ArticleDiagnostic and interventional radiology (Ankara, Turkey)2020
Tubular gastric adenocarcinoma: machine learning-based CT texture analysis for predicting lymphovascular and perineural invasion.
Article in Diagnostic and interventional radiology (Ankara, Turkey), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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18 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Machine learning imaging applications in the differentiation of true tumour progression from treatment-related effects in brain tumours: A systematic review and meta-analysis.Journal of medical imaging and radiation oncology · 2022Pooled it
- Challenges in clinical translation of artificial intelligence and real-time imaging navigation in radical gastrectomy.World journal of gastroenterology · 2025Review
- Preoperative prediction value of 2.5D deep learning model based on contrast-enhanced CT for lymphovascular invasion of gastric cancer.Scientific reports · 2025Article
- Dual-phase contrast-enhanced CT-based intratumoral and peritumoral radiomics for preoperative prediction of lymphovascular invasion in gastric cancer.BMC medical imaging · 2025Article
- A computed tomography‑based radio‑clinical model for the prediction of microvascular invasion in gastric cancer.Molecular and clinical oncology · 2024Article
- Prediction of lymphovascular invasion in esophageal squamous cell carcinoma by computed tomography-based radiomics analysis: 2D or 3D ?Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Preoperative prediction of perineural invasion and lymphovascular invasion with CT radiomics in gastric cancer.European journal of radiology open · 2024Article
- Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning.World journal of gastroenterology · 2024Article
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- Machine learning model based on enhanced CT radiomics for the preoperative prediction of lymphovascular invasion in esophageal squamous cell carcinoma.Frontiers in oncology · 2024Article
- The diagnostic value of a nomogram based on enhanced CT radiomics for differentiating between intrahepatic cholangiocarcinoma and early hepatic abscess.Frontiers in molecular biosciences · 2024Article
- Prediction of lymphovascular invasion of gastric cancer based on contrast-enhanced computed tomography radiomics.Frontiers in oncology · 2024Article
- Spectral CT-based nomogram for preoperative prediction of perineural invasion in locally advanced gastric cancer: a prospective study.European radiology · 2023Article
- Application of preoperative CT texture analysis in papillary gastric adenocarcinoma.BMC cancer · 2022Article
- CT-Based Radiomics Analysis for Noninvasive Prediction of Perineural Invasion of Perihilar Cholangiocarcinoma.Frontiers in oncology · 2022Article
- Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
- Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases.World journal of gastroenterology · 2021Review
- Prediction Model of Ocular Metastases in Gastric Adenocarcinoma: Machine Learning-Based Development and Interpretation Study.Technology in cancer research & treatmentArticle
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10 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeLymphovascular invasion (LVI) and perineural invasion (PNI) are associated with poor prognosis in gastric cancers. In this work, we aimed to investigate the potential role of computed tomography (CT) texture analysis in predicting LVI and PNI in patients with tubular gastric adenocarcinoma (GAC) using a machine learning (ML) approach.
methodsSixty-eight patients who underwent total gastrectomy with curative (R0) resection and D2-lymphadenectomy were included in this retrospective study. Texture features were extracted from the portal venous phase CT images. Dimension reduction was first done with a reproducibility analysis by two radiologists. Then, a feature selection algorithm was used to further reduce the high-dimensionality of the radiomic data. Training and test splits were created with 100 random samplings. ML-based classifications were done using adaptive boosting, k-nearest neighbors, Naive Bayes, neural network, random forest, stochastic gradient descent, support vector machine, and decision tree. Predictive performance of the ML algorithms was mainly evaluated using the mean area under the curve (AUC) metric.
resultsAmong 271 texture features, 150 features had excellent reproducibility, which were included in the further feature selection process. Dimension reduction steps yielded five texture features for LVI and five for PNI. Considering all eight ML algorithms, mean AUC and accuracy ranges for predicting LVI were 0.777-0.894 and 76%-81.5%, respectively. For predicting PNI, mean AUC and accuracy ranges were 0.482-0.754 and 54%-68.2%, respectively. The best performances for predicting LVI and PNI were achieved with the random forest and Naive Bayes algorithms, respectively.
conclusionML-based CT texture analysis has a potential for predicting LVI and PNI of the tubular GACs. Overall, the method was more successful in predicting LVI than PNI.
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