ArticleChinese medical journal2019
Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer.
Article in Chinese medical journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis.BMC cancer · 2021Pooled it
- Deep learning applied to two-dimensional color Doppler flow imaging ultrasound images significantly improves diagnostic performance in the classification of breast masses: a multicenter study.Chinese medical journal · 2021Trial
- Predicting Lymph Node Metastasis in Rectal Cancer: Development and Validation of a Machine Learning Model Using Clinical Data.JMIR medical informatics · 2025Article
- Predictive model for sphincter preservation in lower rectal cancer.World journal of clinical oncology · 2025Article
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- Estimating within-stride metabolic cost from stride-average data using autoencoders and expander networks.Frontiers in bioengineering and biotechnology · 2025Article
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- Ethical guidance for reporting and evaluating claims of AI outperforming human doctors.NPJ digital medicine · 2024Article
- The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer.Cancers · 2024Review
- Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review.Annals of coloproctology · 2024Review
- ARMO: automated and reliable multi-objective model for lymph node metastasis prediction in head and neck cancer.Physics in medicine and biology · 2023Article
- Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review.Exploration of targeted anti-tumor therapy · 2023Review
- Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions.Frontiers in oncology · 2023Review
- Artificial Intelligence in Colorectal Cancer Surgery: Present and Future Perspectives.Cancers · 2022Review
- Article
- Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer.Current oncology (Toronto, Ont.) · 2022Review
- A New Nomogram for Predicting the Postoperative Overall Survival in Patients with Middle-Aged and Elderly Rectal Cancer: A Single Center Retrospective Study in Chinese Population.International journal of general medicine · 2022Article
- A novel classification method of lymph node metastasis in colorectal cancer.Bioengineered · 2021Article
- Artificial intelligence applications in gastroenterology: steps ahead.Medicine and pharmacy reports · 2021Article
- Intra- and Interobserver Variability in Magnetic Resonance Imaging Measurements in Rectal Cancer Patients.Cancers · 2021Article
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14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAn artificial intelligence system of Faster Region-based Convolutional Neural Network (Faster R-CNN) is newly developed for the diagnosis of metastatic lymph node (LN) in rectal cancer patients. The primary objective of this study was to comprehensively verify its accuracy in clinical use.
methodsFour hundred fourteen patients with rectal cancer discharged between January 2013 and March 2015 were collected from 6 clinical centers, and the magnetic resonance imaging data for pelvic metastatic LNs of each patient was identified by Faster R-CNN. Faster R-CNN based diagnoses were compared with radiologist based diagnoses and pathologist based diagnoses for methodological verification, using correlation analyses and consistency check. For clinical verification, the patients were retrospectively followed up by telephone for 36 months, with post-operative recurrence of rectal cancer as a clinical outcome; recurrence-free survivals of the patients were compared among different diagnostic groups, by methods of Kaplan-Meier and Cox hazards regression model.
resultsSignificant correlations were observed between any 2 factors among the numbers of metastatic LNs separately diagnosed by radiologists, Faster R-CNN and pathologists, as evidenced by rradiologist-Faster R-CNN of 0.912, rPathologist-radiologist of 0.134, and rPathologist-Faster R-CNN of 0.448 respectively. The value of kappa coefficient in N staging between Faster R-CNN and pathologists was 0.573, and this value between radiologists and pathologists was 0.473. The 3 groups of Faster R-CNN, radiologists and pathologists showed no significant differences in the recurrence-free survival time for stage N0 and N1 patients, but significant differences were found for stage N2 patients.
conclusionFaster R-CNN surpasses radiologists in the evaluation of pelvic metastatic LNs of rectal cancer, but is not on par with pathologists.
trial registrationwww.chictr.org.cn (No. ChiCTR-DDD-17013842).
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