Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis
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Title
Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis
Authors
Keywords
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Journal
BMC CANCER
Volume 21, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-09-26
DOI
10.1186/s12885-021-08773-w
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Note: Only part of the references are listed.- Artificial intelligence for the diagnosis of lymph node metastases in patients with abdominopelvic malignancy: A systematic review and meta-analysis
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- Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions
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- A clinical-radiomics nomogram for the preoperative prediction of lymph node metastasis in colorectal cancer
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- (2020) Lin Li et al. RADIOLOGY
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- (2020) Aydin Eresen et al. CANCER IMAGING
- Radiomics-Based Preoperative Prediction of Lymph Node Status Following Neoadjuvant Therapy in Locally Advanced Rectal Cancer
- (2020) Xuezhi Zhou et al. Frontiers in Oncology
- Radiomics Approach Outperforms Diameter Criteria for Predicting Pathological Lateral Lymph Node Metastasis After Neoadjuvant (Chemo)Radiotherapy in Advanced Low Rectal Cancer
- (2020) Ryota Nakanishi et al. ANNALS OF SURGICAL ONCOLOGY
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- (2020) Andreas M. Rauschecker et al. RADIOLOGY
- Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node
- (2020) Shin-ei Kudo et al. GASTROENTEROLOGY
- Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study
- (2020) Yuchen Luo et al. JOURNAL OF GASTROINTESTINAL SURGERY
- A deep learning nomogram kit for predicting metastatic lymph nodes in rectal cancer
- (2020) Lei Ding et al. Cancer Medicine
- High-resolution MRI-based radiomics analysis to predict lymph node metastasis and tumor deposits respectively in rectal cancer
- (2020) Yan-song Yang et al. Abdominal Radiology
- How Reliable Is CT Scan in Staging Right Colon Cancer?
- (2019) Laura M. Fernandez et al. DISEASES OF THE COLON & RECTUM
- Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer
- (2019) Lei Ding et al. CHINESE MEDICAL JOURNAL
- FOxTROT: an international randomised controlled trial in 1052 patients (pts) evaluating neoadjuvant chemotherapy (NAC) for colon cancer.
- (2019) Matthew T. Seymour et al. JOURNAL OF CLINICAL ONCOLOGY
- Selective central vascular ligation (D3 lymphadenectomy) in patients undergoing minimally invasive complete mesocolic excision for colon cancer: optimizing the risk benefit equation
- (2019) Tarik Sammour et al. Colorectal Disease
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics
- (2019) Martina Sollini et al. EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
- Radiomics with artificial intelligence: a practical guide for beginners
- (2019) Burak Kocak et al. Diagnostic and Interventional Radiology
- Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-based Convolutional Neural Network
- (2018) Yun Lu et al. CANCER RESEARCH
- Rectal Cancer, Version 2.2018, NCCN Clinical Practice Guidelines in Oncology
- (2018) Al B. Benson et al. Journal of the National Comprehensive Cancer Network
- NCCN Guidelines Insights: Colon Cancer, Version 2.2018
- (2018) Al B. Benson et al. Journal of the National Comprehensive Cancer Network
- Multiparametric radiomics improve prediction of lymph node metastasis of rectal cancer compared with conventional radiomics
- (2018) Li-Da Chen et al. LIFE SCIENCES
- Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
- (2018) Freddie Bray et al. CA-A CANCER JOURNAL FOR CLINICIANS
- Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer
- (2018) Xiaochun Meng et al. EUROPEAN RADIOLOGY
- An overview of deep learning in medical imaging focusing on MRI
- (2018) Alexander Selvikvåg Lundervold et al. Zeitschrift fur Medizinische Physik
- Generic feature learning for wireless capsule endoscopy analysis
- (2016) Santi Seguí et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer
- (2016) Yan-qi Huang et al. JOURNAL OF CLINICAL ONCOLOGY
- Accuracy of High-Resolution MRI with Lumen Distention in Rectal Cancer Staging and Circumferential Margin Involvement Prediction
- (2014) Elsa Iannicelli et al. KOREAN JOURNAL OF RADIOLOGY
- Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research
- (2013) Ewout W. Steyerberg et al. PLOS MEDICINE
- A computer-aided algorithm to quantitatively predict lymph node status on MRI in rectal cancer
- (2012) D M L Tse et al. BRITISH JOURNAL OF RADIOLOGY
- A Novel Approach to Segment and Classify Regional Lymph Nodes on Computed Tomography Images
- (2012) Hongmin Cai et al. Computational and Mathematical Methods in Medicine
- Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging
- (2011) Chunyan Cui et al. EUROPEAN RADIOLOGY
- CT staging of colon cancer
- (2008) S. Dighe et al. CLINICAL RADIOLOGY
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