A Novel Model Based on CXCL8-Derived Radiomics for Prognosis Prediction in Colorectal Cancer

Chu, Yanpeng; Li, Jie; Zeng, Zong Yuan; Huang, Bin; Zhao, Jiaojiao; Liu, Qin; Wu, Huaping; Fu, Jiangping; Yin, Zheng; Zhang, Yefan; Cai, Jianqiang; Zeng, Fanxin

doi:10.3389/fonc.2020.575422

Cited by 15 publications

(11 citation statements)

References 31 publications

(41 reference statements)

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“…Exploring the genomics of cancer patients might help to optimize therapeutic strategies. Previous study has proposed that the gene expression could be evaluated by radiomics features in CRC ( 34 ). However, the potential associations between radiomics, gene expression, and clinical risk remain unclear.…”

Section: Discussionmentioning

confidence: 99%

Individualized Prediction of Colorectal Cancer Metastasis Using a Radiogenomics Approach

Liu

et al. 2021

Front. Oncol.

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Objectives: To evaluate whether incorporating the radiomics, genomics, and clinical features allows prediction of metastasis in colorectal cancer (CRC) and to develop a preoperative nomogram for predicting metastasis.Methods: We retrospectively analyzed radiomics features of computed tomography (CT) images in 134 patients (62 in the primary cohort, 28 in the validation cohort, and 44 in the independent-test cohort) clinicopathologically diagnosed with CRC at Dazhou Central Hospital from February 2018 to October 2019. Tumor tissues were collected from all patients for RNA sequencing, and clinical data were obtained from medical records. A total of 854 radiomics features were extracted from enhanced venous-phase CT of CRC. Least absolute shrinkage and selection operator regression analysis was utilized for data dimension reduction, feature screen, and radiomics signature development. Multivariable logistic regression analysis was performed to build a multiscale predicting model incorporating the radiomics, genomics, and clinical features. The receiver operating characteristic curve, calibration curve, and decision curve were conducted to evaluate the performance of the nomogram.Results: The radiomics signature based on 16 selected radiomics features showed good performance in metastasis assessment in both primary [area under the curve (AUC) = 0.945, 95% confidence interval (CI) 0.892–0.998] and validation cohorts (AUC = 0.754, 95% CI 0.570–0.938). The multiscale nomogram model contained radiomics features signatures, four-gene expression related to cell cycle pathway, and CA 19-9 level. The multiscale model showed good discrimination performance in the primary cohort (AUC = 0.981, 95% CI 0.953–1.000), the validation cohort (AUC = 0.822, 95% CI 0.635–1.000), and the independent-test cohort (AUC = 0.752, 95% CI 0.608–0.896) and good calibration. Decision curve analysis confirmed the clinical application value of the multiscale model.Conclusion: This study presented a multiscale model that incorporated the radiological eigenvalues, genomics features, and CA 19-9, which could be conveniently utilized to facilitate the individualized preoperatively assessing metastasis in CRC patients.

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Section: Discussionmentioning

confidence: 99%

Individualized Prediction of Colorectal Cancer Metastasis Using a Radiogenomics Approach

Liu

et al. 2021

Front. Oncol.

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“…The type of information provided by radiomics conceptually suggests that features not captured by current conventional image analysis methods can be extracted from existing CMR images by means of radiomics, and that this information appears to provide additional insight into myocardial microstructural remodeling patterns (28) so that models combining both radiomics and conventional imaging graphics parameters can be used to improve diagnostic performance. Previous studies (29,30) in other fields have also shown the combined model performs better than single conventional imaging or radiomics model.…”

Section: Cine Images Routinely Provide the Initial Diagnostic Impress...mentioning

confidence: 92%

Differentiating between cardiac amyloidosis and hypertrophic cardiomyopathy on non-contrast cine-magnetic resonance images using machine learning-based radiomics

Jiang

Zhang

Wang

et al. 2022

Front. Cardiovasc. Med.

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ObjectivesThis study aimed to determine whether texture analysis (TA) and machine learning-based classifications can be applied in differential diagnosis of cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) using non-contrast cine cardiac magnetic resonance (CMR) images.MethodsIn this institutional review board-approved study, we consecutively enrolled 167 patients with CA (n = 85), HCM (n = 82), and 84 patients with normal CMR served as controls. All cases were randomized into training [119 patients (70%)] and validation [48 patients (30%)] groups. A total of 275 texture features were extracted from cine images. Based on regression analysis with the least absolute shrinkage and selection operator (LASSO), nine machine learning models were established and their diagnostic performance determined.ResultsNineteen radiomics texture features derived from cine images were used to differentiate CA and HCM. In the validation cohort, the support vector machine (SVM), which had an accuracy of 0.85, showed the best performance (MCC = 0.637). Gray level non-uniformity (GLevNonU) was the single most effective feature. The combined model of radiomics texture features and conventional MR metrics had superior discriminatory performance (AUC = 0.89) over conventional MR metrics model (AUC = 0.79). Moreover, results showed that GLevNonU levels in HCM patients were significantly higher compared with levels in CA patients and control groups (P < 0.001). A cut-off of GLevNonU ≥ 25 was shown to differentiate between CA and HCM patients, with an area under the curve (AUC) of 0.86 (CI:0.804–0.920). Multiple comparisons tests showed that GLevNonU was significantly greater in LGE+, relative to LGE-patient groups (CA+ vs. CA- and HCM+ vs. HCM-, P = 0.01, 0.001, respectively).ConclusionMachine learning-based classifiers can accurately differentiate between CA and HCM on non-contrast cine images. The radiomics-MR combined model can be used to improve the discriminatory performance. TA may be used to assess myocardial microstructure changes that occur during different stages of cardiomyopathies.

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“…Of the 68 studies, 13 [12,26,[61][62][63][64][65][66][67][68][69][70][71](19%) used semi-automatic segmentation, 3 [52,72,73](4%) used automatic segmentation. Except for 12 [21,25,44,45,49,53,[74][75][76][77][78][79] studies which did not describe the segmentation methods used, the remaining 40 [13-20, 22-24, 46-48, 50, 51, 54-56, 80-100]studies adopted the manual segmentation method.…”

Section: Segmentationmentioning

confidence: 99%

Repeatability of Radiomics Studies in Colorectal Cancer: A Systematic Review

Liu

Wei

et al. 2022

Preprint

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Purpose: Recently, radiomics has been widely used in colorectal cancer, but many variable factors affect the repeatability of radiomics research. This review aims to analyze the repeatability of radiomics studies in colorectal cancer and to evaluate the current status of radiomics in the field of colorectal cancer.Methods: A total of 68 eligible studies was included in this review by searching from the PubMed and EmBase databases. Then each study in our review was evaluated using the Radiomics Quality Score (RQS). We analyzed the factors that may affect the repeatability in the radiomics workflow and discussed the repeatability of the included researches.Results: A total of 68 articles was included in this review, of which only 2 (3%) articles controlled the influence of individual factors, just 2 (3%) articles considered the variability between scanners. In addition, the median score of RQS was 12(out of 36), range −7 to + 28. Conclusions: The RQS score was moderately low, and most studies did not consider the repeatability of radiomics features, especially in terms of Intra-individual, scanners, and scanning parameters. In order to improve the generalization of the radiomics model, it is necessary to further control the variable factors of repeatability.

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A Novel Model Based on CXCL8-Derived Radiomics for Prognosis Prediction in Colorectal Cancer

Cited by 15 publications

References 31 publications

Individualized Prediction of Colorectal Cancer Metastasis Using a Radiogenomics Approach

Individualized Prediction of Colorectal Cancer Metastasis Using a Radiogenomics Approach

Differentiating between cardiac amyloidosis and hypertrophic cardiomyopathy on non-contrast cine-magnetic resonance images using machine learning-based radiomics

Repeatability of Radiomics Studies in Colorectal Cancer: A Systematic Review

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