ArticleQuantitative imaging in medicine and surgery2018
Radiomic analysis using contrast-enhanced CT: predict treatment response to pulsed low dose rate radiotherapy in gastric carcinoma with abdominal cavity metastasis.
Article in Quantitative imaging in medicine and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment.European journal of nuclear medicine and molecular imaging · 2021Pooled it
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- Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images.Journal of imaging · 2023Article
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- Role of radiomics in the diagnosis and treatment of gastrointestinal cancer.World journal of gastroenterology · 2022Review
- Radiomics in precision medicine for gastric cancer: opportunities and challenges.European radiology · 2022Review
- Radiomics based on multisequence magnetic resonance imaging for the preoperative prediction of peritoneal metastasis in ovarian cancer.European radiology · 2021Article
- Impact of radiogenomics in esophageal cancer on clinical outcomes: A pilot study.World journal of gastroenterology · 2021Article
- Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases.World journal of gastroenterology · 2021Review
- Status quo and future prospects of artificial neural network from the perspective of gastroenterologists.World journal of gastroenterology · 2021Review
- Differentiation of small (≤ 3 cm) hepatocellular carcinomas from benign nodules in cirrhotic liver: the added additive value of MRI-based radiomics analysis to LI-RADS version 2018 algorithm.BMC gastroenterology · 2021Article
- Artificial Intelligence in the Imaging of Gastric Cancer: Current Applications and Future Direction.Frontiers in oncology · 2021Review
- A Bounding Box-Based Radiomics Model for Detecting Occult Peritoneal Metastasis in Advanced Gastric Cancer: A Multicenter Study.Frontiers in oncology · 2021Article
- Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases.Quantitative imaging in medicine and surgery · 2020Article
- CT radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer.European radiology · 2020Article
- A Delta-radiomics model for preoperative evaluation of Neoadjuvant chemotherapy response in high-grade osteosarcoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2020Article
- Grade Prediction of Bleeding Volume in Cesarean Section of Patients With Pernicious Placenta Previa Based on Deep Learning.Frontiers in bioengineering and biotechnology · 2020Article
- An investigation of the classification accuracy of a deep learning framework-based computer-aided diagnosis system in different pathological types of breast lesions.Journal of thoracic disease · 2019Article
- Radiomics approaches in gastric cancer: a frontier in clinical decision making.Chinese medical journal · 2019Article
- Treatment response prediction of rehabilitation program in children with cerebral palsy using radiomics strategy: protocol for a multicenter prospective cohort study in west China.Quantitative imaging in medicine and surgery · 2019Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo determine the feasibility of radiomic analysis for predicting the therapeutic response of gastric carcinoma (GC) with abdominal cavity metastasis (GCACM) to pulsed low dose rate radiotherapy (PLDRT) using contrast-enhanced computed tomography (CECT) images.
methodsPretreatment CECT images of 43 GCACM patients were analyzed. Patients with complete response (CR) and partial response (PR) were considered responders, while stable disease (SD) and progressive disease (PD) as non-responders. A total of 1,117 image features were quantified from tumor region that segmented from arterial phase CT images. Intra-class correlation coefficient (ICC) and absolute correlation coefficient (ACC) were calculated for selecting influential feature subset. The capability of each influential feature on treatment response classification was assessed using Kruskal-Wallis test and receiver operating characteristic (ROC) analysis. Moreover, artificial neural network (ANN) and k-nearest neighbor (KNN) predictive models were constructed based on the training set (18 responders, 14 non-responders) and the testing set (6 responders, 5 non-responders) validated the reliability of the models. Comparison between the performances of the models was performed by using McNemar's test.
resultsThe analyses showed that 6 features (1 first order-based, 1 texture-based, 1 LoG-based, and 3 wavelet-based) were significantly different between responders and non-responders (AUCs range from 0.686 to 0.728). Both two prediction models based on features extracted from CECT showed potential in predicting the treatment response with higher accuracies (ANN: 0.714, KNN: 0.749 for the training set; ANN: 0.816, KNN: 0.816 for the testing set). No statistical difference was observed between the performance of ANN and KNN (P=0.999).
conclusionsPretreatment radiomic analysis using CECT can potentially provide important information regarding the therapeutic response to PLDRT for GCACM, improving risk stratification.
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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.