ArticleWorld journal of gastroenterology2024
Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning.
Article in World journal of gastroenterology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Preoperative prediction of perineural invasion in pancreatic ductal adenocarcinoma using dual-layer spectral CT.Abdominal radiology (New York) · 2026Article
- Development and validation of an interpretable CT-based scoring model for gastric cancer aggressiveness.Abdominal radiology (New York) · 2026Article
- Imaging characteristics and discrimination model development for early gastric cancer using multi-spectral CT.Surgical endoscopy · 2026Article
- Dual-energy computed tomography-derived extracellular volume fraction and spectral quantitative parameters for predicting early recurrence after gastrectomy: insights from a multicenter study.Abdominal radiology (New York) · 2026Article
- The value of extracellular volume based on spectral CT in lymph node metastasis, perineural invasion, and lymphovascular invasion of colon cancer.Abdominal radiology (New York) · 2026Article
- A Radiomics-Clinical Nomogram for Pre-Treatment Prediction of Neoadjuvant Chemotherapy Response in Locally Advanced Gastric Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Construction and validation of a risk prediction model for perineural invasion in early gastric cancer based on multivariate analysis.BMC cancer · 2026Article
- A visual exploration of the evolutionary trajectory in robotic surgery for gastrointestinal malignancies.Journal of robotic surgery · 2026Article
- Comparison of 2D and 3D measured iodine concentration of gastric adenocarcinoma on spectral-CT and their relationship with perineural invasion.BMC medical imaging · 2025Article
- Interpretable Multimodal Fusion Model Enhances Postoperative Recurrence Prediction in Gastric Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- First clinical investigation to predict lymphovascular and/or perineural invasion in gastric cancer usingEuropean journal of nuclear medicine and molecular imaging · 2025Article
- Predictive factors for successful conversion therapy in gastric cancer with peritoneal metastasis: a case-control study.Journal of gastrointestinal oncology · 2025Article
- Machine Learning-Based Pathomics Signature for Perineural Invasion in Colorectal Cancer.Medical science monitor : international medical journal of experimental and clinical research · 2025Article
- Harnessing the machine learning and nomogram models: elevating prognostication in nonmetastatic gastric cancer with "double invasion" for personalized patient care.European journal of medical research · 2025Article
- Photon-counting CT in cancer radiotherapy: technological advances and clinical benefits.Physics in medicine and biology · 2025Review
- Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.Cancer letters · 2025Review
- Article
- Development of a multimodal predictive model using PET/CT radiomics and clinical data for preoperative assessment of lymphovascular invasion in gastric cancer.American journal of translational research · 2025Article
- Application and progress of nomograms in gastric cancer.Frontiers in medicine · 2025Review
- Review on article of preoperative prediction in chronic hepatitis B virus patients using spectral computed tomography and machine learning.World journal of gastroenterology · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
backgroundLymphovascular invasion (LVI) and perineural invasion (PNI) are important prognostic factors for gastric cancer (GC) that indicate an increased risk of metastasis and poor outcomes. Accurate preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment decisions. However, prior models using conventional computed tomography (CT) images to predict LVI or PNI separately have had limited accuracy. Spectral CT provides quantitative enhancement parameters that may better capture tumor invasion. We hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC patients.
aimTo develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status accurately.
methodsThis study used a retrospective dataset involving 257 GC patients (training cohort,
resultsIn both the training and validation cohorts, CT T3-4 stage, CT-N positive status, and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant (
conclusionThe study found that using portal venous and EP spectral CT parameters allows effective preoperative detection of LVI/PNI in GC, with accuracy boosted by integrating clinical markers.
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