Predicting IDH mutation status of intrahepatic cholangiocarcinomas based on contrast-enhanced CT features

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.

Download Full-text

Addison's disease due to adrenal tuberculosis: Contrast-enhanced CT features and clinical duration correlation

European Journal of Radiology ◽

10.1016/j.ejrad.2006.11.025 ◽

2007 ◽

Vol 62 (1) ◽

pp. 126-131 ◽

Cited By ~ 36

Author(s):

Ying-Kun Guo ◽

Zhi-Gang Yang ◽

Yuan Li ◽

En-Sen Ma ◽

Yu-Ping Deng ◽

...

Keyword(s):

Addison’S Disease ◽

Addison's Disease ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Ct Features

Download Full-text

Can we differentiate histologic subtypes of neuroendocrine tumour liver metastases at a single phase contrast-enhanced CT—correlation with Ga-68 DOTATATE PET/CT findings

British Journal of Radiology ◽

10.1259/bjr.20190735 ◽

2020 ◽

Vol 93 (1106) ◽

pp. 20190735

Author(s):

Basak Gulpinar ◽

Elif Peker ◽

Cigdem Soydal ◽

Mine Araz ◽

Atilla Halil Elhan

Keyword(s):

Liver Metastases ◽

Phase Contrast ◽

Single Phase ◽

Neuroendocrine Tumour ◽

Primary Tumour ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Pet Ct ◽

Enhanced Ct ◽

Ct Features

Objective: To assess the usefulness of a single-phase contrast-enhanced CT to differentiate subtypes of neuroendocrine tumour (NET) liver metastases and to evaluate the correlation between CT features and Ga-68 DOTATATE positron emission tomography/CT (PET/CT) findings. Methods: Between December 2017 and April 2019 patients with liver metastases of neuroendocrine tumours who underwent CT and Ga-68 DOTATATE PET/CT were enrolled in the study. All patients involved in the study had undergone a standardised single-phase contrast-enhanced CT. Whole body PET/CT images were obtained with a combined PET/CT scanner. All CT images were retrospectively analysed by two radiologists. Enhancement patterns of lesions were assessed. For quantitative examination; CT attenuation values of metastatic lesions, liver parenchyma and aorta were measured using a freehand ROI and tumour-to-liver ratio [T–L = (Tumour–Liver) / Liver] and tumour-to-aorta ratio [T–A = (Tumour–Aorta) / Aorta] were calculated. The lesion with the highest Ga-68 DOTATATE uptake in the liver was used for calculations. The metabolic tumour volume (MTV), maximum standardised uptake value (SUV max) and SUV mean were calculated for the target liver lesion. Results: A total of 137 NET liver metastases divided into in three groups: 49 (35.7%) pancreatic, 60 (44.5%) gastroenteric and 26 (18.9%) lung NET liver metastases were analysed. Gastroenteric NET metastases often showed heterogeneous enhancement which was significantly higher than in the pancreas and lung NET liver metastases (p < 0.001). 96.72% (n = 59) of the gastroenteric NET liver metastases were hypoattenuating whereas the most frequent presentation for the pancreatic group was hyperattenuation (63.26%,n = 31). The difference in enhancement patterns of the liver metastases was statistically significant (p < 0.001) with respect to the location of the primary tumour. For quantitative analysis; tumour CT values were significantly different between the groups (p < 0.001). The T–L ratio was statistically different between gastroenteric and pancreatic NET liver metastases and pancreatic and lung NET groups (p < 0.001). The T–A ratio was significantly higher in the pancreatic NET metastases (p < 0.001). SUVmax, SUVmean and MTV values, however, were not significantly different between the subgroups. There was a weak positive correlation between T–L ratio and SUV meanvalues. Conclusion: We noticed statistically significant differences in both qualitative and quantitative CT features between histologic subgroups of neuroendocrine tumour liver metastases at a single phase contrast-enhanced CT. Advances in knowledge: Our study will be the first in the literature which extensively focus on assessing the CT features of liver metastases of NETs at a single phase CT and Ga-68DOTATATE PET/CT. As the different histological subtypes of NET liver metastases exhibit different clinical outcomes, these features might help to identify the primary tumour to provide optimal treatment.

Download Full-text

Using contrast-enhanced CT and non-contrast-enhanced CT to predict EGFR mutation status in NSCLC patients—a radiomics nomogram analysis

European Radiology ◽

10.1007/s00330-021-08366-y ◽

2021 ◽

Author(s):

Xiaoyan Yang ◽

Min Liu ◽

Yanhong Ren ◽

Huang Chen ◽

Pengxin Yu ◽

...

Keyword(s):

Clinical Application ◽

Egfr Mutation ◽

Test Set ◽

Mutation Status ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Test Sets ◽

Nsclc Patients ◽

Radiomics Signature

Abstract Objectives To develop and validate a general radiomics nomogram capable of identifying EGFR mutation status in non-small cell lung cancer (NSCLC) patients, regardless of patient with either contrast-enhanced CT (CE-CT) or non-contrast-enhanced CT (NE-CT). Methods A total of 412 NSCLC patients were retrospectively enrolled in this study. Patients’ radiomics features not significantly different between NE-CT and CE-CT were defined as general features, and were further used to construct the general radiomics signature. Fivefold cross-validation was used to select the best machine learning algorithm. Finally, a general radiomics nomogram was developed using general radiomics signature, and clinical and radiological characteristics. Two groups of data collected at different time periods were used as two test sets to access the discrimination and clinical usefulness. Area under the receiver operating characteristic curve (ROC-AUC) was applied to performance evaluation. Result The general radiomics signature yielded the highest AUC of 0.756 and 0.739 in the two test sets, respectively. When applying to same type of CT, the performance of general radiomics signature was always similar to or higher than that of models built using only NE-CT or CE-CT features. The general radiomics nomogram combining general radiomics signature, smoking history, emphysema, and ILD achieved higher performance whether applying to NE-CT or CE-CT (test set 1, AUC = 0.833 and 0.842; test set 2, AUC = 0.839 and 0.850). Conclusions Our work demonstrated that using general features to construct radiomics signature and nomogram could help identify EGFR mutation status of NSCLC patients and expand its scope of clinical application. Key Points • General features were proposed to construct general radiomics signature using different types of CT of different patients at the same time to identify EGFR mutation status of NSCLC patients. • The general radiomics nomogram based on general radiomics signature, and clinical and radiological characteristics could identify EGFR mutation status of patients with NSCLC and outperformed the general radiomics signature. • The general radiomics nomogram had a wider scope of clinical application; no matter which of NE-CT and CE-CT the patient has, its EGFR mutation status could be predicted.

Download Full-text

Contrast-enhanced CT features of hepatoblastoma: Can we predict histopathology?

Clinical Imaging ◽

10.1016/j.clinimag.2017.03.023 ◽

2017 ◽

Vol 44 ◽

pp. 33-37 ◽

Cited By ~ 4

Author(s):

Akshay D. Baheti ◽

A. Luana Stanescu ◽

Ning Li ◽

Teresa Chapman

Keyword(s):

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Ct Features

Download Full-text

Addison's disease due to adrenal tuberculosis: contrast-enhanced CT features and clinical duration correlation

Clinical Imaging ◽

10.1016/j.clinimag.2007.08.009 ◽

2007 ◽

Vol 31 (6) ◽

pp. 442

Author(s):

Y.-K. Guo ◽

Z.-G. Yang ◽

Y. Li ◽

E.-S. Ma ◽

Y.-P. Deng ◽

...

Keyword(s):

Addison’S Disease ◽

Addison's Disease ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Ct Features

Download Full-text

Potential Value of Radiomics in the Identification of Stage T3 and T4a Esophagogastric Junction Adenocarcinoma Based on Contrast-Enhanced CT Images

Frontiers in Oncology ◽

10.3389/fonc.2021.627947 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xu Chang ◽

Xing Guo ◽

Xiaole Li ◽

Xiaowei Han ◽

Xiaoxiao Li ◽

...

Keyword(s):

Predictive Value ◽

Esophagogastric Junction ◽

Ct Images ◽

Diagnostic Model ◽

Traditional Model ◽

Operating Characteristics ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Ct Features

PurposeThis study was designed to evaluate the predictive performance of contrast-enhanced CT-based radiomic features for the personalized, differential diagnosis of esophagogastric junction (EGJ) adenocarcinoma at stages T3 and T4a.MethodsTwo hundred patients with T3 (n = 44) and T4a (n = 156) EGJ adenocarcinoma lesions were enrolled in this study. Traditional computed tomography (CT) features were obtained from contrast-enhanced CT images, and the traditional model was constructed using a multivariate logistic regression analysis. A radiomic model was established based on radiomic features from venous CT images, and the radiomic score (Radscore) of each patient was calculated. A combined nomogram diagnostic model was constructed based on Radscores and traditional features. The diagnostic performances of these three models (traditional model, radiomic model, and nomogram) were assessed with receiver operating characteristics curves. Sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and areas under the curve (AUC) of models were calculated, and the performances of the models were evaluated and compared. Finally, the clinical effectiveness of the three models was evaluated by conducting a decision curve analysis (DCA).ResultsAn eleven-feature combined radiomic signature and two traditional CT features were constructed as the radiomic and traditional feature models, respectively. The Radscore was significantly different between patients with stage T3 and T4a EGJ adenocarcinoma. The combined nomogram performed the best and has potential clinical usefulness.ConclusionsThe developed combined nomogram might be useful in differentiating T3 and T4a stages of EGJ adenocarcinoma and may facilitate the decision-making process for the treatment of T3 and T4a EGJ adenocarcinoma.

Download Full-text