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

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.

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Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma

Scientific Reports ◽

10.1038/s41598-021-86497-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yong Zhu ◽

Yingfan Mao ◽

Jun Chen ◽

Yudong Qiu ◽

Yue Guan ◽

...

Keyword(s):

Texture Analysis ◽

Ct Images ◽

Textural Features ◽

Mutation Status ◽

Portal Venous Phase ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Venous Phase ◽

Ct Texture Analysis

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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Contrast-enhanced CT radiomics for predicting calcification in metastatic colorectal cancer: Based on machine learning.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e16095 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e16095-e16095

Author(s):

Yuwen Zhou ◽

Qiu Meng

Keyword(s):

Colorectal Cancer ◽

Metastatic Colorectal Cancer ◽

Validation Dataset ◽

Textural Features ◽

Slice Thickness ◽

Invasive Approach ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Radiomics Signature

e16095 Background: In patients with metastatic colorectal cancer (mCRC), calcification is a predictive factor and associated with a better prognosis. This study was aimed to estimate the textural features performance derived from contrast-enhanced CT in prediction of calcification in mCRC. Methods: Four hundred fifty patients from a single center with pathologically diagnosed colorectal adenocarcinoma (training dataset, n = 159; validation dataset, n = 31) were enrolled in our retrospective study. A three-dimensional region of interest (ROI) around the margin of the lesion was manually assessed by two radiologists on the basis of CT scans, and all textural parameters were retrieved from the ROI. The least absolute shrinkage and selection operator (LASSO) method was applied to select the textural feature. The differential diagnostic capabilities of textural features, morphological features, and their combination were analyzed by receiver operating characteristic (ROC). AUC was used as the main indicator. Results: Twenty-one radiomics features extracted from contrast-enhanced CT were screened as a calcification-associated radiomics signature of mCRC. They were able to predict calcification in both the training group (slice thickness of 5 mm, sensitivity 0.84, specificity 0.71, accuracy 0.81, AUC 0.916, 95%CI 0.87-0.97) and the validation group (slice thickness of 5 mm, sensitivity 1.00, specificity 0.88, accuracy 0.77, AUC 0.964, 95%CI 0.904-1.0). Conclusions: In summary, a noninvasive radiomics signature derived from contrast-enhanced CT images was conveniently used for the prediction of calcification in mCRC before therapy, which might be a non-invasive approach in clinical practice to determine whether surgery is needed. However, multi-center studies with larger sample size are needed to confirm these results.

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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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Radiomics Analysis of Contrast-Enhanced CT for the Preoperative Prediction of Microvascular Invasion in Mass-Forming Intrahepatic Cholangiocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.774117 ◽

2021 ◽

Vol 11 ◽

Author(s):

Fei Xiang ◽

Shumei Wei ◽

Xingyu Liu ◽

Xiaoyuan Liang ◽

Lili Yang ◽

...

Keyword(s):

Intrahepatic Cholangiocarcinoma ◽

Microvascular Invasion ◽

Recursive Feature Elimination ◽

Phase Image ◽

Support Vector ◽

Clinical Model ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Radiomics Signature

BackgroundMicrovascular invasion (MVI) has been shown to be closely associated with postoperative recurrence and metastasis in patients with intrahepatic cholangiocarcinoma (ICC). We aimed to develop a radiomics prediction model based on contrast-enhanced CT (CECT) to distinguish MVI in patients with mass-forming ICC.Methods157 patients were included and randomly divided into training (n=110) and test (n=47) datasets. Radiomic signatures were built based on the recursive feature elimination support vector machine (Rfe-SVM) algorithm. Significant clinical-radiologic factors were screened, and a clinical model was built by multivariate logistic regression. A nomogram was developed by integrating radiomics signature and the significant clinical risk factors.ResultsThe portal phase image radiomics signature with 6 features was constructed and provided an area under the receiver operating characteristic curve (AUC) of 0.804 in the training and 0.769 in the test datasets. Three significant predictors, including satellite nodules (odds ratio [OR]=13.73), arterial hypo-enhancement (OR=4.31), and tumor contour (OR=4.99), were identified by multivariate analysis. The clinical model using these predictors exhibited an AUC of 0.822 in the training and 0.756 in the test datasets. The nomogram combining significant clinical factors and radiomics signature achieved satisfactory prediction efficacy, showing an AUC of 0.886 in the training and 0.80 in the test datasets.ConclusionsBoth CECT radiomics analysis and radiologic factors have the potential for MVI prediction in mass-forming ICC patients. The nomogram can further improve the prediction efficacy.

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Abstract P3-01-04: Potential clinical application of mammography conducted immediately after contrast enhanced CT as contrast enhanced subtraction mammography

10.1158/1538-7445.sabcs16-p3-01-04 ◽

2017 ◽

Author(s):

S Ohtani ◽

Y Yoshimura ◽

T Kin ◽

M Fujihara ◽

Y Kajiwara ◽

...

Keyword(s):

Clinical Application ◽

Potential Clinical Application ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct

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Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer

Frontiers in Oncology ◽

10.3389/fonc.2020.552270 ◽

2020 ◽

Vol 10 ◽

Author(s):

Junmeng Li ◽

Chao Zhang ◽

Jia Wei ◽

Peiming Zheng ◽

Hui Zhang ◽

...

Keyword(s):

Gastric Cancer ◽

Disease Free Survival ◽

Stage Ii ◽

Chemotherapy Response ◽

Free Survival ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Disease Free ◽

Radiomics Signature

BackgroundWe evaluated the ability of radiomics based on intratumoral and peritumoral regions on preoperative gastric cancer (GC) contrast-enhanced CT imaging to predict disease-free survival (DFS) and chemotherapy response in stage II/III GC.MethodsThis study enrolled of 739 consecutive stage II/III GC patients. Within the intratumoral and peritumoral regions of CT images, 584 total radiomic features were computed at the portal venous-phase. A radiomics signature (RS) was generated by using support vector machine (SVM) based methods. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to determine the association of the RS and clinicopathological variables with DFS. A radiomics nomogram combining the radiomics signature and clinicopathological findings was constructed for individualized DFS estimation.ResultsThe radiomics signature consisted of 26 features and was significantly associated with DFS in both the training and validation sets (both P<0.0001). Multivariate analysis showed that the RS was an independent predictor of DFS. The signature had a higher predictive accuracy than TNM stage and single radiomics features and clinicopathological factors. Further analysis showed that stage II/III patients with high scores were more likely to benefit from adjuvant chemotherapy.ConclusionThe newly developed radiomics signature was a powerful predictor of DFS in GC, and it may predict which patients with stage II and III GC benefit from chemotherapy.

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A Computed Tomography-Derived Radiomics Approach for Predicting Uncommon EGFR Mutation in Patients With NSCLC

Frontiers in Oncology ◽

10.3389/fonc.2021.722106 ◽

2021 ◽

Vol 11 ◽

Author(s):

Wufei Chen ◽

Yanqing Hua ◽

Dingbiao Mao ◽

Hao Wu ◽

Mingyu Tan ◽

...

Keyword(s):

Egfr Mutation ◽

Validation Cohort ◽

Characteristic Curve ◽

Integrated Model ◽

Curve Analysis ◽

Support Vector ◽

Training Cohort ◽

Contrast Enhanced Ct ◽

Nsclc Patients ◽

Radiomics Signature

PurposeThis study aims to develop a CT-based radiomics approach for identifying the uncommon epidermal growth factor receptor (EGFR) mutation in patients with non-small cell lung cancer (NSCLC).MethodsThis study involved 223 NSCLC patients (107 with uncommon EGFR mutation-positive and 116 with uncommon EGFR mutation-negative). A total of 1,269 radiomics features were extracted from the non-contrast-enhanced CT images after image segmentation and preprocessing. Support vector machine algorithm was used for feature selection and model construction. Receiver operating characteristic curve analysis was applied to evaluate the performance of the radiomics signature, the clinicopathological model, and the integrated model. A nomogram was developed and evaluated by using the calibration curve and decision curve analysis.ResultsThe radiomics signature demonstrated a good performance for predicting the uncommon EGFR mutation in the training cohort (area under the curve, AUC = 0.802; 95% confidence interval, CI: 0.736–0.858) and was verified in the validation cohort (AUC = 0.791, 95% CI: 0.642–0.899). The integrated model combined radiomics signature with clinicopathological independent predictors exhibited an incremental performance compared with the radiomics signature or the clinicopathological model. A nomogram based on the integrated model was developed and showed good calibration (Hosmer–Lemeshow test, P = 0.92 in the training cohort and 0.608 in the validation cohort) and discrimination capacity (AUC of 0.816 in the training cohort and 0.795 in the validation cohort).ConclusionRadiomics signature combined with the clinicopathological features can predict uncommon EGFR mutation in NSCLC patients.

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