New advances in radiomics of gastrointestinal stromal tumors

Cannella, Roberto; Grutta, Ludovico La; Midiri, Massimo; Bartolotta, Tommaso Vincenzo

doi:10.3748/wjg.v26.i32.4729

Cited by 28 publications

(29 citation statements)

References 58 publications

(95 reference statements)

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“…Therefore, an objective and quantitative radiomics approach emerged. Radiomics has offered a novel approach to exploit information encompassed in medical images, which could extract numerous quantitative features from images and have exhibited to improve the preoperative prediction of high-malignant potential GISTs compared with the conventional imaging evaluation methods ( 7 , 16 , 19 , 34 ). Previous studies have demonstrated the predictive ability of radiomics features obtained from contrast-enhanced CT for the discrimination of risk rating of GISTs.…”

Section: Discussionmentioning

confidence: 99%

Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors

Shao

Niu

He³

et al. 2021

Front. Oncol.

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We aimed to build radiomics models based on triple-phase CT images combining clinical features to predict the risk rating of gastrointestinal stromal tumors (GISTs). A total of 231 patients with pathologically diagnosed GISTs from July 2012 to July 2020 were categorized into a training data set (82 patients with high risk, 80 patients with low risk) and a validation data set (35 patients with high risk, 34 patients with low risk) with a ratio of 7:3. Four diagnostic models were constructed by assessing 20 clinical characteristics and 18 radiomic features that were extracted from a lesion mask based on triple-phase CT images. The receiver operating characteristic (ROC) curves were applied to calculate the diagnostic performance of these models, and ROC curves of these models were compared using Delong test in different data sets. The results of ROC analyses showed that areas under ROC curves (AUC) of model 4 [Clinic + CT value of unenhanced (CTU) + CT value of arterial phase (CTA) + value of venous phase (CTV)], model 1 (Clinic + CTU), model 2 (Clinic + CTA), and model 3 (Clinic + CTV) were 0.925, 0.894, 0.909, and 0.914 in the training set and 0.897, 0.866, 0,892, and 0.892 in the validation set, respectively. Model 4, model 1, model 2, and model 3 yielded an accuracy of 88.3%, 85.8%, 86.4%, and 84.6%, a sensitivity of 85.4%, 84.2%, 76.8%, and 78.0%, and a specificity of 91.2%, 87.5%, 96.2%, and 91.2% in the training set and an accuracy of 88.4%, 84.1%, 82.6%, and 82.6%, a sensitivity of 88.6%, 77.1%, 74.3%, and 85.7%, and a specificity of 88.2%, 91.2%, 91.2%, and 79.4% in the validation set, respectively. There was a significant difference between model 4 and model 1 in discriminating the risk rating in gastrointestinal stromal tumors in the training data set (Delong test, p < 0.05). The radiomic models based on clinical features and triple-phase CT images manifested excellent accuracy for the discrimination of risk rating of GISTs.

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Section: Discussionmentioning

confidence: 99%

Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors

Shao

Niu

He³

et al. 2021

Front. Oncol.

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“…The last application field that is worth mentioning is the gastrointestinal application. A plethora of studies on the new advances of radiomics applied to CT and MRI for the evaluation of gastrointestinal stromal tumors have been published, and the consequent potential clinical applications have been discussed [84]. Among these studies regarding the gastrointestinal stromal tumors, it is worth citing an interesting strategy developed for pattern classification based on the integration of radiomics and deep convolutional features [85].…”

Section: Radiomics In Clinical Practicementioning

confidence: 99%

A deep look into radiomics

et al. 2021

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Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It is an evolving field of research with many potential applications in medical imaging. The purpose of this review is to offer a deep look into radiomics, from the basis, deeply discussed from a technical point of view, through the main applications, to the challenges that have to be addressed to translate this process in clinical practice. A detailed description of the main techniques used in the various steps of radiomics workflow, which includes image acquisition, reconstruction, pre-processing, segmentation, features extraction and analysis, is here proposed, as well as an overview of the main promising results achieved in various applications, focusing on the limitations and possible solutions for clinical implementation. Only an in-depth and comprehensive description of current methods and applications can suggest the potential power of radiomics in fostering precision medicine and thus the care of patients, especially in cancer detection, diagnosis, prognosis and treatment evaluation.

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“…Radiomics is defined as the process of extraction, analysis, and modeling of a large quantity of features from medical imaging dataset and to correlate this information to prediction parameters, such as clinical end-points, pathological and genomic features[ 65 ]. Radiomics is a complex process that can be divided into different phases, including: Acquisition of imaging dataset, tumor segmentation, feature extraction, exploratory analysis, and model building[ 66 ].…”

Section: Artificial Intelligence and Radiomic Ultrasound Analysismentioning

confidence: 99%

New frontiers in liver ultrasound: From mono to multi parametricity

Bartolotta¹,

Taibbi²,

Randazzo³

et al. 2021

WJGO

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Modern liver ultrasonography (US) has become a “one-stop shop” able to provide not only anatomic and morphologic but also functional information about vascularity, stiffness and other various liver tissue properties. Modern US techniques allow a quantitative assessment of various liver diseases. US scanning is no more limited to the visualized plane, but three-dimensional, volumetric acquisition and consequent post-processing are also possible. Further, US scan can be consistently merged and visualized in real time with Computed Tomography and Magnetic Resonance Imaging examinations. Effective and safe microbubble-based contrast agents allow a real time, dynamic study of contrast kinetic for the detection and characterization of focal liver lesions. Ultrasound can be used to guide loco-regional treatment of liver malignancies and to assess tumoral response either to interventional procedures or medical therapies. Microbubbles may also carry and deliver drugs under ultrasound exposure. US plays a crucial role in diagnosing, treating and monitoring focal and diffuse liver disease. On the basis of personal experience and literature data, this paper is aimed to review the main topics involving recent advances in the field of liver ultrasound.

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New advances in radiomics of gastrointestinal stromal tumors

Cited by 28 publications

References 58 publications

Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors

Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors

A deep look into radiomics

New frontiers in liver ultrasound: From mono to multi parametricity

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