ArticleAnnals of gastroenterological surgery2022
The Effectiveness of Machine Learning in Predicting Lateral Lymph Node Metastasis From Lower Rectal Cancer: A Single Center Development and Validation Study.
Article in Annals of gastroenterological surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning and deep learning models for preoperative detection of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- MRI for prognosis prediction of T3-stage rectal cancer based on "DISTANCE" structural report.Abdominal radiology (New York) · 2026Article
- Machine learning for predicting surgical difficulty of laparoscopic total mesorectal excision for rectal cancer: integrating MR-based pelvimetry and peritoneal reflection.Frontiers in medicine · 2026Article
- Artificial intelligence and computational prediction models for risk stratification, treatment response, and outcomes in colorectal cancer: a narrative review.Frontiers in oncology · 2026Review
- Interpretable Machine Learning Models for Predicting Lateral Pelvic Lymph Node Metastasis in Rectal Cancer: A Chinese Multicenter Retrospective Study.JCO precision oncology · 2025Article
- Routine Laboratory Tests Predict 72-h Fatality in Patients With D-Dimer Levels ≥ 2 μg/mL: A Retrospective Cohort Study Comparing Statistical and Machine Learning Models.Journal of clinical laboratory analysis · 2025Article
- Preliminary approach to creation of a prediction model for diagnosis of Sjögren's syndrome using radiomics and machine learning techniques on computed tomography images of the parotid glands.Imaging science in dentistry · 2025Article
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- Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review.Cancers · 2025Review
- Long-term outcomes of S-1 and oxaliplatin neoadjuvant chemotherapy with total mesorectal excision and lateral lymph node dissection for rectal cancer.BMC gastroenterology · 2024Article
- Preoperative factors associated with lateral lymph node metastasis in lower rectal cancer and the evaluation of the middle rectal artery.Surgery today · 2024Article
- Visualization analysis of research hotspots and trends in MRI-based artificial intelligence in rectal cancer.Heliyon · 2024Article
- Lateral pelvic lymph node positivity (LPLNP) score: predictive clinic-radiological model of lateral pelvic lymph node involvement in rectal cancer patients.International journal of colorectal disease · 2024Article
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- Identification of lateral pelvic nodes without metastasis in patients with rectal cancer treated with preoperative chemoradiotherapy or chemotherapy based on magnetic resonance imaging.Annals of gastroenterological surgery · 2024Article
- Article
- A Deep Learning Framework with Explainability for the Prediction of Lateral Locoregional Recurrences in Rectal Cancer Patients with Suspicious Lateral Lymph Nodes.Diagnostics (Basel, Switzerland) · 2023Article
- The Effectiveness of Machine Learning in Predicting Lateral Lymph Node Metastasis From Lower Rectal Cancer: A Single Center Development and Validation Study.Annals of gastroenterological surgery · 2022Article
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Authors and funding
9 authors.
Funding
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Abstract
aimAccurate preoperative diagnosis of lateral lymph node metastasis (LLNM) from lower rectal cancer is important to identify patients who require lateral lymph node dissection (LLND). We aimed to create an effective prediction model for LLNM using machine learning by combining preoperative information.
methodsWe retrospectively examined patients who underwent primary rectal cancer surgery with unilateral or bilateral LLND between April 2010 and March 2020 at a single institution. Using the machine learning software "Prediction One" (Sony Network Communications), we developed a prediction model in the training cohort that included 267 consecutive patients (500 sides) from April 2010. Clinicopathological data obtained from the preoperative examinations were used as the learning items. In the validation cohort that included subsequent patients until March 2020, we compared the discriminating powers of the prediction model and the conventional method using the short-axis diameter of the largest lateral lymph node, as detected on magnetic resonance imaging.
resultsThe area under the receiver operating characteristic curve (AUC) of the prediction model was 0.903 in the validation cohort comprising 56 patients (107 sides). This indicated significantly higher predictive power than that of the conventional method (AUC = 0.754;
conclusionMachine learning contributed to the creation of an effective prediction model for LLNM.
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