Tumor heterogeneity assessed by texture analysis on contrast-enhanced CT in lung adenocarcinoma: association with pathologic grade

AbstractTo explore the value of contrast-enhanced CT texture analysis in predicting isocitrate dehydrogenase (IDH) mutation status of intrahepatic cholangiocarcinomas (ICCs). Institutional review board approved this study. Contrast-enhanced CT images of 138 ICC patients (21 with IDH mutation and 117 without IDH mutation) were retrospectively reviewed. Texture analysis was performed for each lesion and compared between ICCs with and without IDH mutation. All textural features in each phase and combinations of textural features (p < 0.05) by Mann–Whitney U tests were separately used to train multiple support vector machine (SVM) classifiers. The classification generalizability and performance were evaluated using a tenfold cross-validation scheme. Among plain, arterial phase (AP), portal venous phase (VP), equilibrium phase (EP) and Sig classifiers, VP classifier showed the highest accuracy of 0.863 (sensitivity, 0.727; specificity, 0.885), with a mean area under the receiver operating characteristic curve of 0.813 in predicting IDH mutation in validation cohort. Texture features of CT images in portal venous phase could predict IDH mutation status of ICCs with SVM classifier preoperatively.

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Can texture features improve the differentiation of infiltrative lung adenocarcinoma appearing as ground glass nodules in contrast-enhanced CT?

European Journal of Radiology ◽

10.1016/j.ejrad.2019.06.010 ◽

2019 ◽

Vol 117 ◽

pp. 126-131 ◽

Cited By ~ 4

Author(s):

Chen Gao ◽

Ping Xiang ◽

Jianfeng Ye ◽

Peipei Pang ◽

Shiwei Wang ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Texture Features ◽

Ground Glass ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Ground Glass Nodules

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Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma

Molecular Imaging ◽

10.1177/1536012119883161 ◽

2019 ◽

Vol 18 ◽

pp. 153601211988316 ◽

Cited By ~ 4

Author(s):

Guangjie Yang ◽

Aidi Gong ◽

Pei Nie ◽

Lei Yan ◽

Wenjie Miao ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Texture Analysis ◽

Renal Cell ◽

Texture Features ◽

Regions Of Interest ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

2D And 3D

Objective: To evaluate the value of 2-dimensional (2D) and 3-dimensional (3D) computed tomography texture analysis (CTTA) models in distinguishing fat-poor angiomyolipoma (fpAML) from chromophobe renal cell carcinoma (chRCC). Methods: We retrospectively enrolled 32 fpAMLs and 24 chRCCs. Texture features were extracted from 2D and 3D regions of interest in triphasic CT images. The 2D and 3D CTTA models were constructed with the least absolute shrinkage and selection operator algorithm and texture scores were calculated. The diagnostic performance of the 2D and 3D CTTA models was evaluated with respect to calibration, discrimination, and clinical usefulness. Results: Of the 177 and 183 texture features extracted from 2D and 3D regions of interest, respectively, 5 2D features and 8 3D features were selected to build 2D and 3D CTTA models. The 2D CTTA model (area under the curve [AUC], 0.811; 95% confidence interval [CI], 0.695-0.927) and the 3D CTTA model (AUC, 0.915; 95% CI, 0.838-0.993) showed good discrimination and calibration ( P > .05). There was no significant difference in AUC between the 2 models ( P = .093). Decision curve analysis showed the 3D model outperformed the 2D model in terms of clinical usefulness. Conclusions: The CTTA models based on contrast-enhanced CT images had a high value in differentiating fpAML from chRCC.

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Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In-Phase and Opposed-Phase MRI?

American Journal of Roentgenology ◽

10.2214/ajr.18.20097 ◽

2019 ◽

Vol 212 (3) ◽

pp. 554-561 ◽

Cited By ~ 8

Author(s):

Lisa M. Ho ◽

Ehsan Samei ◽

Maciej A. Mazurowski ◽

Yuese Zheng ◽

Brian C. Allen ◽

...

Keyword(s):

Texture Analysis ◽

Unenhanced Ct ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Ct Contrast

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Hypervascular hepatic focal lesions on dynamic contrast-enhanced CT: preliminary data from arterial phase scans texture analysis for classification

Clinical Radiology ◽

10.1016/j.crad.2019.05.010 ◽

2019 ◽

Vol 74 (8) ◽

pp. 653.e11-653.e18 ◽

Cited By ~ 3

Author(s):

S. Song ◽

Z. Li ◽

L. Niu ◽

X. Zhou ◽

G. Wang ◽

...

Keyword(s):

Texture Analysis ◽

Preliminary Data ◽

Arterial Phase ◽

Focal Lesions ◽

Dynamic Contrast Enhanced ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Dynamic Contrast Enhanced Ct

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Radiomics signature on CECT as a predictive factor for invasiveness of lung adenocarcinoma manifesting as subcentimeter ground glass nodules

Scientific Reports ◽

10.1038/s41598-021-83167-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Wufei Chen ◽

Ming Li ◽

Dingbiao Mao ◽

Xiaojun Ge ◽

Jiaofeng Wang ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Predictive Performance ◽

Ground Glass ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Validation Set ◽

Lung Adenocarcinomas ◽

Radiomics Signature ◽

Ground Glass Nodules

AbstractControversy and challenges remain regarding the cognition of lung adenocarcinomas presented as subcentimeter ground glass nodules (GGNs). Postoperative lymphatic involvement or intrapulmonary metastasis is found in approximately 15% to 20% of these cases. This study aimed to develop and validate a radiomics signature to identify the invasiveness of lung adenocarcinoma appearing as subcentimeter ground glass nodules. We retrospectively enrolled 318 subcentimeter GGNs with histopathology-confirmed adenocarcinomas in situ (AIS), minimally invasive adenocarcinomas (MIA) and invasive adenocarcinomas (IAC). The radiomics features were extracted from manual segmentation based on contrast-enhanced CT (CECT) and non-contrast enhanced CT (NCECT) images after imaging preprocessing. The Lasso algorithm was applied to construct radiomics signatures. The predictive performance of radiomics models was evaluated by receiver operating characteristic (ROC) analysis. A radiographic-radiomics combined nomogram was developed to evaluate its clinical utility. The radiomics signature on CECT (AUC: 0.896 [95% CI 0.815–0.977]) performed better than the radiomics signature on NCECT data (AUC: 0.851[95% CI 0.712–0.989]) in the validation set. An individualized prediction nomogram was developed using radiomics model on CECT and radiographic model including type, shape and vascular change. The C index of the nomogram was 0.915 in the training set and 0.881 in the validation set, demonstrating good discrimination. Decision curve analysis (DCA) revealed that the proposed model was clinically useful. The radiomics signature built on CECT could provide additional benefit to promote the preoperative prediction of invasiveness in patients with subcentimeter lung adenocarcinomas.

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