erythematosus (SLE) patients without LN and 786 SLE patients with LN was established, and a total of 95 clinical, laboratory data and 17 meteorological indicators were collected. After tenfold cross-validation, the patients were divided into training set and test set. The features selected by collective feature selection method of mutual information (MI) and multisurf were used to construct the models of logistic regression, decision tree, random forest, naive Bayes, support vector machine (SVM), light gradient boosting (LGB), extreme gradient boosting (XGB), and artificial neural network (ANN), the models were compared and verified in post-analysis. Results Collective feature selection method screens out antistreptolysin (ASO), retinol binding protein (RBP), lupus anticoagulant 1 (LA1), LA2, proteinuria and other features, and the hyperparameter optimized XGB (ROC: AUC = 0.995; PRC: AUC = 1.000, APS = 1.000; balance accuracy: 0.990) has the best performance, followed by LGB (ROC: AUC = 0.992; PRC: AUC = 0.997, APS = 0.977; balance accuracy: 0.957). The worst performance is naive Bayes model (ROC: AUC = 0.799; PRC: AUC = 0.822, APS = 0.823; balance accuracy: 0.693). In the composite feature importance bar plots, ASO, RF, Up/Ucr, and other features play important roles in LN. Conclusion We developed and validated a new and simple machine learning pathway for diagnosis of LN, especially the XGB model based on ASO, LA1, LA2, proteinuria, and other features screened out by collective feature selection. Supplementary Information The online version contains supplementary material available at 10.1007/s00011-023-01755-7.

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Section: Discussionmentioning

confidence: 99%

Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis

Wang

Wang³

et al. 2023

Inflamm. Res.

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“…Hartwig et al (2022) used lymph node metastasis attributes of EGC patients and various machine learning predictive models, including a linear support vector classifier (Linear SVC), logistic regression model, extreme gradient boosting model (XGBoost), light gradient boosting machine model (LightGBM), and Gaussian process classification model, to predict the early detection of lymph node metastasis in the early stages of Colorectal Cancer. A Least Absolute Shrinkage and Selection Operator (LASSO) Logistic regression model is used in (Wang et al, 2022) for early prediction of colorectal cancer from four Danish health databases on patients diagnosed with colorectal cancer. The Danish health databases primarily consist of electronic health records (EHRs), which contain information such as patient demographics, medical history, diagnostic codes, medication use, and laboratory results.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to EHRs, the Danish health databases may also contain some imaging data and histological samples, but these are typically limited in scope and may not be readily accessible for research purposes. Evaluation of outcomes through Area under the receiver operating characteristic curve (AUROC) and Area under the precision and recall curve (AUPRC) showed Machine learning predictions outperformed present LASSO Logistic regression technique in use (Wang et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques

Azar

Tounsi

Fati

et al. 2023

International Journal of Sociotechnology and Knowledge Development

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Colon cancer is one of the world's three most deadly and severe cancers. As with any cancer, the key priority is early detection. Deep learning (DL) applications have recently gained popularity in medical image analysis due to the success they have achieved in the early detection and screening of cancerous tissues or organs. This paper aims to explore the potential of deep learning techniques for colon cancer classification. This research will aid in the early prediction of colon cancer in order to provide effective treatment in the most timely manner. In this exploratory study, many deep learning optimizers were investigated, including stochastic gradient descent (SGD), Adamax, AdaDelta, root mean square prop (RMSprop), adaptive moment estimation (Adam), and the Nesterov and Adam optimizer (Nadam). According to the empirical results, the CNN-Adam technique produced the highest accuracy with an average score of 82% when compared to other models for four colon cancer datasets. Similarly, Dataset_1 produced better results, with CNN-Adam, CNN-RMSprop, and CNN-Adadelta achieving accuracy scores of 0.95, 0.76, and 0.96, respectively.

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“…Among the most promising methods are deep learning methods, which are able to recognise undersampled data and also allow the conversion of low-resolution data into high-resolution data [75]. Preliminary studies show the potential of a variational network to classify many different anatomical regions and achieve the diagnostic accuracy of conventional methods [76].…”

Section: Acquisition Analysis and Post Processingmentioning

confidence: 99%

Magnetic Resonance Imaging of the Gastrointestinal Tract: Current Role, Recent Advancements and Future Prospectives

et al. 2023

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This review focuses on the role of magnetic resonance imaging (MRI) in the evaluation of the gastrointestinal tract (GI MRI), analyzing the major technical advances achieved in this field, such as diffusion-weighted imaging, molecular imaging, motility studies, and artificial intelligence. Today, MRI performed with the more advanced imaging techniques allows accurate assessment of many bowel diseases, particularly inflammatory bowel disease and rectal cancer; in most of these diseases, MRI is invaluable for diagnosis, staging, and disease monitoring under treatment. Several MRI parameters are currently considered activity biomarkers for inflammation and neoplastic disease. Furthermore, in younger patients with acute or chronic GI disease, MRI can be safely used for short-term follow-up studies in many critical clinical situations because it is radiation-free. MRI assessment of functional gastro-esophageal and small bowel disorders is still in its infancy but very promising, while it is well established and widely used for dynamic assessment of anorectal and pelvic floor dysfunction; MRI motility biomarkers have also been described. There are still some limitations to GI MRI related to high cost and limited accessibility. However, technical advances are expected, such as faster sequences, more specific intestinal contrast agents, AI analysis of MRI data, and possibly increased accessibility to GI MRI studies. Clinical interest in the evaluation of bowel disease using MRI is already very high, but is expected to increase significantly in the coming years.

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Development and validation of artificial intelligence models for preoperative prediction of inferior mesenteric artery lymph nodes metastasis in left colon and rectal cancer

Cited by 10 publications

References 25 publications

Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis

Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis

Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques

Magnetic Resonance Imaging of the Gastrointestinal Tract: Current Role, Recent Advancements and Future Prospectives

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