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

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.

scholarly journals Prediction models for clear cell renal cell carcinoma ISUP/WHO grade: comparison between CT radiomics and conventional contrast-enhanced CT

2020 ◽  
Vol 93 (1114) ◽  
pp. 20200131
Author(s):  
Dong Han ◽  
Yong Yu ◽  
Nan Yu ◽  
Shan Dang ◽  
Hongpei Wu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Statistical Significance ◽  
Net Benefit ◽  
Cell Renal Cell Carcinoma ◽  
Who Grade ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Validation Set

Objective: Comparing the prediction models for the ISUP/WHO grade of clear cell renal cell carcinoma (ccRCC) based on CT radiomics and conventional contrast-enhanced CT (CECT). Methods: The corticomedullary phase images of 119 cases of low-grade (I and II) and high-grade (III and IV) ccRCC based on 2016 ISUP/WHO pathological grading criteria were analyzed retrospectively. The patients were randomly divided into training and validation set by stratified sampling according to 7:3 ratio. Prediction models of ccRCC differentiation were constructed using CT radiomics and conventional CECT findings in the training setandwere validated using validation set. The discrimination, calibration, net reclassification index (NRI) and integrated discrimination improvement index (IDI) of the two prediction models were further compared. The decision curve was used to analyze the net benefit of patients under different probability thresholds of the two models. Results: In the training set, the C-statistics of radiomics prediction model was statistically higher than that of CECT (p < 0.05), with NRI of 9.52% and IDI of 21.6%, both with statistical significance (p < 0.01).In the validation set, the C-statistics of radiomics prediction model was also higher but did not show statistical significance (p = 0.07). The NRI and IDI was 14.29 and 33.7%, respectively, both statistically significant (p < 0.01). Validation set decision curve analysis showed the net benefit improvement of CT radiomics prediction model in the range of 3–81% over CECT. Conclusion: The prediction model using CT radiomics in corticomedullary phase is more effective for ccRCC ISUP/WHO grade than conventional CECT. Advances in knowledge: As a non-invasive analysis method, radiomics can predict the ISUP/WHO grade of ccRCC more effectively than traditional enhanced CT.

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

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.

scholarly journals Computed Tomography-Based Radiomics Model to Preoperatively Predict Microsatellite Instability Status in Colorectal Cancer: A Multicenter Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhi Li ◽  
Qi Zhong ◽  
Liang Zhang ◽  
Minhong Wang ◽  
Wenbo Xiao ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Feature Selection ◽  
Clinical Characteristics ◽  
External Validation ◽  
Training Set ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Auc Value ◽  
Validation Set

ObjectivesTo establish and validate a combined radiomics model based on radiomics features and clinical characteristics, and to predict microsatellite instability (MSI) status in colorectal cancer (CRC) patients preoperatively.MethodsA total of 368 patients from four hospitals, who underwent preoperative contrast-enhanced CT examination, were included in this study. The data of 226 patients from a single hospital were used as the training dataset. The data of 142 patients from the other three hospitals were used as an independent validation dataset. The regions of interest were drawn on the portal venous phase of contrast-enhanced CT images. The filtered radiomics features and clinical characteristics were combined. A total of 15 different discrimination models were constructed based on a feature selection strategy from a pool of 3 feature selection methods and a classifier from a pool of 5 classification algorithms. The generalization capability of each model was evaluated in an external validation set. The model with high area under the curve (AUC) value from the training set and without a significant decrease in the external validation set was final selected. The Brier score (BS) was used to quantify overall performance of the selected model.ResultsThe logistic regression model using the mutual information (MI) dimensionality reduction method was final selected with an AUC value of 0.79 for the training set and 0.73 for the external validation set to predicting MSI. The BS value of the model was 0.12 in the training set and 0.19 in the validation set.ConclusionThe established combined radiomics model has the potential to predict MSI status in CRC patients preoperatively.

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

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.

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

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&lt;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.

scholarly journals Evaluation of the Radiomics Method for the Prediction of Atypical Adenomatous Hyperplasia in Patients With Subcentimeter Pulmonary Ground-Glass Nodules

2021 ◽  
Vol 11 ◽  
Author(s):  
Bin Wang ◽  
Preeti Hamal ◽  
Xue Meng ◽  
Ke Sun ◽  
Yang Yang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Clinical Features ◽  
Discrimination Performance ◽  
Ground Glass ◽  
Atypical Adenomatous Hyperplasia ◽  
Adenomatous Hyperplasia ◽  
Model Based ◽  
Validation Set ◽  
Lung Adenocarcinomas ◽  
Ground Glass Nodules

ObjectivesWe aimed to develop a prediction model to distinguish atypical adenomatous hyperplasia (AAH) from early lung adenocarcinomas in patients with subcentimeter pulmonary ground-glass nodules (GGNs), which may help avoid aggressive surgical resection for patients with AAH.MethodsSurgically confirmed cases of AAH and lung adenocarcinomas manifesting as GGNs of less than 1 cm were retrospectively collected. A prediction model based on radiomics and clinical features identified from a training set of cases was built to differentiate AAH from lung adenocarcinomas and tested on a validation set.ResultsFour hundred and eighty-five eligible cases were included and randomly assigned to the training (n = 339) or the validation sets (n = 146). The developed radiomics prediction model showed good discrimination performance to distinguish AAH from adenocarcinomas in both the training and the validation sets, with, respectively, 84.1% and 82.2% of accuracy, and AUCs of 0.899 (95% CI: 0.867–0.931) and 0.881 (95% CI: 0.827–0.936).ConclusionThe prediction model based on radiomics and clinical features can help differentiate AAH from adenocarcinomas manifesting as subcentimeter GGNs and may prevent aggressive resection for AAH patients, while reserving this treatment for adenocarcinomas.

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

Oncotarget ◽  
2017 ◽  
Vol 8 (32) ◽  
pp. 53664-53674 ◽  
Author(s):  
Ying Liu ◽  
Shichang Liu ◽  
Fangyuan Qu ◽  
Qian Li ◽  
Runfen Cheng ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Texture Analysis ◽  
Tumor Heterogeneity ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

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

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.

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