Application of radiomics in precision prediction of diagnosis and treatment of gastric cancer

Du, Getao; Zeng, Yun; Chen, Dan; Zhan, Wenbin; Zhan, Yonghua

doi:10.1007/s11604-022-01352-4

Cited by 7 publications

(5 citation statements)

References 60 publications

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“…Previous studies have demonstrated these limitations [ 9 , 30 ]. In the past few years, radiomics has emerged as a useful and objective technique to identify associations between radiomics features and tumor biomarkers, offering valuable insights into the pathological and clinical characteristics of lesions without the need for invasive procedures [ 31 , 32 ].…”

Section: Discussionmentioning

confidence: 99%

The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma

Chen,

Wang,

Xia

et al. 2024

Discov Onc

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Objective Mitosis karyorrhexis index (MKI) can reflect the proliferation status of neuroblastoma cells. This study aimed to investigate the contrast-enhanced computed tomography (CECT) radiomics features associated with the MKI status in neuroblastoma. Materials and methods 246 neuroblastoma patients were retrospectively included and divided into three groups: low-MKI, intermediate-MKI, and high-MKI. They were randomly stratified into a training set and a testing set at a ratio of 8:2. Tumor regions of interest were delineated on arterial-phase CECT images, and radiomics features were extracted. After reducing the dimensionality of the radiomics features, a random forest algorithm was employed to establish a three-class classification model to predict MKI status. Results The classification model consisted of 5 radiomics features. The mean area under the curve (AUC) of the classification model was 0.916 (95% confidence interval (CI) 0.913–0.921) in the training set and 0.858 (95% CI 0.841–0.864) in the testing set. Specifically, the classification model achieved AUCs of 0.928 (95% CI 0.927–0.934), 0.915 (95% CI 0.912–0.919), and 0.901 (95% CI 0.900–0.909) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively, in the training set. In the testing set, the classification model achieved AUCs of 0.873 (95% CI 0.859–0.882), 0.860 (95% CI 0.852–0.872), and 0.820 (95% CI 0.813–0.839) for predicting low-MKI, intermediate-MKI, and high-MKI, respectively. Conclusions CECT radiomics features were found to be correlated with MKI status and are helpful for reflecting the proliferation status of neuroblastoma cells.

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Section: Discussionmentioning

confidence: 99%

The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma

Chen,

Wang,

Xia

et al. 2024

Discov Onc

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“…75 Radiomics has become one of the most popular analytical methods of multiparametric MRI, which is based on the extraction of potentially innumerable numbers of quantitative imaging metrics, or "radiomic features," which are difficult to be captured by the human eye. 76 Radiomic features are collectively used for the prediction of diagnosis, [77][78][79][80] classification, 37 prognostication, 81 assessment of treatment effects, 82 and gene expression profiling, among others. Focal lesions (eg, tumors) or other areas of concern are segmented to be used as regions of interest for extracting radiomic features.…”

Section: Machine Learning and Radiomicsmentioning

confidence: 99%

Multiparametric MRI

et al. 2023

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With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.

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“…Prognosis prediction contributes to the optimization of treatment strategies, including the selection of initial therapy and the indication for adjuvant therapies, and the prediction is also considered useful to validate the treatment plan for each patient with head or neck cancer. Over the past few decades, imaging methods have been well developed and used together with advanced imaging techniques such as DWI and DCE, [99][100][101][102][103][104][105][106][107][108][109][110] or a high-dimensional analytical method such as a texture analysis or radiomics method 14,22,24,111,112 in various diseases as well as head and neck lesions. However, achieving accurate predictions is still challenging.…”

Section: Prognosis Predictionmentioning

confidence: 99%

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging

Fujima,

Kamagata,

Ueda

et al. 2023

MRMS

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Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.

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Application of radiomics in precision prediction of diagnosis and treatment of gastric cancer

Cited by 7 publications

References 60 publications

The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma

The relationship between contrast-enhanced computed tomography radiomics features and mitosis karyorrhexis index in neuroblastoma

Multiparametric MRI

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging

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