ArticlePloS one2016
Tumor Heterogeneity in Human Epidermal Growth Factor Receptor 2 (HER2)-Positive Advanced Gastric Cancer Assessed by CT Texture Analysis: Association with Survival after Trastuzumab Treatment.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Radiomics analysis of 3D spheroids captures irradiation-induced structural and textural changes.Scientific reports · 2026Article
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- Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer.World journal of gastrointestinal oncology · 2024Article
- Computed tomography-based radiomics nomogram for predicting therapeutic response to neoadjuvant chemotherapy in locally advanced gastric cancer : A scale for treatment predicting.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Article
- Article
- A CT-based radiomics signature for prediction of HER2 overexpression and treatment efficacy of trastuzumab in advanced gastric cancer.Translational cancer research · 2022Article
- Prognostic role of iodine values for gastric cancer after neoadjuvant chemotherapy: a strong independent prognostic factor.Diagnostic and interventional radiology (Ankara, Turkey) · 2022Article
- Radiomics in precision medicine for gastric cancer: opportunities and challenges.European radiology · 2022Review
- A multiphase contrast-enhanced CT radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer.Frontiers in genetics · 2022Article
- Computed Tomography Texture Features and Risk Factor Analysis of Postoperative Recurrence of Patients with Advanced Gastric Cancer after Radical Treatment under Artificial Intelligence Algorithm.Computational intelligence and neuroscience · 2022Article
- CT-based delta radiomics in predicting the prognosis of stage IV gastric cancer to immune checkpoint inhibitors.Frontiers in oncology · 2022Article
- Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer-a multicenter study of GIRCG (Italian Research Group for Gastric Cancer).Quantitative imaging in medicine and surgery · 2021Article
- Contrast-Enhanced CT-based Textural Parameters as Potential Prognostic Factors of Survival for Colorectal Cancer Patients Receiving Targeted Therapy.Molecular imaging and biology · 2021Article
- Prospective evaluation of metabolic intratumoral heterogeneity in patients with advanced gastric cancer receiving palliative chemotherapy.Scientific reports · 2021Article
- Artificial Intelligence in the Imaging of Gastric Cancer: Current Applications and Future Direction.Frontiers in oncology · 2021Review
- CT-based radiomics scores predict response to neoadjuvant chemotherapy and survival in patients with gastric cancer.BMC cancer · 2020Article
- HER2 Heterogeneity in Personalized Therapy of Gastro-Oesophageal Malignancies: An Overview by Different Methodologies.Journal of personalized medicine · 2020Review
- A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2020Article
- Preoperative Splenic Density for the Prediction of Survival and Adjuvant Chemotherapy Benefits in Gastric Cancer.Journal of Cancer · 2020Article
- Spatial heterogeneity of nanomedicine investigated by multiscale imaging of the drug, the nanoparticle and the tumour environment.Theranostics · 2020Review
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
backgroundImage texture analysis is a noninvasive technique for quantifying intratumoral heterogeneity, with derived texture features reported to be closely related to the treatment outcome of tumors. Gastric cancer is one of the most common tumors and the third leading cause of cancer-related deaths worldwide. Although trastuzumab is associated with a survival gain among patients with human epidermal growth factor receptor 2 (HER2)-positive advanced gastric cancer, optimal patient selection is challenging. The purpose of this study was to determine whether CT texture features of HER2-positive gastric cancer were related to the survival rate after trastuzumab treatment. METHODS AND
findingsPatients diagnosed with HER2-positive advanced gastric cancer from February 2007 to August 2014 were retrospectively selected. Using in-house built software, histogram features (kurtosis and skewness) and gray-level co-occurrence matrices (GLCM) features (angular second moment [ASM], contrast, entropy, variance, and correlation) were derived from the CT images of HER2-positive advanced gastric cancer in 26 patients. All the patients were followed up for more than 6 months, with no confirmed deaths. The patients were dichotomized into a good and poor survival group based on cutoff points of overall survival of 12 months. A receiver-operating characteristics (ROC) analysis was performed to test the ability of each texture parameter to identify the good survival group. Kaplan-Meier curves for patients above and below each threshold were constructed. Using a threshold of >265.8480 for contrast, >488.3150 for variance, and ≤0.1319×10-3. for correlation, all of the area under the ROC curves showed fair accuracy (>0.7). Kaplan-Meier analysis showed statistically significant survival difference between two groups according to optimal cutoff values of contrast, variance, correlation and ASM. However, as this study had a small number of patients, a further study with a larger population will be needed to validate the results.
conclusionsHeterogeneous texture features on CT images were associated with better survival in patients with HER2-positive advanced gastric cancer who received trastuzumab-based treatment. Therefore, texture analysis shows potential to be a clinically useful imaging biomarker providing additional prognostic information for patient selection.
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