ArticleEuropean radiology2023
CT morphological features for predicting the risk of lymph node metastasis in T1 colorectal cancer.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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15 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- 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
- Current Status and Challenges of Imaging-Based Diagnosis of Lymph Node Metastasis in Colon Cancer: The Lymph Node Committee of the Japanese Society for Cancer of the Colon and Rectum.Annals of gastroenterological surgery · 2026Review
- Nomogram for preoperative prediction of lymph node metastasis in patients with early submucosal right-sided colon cancer: a dual-center retrospective cohort study.Surgical endoscopy · 2026Article
- The predictive value of contrast-enhanced CT characteristics combined with composite inflammatory markers for lymph node metastasis in T1 colorectal cancer.International journal of colorectal disease · 2026Article
- Multitask radioclinical decision stratification in non-metastatic colon cancer: integrating MMR status, pT staging, and high-risk pathological factors.Abdominal radiology (New York) · 2026Article
- Quantitative parameters of dual-layer spectral CT for differentiating metastatic and non-metastatic lymph nodes in colorectal cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- MRI-derived radiomics for risk stratification of tumor deposits in rectal cancer: a dual-center study.Insights into imaging · 2026Article
- Artificial intelligence in the surgical management for colorectal cancer: current applications and future directions.Frontiers in oncology · 2026Review
- Artificial intelligence and computational prediction models for risk stratification, treatment response, and outcomes in colorectal cancer: a narrative review.Frontiers in oncology · 2026Review
- Development and explanation of a machine learning model for identifying non-localized early-onset T1 colorectal cancer.Discover oncology · 2025Article
- Predicting Lymph Node Metastasis in Rectal Cancer: Development and Validation of a Machine Learning Model Using Clinical Data.JMIR medical informatics · 2025Article
- Mesentery morphological features on computed tomography for preoperative prediction of tumor invasion and lymph node metastasis in colon cancer.World journal of clinical oncology · 2025Article
- Factors associated with lymph node metastasis and survival in T2 colon cancer.BMC gastroenterology · 2025Article
- Diagnostic performance of Node Reporting and Data System (Node-RADS) for assessing mesorectal lymph node in rectal cancer by CT.BMC cancer · 2024Article
- Weighing the benefits of lymphadenectomy in early-stage colorectal cancer.Annals of surgical treatment and research · 2023Review
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11 authors at 4 institutions in 1 country.
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Abstract
objectivesThe aim of this study is to evaluate the feasibility of clinicopathological characteristics and computed tomography (CT) morphological features in predicting lymph node metastasis (LNM) for patients with T1 colorectal cancer (CRC).
methodsA total of 144 patients with T1 CRC who underwent CT scans and surgical resection were retrospectively included in our study. The clinicopathological characteristics and CT morphological features were assessed by two observers. Univariate and multiple logistic regression analyses were used to identify significant LNM predictive variables. Then a model was developed using the independent predictive factors. The predictive model was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate the calibration curve and relative C-index.
resultsLNM were found in 30/144 patients (20.83%). Four independent risk factors were determined in the multiple logistic regression analysis, including presence of necrosis (adjusted odds ratio [OR] = 10.32, 95% confidence interval [CI] 1.96-54.3, p = 0.004), irregular outer border (adjusted OR = 5.94, 95% CI 1.39-25.45, p = 0.035), and heterogeneity enhancement (adjusted OR = 7.35, 95% CI 3.11-17.38, p = 0.007), as well as tumor location (adjusted OR
conclusionsThere are significant associations between lymphatic metastasis status and tumor location as well as CT morphologic features in T1 CRC, which could help the doctor make decisions for additional surgery after endoscopic resection. KEY POINTS: • LNM more frequently occurs in left-sided T1 colon cancer than in right-sided T1 colon and rectal cancer. • CT morphologic features are risk factors for LNM of T1 CRC, which may be related to fundamental biological behaviors. • The combination of tumor location and CT morphologic features can more effectively assist in predicting LNM in patients with T1 CRC, and decrease the rate of unnecessary extra surgeries after endoscopic resection.
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