ArticleCancers2024
Preoperative Prediction of Perineural Invasion and Prognosis in Gastric Cancer Based on Machine Learning through a Radiomics-Clinicopathological Nomogram.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 20 citations in OpenAlex.
- The added value of intratumoral and peritumoral radiomic scores based on iodine density map for preoperative prediction of lymphovascular invasion in patients with gastric adenocarcinoma: a two-center study.Abdominal radiology (New York) · 2026Article
- CT radiomics of adenocarcinoma of the esophagogastric junction: machine learning-based prediction of perineural invasion status.World journal of surgical oncology · 2026Article
- A novel combined model integrating collagen properties, radiomics and clinical data to predict gastric cancer prognosis.Frontiers in oncology · 2026Article
- Interpretable deep learning-based hierarchical multi-modal fusion model for predicting HER2 expression in gastric cancer.Frontiers in oncology · 2026Article
- Article
- The Neuroimmune Axis in Gastric Cancer: Bridging Neural Regulation, Tumor Microenvironment, and Immunotherapy.Oncology research · 2026Review
- 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
- A comprehensive study on the radiomic score derived from perineural invasion in gastric cancer and its correlation with the overall survival of patients.La Radiologia medica · 2025Article
- Evaluation of radiomics as an assistant tool for radiologists in the diagnosis of necrotizing enterocolitis.Translational pediatrics · 2025Article
- Article
- Development and validation of a postoperative risk model for esophageal squamous cell carcinoma after neoadjuvant immunochemotherapy.Frontiers in medicine · 2025Article
- Predictive modeling of acute radiation-induced dermatitis in nasopharyngeal carcinoma patients undergoing tomotherapy using machine learning with multimodal data integration.Frontiers in oncology · 2025Article
- Establishment and validation of a prediction model for gastric cancer with perineural invasion based on preoperative inflammatory markers.Translational cancer research · 2024Article
- Construction and interpretation of machine learning-based prognostic models for survival prediction among intestinal-type and diffuse-type gastric cancer patients.World journal of surgical oncology · 2024Article
- Utilizing radiomics and dosiomics with AI for precision prediction of radiation dermatitis in breast cancer patients.BMC cancer · 2024Article
- Review
- Perineural Invasion in Cervical Cancer: A Hidden Trail for Metastasis.Diagnostics (Basel, Switzerland) · 2024Review
- Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
purposeThe aim of this study was to construct and validate a nomogram for preoperatively predicting perineural invasion (PNI) in gastric cancer based on machine learning, and to investigate the impact of PNI on the overall survival (OS) of gastric cancer patients.
methodsData were collected from 162 gastric patients and analyzed retrospectively, and radiomics features were extracted from contrast-enhanced computed tomography (CECT) scans. A group of 42 patients from the Cancer Imaging Archive (TCIA) were selected as the validation set. Univariable and multivariable analyses were used to analyze the risk factors for PNI. The
resultsThe univariable and multivariable analyses showed that the T stage, N stage and radscore were independent risk factors for PNI (
conclusionsA machine learning-based radiomics-clinicopathological model could effectively predict PNI in gastric cancer preoperatively through a non-invasive approach, and gastric cancer patients with PNI had relatively poor prognoses.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.